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CN103642902A - Genetic analysis systems and methods - Google Patents

Genetic analysis systems and methods Download PDF

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Publication number
CN103642902A
CN103642902A CN201310565723.1A CN201310565723A CN103642902A CN 103642902 A CN103642902 A CN 103642902A CN 201310565723 A CN201310565723 A CN 201310565723A CN 103642902 A CN103642902 A CN 103642902A
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phenotype
individual
individuality
disease
snp
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CN103642902B (en
Inventor
D·A·斯坦芬
M·F·菲利普庞
J·韦塞尔
M·卡吉尔
E·哈尔佩里恩
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Navigenics Inc
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Navigenics Inc
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Priority claimed from US11/781,679 external-priority patent/US20080131887A1/en
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Priority claimed from CN2007800500195A external-priority patent/CN101617227B/en
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Abstract

The present invention provides methods of determining a Genetic Composite Index score by assessing the association between an individual's genotype and at least one disease or condition. The assessment comprises comparing an individual's genomic profile with a database of medically relevant genetic variations that have been established to associate with at least one disease or condition.

Description

Genetic analysis systems and method
The application is to be dividing an application of on November 30th, 2007 and denomination of invention No. 200780050019.5 applications for a patent for invention that are " genetic analysis systems and method " the applying date.
Background technology
Other latest developments that human genome order-checking and human genome are learned disclose, and any two person-to-person genome constitutions have the similarity that surpasses 99.9%.Between Different Individual, in DNA, relatively a small amount of variation is the reason that causes phenotypic character difference, and with many human diseasess, to the susceptibility of various diseases and relevant to the reaction of disease treatment.Between individuality, the variation of DNA occurs in coding region and non-coding region, and comprises the variation of base on specific site in genomic dna sequence, and the insertion of DNA and disappearance.Occur in the locational variation of single base in genome and be called single nucleotide polymorphism, or " SNP ".
Although SNP is relatively rare in human genome, but it accounts for the major part of mutant dna sequence between individuality, there is a SNP (referring to International HapMap Project, www.hapmap.org) in approximately every 1,200 base pair in human genome.Owing to can obtaining more human inheritance's information, the complicacy of SNP starts to be understood by people.Thereupon, in genome, the generation of SNP occurs associated with existence and/or the susceptibility of various diseases and state.
Due to other progress obtaining in these dependencys and human genetics, generally speaking medical treatment and individual health care are just towards personalized approach development, and wherein patient selects and other selection make suitable medical treatment in the situation that other factors is considered his or her genomic information in addition.Therefore, just need to provide the genomic information of individual specific to this individuality to individual and their healthcare provider, thereby personalized medicine and other decision-making are provided.
Summary of the invention
The invention provides a kind of method of assessing individual genotypic correlation, the method comprises: the hereditary sample that a) obtains this individuality, b) generate this individual Genome Atlas, c) by this individual Genome Atlas is compared with the current database of the dependency of Human genome type and phenotype, determine the dependency of this idiotype and phenotype, d) to this individuality or this individual health care management person report by step c) result that obtains, e) when known additional Human genome type dependency, by this additional Human genome type dependency, upgrade Human genome type correlation data storehouse, f) by by by step c) this individual Genome Atlas or its part that obtain compare and upgrade this individual genotypic correlation with additional Human genome type dependency, and definite this individual episome type dependency, and g) to this individuality or this individual health care management person report by step f) result that obtains.
The present invention further provides a kind of business method of assessing individual genotypic correlation, the method comprises: the hereditary sample that a) obtains this individuality; B) generate this individual Genome Atlas, c) by this individual Genome Atlas is compared with Human genome type correlation data storehouse, determine this individual genotypic correlation; D) in the mode of encrypting, to this individuality, provide the result of determining individual genotypic correlation; E), when known additional Human genome type dependency, by this additional Human genome type dependency, upgrade Human genome type correlation data storehouse; F) by this individual Genome Atlas or its part are compared and upgraded this individual genotypic correlation with additional Human genome type dependency, and determine this individual episome type dependency; And g) to this individuality or this individual health care management person, provide the result of upgrading this individual genotypic correlation.
Another aspect of the present invention is a kind of method that generates individual phenotypic spectrum, the method comprises: a) provide and comprise regular rule set (rule set), each rule shows the dependency between at least one genotype and at least one phenotype, b) provide the data set that comprises each individual Genome Atlas in a plurality of individualities, wherein each Genome Atlas comprises Multi-genotype; C), with at least one this rule set of new regulation regular update, wherein this at least one new regulation shows previous genotype not associated with each other in rule set and the dependency between phenotype; D) each new regulation is applied to at least one individual Genome Atlas, thereby makes at least one genotype and at least one phenotypic correlation connection of this individuality, and optionally, e) generate the report that comprises the phenotypic spectrum that this is individual.
The present invention also provides a kind of system, and this system comprises: a) comprise regular rule set, each rule shows the dependency between at least one genotype and at least one phenotype; B) with the code of at least one this rule set of new regulation regular update, wherein this at least one new regulation shows previous genotype not associated with each other in rule set and the dependency between phenotype; C) comprise the database of the Genome Atlas of a plurality of individualities; D) this rule set is applied to individual Genome Atlas to determine the code of individual phenotypic spectrum; And e) generate the code of the report of each individuality.
Another aspect of the present invention is in the mode of encrypting or do not encrypt, by network, to transmit in above-mentioned method and system.
The reference content of introducing
All publications and the patent application in specification sheets, mentioned are hereby incorporated by, and as each single publication or patent application, are incorporated herein by reference the same especially with explanation individually.
Particularly, the present invention relates to the following:
1. a method of assessing individual genotypic correlation, the method comprises:
A) obtain the hereditary sample of described individuality;
B) generate the Genome Atlas of described individuality;
C) by the Genome Atlas of described individuality is compared and is determined the genotype of described individuality and the dependency of phenotype with the correlation data storehouse of phenotype with current mankind genotype;
D) to the health care management person report of described individuality or described individuality by step c) the described result that obtains;
E), when knowing additional Human genome type dependency, by described additional Human genome type dependency, upgrade described Human genome type correlation data storehouse; With
F) by by step c) the Genome Atlas of described individuality or its part compare with described additional Human genome type dependency and upgrade the genotypic correlation of described individuality the episome type dependency of definite described individuality; With
G) to the health care management person report of described individuality or described individuality by step f) the described result that obtains.
2. the method described in the 1st, wherein, third party obtains described hereditary sample.
3. the method described in the 1st, wherein, described generation Genome Atlas is undertaken by third party.
4. the method described in the 1st, wherein, described result is based on GCI or GCI Plus scoring.
5. the method described in the 1st, wherein, described report comprises by result described in Internet Transmission.
6. the method described in the 1st, wherein, the described report of described result is by online entrance.
7. the method described in the 1st, wherein, the described report of described result is by paper part or passes through e-mail.
8. the method described in the 1st, wherein, the mode that described report comprises encrypting is reported described result.
9. the method described in the 1st, wherein, described report comprises in unencrypted mode reports described result.
10. the method described in the 1st, wherein, the Genome Atlas of described individuality is stored in encrypting database or strong room.
Method described in 11. the 1st, wherein, described individuality is registered user.
Method described in 12. the 1st, wherein, described individuality is nonregistered user.
Method described in 13. the 1st, wherein, described hereditary sample is DNA.
Method described in 14. the 1st, wherein, described hereditary sample is RNA.
Method described in 15. the 1st, wherein, described Genome Atlas is single nucleotide polymorphism Genome Atlas, and described Human genome type correlation data storehouse is mankind's single nucleotide polymorphism dependency, and described additional Human genome type dependency is single nucleotide polymorphism dependency.
Method described in 16. the 1st, wherein, that described Genome Atlas comprises is truncate, insert, disappearance or repeat, described Human genome type correlation data storehouse be the mankind truncate, insert, disappearance or repeat dependency, and described additional Human genome type dependency be truncate, insert, disappearance or repeat dependency.
Method described in 17. the 1st, wherein, the full genome that described Genome Atlas is described individuality.
Method described in 18. the 1st, wherein, described method comprises 2 of assessments or more genotypic correlation.
Method described in 19. the 1st, wherein, described method comprises 10 of assessments or more genotypic correlation.
Method described in 20. the 1st, wherein, described Human genome type correlation data storehouse comprises the genetic variant and the phenotype relevant to described genetic variants useful in one or more genes of listing in table 1.
Method described in 21. the 1st, wherein, described Human genome type correlation data storehouse comprises the genetic variant and the phenotype relevant to described genetic variant of listing in Fig. 4,5,6, one or more genes of 22 or 25.
Method described in 22. the 1st, wherein, described Human genome type correlation data storehouse comprises by the definite genetic variant of the described Genome Atlas of described individuality and the predetermined phenotype that appeared by described individuality.
Method described in 23. the 1st, wherein, described Human genome type correlation data storehouse is included in the single nucleotide polymorphism and the phenotype relevant to described single nucleotide polymorphism in table 1 or Fig. 4,5,6,22 or 25 listed described genes.
Method described in 24. the 1st, wherein, described hereditary sample is from the biological sample that is selected from blood, hair, skin, saliva, seminal fluid, urine, fecal materials, sweat and oral cavity sample.
Method described in 25. the 15th, wherein, described genotypic correlation is the dependency of single nucleotide polymorphism and disease and state.
Method described in 26. the 15th, wherein, described genotypic correlation is the dependency of the phenotype of single nucleotide polymorphism and non-medical state.
Method described in 27. the 1st, wherein, described Genome Atlas is used high-density DNA microarray to generate.
Method described in 28. the 1st, wherein, described Genome Atlas is used genomic dna order-checking to generate.
Method described in 29. the 24th, wherein, described hereditary sample is that genomic dna and described biological sample are saliva.
30. 1 kinds of methods, the method comprises:
A) provide and comprise that regular rule set, each rule show the dependency between at least one genotype and at least one phenotype;
B) provide the data set that comprises each individual Genome Atlas in a plurality of individualities, wherein each Genome Atlas comprises Multi-genotype;
C) use termly at least one new regulation to upgrade described rule set, wherein said at least one new regulation shows previously in described rule set the dependency between incoherent genotype and phenotype each other; With
D) each new regulation is applied to the described Genome Atlas of one of at least described individuality, thereby makes at least one genotype and at least one phenotypic correlation connection for described individuality.
Method described in 31. the 30th, the method further comprises:
E) generate the report of the described phenotypic spectrum that comprises described individuality.
Method described in 32. the 30th, the method further comprises: at step b) afterwards
I) by the described rule application of described rule set in the described Genome Atlas of described individuality to determine a set of phenotypic spectrum of described individuality; With
Ii) generate the report of the initial table type spectrum that comprises described individuality.
33. the 31st or 32 described in method, wherein, provide described report to comprise by described in Internet Transmission and report.
34. the 31st or 32 described in method, wherein, described report provides with cipher mode.
35. the 31st or 32 described in method, wherein, described report provides in non-encrypted mode.
36. the 31st or 32 described in method, wherein, described report provides by online entrance.
37. the 31st or 32 described in method, wherein, described report provides with paper part or e-mail.
Method described in 38. the 30th, wherein, described new regulation makes not associated genotype and phenotypic correlation connection.
Method described in 39. the 30th, wherein, described new regulation makes associated genotype and the previous not phenotypic correlation of associated connection in described rule set.
Method described in 40. the 30th, wherein, described new regulation changes the rule in described rule set.
Method described in 41. the 30th, wherein, described new regulation generates by the dependency of the genotype of the described Genome Atlas from described individuality and the predetermined phenotype of described individuality.
Method described in 42. the 30th, wherein, described rule makes Multi-genotype and a kind of phenotypic correlation connection.
Method described in 43. the 30th, wherein, applies described new regulation and further comprises that the feature based on being selected from the described individuality of race, family, geography, sex, age, family history and predetermined phenotype is determined described phenotypic spectrum at least partly.
Method described in 44. the 30th, wherein, described genotype comprises that Nucleotide repetition, Nucleotide insertion, nucleotide deletion, chromosome translocation, karyomit(e) repeat or copy number variation.
Method described in 45. the 44th, wherein, described copy number variation is for micro-satellite repeats, Nucleotide repeats, repeat in kinetochore or telomere repeats.
Method described in 46. the 30th, wherein, described genotype comprises single nucleotide polymorphism.
Method described in 47. the 30th, wherein, described genotype comprises haplotype and double body type.
Method described in 48. the 30th, wherein, described genotype comprises the genetic marker with the single nucleotide polymorphism linkage disequilibrium of phenotypic correlation.
Method described in 49. the 30th, wherein, described phenotypic spectrum shows whether described quantitative proterties exists or produce the risk of described quantitative proterties.
Method described in 50. the 30th, wherein, described phenotypic spectrum shows to have that genotypic individuality has or will have the probability of phenotype.
Method described in 51. the 50th, wherein, described probability is based on GCI or GCI Plus scoring.
Method described in 52. the 50th, wherein, the lifetime risk of described probability for estimating.
Method described in 53. the 30th, wherein, described dependency is through checking.
Method described in 54. the 30th, wherein, described rule set comprises at least 20 rules.
Method described in 55. the 30th, wherein, described rule set comprises at least 50 rules.
Method described in 56. the 30th, wherein, described rule set comprises the rule of the described genotypic correlation based in table 1.
Method described in 57. the 30th, wherein, described rule set comprises the rule of the described genotypic correlation based in Fig. 4,5,6,22 or 25.
Method described in 58. the 30th, wherein, described phenotype comprises quantitative proterties.
Method described in 59. the 58th, wherein, described quantitative proterties comprises medical condition.
Method described in 60. the 59th, wherein, described phenotypic spectrum shows whether described medical condition exists, produces the risk of described medical condition, the result for the treatment of of the prognosis of described medical condition, described medical condition or for the reaction of the treatment of described medical condition.
Method described in 61. the 58th, wherein, described quantitative proterties comprises the phenotype of non-medical state.
Method described in 62. the 58th, wherein, described quantitative proterties is selected from health proterties, physiological character, spiritual proterties, mood proterties, race, family or age.
Method described in 63. the 30th, wherein, described individuality is the mankind.
Method described in 64. the 30th, wherein, described individuality is non-human.
Method described in 65. the 30th, wherein, described individuality is registered user.
Method described in 66. the 30th, wherein, described individuality is nonregistered user.
Method described in 67. the 30th, wherein, described Genome Atlas comprises at least 100,000 kind of genotype.
Method described in 68. the 30th, wherein, described Genome Atlas comprises at least 400,000 kind of genotype.
Method described in 69. the 30th, wherein, described Genome Atlas comprises at least 900,000 kind of genotype.
Method described in 70. the 30th, wherein, described Genome Atlas comprises at least 1,000,000 kind of genotype.
Method described in 71. the 30th, wherein, described Genome Atlas comprises substantially whole genome sequence completely.
Method described in 72. the 30th, wherein, described data set comprises a plurality of data points, wherein each data point relates to individuality and comprises a plurality of data elements, wherein said data element comprises the unique identification thing that is selected from described individuality, genotype information, microarray SNP identifier, SNPrs identifier, chromosome position, polymorphic nucleotide, quality metric, raw data file, image, the intensity score of extracting, physical data, medical data, race, family, geographical, sex, age, family history, known phenotype, demographic data, expose data, at least one element of mode of life data and behavioral data.
Method described in 73. the 30th, wherein, regular update and application occur at least one times for 1 year.
Method described in 74. the 30th, wherein, provides described data set to comprise by following steps and obtains each the individual Genome Atlas in a plurality of individualities:
I) the hereditary sample being obtained by described individuality is carried out to genetic analysis, and
Ii) with computer-reader form, described analysis is encoded.
Method described in 75. the 30th, wherein, described phenotypic spectrum comprises single-gene phenotype.
Method described in 76. the 30th, wherein, described phenotype comprises polygene phenotype.
Method described in 77. the 30th, wherein, described report comprises initial table type spectrum.
Method described in 78. the 30th, wherein, described report comprises the phenotypic spectrum of renewal.
Method described in 79. the 30th, wherein, described report further comprises that this information is selected from one or more of the following stated about the information of the described phenotype of described phenotypic spectrum: accurate discriminating and the disaggregated classification of phenotype described in preventive measure, health and fitness information, therapy, symptom understanding, early detection scheme, intervention plan and described phenotypic spectrum.
Method described in 80. the 30th, the method further comprises:
E) the new Genome Atlas of new individuality being joined to described individual data items concentrates;
F) described rule set is applied to the described Genome Atlas of described new individuality; With
G) generate the Initial Report of the phenotypic spectrum of described new individuality.
Method described in 81. the 30th, the method comprises:
E) add the new Genome Atlas of described individuality;
F) described rule set is applied to the described new Genome Atlas of described individuality; With
G) generate the latest report of the phenotypic spectrum of described individuality.
82. 1 kinds of systems, this system comprises:
A) comprise regular rule set, each rule shows the dependency between at least one genotype and at least one phenotype;
B) code of rule set described in use at least one new regulation regular update, wherein said at least one new regulation shows the previous genotype not being relative to each other in described rule set and the dependency between phenotype;
C) comprise the database of the Genome Atlas of a plurality of individualities;
D) described rule set is applied to individual described Genome Atlas to determine the code of the phenotypic spectrum of described individuality; With
E) generate the code of the report of each individuality.
System described in 83. the 82nd, wherein, Internet Transmission is passed through in described report.
System described in 84. the 82nd, wherein, described report provides with cipher mode.
System described in 85. the 82nd, wherein, described report provides in non-encrypted mode.
System described in 86. the 82nd, wherein, described report provides by online entrance.
System described in 87. the 82nd, wherein, described report provides by paper part or e-mail.
System described in 88. the 82nd, this system further comprises to the described individual code of noticing dependency new or that revise.
System described in 89. the 82nd, this system further comprises the regular code new or that revise that can be applied to the described Genome Atlas of described individuality to described individual notice.
System described in 90. the 82nd, this system further comprises to described individual notice about the prevention new or that revise of the described phenotype of the described phenotypic spectrum of described individuality and the code of health and fitness information.
91. 1 kinds of test kits, this test kit comprises:
A) at least one collection containers;
B) for obtain the operation instruction of sample from individuality;
C) for access the operation instruction of the Genome Atlas of the described individuality being obtained by described sample by online entrance;
D) for access the operation instruction of the phenotypic spectrum of the described individuality being obtained by described sample by online entrance; With
E) for described collection containers being delivered to the packing of described sample preparation mechanism.
92. 1 kinds of online entrances, this online entrance comprises that individuality can access the website of described phenotypic spectrum, wherein said website allows described individuality to carry out at least one operation as described below:
A) select described rule to be applied to the Genome Atlas of described individuality;
B) on described website, check the initial report with upgrading;
C) from described website, print initial and the report of upgrading;
D) the initial report with upgrading from described website is saved on the computer of described individuality;
E) obtain prevention and the health and fitness information about the phenotypic spectrum of described individuality;
F) obtain genetic counseling online or that phone connects;
G) information extraction is to share with doctor/genetic consultant; And/or
H) obtain the service of collocation and the product providing.
Online entrance described in 93. the 92nd, wherein, described information exchange is crossed Internet Transmission.
Online entrance described in 94. the 92nd, wherein, encrypt described website.
Online entrance described in 95. the 92nd, wherein, does not encrypt described website.
Online entrance described in 96. the 92nd, wherein, described individuality has one or more options of the described security level that relates to this individual information or its one or more parts.
Online entrance described in 97. the 92nd, wherein, described phenotypic spectrum comprises the medical condition that can dispose.
Online entrance described in 98. the 92nd, wherein, described phenotypic spectrum comprises the medical condition without existing preventive measures or Current Therapy.
Online entrance described in 99. the 92nd, wherein, described phenotypic spectrum comprises non-medical state.
Assess the individual a kind of method that obtains risk of state for 100. one kinds, the method comprises:
A) obtain individual genotype;
B) by described genotype, determine that GCI or GCI Plus mark;
C) by described GCI or GCI Plus scoring, generate report; With
D) described report is offered to the health care management person of described individuality or described individuality.
Assess the individual a kind of method that obtains risk of state for 101. one kinds, the method comprises:
A) obtain individual genotype;
B) generate the Genome Atlas of described individuality;
C) by described Genome Atlas and genotypic correlation database, determined the risk of individual acquisition state;
D) by c) generate and report;
E) from the described individual new information that obtains;
F) by introducing described new information, determine the new risk of acquisition state;
G) by f) generate and report; With
H) described report is offered to the health care management person of described individuality or described individuality.
Assess the individual a kind of method that obtains risk of state for 102. one kinds, the method comprises:
A) obtain individual genotype;
B) generate the Genome Atlas of described individuality;
C) by described Genome Atlas and genotypic correlation database, determined the risk of individual acquisition state, the SNP of wherein said risk based on more than a kind of;
D) by c) generate and report;
E) described report is offered to the health care management person of described individuality or described individuality.
Method described in 103. the 100th, 101 or 102, wherein, the genotype of described individuality is directly from described individual acquisition.
Method described in 104. the 100th, 101 or 102, wherein, the genotype of described individuality obtains from third party.
Method described in 105. the 100th, 101 or 102, wherein, described in to provide be to pass through Internet Transmission.
Method described in 106. the 101st, wherein, described new information obtains from the biological sample of described individuality.
Method described in 107. the 101st, wherein, described new information obtains from individual somatometry.
108. the 101st or 102 described in method, wherein, described risk is obtained by GCI or GCI Plus scoring.
109. the 100th or 108 described in method, wherein, described GCI or GCI Plus scoring comprises the family of described individuality.
110. the 100th or 108 described in method, wherein, described GCI or GCI Plus scoring comprises the sex of described individuality.
111. the 100th or 108 described in method, wherein, described GCI or GCI Plus scoring comprises that the factor specific to described individuality, wherein said factor are not to be derived from described genotype.
Method described in 112. the 111st, wherein, described factor is selected from: individual birthplace, father and mother and/or grand parents, relationship family, position, residence, ancestors' position, residence, envrionment conditions, known healthy state, known drug interaction, domestic hygiene condition, mode of life situation, diet, exercise custom, marital status and somatometry.
113. the 107th or 112 described in method, wherein, the somatometry of described individuality is selected from: blood pressure, heart rate, glucose level, metabolite level, ion concentration, body weight, height, cholesterol levels, VITAMIN level, cytometry, weight index (BMI), protein level and transcript level.
Assess the individual a kind of method that obtains risk of state for 114. one kinds, the method comprises:
A) obtain individual genotype;
B) generate the Genome Atlas of described individuality;
C) determine the individual risk that obtains Alzheimer (AD), colorectal carcinoma (CRC), osteoarthritis (OA) or exfoliative glaucoma (XFG), wherein, described risk is based on rs4420638, for CRC, is based on rs6983267, for OA, is based on rs4911178 and is based on rs2165241 for XFG for AD;
D) by c) generate and report;
E) described report is offered to the health care management person of described individuality or described individuality.
Method described in 115. the 102nd, wherein, described risk is determined by least 3,4,5,6,7,8,9,10 or 11 SNP.
Method described in 116. the 102nd, wherein, described risk is determined by least 2 SNP.
Method described in 117. the 116th, wherein, described risk is to be rs9939609 or rs9291171 at least one in fat (BMIOB) and described at least 2 SNP.
Method described in 118. the 116th, wherein, described risk be in Graves' disease (GD) and described at least 2 SNP at least one for rs3087243, DRB1*0301DQA1*0501 or with the linkage disequilibrium of DRB1*0301DQA1*0501.
Method described in 119. the 116th, wherein, described risk is for rs1800562 or rs129128 at least one in hemochromatosis disease (HEM) and described at least 2 SNP.
Method described in 120. the 116th, wherein, described risk is for rs1866389, rs1333049 or rs6922269 at least one in myocardial infarction (MI) and described at least 2 SNP.
Method described in 121. the 116th, wherein, described risk is for rs6897932, rs12722489 or DRB1*1501 at least one in multiple sclerosis (MS) and described at least 2 SNP.
I22. the method described in the 116th, wherein, described risk is to be rs6859018, rs11209026 or HLAC*0602 at least one in psoriasis (PS) and described at least 2 SNP.
Method described in 123. the 116th, wherein, described risk is for rs6904723, rs2300478, rs1026732 or rs9296249 at least one in restless legs syndrome (RLS) and described at least 2 SNP.
Method described in 124. the 116th, wherein, described risk is for rs6840978, rs11571315, rs2187668 or DQA1*0301DQB1*0302 at least one in celiac disease (CelD) and described at least 2 SNP.
Method described in 125. the 116th, wherein, described risk is for rs4242384, rs6983267, rs16901979, rs17765344 or rs4430796 at least one in prostate cancer (PC) and described at least 2 SNP.
Method described in 126. the 116th, wherein, described risk is for rs12531711, rs10954213, rs2004640, DRB1*0301 or DRB1*1501 at least one in lupus (SLE) and described at least 2 SNP.
Method described in 127. the 116th, wherein, described risk is to be rs10737680, rs10490924, rs541862, rs2230199, rs1061170 or rs9332739 at least one in macular degeneration (AMD) and described at least 2 SNP.
Method described in 128. the 116th, wherein, described risk is to be rs6679677, rs11203367, rs6457617, DRB*0101, DRB1*0401 or DRB1*0404 at least one in rheumatoid arthritis (RA) and described at least 2 SNP.
Method described in 129. the 116th, wherein, described risk is to be rs3803662, rs2981582, rs4700485, rs3817198, rs17468277, rs6721996 or rs3803662 at least one in mammary cancer (BC) and described at least 2 SNP.
Method described in 130. the 116th, wherein, described risk is to be rs2066845, rs5743293, rs10883365, rs17234657, rs10210302, rs9858542, rs11805303, rs1000113, rs17221417, rs2542151 or rs10761659 at least one in Crohn's disease (CD) and described at least 2 SNP.
Method described in 131. the 116th, wherein, described risk is to be rs13266634, rs4506565, rs10012946, rs7756992, rs10811661, rs12288738, rs8050136, rs1111875, rs4402960, rs5215 or rs1801282 at least one in diabetes B (T2D) and described at least 2 SNP.
Accompanying drawing explanation
Fig. 1 is the schema that illustrates method of the present invention aspect.
Fig. 2 is the example of genomic dna quality control method.
Fig. 3 is the example of hybridization quality control method.
Fig. 4 is from the table with the typical genotypic correlation of the SNP of test and the open source literature of Effect Evaluation.A-I) represent the genotypic correlation of individual gene seat; J) represent the genotypic correlation of two locus; K) represent the genotypic correlation of three locus; L) race and the national index of abridging for using in A-K; M) be the reference of index, heritability and the heritability of phenotype title abbreviation (the Short Phenotype Name) abbreviation in A-K.
Fig. 5 A-J is the table with the typical genotypic correlation of Effect Evaluation.
Fig. 6 A-F is the table of the relative risk of typical genotypic correlation and estimation.
Fig. 7 is example report.
Fig. 8 is for analyzing and pass through the diagram of the system of Internet Transmission Genome Atlas and phenotypic spectrum.
Fig. 9 is the schema that illustrates business method of the present invention aspect.
Figure 10: popularity (prevalence) is evaluated the effect to relative risk assessment.Suppose that, in the situation of Hardy-Weinberg equilibrium (Hardy-Weinberg Equilibrium), each curve is corresponding to the different numerical value of gene frequency in colony.Article two, black line is corresponding to 9 and 6 odds ratio, and two red lines are corresponding to 6 and 4 odds ratio, and two blue lines are corresponding to 3 and 2 odds ratio.
Figure 11: the effect of gene frequency evaluation to relative risk assessment.Each curve is corresponding to the different numerical value of popularity in colony.Article two, black line is corresponding to 9 and 6 odds ratio, and two red lines are corresponding to 6 and 4 odds ratio, and two blue lines are corresponding to 3 and 2 odds ratio.
Figure 12: the paired comparisons of the absolute value of different models.
Figure 13: the paired comparisons of the grade point based on different models (GCI scoring).In table 2, provided different between Spearman dependency.
Figure 14: the effect of popularity report to GCI scoring.Spearman dependency between any two popularity values is at least 0.99.
Figure 15: be the figure of the example web page from individual entrance.
Figure 16: for illustrating that individual suffers from the figure of the example web page from individual entrance of the risk of prostate cancer.
Figure 17: for illustrating that individual suffers from the figure of the example web page from individual entrance of the risk of Crohn disease.
Figure 18: be the histogram that uses the GCI of the multiple sclerosis based on HapMAP of 2 SNP to mark.
Figure 19: for using the individual lifetime risk of the multiple sclerosis of GCI Plus.
Figure 20: be the histogram of the GCI scoring of Crohn disease.
Figure 21: be the table of polygene seat dependency.
Figure 22: be the table of SNP and phenotypic correlation.
Figure 23: be the table of phenotype and popularity.
Figure 24: be the glossary of abbreviation in Figure 21,22 and 25.
Figure 25: be the table of SNP and phenotypic correlation.
Embodiment
The storage Genome Atlas the invention provides based on individuality or group of individuals generates phenotypic spectrum, and the Genome Atlas based on storage generates method and system original and phenotypic spectrum renewal easily.By determining that by deriving from individual biological sample genotype generates Genome Atlas.The biological sample obtaining from individuality can be to be obtained by it any sample of hereditary sample.Sample can be from the tissue sample of buccal swab, saliva, blood, hair or any other type.Then can determine genotype by biological sample.Genotype can be any genetic variant or biomarker, for example, and single nucleotide polymorphism (SNPs), haplotype (haplotype)) or genomic sequence.Genotype can be individual full gene group sequence.Genotype can be obtained by the high throughput analysis that produces thousands of or millions of data points, for example, and for the microarray analysis of great majority or all known SNP.In other embodiments, genotype also can be determined by high throughput order-checking.
Genotype forms individual Genome Atlas.Genome Atlas carries out stored digital and is easy to putting and conducting interviews to generate phenotypic spectrum at any time.By application, make the rule of genotype and phenotypic correlation connection or combination generate phenotypic spectrum.Rule can be formulated in the scientific research based on showing the dependency between genotype and phenotype.The council that dependency can be comprised of one or more experts verifies (curate) or confirms.By by rule application in individual Genome Atlas, can determine individual genotype and the association between phenotype.Individual phenotypic spectrum will have this determinacy.This determines it can is the positive correlation between individual genotype and given phenotype, thereby this individuality has given phenotype or will produce this phenotype.Or, can determine that individuality does not have or will not produce given phenotype.In other embodiments, this determine can be risk factor, estimated value or individual have maybe will produce the probability of phenotype.
Can determine based on multiple rule, for example, multiple rule can be applied to Genome Atlas to determine the associated of idiotype and particular phenotype.Deterministic process also can comprise specific to individual factor, for example race, sex, mode of life (for example, diet and temper custom), age, environment (for example, inhabitation position), family's medical history, personal history and other known phenotype.Being incorporated to of specific factor can comprise these factors by revising existing rule.Or, can generate independent rule and after applying existing rule, be applied to individual phenotype by these factors and determine.
Phenotype can comprise any measurable proterties or characteristic, for example, for the susceptibility of certain disease or for the reaction of pharmacological agent.Other phenotype that can comprise is body and spiritual proterties, for example, and height, body weight, hair color, eye color, sunburn susceptibility, size, memory, intelligence, optimistic degree, whole disposition.Phenotype also can comprise the heredity comparison with other individualities or organism.For example, individuality may be interested in the similarity between their Genome Atlas and famous person's Genome Atlas.They also may make their gene mapping and other organism (for example bacterium, plant or other animal) compare.
In a word, the set for the determined relevant phenotype of individuality forms this individual phenotypic spectrum.Phenotypic spectrum can be accessed by online entrance.Or phenotypic spectrum can provide with paper part form according to the form existing at specified time, follow-up renewal also provides with paper part form.Phenotypic spectrum also can provide by online entrance.This online entrance is the online entrance for encrypting optionally.The access right of phenotypic spectrum can offer registered user, the rule that this registered user is the dependency between customized generation phenotype and genotype, determine individual Genome Atlas, by rule application in Genome Atlas with generate the individuality of the service of individual phenotypic spectrum.Access right also can offer nonregistered user, and wherein they can have their phenotypic spectrum of access and/or the limited authority of report, or can allow to generate Initial Report or phenotypic spectrum, but only has by the customized report of upgrading that just generates of paying.Health care management person and supplier, for example paramedic, doctor and genetic consultant also can have the access right of phenotypic spectrum.
In another aspect of this invention, can be registered user and nonregistered user generation Genome Atlas, and carry out stored digital, but can be limited to registered user for the access of phenotypic spectrum and report.In another modification, registered user and nonregistered user can be accessed its genotype and phenotypic spectrum, but nonregistered user has restricted access rights or allows the limited report of generation, yet registered user has complete access rights and can allow to generate complete report.In another embodiment, registered user and nonregistered user can have access rights or complete Initial Report completely at first, but only registered user can access the report that the Genome Atlas based on its storage upgrades.
In another aspect of this invention, organized combined analysis and to obtain hereditary aggregative index (genetic composite index), (GCI) marked with the associated information of one or more diseases or state about multiple genetic marker.This scoring has comprised known risk factor and out of Memory and hypothesis, for example, and the popularity of gene frequency and disease.GCI can be for the combined effect of qualitative assessment disease or state and a series of genetic markers associated.GCI scoring can for example, for providing reliable (, firm) about compare its Personal Risk with Reference Group, intelligible and/or be familiar with intuitively based on existing scientific research to the people who was not subject to genetics training.GCI scoring can be for generating GCI Plus scoring.GCI Plus scoring can comprise all GCI hypothesis, the sickness rate that this hypothesis comprises the risk (for example, lifetime risk) of state, the popularity of age restriction and/or age limit.Then individual lifetime risk may be calculated to individual GCI scoring divided by average GCI proportional GCI Plus scoring of marking.Average GCI scoring can be determined by the group of individuals with similar family background, and for example one group of Caucasian, Aisa people, people from East India or other have the group of common family background.Described group can be comprised of at least 5,10,15,20,25,30,35,40,45,50,55 or 60 individualities.In some embodiments, average GCI scoring can be determined by least 75,80,95 or 100 individualities.GCI Plus scoring can be removed this GCI by average relative risk and mark by determining that individual GCI mark, and the lifetime risk that is multiplied by state or phenotype is determined.For example, use from data and the information in Figure 24 of Figure 22 and/or Figure 25 and calculate GCI Plus scoring, for example, in Figure 19.
The present invention includes and use GCI scoring described here, and those skilled in the art are easy to recognize that GCI Plus scoring or its modification replace the purposes of GCI scoring described here.
In one embodiment, for each interested disease or state, generate GCI scoring.Can concentrate these GCI to mark to form individual risk distribution figure (risk profile).Can carry out stored digital to this GCI scoring and conduct interviews easily to generate risk distribution figure so that they can be put at any time.Risk distribution figure can decompose according to large classification of diseases, for example, and cancer, heart trouble, metabolism disorder, abalienation, osteopathy or senile disease (age on-set disorder).Large classification of diseases can further be broken down into subclass.For example, for as the large classification of cancer, can or by tissue specificity (nerve, mammary gland, ovary, testis, prostate gland, bone, lymphoglandula, pancreas, esophagus, stomach, liver, brain, lung, kidney etc.), list the subclass of cancer such as (sarcoma, cancer knurl or leukemia etc.) by type.
In another embodiment, generate individual GCI scoring, it provides to hold and intelligiblely about individuality, obtains the risk of at least one disease or state or for the information of the susceptibility of at least one disease or state.In one embodiment, for different diseases or state, generate multinomial GCI scoring.In another embodiment, can mark by least one GCI of online entrance access.Or, can provide at least one GCI scoring with paper part form, follow-up renewal also provides with paper part form.In one embodiment, to registered user, provide the access at least one GCI scoring, this registered user is the individuality of booking service.In an alternative embodiment, to nonregistered user, provide access rights, wherein they can have the limited access rights of at least one in their GCI scoring of access, or they can allow to generate the Initial Report of at least one in their GCI scoring, but only by the customized report of upgrading that just generates of paying.In another embodiment, health care management person and supplier, for example paramedic, doctor and genetic consultant, also can have the authority of at least one in the individual GCI scoring of access.
Here also can there is basic registration mode.Basic registration can provide phenotypic spectrum, and wherein registered user can select all existing rule application in their Genome Atlas, or existing well-regulated subset is applied to their Genome Atlas.For example, they can select only to apply the rule of the disease phenotype that can dispose (actionable).Basic registration can have different levels in registration grade.For example, different levels can depend on that registered user wants the phenotype number associated with their Genome Atlas, or depends on the personnel's of the phenotypic spectrum that can access them number.Another level of basic registration can be by the factor specific to individual, and the phenotype of for example knowing already (as age, sex or medical history) is incorporated to their phenotypic spectrum.Another level again of basic registration can allow individual at least one the GCI scoring generating for disease or state.If owing to causing any variation of at least one GCI scoring, the variations of this level can further allow the individual automatic renewal generating at least one GCI scoring of disease or state of specifying for generating the variation of the analysis of at least one GCI scoring.In some embodiments, can pass through e-mail, voice messaging, text message, postal delivery or fax notices automatically and upgrades to individuality.
Registered user also can generate has their phenotypic spectrum and for example, about the report of the information of phenotype heredity and the medical information of phenotype (about).For example, in report, can comprise the popularity of phenotype in colony, for the genetic variant of dependency, cause the molecular mechanism of phenotype, for the methods for the treatment of of phenotype, for treatment selection and the protective action of phenotype.In other embodiments, report can also comprise for example individual genotype and the information of the similarity between the genotype of other individualities (as famous person or other celebrities).Information about similarity may be, but not limited to, the number of percent homology, identical variation and phenotype that may be similar.These reports may further include at least one GCI scoring.
If online access report, report also can provide and be connected to the link that has about other positions of the further information of phenotype, is connected to the online support group of the people with identical phenotype or one or more similar phenotypes and the link of message board, contacts online genetic consultant or doctor's link or be connected to and arrange genetic consultant or doctor's phone or the link of on-the-spot reservation.If report is paper part form, information can be the site location of above-mentioned link or genetic consultant or doctor's telephone number and address.Which information is the phenotypic spectrum which phenotype registered user also can select be included in them neutralize is included in their report.Phenotypic spectrum and report also can be obtained by individual health care management person or supplier, for example paramedic, doctor, psychiatrist, psychologist, treatment expert or genetic consultant.Whether registered user also can select phenotypic spectrum and report or its partial content to be obtained by individual health care management person or supplier.
The present invention also can comprise the senior level (premium level) of registration.The senior level of registration keeps its Genome Atlas to digitizing after generating initial table type spectrum and report, and registered user can utilize the dependency of the renewal being obtained by nearest research to generate phenotypic spectrum and report.In another embodiment, registered user can utilize the dependency of the renewal being obtained by nearest research to generate risk distribution figure and report.Because research discloses the new dependency between genotype and phenotype, disease or state, based on these new dependencys, new rule will be produced, and new rule can be applied to the Genome Atlas of having stored and having kept.New rule can associated previously not associated with any phenotype genotype, make genotype with new phenotypic correlation connection, existing dependency or the associated adjustment basis that GCI marks that provides based between newfound genotype and disease or state are provided.Can inform the dependency that registered user is new by e-mail or other electronics mode, and if be interested phenotype, they can select to upgrade by new dependency their phenotypic spectrum.Registered user can be chosen as each renewal and pay, is for example, repeatedly upgrading or the logon mode of unlimited renewal paying in time limit (, 3 months, 6 months or 1 year) at the appointed time.Another registration level can be, no matter when the dependency based on new has produced new rule, and registered user automatically upgrades their phenotypic spectrum or risk distribution figure, rather than when individual selection upgrades their phenotypic spectrum or risk distribution figure.
In registration on the other hand, registered user can serve below to nonregistered user introduction: generate the association rules between phenotype and genotype, determine individual Genome Atlas, rule application, in Genome Atlas, and is generated to individual phenotypic spectrum.Registered user can make registered user mention preferential service subscription price or its existing registration is upgraded by introducing.Recommended individuality can be in finite time free access or enjoy discount cost of registering.
Can be for the mankind and non-human individual generation phenotypic spectrum and report and risk distribution figure and report.For example, individuality can comprise other Mammals, for example ox, horse, sheep, dog or cat.As used in this, registered user is the human individual of subscribed services by buying or pay one or more service.Service can include, but are not limited to following one or more: the Genome Atlas of determining themselves or another individuality (for example registered user's child or pet); Obtain phenotypic spectrum; Updating form type spectrum and obtain Genome Atlas based on them and the report of phenotypic spectrum.
In another aspect of this invention, can assemble from individuality and show that " (field-deployed) disposed in region " mechanism is to generate individual phenotypic spectrum.In a preferred embodiment, individuality can have the initial table type spectrum generating based on genetic information.For example, generate and comprise for the treatment of not isophenic risk factor and suggestion or the initial table type spectrum of preventive measures.For example, phenotypic spectrum can comprise for the information of the available pharmacological agent about a certain state and/or for the suggestion of changes in diet or workout scheme.Individual can select to see the doctor or genetic consultant or by Web portal or phone contact doctor or genetic consultant so that their phenotypic spectrum to be discussed.Individuality can determine to take certain course of action, for example, adopts specific pharmacological agent, changes their diet etc.
Then, individuality can submit to biological sample to assess the variation of its physical state and may changing of risk factor subsequently.The individual mechanism that can generate by directly biological sample being submitted to Genome Atlas and phenotypic spectrum (or associated mechanisms, the mechanism for example being concludeed a contract or treaty by the entity that generates hereditary distribution plan and phenotypic spectrum) determines this variation.Or individuality can utilize " region deployment " mechanism, wherein individuality can be submitted to their saliva, blood or other biological sample in the proofing unit at its family place, analyzed, and data is through transmitting to be included in another phenotypic spectrum by third party.For example, thus individuality can receive initial phenotype report based on its genetic data to the individuality report with myocardial infarction (MI) lifetime risk of increase.This report for example also can have the suggestion of preventive measures, to reduce the risk of MI, anticholesteremic agent and metatrophia.Individuality can select to contact genetic consultant or doctor changes their diet so that this report and preventive measures and decision to be discussed.Adopting new diet after for some time, individuality can go to see that their individual doctor is to measure its cholesterol levels.New information (cholesterol levels) (for example can be transmitted, pass through Internet) to the entity with genomic information, and new information is for generating individual new phenotypic spectrum, and the new risk factor of myocardial infarction and/or other state.
Individuality also can be used " region deployment " mechanism or directly machine-processed to determine that it is for the individual reaction of concrete pharmacological agent.For example, individuality can be measured it for the reaction of medicine, and this information can be for determining more effective treatment.Measurable information comprises, but (be for example not limited to meta-bolites level, glucose level, ion concentration, calcium, sodium, potassium, iron), VITAMIN, cytometry, weight index (BMI), protein level, transcript level, heart rate etc., these information can by the method for easy utilization determine and can be included in algorithm with initial gene picture group spectrum in conjunction with determining that the overall risk assessment of revising marks.
Term " biological sample " refer to any can be from individual separated biological sample, it comprises the therefrom sample of separated genetic material.Just as used herein, " hereditary sample " refer to from individuality, obtain or be derived from individual DNA and/or RNA.
As used herein, term " genome " is used for being illustrated in a whole set of chromosomal DNA of finding in the nucleus of human body cell.Term " genomic dna " refers to that nature is present in the one or more chromosomal DNA molecules in the nucleus of human body cell, or a part for chromosomal DNA molecule.
Term " Genome Atlas " refers to one group of information about genes of individuals, and for example whether specific SNP or sudden change exist.Genome Atlas comprises individual genotype.Genome Atlas can be also individual basic complete genome group sequence.In some embodiments, Genome Atlas can be at least 60%, 80% or 95% of individual complete genome group sequence.Genome Atlas can be about 100% individual complete genome group sequence.When mentioning Genome Atlas, " its part " refers to the Genome Atlas of the subset of complete genomic Genome Atlas.
Term " genotype " refers to the specific genetic composition of individual DNA.Genotype can comprise individual genetic variant and genetic marker.Genetic marker and genetic variant can comprise that Nucleotide repetition, Nucleotide insertion, nucleotide deletion, chromosome translocation, karyomit(e) repeat or copy number variation.Copy number variation can comprise that micro-satellite repeats, Nucleotide repeats, repeat in kinetochore or telomere repeats.Genotype can be also SNP, haplotype or double body type (diplotype).Haplotype can refer to locus or allelotrope.Haplotype also can be called the one group of single nucleotide polymorphism (SNP) on statistically associated single chromatid.Double body type is one group of haplotype.
Term single nucleotide polymorphism or " SNP " refer to the specific gene seat that shows variation (for example at least 1 percentage point (1%)) on karyomit(e) with respect to the identity that is present in the nitrogenous choline on a locus in mankind population.For example, at body one by one in the situation that may there is adenosine (A) on the specific nucleotide position of given gene, another individuality may have cytosine(Cyt) (C), guanine (G) or thymus pyrimidine (T) on this position, thereby has SNP on this specific position.
Just as used herein, term " SNP gene element Butut " refers to the base contents of individual DNA given on the SNP position of whole individual whole genome DNA sequence dna." SNP distribution plan " refers to complete gene element Butut, or refers to one part, more local SNP distribution plan that for example may be relevant with specific gene or specific one group of gene.
Term " phenotype " is for describing individual quantitative proterties or feature.Phenotype includes, but are not limited to medical science and non-medical state.Medical condition comprises disease and disorder.Phenotype also can comprise health proterties, for example color development, the spiritual proterties keeping as the physiological character of lung volume, as memory, as the mood proterties of angry controllability, as the racial traits of ethnic background, as the family feature of individuality class origin position and as age expectation or the age characteristics of isophenic age of onset not.Phenotype can be also monogenic, wherein it is believed that a gene may join with phenotypic correlation; Or polygenic, the gene that one of them is above and phenotypic correlation connection.
" rule " is for defining the dependency between genotype and phenotype.Rule can define dependency by numerical value, for example, by percentage, risk factor or degree of confidence, mark.Rule can comprise the dependency of a plurality of genotype and phenotype." rule set " comprises more than one rule." new regulation " can be the rule that shows the dependency between the current still non-existent genotype of its rule and phenotype.New regulation can be by not associated genotype and phenotypic correlation connection.New regulation also can will join with previously not associated phenotypic correlation with the genotype of phenotypic correlation connection." new regulation " can be also the existing rule of being revised by other factors (comprising another rule).Existing rule can be due to individual known features, the previous definite phenotype of race, family, geography, sex, age, family history or other for example, and revise.
As used in this, " genotypic correlation " refers to for example, statistic correlation between idiotype (existence of a certain sudden change or a plurality of sudden changes), and the possibility of tending to occur a kind of phenotype (for example specified disease, state, physical state and/or the mental status).The frequency of observing particular phenotype under specific gene type exists has determined the degree of genotypic correlation or has occurred the possibility of specific phenotype.For example, as what describe in detail at this, the SNP that causes apolipoprotein E isotype to bring out that early hair style Alzheimer is relevant.Genotypic correlation also can refer to wherein be not inclined to dependency or the negative correlation that produces phenotype.Genotypic correlation also can represent that individuality has phenotype or tends to occur the assessment of phenotype.Can be by numeric representation genotypic correlation, for example percentage ratio, the relative risk factor, Effect Evaluation or degree of confidence scoring.
Term " phenotypic spectrum " refers to the set with a genotype of individuality or a plurality of phenotypes of a plurality of genotypic correlations.Phenotypic spectrum can comprise information or the relevant information that is applied to the genotypic correlation of Genome Atlas by one or more rule application is produced in Genome Atlas.Can generate phenotypic spectrum by a plurality of genotype of application rule associated with phenotype.Probability or assessment can be expressed as numerical value, for example the risk factor of percentage ratio, numeral or the fiducial interval of numeral.Probability also can be expressed as height, in or low.Phenotypic spectrum also can show whether phenotype exists or produce the risk of phenotype.For example, phenotypic spectrum can show the existence of blue eyes or the excessive risk of generation diabetes.Phenotypic spectrum also can show prognosis, result for the treatment of or the reaction to the treatment of medical condition of prediction.
Term risk distribution plan refers to the set for the GCI scoring of more than one disease or state.The GCI scoring associated analysis based on between idiotype and one or more diseases or state.Risk distribution figure can show the GCI scoring by classification of diseases grouping.Further, risk distribution figure can show the information of how predicting the variation of GCI scoring with the adjustment of Individual Age or multiple risk factor.For example, for the GCI scoring of specified disease, can consider changes in diet or the effect of the preventive measures taked (stop smoking, take medicine, underwent bilateral radical mastectomy, uterectomy).GCI scoring can be shown as the combination of numerical value metering, figure demonstration, audio feedback or any aforementioned manner.
As used herein, term " online entrance " refers to individual by computer and internet site, phone or allow information to carry out the information source that the alternate manner of similar access is accessed easily.Online entrance can be to encrypt website.This website can provide encrypts with other and the linking of non-encrypted website, for example, connect the link of the encryption website with individual phenotypic spectrum or connect the link of (as having the individual message board of particular phenotype) of non-encrypted website.
Except as otherwise noted, enforcement of the present invention can utilize molecular biology, cytobiology, biological chemistry and immunologic routine techniques and the operation instruction in those skilled in the art's limit of power.These routine techniquess comprise separate nucleic acid, polymer array synthetic (polymer array synthesis), hybridization, connect the hybridization check of (ligation) and applying marking thing.The present invention for example understands the concrete illustration of proper technology and has provided reference.But, also can use other equivalent ordinary method.Other routine techniques and operation instruction can find in following standard laboratory handbook and document: for example, genome analysis: laboratory manual series (volume I-IV) (Genome Analysis:A Laboratory Manual Series (Vols.I-IV)), PCR primer: laboratory manual (PCR Primer:A Laboratory Manual), molecular cloning: laboratory manual (Molecular Cloning:A Laboratory Manual) (being all derived from press of cold spring harbor laboratory (Cold Spring Harbor Laboratory Press)), Stryer, L. (1995) biological chemistry (the 4th edition) Freeman, New York, Gait, " oligonucleotide is synthetic: hands-on approach (Oligonucleotide Synthesis:A Practical Approach) " 1984, IRL press, London, Nelson and Cox (2000), Lehninger, biochemical theory, the third edition, W.H.Freeman Pub., New York, N.Y., and (2002) biological chemistry such as Berg, the 5th edition, W.H.Freeman Pub., New York, N.Y., the full content of above-mentioned all documents is incorporated herein by reference at this.
Method of the present invention comprises analyzes genes of individuals picture group spectrum so that the molecular information about phenotype to be provided to individuality.As what describe in detail at this, individuality provides the hereditary sample that generates individual Genome Atlas.By Genome Atlas is compared with the database of the Human genome type dependency of establishing and verifying, the data of query individual Genome Atlas related gene type dependency.The database of the genotypic correlation of having established and having verified can be from the document of the peer review (peer-reviewed), and one or more experts' in this area (for example geneticist, epidemiologist or statistician) the council is further passed judgment on, and verifies.In a preferred embodiment, the genotypic correlation of rule based on empirical tests formulated, and is applied to individual Genome Atlas to generate phenotypic spectrum.The analytical results (phenotypic spectrum) of genes of individuals picture group spectrum offers individuality or individual's health care management person together with supportive information with explanation, thereby give the health care to individuality, carry out the personalized ability of selecting.
Method of the present invention is described in detail in Fig. 1, wherein first generates individual Genome Atlas.Genes of individuals picture group spectrum is by the information comprising about the genes of individuals based on heritable variation and genetic marker.Heritable variation is genotype, its constitutive gene picture group spectrum.These heritable variations or genetic marker comprise, but be not limited to single nucleotide polymorphism, list and/or polynucleotide repetition, list and/or polynucleotide disappearance, micro-satellite and repeat that (a small amount of Nucleotide conventionally with 5～1,000 repeating unit repeats), dinucleotides repeat, trinucleotide repeats, sequence is reset (comprising transposition and repetition), copy number variation (disappearance on specific gene seat and increase) etc.Other heritable variation comprises that karyomit(e) repetition and transposition and kinetochore repeat and telomere repeats.
Genotype also can comprise haplotype and double body type.In some embodiments, Genome Atlas can have at least 100,000,300,000,500,000 or 1,000,000 genotype.In some embodiments, Genome Atlas can be substantially individual complete genome group sequence.In other embodiments, Genome Atlas is at least 60%, 80% or 95% individual complete genome group sequence.Genome Atlas can be about 100% individual complete genome group sequence.The DNA (or cDNA) of the genomic dna that the hereditary sample that comprises target material includes, but are not limited to not increase or RNA sample or amplification.Target material can be the specific region of the genomic dna that comprises interested especially genetic marker.
In the step 102 of Fig. 1, individual hereditary sample is separated from individual biological sample.These biological samples include, but are not limited to blood, hair, skin, saliva, seminal fluid, urine, fecal materials, sweat, oral cavity (buccal) and various bodily tissue.In some embodiments, tissue sample can directly gather from individuality, and for example oral cavity sample can swab inner side, its cheek with swab by individuality and obtains.For example other sample of saliva, seminal fluid, urine, fecal materials or sweat also can be provided by individuality.Other biological sample can for example, be extracted by health professional (bleeder, nurse or doctor).For example, blood sample can be extracted from individuality by nurse.Biopsy can be undertaken by health professional, and health professional also can utilize test kit effectively to obtain sample.Can pipette little cylinder skin samples or use pin to pipette little tissue or fluid sample.
In some embodiments, to individuality, provide the test kit having for the specimen collection container of individual biological sample.Test kit also can provide the individual specification sheets that directly gathers himself sample, for example, need to provide how many hairs, urine, sweat or saliva.Test kit also can comprise the individual specification sheets that requires to be extracted by health professional tissue sample.Test kit can comprise can be by the place of third party's collected specimens, for example, test kit can be offered subsequently to the health institution from individual collected specimens.Test kit can also be provided for sample to be delivered to the return package of sample preparation mechanism, genetic material separated (step 104) from biological sample in Gai mechanism.
Can be according to the hereditary sample of DNA isolation or the RNA from biological sample of any method in several known organisms chemistry and molecular biology method, referring to such as people such as Sambrook, molecular cloning: laboratory manual (Molecular Cloning:A Laboratory Manual) (cold spring harbor laboratory, New York) (1989).Also have several for commercially available test kit and reagent from biological sample DNA isolation or RNA, test kit and the reagent that for example can obtain from DNA Genotek, Gentra Systems, Qiagen, Ambion and other supplier.Oral cavity sample reagent box is easy to be commercially available, for example, derive from the MasterAmp of Epicentre Biotechnologies tMbuccal Swab DNA extraction test kit extracts the test kit of DNA equally in addition from blood sample, for example, derive from the Extract-N-Amp of Sigma Aldrich tM.The DNA that is derived from other tissue can be by with protease digestion tissue with heat-treat, centrifugal sample and the unwanted material of use benzene phenol-chloroform extracting, DNA stayed in water and obtained.Then can be by the further DNA isolation of ethanol precipitation.
In a preferred embodiment, isolation of genomic DNA from saliva.For example, the DNA that use can obtain from DNA Genotek is from gathering test kit technology, and the individual saliva sample that gathers is for Clinical Processing.Sample can at room temperature store easily and transport.After sample being delivered to the suitable laboratory of processing, by sample is carried out to thermally denature and protease digestion (conventionally utilizing the reagent being provided by collection test kit supplier to carry out at least 1 hour) at 50 ℃, carry out DNA isolation.Follow centrifugal sample, and supernatant liquid is carried out to ethanol precipitation.DNA precipitation is suspended in the damping fluid that is suitable for subsequent analysis.
In another embodiment, can use RNA as hereditary sample.Especially, can identify the heritable variation of expressing from mRNA.Term " messenger RNA(mRNA) " or " mRNA " include, but are not limited to premessenger RNA transcript, transcript processing intermediate, prepare for the translation of a gene or a plurality of genes and the ripe mRNA transcribing or the nucleic acid that is derived from mRNA transcript.Transcript processing can comprise montage, editor and degraded.As used in this, the nucleic acid that is derived from mRNA transcript refers to that mRNA transcript or its subsequence finally serve as the nucleic acid of its synthetic template.Therefore, by the cDNA of mRNA reverse transcription, from the DNA of cDNA amplification, the RNA that transcribes from the DNA of amplification etc., be to be all derived from mRNA transcript.Can use methods known in the art any one isolation of RNA from several bodily tissues, for example, use the PAXgene obtaining from PreAnalytiX tMblood rna system is isolation of RNA from unassorted (unfractionated) whole blood.Typically, mRNA will be for reverse transcription cDNA, and cDNA is used subsequently or increases and analyze for genovariation.
Before Genome Atlas is analyzed, conventionally by the cDNA of DNA or the RNA reverse transcription hereditary sample that increases.Can pass through several different methods DNA amplification, the many PCR that used in these methods.Referring to for example, round pcr: DNA cloning mechanism and application (PCR Technology:Principles and Applications for DNA Amplification) (Ed.H.A.Erlich, Freeman Press, NY, N.Y., 1992); PCR scheme: methods and applications guide (PCR Protocols:A Guide to Methods and Applications) (people such as Eds.Innis, Academic Press, San Diego, Calif., 1990); The people such as Mattila, Nucleic Acids Res.19,4967 (1991); The people such as Eckert, PCR method and application (PCR Methods and Applications) 1,17 (1991); PCR (people such as Eds.McPherson, IRL Press, Oxford); With United States Patent (USP) the 4th, 683,202,4,683,195,4,800,159,4,965,188 and 5,333, No. 675, above-mentioned each document is incorporated herein by reference with its full content at this.
Other applicable amplification method (for example comprises ligase chain reaction (LCR), Wu and Wallace, genomics, 4, 560 (1989), the people such as Landegren, science, 241, 1077 (1988) and the people such as Barringer, gene, 89:117 (1990)), transcription amplification (the people such as Kwoh, Proc.Natl.Acad.Sci.USA86:1173-1177 (1989) and WO88/10315), self-sustained sequence replication (the people such as Guatelli, Proc.Nat.Acad.Sci.USA, 87:1874-1878 (1990) and WO90/06995), the selective amplification of target polynucleotide sequence (United States Patent (USP) the 6th, 410, No. 276), consensus sequence primer-oligomerization polymerase chain reaction (CP-PCR) (United States Patent (USP) the 4th, 437, No. 975), arbitrarily primed polymerase chain reaction (AP-PCR) (United States Patent (USP) the 5th, 413, 909, 5, 861, No. 245), sequence amplification based on nucleic acid (nucleic acid based sequence amplification) (NABSA), rolling circle amplification (RCA), multiple displacement amplification (multiple displacement amplification) is (United States Patent (USP) the 6th (MDA), 124, 120 and 6, 323, No. 009) and encircle to (the C2CA) (people such as Dahl of circle amplification (circle-to-circle amplification), Proc.Natl.Acad.Sci101:4548-4553 (2004)).(referring to United States Patent (USP) the 5th, 409,818,5,554,517 and 6,063, No. 603, above-mentioned each document is incorporated herein by reference at this).In United States Patent (USP) the 5th, 242,794,5,494,810,5,409,818,4,988,617,6,063,603 and 5,554, No. 517 and U.S. Patent application the 09/854th, described operable other amplification method in No. 317, and above-mentioned each document is incorporated herein by reference at this.
Use the generation of the Genome Atlas of any one completing steps 106 in several method.Known in the art in order to identify the several method of heritable variation, and these methods comprise, but be not limited to by any one DNA sequencing carrying out in several method, the method of PCR-based, fragment length polymorphism analysis (restriction fragment length polymorphism (RFLP), crack fragment length polymorphism (CFLP)), use allele specific oligonucleotide as the hybridizing method of template (for example, TaqMan PCR method, invader method (invader method), DNA chip method), use the method for primer extension reaction, mass spectrometry (MALDI-TOF/MS method) etc.
In one embodiment, high-density DNA array is identified for SNP and distribution plan generation.These arrays can be buied (referring to Affymetrix from Affymetrix and Illumina
Figure BSA0000097585230000311
500K Assay Manual, Affymetrix, Santa Clara, CA (being incorporated herein by reference); humanHap650Y gene type superbead chip (genotyping beadchip), Illumina, San Diego, CA).
For example, can use Affymetrix Genome Wide Human SNP Array6.0 by carrying out gene type to generate SNP distribution plan to surpassing 900,000 SNP.Or, can be by using Affymetrix GeneChip Human Mapping500K Array Set to determine 500,000 SNP that surpass through complete genome sampling analysis.In these analytical procedures, the subset of human genome is used human gene group DNA digestion with restriction enzyme, that joint connects to be reacted and increased by single primer amplification.As shown in Figure 2, then can determine the concentration of the DNA of connection.The DNA break then increasing, and in the quality that continues the front definite sample of step 106.If samples met PCR and fragmentation standard, to sample carry out sex change, mark and subsequently with the quartzy face applying on the microarray that forms of the little DNA probe of specific position hybridize.The monitoring amount with marker each probe hybridization that change with the DNA sequence dna of amplification, thus sequence information and final SNP gene type produced.
The use of Affymetrix GeneChip 500K Assay is carried out according to the guidance of manufacturers.In brief, first with NspI or StyI restriction endonuclease, digest separated genomic dna.Then the DNA of digestion is connected with NspI or the StyI joint oligonucleotide of annealing with NspI or StyI restricted DNA respectively.Then the DNA that comprises joint after connecting increases to be created in the amplification of DNA fragments between approximately 200 to 1100 base pairs by PCR, and this is confirmed by gel electrophoresis.The PCR product that meets amplification standard carries out purifying and quantitatively to carry out fragmentation.PCR product ruptures the DNA chip hybridization that reaches best with DNase I.After fracture, DNA fragmentation should be less than 250 base pairs, and average out to 180 base pairs, and this confirms by gel electrophoresis.Then use terminal deoxynucleotidyl transferase with biotin compound mark, to meet the sample of fragmentation standard.Then by the fragment sex change of mark, then hybridize in GeneChip 250K array.After hybridization, treating processes pair array by three steps before scanning dyes, three described treating processess are comprised of the following step: streptavidin phycoerythrin (SAPE) dyeing, the antibody amplification step of utilizing biotinylated anti-streptavidin antibody (goat) subsequently, and with the final dyeing of streptavidin phycoerythrin (SAPE).After mark, array keeps damping fluid to cover with array, then with for example scanner of Affymetrix GeneChip Scanner3000, scans.
After Affymetrix GeneChip Human Mapping500K Array Set scanning, according to the guidance of manufacturers, carry out data analysis, as shown in Figure 3.In brief, use GeneChip function software (GCOS) to obtain raw data.Also can be by using Affymetrix GeneChip Command Console tMobtain data.After obtaining primary data, with GeneChip gene type analysis software (GTYPE), analyze.For the purposes of the present invention, eliminating GTYPE calls rate (call rate) and is less than 80% sample.Then with BRLMM and/or SNiPer Algorithm Analysis, sample is tested.Get rid of BRLMM call rate be less than 95% or SNiPer call the sample that rate is less than 98%.Finally, carry out association analysis, and get rid of SNiPer quality index and be less than 0.45 and/or Ha Di-Weinberg p-value sample of being less than 0.00001.
That as DNA microarray, analyzes substituting or adding, and can detect heritable variation by DNA sequencing, for example SNP and sudden change.Also can use DNA sequencing to check order to individual major portion or full gene group sequence.Conventionally, conventional DNA sequencing is with analytic thread dististyle stage group (people such as Sanger, Proc.Natl.Acad.Sci.USA74:5463-5467 (1977)) based on polyacrylamide gel fractional separation.Alternative method that developed and that proceed to develop has improved speed and the simplicity of DNA sequencing.For example, high-throughput and single-molecule sequencing platform can be from 454 Life Sciences (Branford, CT) (the people such as Margulies, nature, (2005) 437:376-380 (2005)), Solexa (Hayward, CA), (Cambridge of Helicos BioSciences company, MA) (No. 11/167046th, the U. S. application of submitting on June 23rd, 2005) and Li-Cor Biosciences (Lincoln, NE) (No. 11/118031st, the U. S. application of submitting on April 29th, 2005) is commercially available, or just by them, developed.
Generate individual Genome Atlas in step 106 after, in step 108, this collection of illustrative plates is stored in digitizing, and this collection of illustrative plates can be stored with cipher mode digitizing.With computer-readable format, this Genome Atlas is encoded to be stored as the part of data set, and can be stored as database, wherein Genome Atlas can be by " savings ", and access again later.Data set comprises a plurality of data points, and wherein each data point relates to body one by one.Each data point can have a plurality of data elements.A data element is the unique identifier of identifying individual Genome Atlas.It can be also barcode.Another data element is genotype information, for example the SNP of genes of individuals group or nucleotide sequence.Data element corresponding to genotype information also can be included in data point.For example, if genotype information comprises the SNP being identified by microarray analysis, other data element can comprise microarray SNP identifier, No. SNPrs and polymorphic nucleotide (polymorphic nucleotide) so.Other data element can be that the chromosome position of genotype information is, the quality metrics of data, raw data file, data image and extraction intensity score.
Individual specific factors, for example body data, medical data, race, family, geography, sex, age, family history, known phenotype, demographic data, exposure data (exposure data), mode of life data, behavioral data and other known phenotype, also can be used as data element and included.For example, these factors can comprise, but be not limited to individual: birthplace, father and mother and/or grand parents, relationship family, position, residence, ancestors' position, residence, envrionment conditions, known healthy state, known drug interaction, domestic hygiene condition, mode of life condition, diet, exercise custom, marital status and somatometry data (for example, body weight, height, cholesterol levels, heart rate, blood pressure, gentle other take off data known in the art of G/W).Individual relative or ancestors' (for example, father and mother and grand parents) above-mentioned factor also can be introduced as data element and for determining individual phenotype or the risk of state.
Specific factor can obtain from questionnaire or from individual health care management person.Then, can access from the information of the collection of illustrative plates of " savings " and use by required.For example, in the initial assessment of individual genotypic correlation, will analyze individual full detail (SNP on whole genome or that obtain from whole genome or other genome sequence conventionally) for determining genotypic correlation.In follow-up analysis, can be on demand or suitably access from full detail or its part of Genome Atlas storage or savings.
the comparison of Genome Atlas and genotypic correlation database
In step 1l0, genotypic correlation obtains from scientific literature.By to whether there are one or more interested phenotypic characters and gene type spectrum having been carried out determining in analysis that the individual colony of test carries out in the genotypic correlation of heritable variation.Then the allelotrope of each heritable variation or polymorphism in gene type spectrum is detected to determine whether that specific allelic existence with interested proterties is associated.Can carry out correlation analysis by standard statistical routines, and record the dependency of the statistically significant between heritable variation and phenotypic characteristic.Such as, may determine, the existence of the allelotrope A1 of polymorphism A is relevant to heart trouble.The combination existence of the allelotrope A1 that may find at polymorphism A as a further example, and the allelotrope B1 of polymorphism B is relevant to the increase of risk of cancer.The result of analyzing can be announced in peer review document, is confirmed, and/or for example, is analyzed by Committee of Experts's (, geneticist, statistician, epidemiologist and doctor), and also can verify by other study group.
In Fig. 4,5 and 6, be the example of the dependency between genotype and phenotype, be wherein applied to the genotype of Genome Atlas and the rule between phenotype based on these dependencys.For example, in Fig. 4 A and B, each row is corresponding to phenotype/locus/race, and wherein Fig. 4 C to I comprises the further information of the dependency of each row in these row.As an example, in Fig. 4 A, BC " abbreviation of phenotype title " is the abbreviation of mammary cancer as what indicate in the index of Fig. 4 M phenotype title abbreviation.In this line of BC_4 (its class name that is locus), gene LSP1 is relevant to mammary cancer.As shown in Fig. 4 C, the disclosed or functional SNP confirming for this dependency is rs3817198, and disclosed risk allelotrope is C, and non-risk allelotrope is T.Disclosed SNP and allelotrope for example, are confirmed by publication (, the basic open source literature in Fig. 4 E-G).In the example of the LSP1 of Fig. 4 E, people, nature, the 447:713-720 (2007) such as basic open source literature is Easton.Figure 22 and 25 has been further listed in dependency.Can use correlation calculations individuality in Figure 22 and 25 for the risk of a kind of state or phenotype, for example, calculate GCI or GCI Plus scoring.GCI or GCI Plus scoring also can be introduced for example information of the popularity of state, as in Figure 23.
Or, can form dependency by the Genome Atlas of storing.For example, the individuality that has the Genome Atlas of storage also may have been stored known phenotype information.Analysis to the Genome Atlas of storage and known phenotype can form genotypic correlation.As an example, 250 individualities with storage Genome Atlas also have and had previously been diagnosed as the storage information of suffering from diabetes.Their Genome Atlas is analyzed and compared with the control group of non-diabetic individuality.Then determine that be previously diagnosed as the individuality of suffering from diabetes compares with control group that to have the ratio of specific genetic variant higher, thereby can between specific genetic variant and diabetes, draw genotypic correlation.
In step 112, the dependency formation rule based between certified genetic variant and particular phenotype.For example can be based on table 1 listed be mutually related genotype and phenotype create-rule.Rule based on dependency can be introduced other factors, for example, sex (as, Fig. 4) or race (Figure 4 and 5) to produce as the Effect Evaluation in Figure 4 and 5.Other generation by rule measured the relative risk that can assess as in Fig. 6 to be increased.The relative risk increase of Effect Evaluation and estimation can be from disclosed document, or is calculated by disclosed document.Or, the dependency that the Genome Atlas that rule can be based on by storing and the phenotype of previously known produce.In some embodiments, the dependency that rule can be based in Figure 22 and 25.
In a preferred embodiment, genetic variant is SNP.Although SNP occurs on unit point, be carried at the common measurable special SNP allelotrope that carries of the allelic individuality of specific SNP on a site on other site.SNP produces by linkage disequilibrium (linkage disequilibrium) with making the individual allelic dependency of easily sending out disease or state, and the frequency that wherein nonrandom association occurs the allelotrope on two or more locus in colony is greater than or less than to be estimated by the random frequency obtaining that forms of recombinating.
Other genetic marker or modification (for example Nucleotide repeat or insert) also can with the genetic marker generation linkage disequilibrium being shown as with specific phenotypic correlation.For example, Nucleotide inserts and phenotypic correlation, and SNP and Nucleotide insertion generation linkage disequilibrium.Dependency formation rule based between SNP and phenotype.Also can form the rule of the dependency based between Nucleotide insertion and phenotype.Can be by arbitrary rule or two rule application in Genome Atlas because the existence of a SNP can provide a certain risk factor, another rule can provide another risk factor, and when they in conjunction with time can increase risk.
By linkage disequilibrium, the specific allelic combination of easily sending out the allelotrope of disease and the specific allelotrope of SNP or SNP is divided into from (cosegregate).Along the allelic particular combination of chromosomal SNP, be called haplotype, and the DNA region that wherein they occur to combine can be called haplotype section.Although haplotype section can be comprised of a SNP, typical haplotype segment table is shown in the series that shows low haplotype diversity between individuality and conventionally have the SNP of 2 of low recombination frequency or a plurality of vicinities.Can be tested and appraised the one or more SNP that are arranged in haplotype section and carry out the evaluation of haplotype.Like this, conventionally, SNP distribution plan can rather than must be identified all SNP in given haplotype section for the identification of haplotype section.
Genotypic correlation between SNP haplotype pattern and disease, state or physical state becomes known gradually.For given disease, the known haplotype pattern with the lineup of this disease is compared with the lineup without this disease.By analyzing many individualities, can determine the frequency of polymorphism in colony, and these frequencies or genotype can for example, be associated with specific phenotype (disease or state) subsequently.The polymorphism (people such as Klein, science, 308:385-389, (2005)) that the example of known SNP-disease-related is included in complement factor H in age-dependent macular degeneration is with relevant to obesity close iNSIG2the modification of gene (people such as Herbert, science, 312:279-283 (2006)).Other known SNP dependency for example comprises, comprise that polymorphism in the 9p21 region of CDKN2A and B is (such as the rs10757274 relevant with myocardial infarction, rs2383206, rs13333040, rs2383207 and the rs10116277 (people such as Helgadottir, science, 316:1491-1493 (2007); The people such as McPherson, science, 316:1488-1491 (2007)).
SNP can be functional or non-functional.For example, functional SNP cellular function has impact, thereby causes phenotype, however non-functional SNP in function, mourn in silence, but can there is linkage disequilibrium with functional SNP.SNP can be also synonym or non-synonym.The SNP of synonym is the multi-form SNP that causes identical peptide sequence wherein, and is non-functional SNP.If SNP causes not homopolypeptide, so SNP be non-synonym and can be functional or non-functional.SNP or other genetic marker for the identification of the haplotype in double body type (it is 2 or a plurality of haplotype) also can be for the associated phenotypes relevant to double body type.Information about individual haplotype, double body type and SNP distribution plan can be in individual Genome Atlas.
In a preferred embodiment, form the rule of the genetic marker generation of linkage disequilibrium for another genetic marker based on associated with phenotype, this genetic marker can have the r that is greater than 0.5 2or D ' score, this score is conventionally in the art for determining linkage disequilibrium.In a preferred embodiment, score is greater than 0.6,0.7,0.8,0.90,0.95 or 0.99.As a result, in the present invention, for can be identical by the phenotype genetic marker associated with individual Genome Atlas or be different from the functional or disclosed SNP with phenotypic correlation.For example, use BC_4, test SNP and disclosed SNP are identical, as risk and the non-risk allelotrope tested, are identical (Fig. 4 A and C) with disclosed risk and non-risk allelotrope.But, for BC_5, CASP8 and with the dependency of mammary cancer, test SNP is functional from it or disclosed SNP is different, the same with non-risk allelotrope for disclosed risk with non-risk allelotrope as the risk of testing.That tests is directed with respect to genomic normal chain with disclosed allelotrope, and can infer homozygous risk or non-risk genes type from these row, and this can generate the rule for for example registered user's individual Genome Atlas.In some embodiments, also characterization test SNP not, but use disclosed SNP information, can for example, based on another analytical procedure (TaqMan), identify allelotrope difference or SNP.For example, the AMD_5 in Figure 25 A, disclosed SNP is rs1061170, but there is no characterization test SNP.Can be by the LD Analysis and Identification test SNP of disclosed SNP.Or, can not use test SNP, but with TaqMan or other suitable analytical procedure evaluation, there is the genes of individuals group of this test SNP.
Test SNP can be " directly (DIRECT) " or " label (TAG) " SNP (Fig. 4 E-G, Fig. 5).Directly SNP is the test SNP identical with disclosed or functional SNP, for example, for BC_4.Use European and Asian SNP rs1073640, directly SNP also can be for the FGFR2 dependency of mammary cancer, and wherein less important allelotrope is that A and other allelotrope are G people such as (, nature, 447:1087-1093 (2007)) Easton.Also another the disclosed or functional SNP that is the FGFR2 dependency of the mammary cancer in European and Aisa people is rs1219648 (people such as Hunter, Nat.Genet.39:870-874 (2007)).Tag SNP is for the test SNP situation different from functional or disclosed SNP, as the situation of BC_5.Tag SNP also can be for other genetic variant, for example,, for the SNP of CAMTA1 (rs4908449), 9p21 (rs10757274, rs2383206, rs13333040, rs2383207, rs10116277), COL1A1 (rs1800012), FVL (rs6025), HLA-DQA1 (rs4988889, rs2588331), eNOS (rs1799983), MTHFR (rs1801133) and APC (rs28933380).
The database of SNP can openly obtain from following place: for example, International HapMap Project is (referring to www.hapmap.org, The International HapMap Consortium, nature, 426.789-796 (2003), with The International HapMap Consortium, nature, 437:1299-1320 (2005)), human mutation database (the Human Gene Mutation Database) is public data storehouse (referring to www.hgmd.org) and single nucleotide polymorphism database (the Single Nucleotide Polymorphism database) (dbSNP) (referring to www.ncbi.nlm.nih.gov/SNP/) (HGMD).These databases provide SNP haplotype, or make it possible to determine SNP haplotype pattern.Therefore, these snp databases make it possible to detect for example, the basic genetic risk factor as large-scale disease and state (cancer, inflammatory diseases, cardiovascular diseases, neurodegenerative disease and transmissible disease).These diseases or state can be disposed, wherein its processing of current existence and methods for the treatment of.Processing can comprise preventive treatment and improve the processing of symptom and state, comprise and changing lifestyles.
Also can detect many other phenotypes, for example health proterties, physiological character, spiritual proterties, mood proterties, race, family and age.Health proterties can comprise height, color development, eye color, body or the proterties of energy, endurance and agility for example.Spirit proterties can comprise intelligence, memory capability or learning capacity.Race and family can comprise family or race's evaluation, or where individual ancestors come from.Age can be to determine individual actual age, or individual genetics characteristics makes it with respect to the residing age of total colony.For example, individual actual age is 38 years old, but can to determine its memory capability or health states may be average 28 years old to its genetics characteristics.Other age proterties can be individual predicted life.
Other phenotype also can comprise non-medical state, for example " amusement " phenotype.These phenotypes can comprise the contrast with well-known individuality, for example, and foreign noble, statesman, famous person, inventor, sportsmen, musician, artist, businessperson and notorious individuality (for example criminal).Other " amusement " phenotype can comprise the contrast with other organism, for example, and bacterium, insect, plant or inhuman animal.For example, the individual Genome Atlas contrast meeting that may interestedly look at its Genome Atlas and its pet dog or ex-president how.
In step 114, by rule application in the Genome Atlas of storage to generate the phenotypic spectrum of step 116.For example, the information in Fig. 4,5 or 6 can formation rule or the basis of test to be applied to individual Genome Atlas.Rule can comprise in Fig. 4 the information about test SNP and allelotrope and Effect Evaluation, wherein, and the unit that the UNITS of Effect Evaluation is Effect Evaluation, for example OR, or odds ratio (95% fiducial interval) or mean value.Effect Evaluation can be genotype risk (Fig. 4 C-G) in a preferred embodiment, for example, for homozygous risk (homoz or RR), risk heterozygote (heteroz or RN) and non-risk homozygote (homoz or NN).In other embodiments, Effect Evaluation can be carrier's risk (carrier risk), and it is that RR or RN are to NN.In other again embodiment, Effect Evaluation can be based on allelotrope, allelotrope risk, and for example R is to N.Here also there is the genotype Effect Evaluation (for example,, for 9 kinds of two locus Effect Evaluation possible genotype combinations: RRRR, RRNN etc.) of two locus (Fig. 4 J) or three locus (Fig. 4 K).In Fig. 4 H and I, also recorded the test SNP frequency in public HapMap.
In other embodiments, from Figure 21,22,23 and/or 25 information can be for information generated to be applied to individual Genome Atlas.For example, information can for example, for generating individual GCI or GCI Plus scoring (, Figure 19).Scoring can for be created on one or more states in individual phenotypic spectrum genetic risk (for example estimate lifetime risk) information (for example, Figure 15).The method allow to be calculated as Figure 22 or 25 listed one or more phenotypes or estimation lifetime risk or the relative risk of state.The risk of single status can be based on one or more SNP.For example, can be based at least 2,3,4,5,6,7,8,9,10,11 or 12 SNP for the calculated risk of phenotype or state, wherein for the SNP of calculated risk can for disclosed SNP, test SNP or above both (for example, Figure 25).
Calculated risk for state can be based on Figure 22 or 25 listed SNP.In some embodiments, the risk of state can be based at least one SNP.For example, the assessment of the individual risk for Alzheimer's disease (AD), colorectal carcinoma (CRC), osteoarthritis (OA) or exfoliative glaucoma (XFG) can for example, based on 1 SNP (, be rs4420638, be rs6983267, be rs4911178 and be rs2165241 for XFG for OA for CRC for AD).For other state, for example fat (BMIOB), Graves' disease (GD) or hemochromatosis (HEM), individual calculated risk can (be for example, rs9939609 and/or rs9291171 for BMIOB based at least 1 or 2 SNP; For GD, be DRB1*0301DQA1*0501 and/or rs3087243; For HEM, be rs1800562 and/or rs129128).For for example, but be not limited to the state of myocardial infarction (MI), multiple sclerosis (MS) or psoriasis (PS), 1,2 or 3 SNP can (be for example, rs1866389, rs1333049 and/or rs6922269 for MI for the risk of these states for assessment of individuality; For MS, be rs6897932, rs12722489 and/or DRB1*1501; For PS, be rs6859018, rs11209026 and/or HLAC*0602).In order to assess the individual risk of restless leg syndrome (RLS) or celiac disease (CelD), can use 1,2,3 or 4 SNP (is for example, rs6904723, rs2300478, rs1026732 and/or rs9296249 for RLS; For CelD, be rs6840978, rs11571315, rs2187668 and/or DQA1*0301DQB1*0302).For prostate cancer (PC) or lupus (SLE), 1,2,3,4 or 5 SNP can (be for example, rs4242384, rs6983267, rs16901979, rs17765344 and/or rs4430796 for PC for the risk of PC or SLE for assessment of individuality; For SLE, be rs12531711, rs10954213, rs2004640, DRB1*0301 and/or DRB1*1501).In order to assess the individual lifetime risk of macular degeneration (AMD) or rheumatoid arthritis (RA), can use 1,2,3,4,5 or 6 SNP (is for example, rs10737680, rs10490924, rs541862, rs2230199, rs1061170 and/or rs9332739 for AMD; For RA, be rs6679677, rs11203367, rs6457617, DRB*0101, DRB1*0401 and/or DRB1*0404).In order to assess the individual lifetime risk of mammary cancer (BC), can use 1,2,3,4,5,6 or 7 SNP (for example, rs3803662, rs2981582, rs4700485, rs3817198, rs17468277, rs6721996 and/or rs3803662).In order to assess the individual lifetime risk of Crohn disease (CD) or diabetes B (T2D), can use 1,2,3,4,5,6,7,8,9,10 or 11 SNP (is for example, rs2066845, rs5743293, rs10883365, rs17234657, rs10210302, rs9858542, rs11805303, rs1000113, rs17221417, rs2542151 and/or rs10761659 for CD; For T2D, be rs13266634, rs4506565, rs10012946, rs7756992, rs10811661, rs12288738, rs8050136, rs1111875, rs4402960, rs5215 and/or rs1801282).In some embodiments, the basic SNP determining as risk can form linkage disequilibrium with SNP above-mentioned or that list in Figure 22 or 25.
Individual phenotypic spectrum can comprise many phenotypes.Especially, no matter before having symptom, symptom or in asymptomatic individuality (the allelic carrier of susceptible who comprises one or more disease/states), by method evaluating patient of the present invention, (for example take a disease disease or other state, possible drug reaction, comprises metabolism, effect and/or security) risk make it possible to the susceptibility of multiple incoherent disease and state to carry out prognosis or diagnositc analysis.Therefore, these methods provide for the overall merit of the individual susceptibility of disease or state and have not needed to imagine in advance the test of any specified disease or state.For example, any individual susceptibility that method of the present invention makes it possible in the various states based on listed in genes of individuals picture group spectrum his-and-hers watches 1, Fig. 4,5 or 6 is evaluated.For example, and these methods allow the individuality of evaluating one or more phenotypes or state to estimate lifetime risk or relative risk, those phenotypes in Figure 22 or 25.
Described prior appraisal provides 2 kinds or multiple information in relevant these states, and the more preferably information of 3,4,5,10,20,50,100 or even more kinds of states in these states.In a preferred embodiment, at least 20 rules are applied to individual Genome Atlas and obtain phenotypic spectrum.In other embodiment, at least 50 rules are applied to individual Genome Atlas.The single rule of phenotype can be applied to monogenic phenotype.Also can be for single phenotype more than the rule of, there is the monogenic phenotype of the probability of this phenotype in a plurality of genetic variant impacts in polygenic phenotype or term single gene for example.
After indivedual patient's Genome Atlas are carried out to preliminary sweep, when knowing additional Nucleotide modification, for example, by the Nucleotide modification additional with these (, renewal of relatively carrying out SNP) (or employing) idiotype dependency.For example, step 110 can be carried out to find one of genetics field of new genotypic correlation or several those of ordinary skill termly by search scientific literature, as, every day, carry out weekly or monthly.Then, new genotypic correlation further the council of the one or more experts in this area confirm.Then, step 112 can be upgraded termly with the new regulation of the effective dependency of confirmation based on new.
New regulation can be included in genotype or the phenotype outside existing rule.For example, not associated with any phenotype genotype is found and new or existing phenotypic correlation.New regulation also can be for previously without the dependency between the genotype phenotype associated with it.New regulation also can be identified for having had now well-regulated genotype and phenotype.For example, the rule of the existing dependency based between genotype A and phenotype A.It is relevant to phenotype A that new research has disclosed genotype B, thereby produce the new regulation based on this dependency.Another example is relevant to genotype A for finding phenotype B, and therefore formulates new regulation.
Can find based on known but lay down a regulation while not carrying out the initial dependency of confirming in disclosed scientific literature.For example, may someone report, genotype C is relevant to phenotype C.Other publication report, genotype D is relevant to phenotype D.Phenotype C and D are relevant symptoms, and for example phenotype C is short of breath, and phenotype D is less lung volume.Utilize the individual Genome Atlas with genotype C and D and phenotype C and D of existing storage by statistical method, or can find and confirm the dependency between genotype C and phenotype D or genotype D and phenotype C by further research.Then, can generate new regulation based on dependency newfound and that confirm.In another embodiment, can study the gene type spectrum of a plurality of individualities of the specific or relevant phenotype of having of storage and determine these individual total genotype, and definite dependency.Based on this dependency, can generate new regulation.
Also can lay down a regulation to revise existing rule.For example, the dependency between genotype and phenotype may partly be determined by known personal feature, for example, and race, family, geography, sex, age, family history or individual any other known phenotype.Can formulate the rule based on these known personal features and introduce in existing rule so that the rule of correction to be provided.The regular selection that application is revised will be depended on individual particular individual factor.For example, rule may be based on being 35% when the individual probability that individuality has a phenotype E while having genotype E.But if individuality is specific race, described probability is 5%.The individuality of this particular race characteristic can formulate and be applied to have based on this result to new regulation.Or, can apply determined value and be 35% existing rule, then apply another rule of the racial traits based on this phenotype.Rule based on known personal feature can be determined or the determining of Genome Atlas based on to storage by scientific literature.When having produced new regulation, can in step 114, add new rule and be applied to Genome Atlas, or can apply termly them, for example 1 year at least one times.
The information of the individual risk of disease also can be expanded along with the technical progress of high resolving power SNP Genome Atlas more.As mentioned above, the microarray technology that is used for scanning 500,000 SNP can generate initial SNP gene element Butut at an easy rate.Suppose the situation of haplotype section, this numeral can be used for the typical profile of all SNP in genes of individuals group.Even so, in human genome, estimate conventionally to occur about 1,000 ten thousand SNP (the International HapMap Project; Www.hapmap.org).For example, along with carrying out practical and economic parsing (1 to SNP with higher level of detail, 000,000,1,500,000,2,000,000,3, the technical progress microarray of 000,000 or more SNP) or genome sequencing aspect, can generate more detailed SNP gene element Butut.Similarly, the progress by computer analysis method technology will make the economic analysis of meticulousr SNP gene element Butut and the renewal of SNP-disease-related master data base become possibility.
After step 116 generates phenotypic spectrum, registered user or its health care management person can as in step 118 by online entrance or their Genome Atlas of website visiting or phenotypic spectrum.Also can be by comprising that phenotypic spectrum and other report about the information of phenotypic spectrum and Genome Atlas offer registered user or its health care management person, described in step 120 and 122.Can by reporting printing out, be stored in registered user's computer or watch online.
Fig. 7 shows the online report of example.Registered user can select to show single phenotype or more than one phenotype.Registered user also can have the different options of watching, for example, and " Quick View " option as shown in Figure 7.Phenotype can be that medical condition and different treatment and symptom in quick report can link to the webpage that other comprises the relevant further information of processing.For example, by clicking medicine, can lead and comprise the website about the information of dosage, expense, side effect and effect.Also medicine and other treatment can be compared.Website also can comprise the link of the website of targeted drug manufacturers.Another link can provide to registered user the option of generating medicine genomics (pharmacogenomic) collection of illustrative plates, this by comprise based on its Genome Atlas they for the information that may react of medicine.Also can provide the link for the replacement scheme of medicine, for example preventative behavior (as sports (fitness) and lose weight); And also can provide for diet supplement, the link of dietary program and for the link of near health club, healthy clinic, health care and rehabilitation supplier, city type spa (day spa) etc.Education and information video, the summary of available treatment, possible therapy and general recommendations also can be provided.
Online report also can provide and arrange individual doctor or the link of genetic counseling reservation or the link of accessing online genetic consultant or doctor, thereby provides the chance of the more information about its phenotypic spectrum of inquiry for registered user.In online report, also can be provided in the link of line genetic counseling and doctor's inquiry.
Also can watch report with other form, for example, for the comprehensive observing of single phenotype, wherein provide the more details for each classification.For example, can there is the more detailed statistics that occurs the possibility of phenotype about registered user; About the more information of classical symptom or phenotype, the scope that represents symptom or health non-medical state (as height) of medical condition for example; Or about the more information of gene and genetic variant, colony's popularity for example, as in the world or in country variant, or the colony's popularity in different ages scope or sex.For example, Figure 15 has shown the summary of being permitted multi-mode estimation lifetime risk.Individuality can be watched the more information of particular state (for example prostate cancer (Figure 16) or Crohn disease (Figure 17)).
In another embodiment, report can be the report of " amusement " phenotype, for example, and the similarity of the Genome Atlas of genes of individuals picture group spectrum and well-known individuality (as Alberta einstein).Report can show the per-cent similarity between genes of individuals picture group spectrum and Einsteinian genes of individuals picture group spectrum, and can further show the prediction IQ of Einsteinian prediction IQ and this individuality.Further information can comprise the Genome Atlas of total group and the situation of its IQ and this individuality and Einsteinian Genome Atlas and IQ comparison.
In another embodiment, report can show all phenotypes that have been associated with registered user's Genome Atlas.In other embodiment, report can only show to be determined and the individual positively related phenotype of Genome Atlas.The individual specific subclass that can select to show with other form phenotype, for example only medical science phenotype or the medical science phenotype that only can dispose.For example, the phenotype that can dispose and relevant genotype thereof can comprise Crohn disease (relevant to IL23R and CARD15), type 1 diabetes (relevant with HLA-DR/DQ), lupus (relevant with HLA-DRB1), psoriasis (HLA-C), multiple sclerosis (HLA-DQA1), Graves disease (HLA-DRB1), rheumatoid arthritis (HLA-DRB1), diabetes B (TCF7L2), mammary cancer (BRCA2), colorectal carcinoma (APC), episodic memory (KIBRA) and osteoporosis (COL1A1).Individuality also can be chosen in the subclass that shows phenotype in report, for example, and the only inflammatory diseases of medical condition or only the health proterties of non-medical state.In some embodiments, individual can select by highlight calculated calculated risk those states (for example, Figure 15 A, D), only there is the state (Figure 15 B) of high risk or only have compared with the state of low risk (Figure 15 C) and show all states that this individuality calculated to calculated risk.
It can be that encrypt and secret paying and be sent to individual information, and can control the individual access to these information.The information being obtained by complex genome collection of illustrative plates can offer individual as approved by management, intelligible, medical treatment data relevant and/or that have effect of altitude.Information can be also to have general importance, and irrelevant with medical treatment.Can to individuality, cryptographically transmit information by several modes, described mode includes, but are not limited to Entry Interface and/or mailing.More preferably, information exchange is crossed Entry Interface and cryptographically (if individual, is so selected) to provide to individuality, and wherein individual have safe and secret access rights to this Entry Interface.This interface preferably provides by online, internet site's entrance, or selectively, by phone or allow to provide the alternate manner of secret, safety and wieldy access.Genome Atlas, phenotypic spectrum and report provide to individual or its health care management person by the data transmission of network.
Therefore, Fig. 8 has shown to generate by it block diagram of the representative illustration logical device of phenotypic spectrum and report.Fig. 8 has shown computer system (or digital device) 800, its for receive and storage Genome Atlas, analyzing gene type dependency, based on genotypic correlation create-rule, by rule application in Genome Atlas with produce phenotypic spectrum and report.Computer system 800 can be understood as can be from the logical device of medium 811 and/or the network port 805 reading command, and this network port 805 can optionally be connected with the server 809 with mounting medium 812.The system showing in Fig. 8 comprises CPU801, disc driver 803, optional input unit (for example keyboard 815 and/or mouse 816) and optional watch-dog 807.With the data corresponding of the server 809 of this locality or remote location can by shown in telecommunication media complete.Telecommunication media can comprise any means that transmit and/or receive data.For example, telecommunication media can be that network connection, wireless connections or internet connect.This connection can provide the communication on World Wide Web (World Wide Web).Can envision, the relevant data of the present invention can for a side 822, receive by these means and/or network or the connection of check transmit.Take over party 822 can be individuality, registered user, healthcare provider or health care management person, but is not limited to this.In one embodiment, computer-readable medium comprises the medium of the analytical results that is suitable for transmitting biological sample or genotypic correlation.Described medium can comprise the result about the phenotypic spectrum of individual subject, wherein uses method described herein to obtain this result.
Individual's entrance will be preferably used as the individual basic interface that receives and evaluate genomic data.Entrance also can tracking results from collecting the process of test by making individuality can follow the tracks of its sample.By entrance, access, based on its Genome Atlas, to individuality, introduce the relative risk of common inherited disease.Which rule application registered user can select in its Genome Atlas by entrance.
In one embodiment, one or more webpages will have the list of phenotype and have a square frame near each phenotype, and registered user can select square frame to be included in their phenotypic spectrum.Phenotype can link to the information relevant with this phenotype, to help registered user to select advisably to wish to be included in the phenotype in its phenotypic spectrum about them.Webpage for example also can have, by the phenotype of disease grouping (disease that the disease that can dispose maybe can not be disposed) tissue.For example, registered user can only select the phenotype that can dispose, for example HLA-DQA1 and celiac disease.Before registered user also can select to show the symptom of phenotype or treat after symptom.For example, individuality can select to have the phenotype disposed (beyond further examination) for the treatment of before symptom, is to treat before the symptom of GF diet for celiac disease.Another example can be Alzheimer, and before symptom, treatment is statins, exercise, VITAMIN and mentation.Thrombosis is another example, and before symptom, treatment is to avoid oral contraceptive and avoid normal time sitting.The example with the phenotype for the treatment of after the symptom of approval is the moist AMD relevant with CFH, and wherein individuality can carry out the laser therapy to its state.
Phenotype also can be organized by the type of disease or state or kind, for example neuroscience, cardiovascular, internal secretion, immunity etc.Phenotype also can be grouped into medical science and non-medical phenotype.Other classification of phenotype on webpage can be carried out according to health proterties, physiological character, spiritual proterties or mood proterties.Webpage can further provide by selecting a square frame to select the subregion of one group of phenotype.For example, select all phenotypes, the only phenotype relevant from medical science, the phenotype that only non-medical is relevant, the phenotype that only can dispose, the phenotype that only can not dispose, different disease group or " amusement " phenotype." amusement " phenotype can comprise the contrast with famous person or other well-known individualities, or with other animal or the even contrast of other organism.The list that can be used for the Genome Atlas of contrast also can provide for being selected by registered user and contrast with registered user's Genome Atlas on webpage.
Online entrance also can provide search engine, to help registered user to browse entrance, retrieval particular phenotype or retrieval by its phenotypic spectrum or particular term or information that report was disclosed.Also can be provided by entrance the link of the product of accessing the service of collocation and providing.The other link that is connected to support group, message board and has the individual chatroom of common or similar phenotype also can be provided.Online entrance also can provide and be connected to the linking of other address with more information relevant with phenotype in registered user's phenotypic spectrum.Online entrance also can provide and allow registered user to share the service of its phenotypic spectrum and report with friend, household or health care management person.Registered user can be chosen in phenotypic spectrum and show that they wish the phenotype of sharing with its friend, household or health care management person.
Phenotypic spectrum and report provide individual individualized genotypic correlation.The genotypic correlation performance providing to individuality is enough in determines individual health care and mode of life selection.If found the strong correlation between genetic variant and the disease that can treat, the detection of genetic variant can help to determine to start disease treatment and/or Personal monitoring.In the situation that having statistically significant dependency but not thinking strong correlation, individuality can be discussed this information and determine suitable, useful action scheme with individual doctor.With regard to specific gene type dependency, may be of value to individual potential action scheme comprises and treats processing, monitor potential treatment needs or result for the treatment of or change lifestyles at diet, exercise and other personal habits/aspect such as activity.For example, can dispose the symptom treatment that phenotype (as celiac disease) can be carried out GF diet.Equally, by pharmacogenomics, genotypic correlation information can be applicable to individual may the reacting that prediction must be treated by certain drug or courses of pharmaceuticals, for example possible effect or the security of particular medication.
Registered user can select Genome Atlas and phenotypic spectrum to offer its health care management person, for example doctor or genetic consultant.Genome Atlas and phenotypic spectrum can directly be accessed by health care management person, by registered user, print portion to give health care management person, or by online entrance (for example, by the link in online report), it are directly sent to health care management person.
The transmission of this relevant information will make patient carry out the action of coordinating with its doctor.Particularly, the discussion between patient and its doctor becomes possibility in can being attached to its medical record by individual entrance and the genomic information that links and make patient that is connected to medical information.Medical information can comprise prevention and health and fitness information.By the invention provides information to individual patient, can make patient make for the wisdom of its health care to select.In this mode, patient can select to help them to avoid and/or postpone the disease that its genes of individuals picture group spectrum (DNA of heredity) more may cause.In addition, patient can adopt the treatment plan of the specific medical needs that are applicable to its people itself.Individual also by thering is the ability of its genotype data of access, if they disease occur and need this information to help its doctor to form treatment countermeasure.
Genotypic correlation information also can be combined with genetic counseling and be advised for the Mr. and Mrs to considering fertility, and proposes to pay close attention to for mother, father and/or child's potential heredity.Genetic consultant can provide information and support to the registered user with the phenotypic spectrum that shows the particular state of increase or the risk of disease.They can explain information, analysis hereditary pattern and the risk of recurrence about this illness and with registered user, available selection are discussed.Genetic consultant also can provide support sexual counseling to recommend community or country to support service to registered user.Genetic counseling can comprise specific registration plan.In some embodiments, genetic counseling can be arranged in asked 24 hours and can within as evening, Saturday, Sunday and/or false object time, utilize.
Individual entrance also will be convenient to transmit initial examination Additional Information in addition.The individual new scientific discovery that will be apprised of relevant its individual inheritance's collection of illustrative plates, for example or the new treatment of sneak condition or the information of preventive measure current about it.New discovery also can pass to its health care management person.In a preferred embodiment, by electronics, to mail registered user or its healthcare provider, notice new genotypic correlation and the recent studies on about the phenotype in registered user's phenotypic spectrum.In other embodiments, the e-mail of " amusement " phenotype is sent to registered user, and for example electronic mail can inform that 77% and further information exchange identical with A Bailahan Lincoln's Genome Atlas of their its Genome Atlas cross online entrance and provide.
The present invention also provide a kind of for generate new regulation, modification rule, combining rule, regularly with new regulation update rule collect, maintain safely Genome Atlas database, by rule application in Genome Atlas to determine phenotypic spectrum and for generating the computer generation code system of report.Computer code is informed registered user's dependency new or that revise and report new or that revise, for example, have new prevention and health and fitness information, about the information of new treatment or the report of obtainable new treatment in exploitation.
business method
The invention provides a kind of business method, the clinical database of the Genome Atlas of the method based on patient and the medical science associated nucleotide modification of having established relatively assess individual genotypic correlation.The present invention further provides a kind of business method, the method is used the initial unknown new dependency of genes of individuals picture group spectrum assessment of storage to generate individual updating form type spectrum, and submits other biological sample to without individuality.Fig. 9 is the schema that illustrates this business method.
At the individual because genotypic correlation of multiple common human diseases, state and physical state and when initial request and purchase individual Genome Atlas, the revenue stream of the raw business method of the present invention of part real estate in step 101.Request and purchase can be undertaken by many sources, include but not limited to online Web portal, online health service and the individual doctor of individuality or the source of similar individual medical attention.In alternative embodiment, Genome Atlas can provide free, and can for example, in step (step 103) subsequently, generate revenue stream.
Registered user or human consumer make the request of buying phenotypic spectrum.In response to demand and purchase, to human consumer, provide and gather test kit for being captured in the biological sample that carries out hereditary sample separation in step 103.When online, by phone or other human consumer, be not easy in person when obtaining the source that gathers test kit and making request, by express delivery, provide collection test kit, the express delivery service of the same day or payment overnight is for example provided.What gather that test kit comprises is the container of sample and for sample being delivered to fast to the wrapping material in the laboratory that generates Genome Atlas.Test kit also can comprise the explanation of sample being delivered to the explanation in sample preparation mechanism or laboratory and accessing its Genome Atlas and phenotypic spectrum, and this can be undertaken by online entrance.
Just as described above in detail, can any type from polytype biological sample obtain genomic dna.Preferably, use collection test kit (test kit of for example buying from the DNA Genotek) isolation of genomic DNA from saliva being purchased.The use of saliva and this test kit makes it possible to carry out not damaged sample collecting, because human consumer easily provides saliva sample in the container from collection test kit, then seals this container.In addition, saliva sample can at room temperature store and transport.
After in biological sample being left in to collection or specimen container, in step 105, human consumer is delivered to sample in the laboratory of processing.Typically, by for example on the same day or the sending fast of Courier Service overnight, human consumer can use the wrapping material that provide in gathering test kit that sample is sent/sent to laboratory.
Processing sample the laboratory that generates Genome Atlas can follow that suitable government organs instruct and regulation.For example, in the U.S., treating lab can be by for example FDA (FDA) or medical insurance and medical subsidy service centre (Centers for Medicare and Medicaid Services) one or more federal agencies and/or one or more state organization management (CMS).In the U.S., can according to the Clinical Laboratory Improvement Amendments (CLIA) of 1988, authorize or approval clinical labororatory.
In step 107, the hereditary sample with DNA isolation or RNA is processed to sample in laboratory as previously described.Then, in step 109, Genome Atlas is analyzed and generated to separated hereditary sample.Preferably, generate genome SNP distribution plan.As mentioned above, can use several method to generate SNP distribution plan.Preferably, high density arrays (for example from Affymetrix or Illumina the platform that is purchased) is identified for SNP and distribution plan generation.For example, as above, describe in more detail, use Affymetrix GeneChip assay to generate SNP distribution plan.Along with technical development, may have other technology suppliers of energy generating high density SNP distribution plan.In another embodiment, registered user's Genome Atlas is by the genome sequence that is registered user.
After generating individual Genome Atlas, in step 111, preferably genotype data is encrypted, is inputted, and in step 113 by this deposit data in encrypting database or strong room, wherein information storage is in order to being used in the future.Genome Atlas and can be secret for information about, limits accessing this private information and Genome Atlas according to individual and/or his or her individual doctor's instruction.Other people (for example individual household and genetic consultant) also can be by registered user's permits access.
Database or strong room can be positioned at treating lab place on the spot.Or database can be positioned at independently place.In this case, in step 111, the Genome Atlas data that generated can be transported to the independent mechanism that comprises database by treating lab.
After generating individual Genome Atlas, in step 115, individual heritable variation is compared to the fixed medically clinical database of relevant genetic variant subsequently.Or, genotypic correlation can not be medical science relevant but still be included in genotypic correlation database, for example, as the health proterties of eye color, or as with " amusement " phenotype of the similarity of famous person's Genome Atlas.
Medically relevant SNP can set up by scientific literature and relevant sources.Also can set up non-SNP genetic variant to join with phenotypic correlation.Conventionally, by the intimate haplotype pattern with the lineup of disease is compared to set up the SNP dependency of given disease with the lineup who there is no disease.By analyzing many individualities, can determine the frequency of polymorphism in colony, and these genotype frequencies can for example, be associated with particular phenotype (disease or state) thereupon.Or phenotype can be non-medical condition.
Also can determine relevant SNP and non-SNP genetic variant by the genes of individuals picture group spectrum of analyzing stored, rather than determine by available open source literature.The individuality with the Genome Atlas of storage can disclose previously definite phenotype.Can be by the analysis of the phenotype of the genotype to individual and announcement with the individual relative ratio of this phenotype then can be for the dependency of other Genome Atlas to determine.The individuality of determining its Genome Atlas can be filled in about the previous questionnaire of definite phenotype.Questionnaire can comprise the problem of relevant medical science and non-medical state, such as the disease of previous diagnosis, family history of medical condition, mode of life, health proterties, spiritual proterties, age, social life, environment etc.
In one embodiment, if individuality has been filled in questionnaire, they just can freely determine its Genome Atlas.In some embodiments, individuality regularly fills out a questionnaire with its phenotypic spectrum of free access and report.In other embodiments, filled in the individuality of questionnaire and can register upgrading, so that they have than the access rights of its previous registration higher level, or they can buy or more new registration with lower price.
In order to guarantee science accuracy and importance, first all information leaving in step 121 in the genetic variant database that medical science is relevant is checked and approved by research/clinical advisor group, if simultaneously authorized in step 119, by suitable government organs, checked and supervision.For example, in the U.S., FDA can be by checking and approving for confirming that the algorithm of genetic variant (being generally SNP, transcript level or sudden change) related data exercises supervision.In step 123, for additional genetic variant-disease or state dependency, scientific literature and other relevant sources are monitored, and after confirming their accuracy and importance, and inspection and the approval of process government organs, in these additional genotypic correlation steps 125, add in master data base.
The database of the medical science correlated inheritance modification through checking and approving and verifying combines, by advantageously allowing, a large amount of diseases or state is carried out to genetic risk assessment with the individual collection of illustrative plates of full genome.After the individual Genome Atlas of compilation, can be by individual Nucleotide (heredity) modification or genetic marker be compared and are determined idiotype dependency with the database of the human nucleotide modification for example, being associated with particular phenotype (disease, state or physical state).By genes of individuals picture group spectrum is compared with the master data base of genotypic correlation, can inform individuality whether find they for the genetic risk factor be positive or negative and degree how.Individual relative risk and/or the ill physique data that for example will receive, about the large-scale morbid state (, Alzheimer, cardiovascular diseases, blood coagulation) through scientific validation.For example, can comprise the genotypic correlation in table 1.In addition, the SNP disease-related in database can include, but are not limited to those dependencys shown in Fig. 4.Also can comprise other dependency in Fig. 5 and 6.Therefore business method of the present invention provides for the venture analysis of a large amount of diseases and state may cause any risk without understanding in advance those diseases and state.
In other embodiments, the genotypic correlation combining to the individual collection of illustrative plates of full genome is the relevant phenotype of non-medical, for example " amusement " phenotype or for example the health proterties of color development.In a preferred embodiment, as mentioned above, rule or rule set are applied to individual Genome Atlas or SNP distribution plan.Rule application is generated for individual phenotypic spectrum in Genome Atlas.
Therefore, when finding and verifying new dependency, by the master data base of additional genotypic correlation expansion Human genome type dependency.In the time of when needed or suitably, can from the relevant information in the genes of individuals picture group spectrum being stored in database, upgrade by access.For example, the new genotypic correlation of knowing can be based on specific gene modification.Then, can by only obtain and more individual complete genome picture group spectrum in only the part of this gene determine that individual possibility is subject to the impact of this new genotypic correlation.
Preferably the result of genome inquiry is analyzed and explained to be and pass individuality with understandable form.Then, in step 117, as what describe in detail above, pass through mailing or with safety, secret mode, to patient, provide the result of initial examination by online Entry Interface.
Report can comprise phenotypic spectrum and about the genomic information of phenotype in phenotypic spectrum, for example, about the basic genetic of related gene, learn information or the demographic information of genetic variant in different groups.The out of Memory based on phenotypic spectrum that can be included in report is further evaluation and the classification of preventive measure, health and fitness information, methods for the treatment of, symptom understanding, early detection scheme, intervention plan and phenotype.After the initial examination of genes of individuals picture group spectrum, carry out maybe can carrying out renewal controlled, appropriateness.
When new genotypic correlation occurs and is verified and checks and approves, in conjunction with the renewal of master data base, genes of individuals picture group spectrum is upgraded or can be obtained renewal.The new regulation of the genotypic correlation based on new can be applied to the phenotypic spectrum that initial gene picture group composes to provide renewal.In step 127, by the relevant portion of individual Genome Atlas is compared with new genotypic correlation, can generate the genotypic correlation distribution plan of renewal.For example, if new genotypic correlation is found in the variation based in specific gene, can to this Gene Partial of genes of individuals picture group spectrum, analyze with regard to new genotypic correlation.In this case, the phenotypic spectrum that one or more rule application can be upgraded in generation, rather than with thering is the regular whole rule set updating form type spectrum of having applied.In step 129, in the mode of encrypting, provide the result of individual renewal genotypic correlation.
The initial phenotypic spectrum with upgrading can be to provide the service to registered user or human consumer.Difference registration level and the combination thereof that can provide Genome Atlas to analyze.Similarly, registration level can change to individuality, to provide them to wish the selection of the volume of services with its genotypic correlation of acceptance.Like this, the grade of service providing changes the service registry level along with individual acquisition.
Registered user's entry level registration can comprise Genome Atlas and initial table type spectrum.This can be basic registration level.In basis registration level, can there is the different grades of service.For example, specifically registration level can provide for genetic counseling, aspect treatment or prevention specified disease, have the doctor of special expertise and the introduction of other service option.Can online or obtain genetic counseling by phone.In another embodiment, the price of registration may depend on that individual selection is for the quantity of the phenotype of its phenotypic spectrum.Another option may be for whether registered user selects to access online genetic counseling.
In another situation, registration can provide initial complete genomic genotypic correlation, maintains individual Genome Atlas simultaneously in database; If individual, so select, this database can be encrypted.After this initial analysis, subsequent analysis and additional result can complete when individual requests and other payment.This can be advanced resistry.
In an embodiment of business method of the present invention, carry out the renewal of individual risk and can provide corresponding information to individuality on registration basis.The registered user who buys advanced resistry can obtain renewal.Registration for genotypic correlation analysis can provide the particular type of new genotypic correlation or the renewal of subclass according to individual preference.For example, individuality may only wish to learn the genotypic correlation that has known treatment or prevention process.In order to help individual decision whether to carry out other analysis, can provide the information about available other genotypic correlation to individuality.E-mail can be posted or send to this information easily to registered user.
In advanced resistry, can there is the more grade of service, for example mentioned those in basis registration.Other registration mode can be provided in high-grade.For example, highest ranking can provide unconfined renewal and report to registered user.When determining new dependency and rule, can upgrade registered user's distribution plan.In this grade, registered user also can allow the individuality of unrestricted number to conduct interviews, for example kinsfolk and health care management person.Registered user also can unrestrictedly access online genetic consultant and doctor.
Next registration level in high-grade can provide more restrictions aspect, for example renewal of limited number of times.Registered user can carry out the renewal of limited number of times in period of registration to its Genome Atlas, for example, and 1 year 4 times.In another registration level, registered user can be weekly, once or annually upgrade the Genome Atlas of its storage January.In another embodiment, registered user only can have a limited number of phenotype that can select to upgrade its Genome Atlas.
Individual's entrance also will make individuality can maintain the registration for risk or dependency renewal and/or information updating easily, or risk assessment and the information of request renewal.As mentioned above, can provide different registration levels so that individuality can be selected genotypic correlation result and the renewal of various levels, and registered user can select different registration levels by its people's entrance.
Any one in these registration options is made contributions the revenue stream to business method of the present invention.The revenue stream of business method of the present invention also increases by adding new human consumer and registered user, and wherein new Genome Atlas joins in database.
Table 1: there is the typical gene with the genetic variant of phenotypic correlation.
Gene Phenotype
A2M Alzheimer
ABCA1 Cholesterol, HDL
ABCB1 HIV
ABCB1 Epilepsy
ABCB1 Complication of transplanted kidney
ABCB1 Digoxin, serum-concentration
ABCB1 Crohn disease; Ulcerative colitis
ABCB1 Parkinson's disease
ABCC8 Diabetes B
ABCC8 Diabetes, 2 types
ABO Myocardial infarction
ACADM Medium chain Acyl-CoA dehydrogenase deficiency
ACDC
2 types, diabetes
ACE Diabetes B
ACE Hypertension
ACE Alzheimer
ACE Myocardial infarction
ACE Cardiovascular
ACE Left ventricular hypertrophy
Gene Phenotype
ACE Coronary artery disease
ACE Atherosclerosis, crown
ACE Retinopathy, diabetes
ACE Systemic lupus erythematous
ACE Blood pressure, artery
ACE Erectile dysfunction
ACE Lupus
ACE POLYCYSTIC KIDNEY DISEASE
ACE Apoplexy
ACP1 Diabetes, 1 type
ACSM1(LIP)c Cholesterol levels
ADAM33 Asthma
ADD1 Hypertension
ADD1 Blood pressure, artery
ADH1B Alcohol abuse
ADH1C Alcohol abuse
ADIPOQ Diabetes, 2 types
ADIPOQ Fat
ADORA2A Panic-stricken
ADRB1 Hypertension
ADRB1 In heart failure
ADRB2 Asthma
ADRB2 Hypertension
ADRB2 Fat
ADRB2 Blood pressure, artery
ADRB2 Diabetes B
ADRB3 Fat
Gene Phenotype
ADRB3 Diabetes B
ADRB3 Hypertension
AGT Hypertension
AGT Diabetes B
AGT Essential hypertension
AGT Myocardial infarction
AGTR1 Hypertension
AGTR2 Hypertension
AHR Mammary cancer
ALAD Toxicity of Lead
ALDH2 Alcoholism
ALDH2 Alcohol abuse
ALDH2 Colorectal carcinoma
ALDRL2 Diabetes B
ALOX5 Asthma
ALOX5AP Asthma
APBB1 Alzheimer
APC Colorectal carcinoma
APEX1 Lung cancer
APOA1 Atherosclerosis, crown
APOA1 Cholesterol, HDL
APOA1 Coronary artery disease
APOA1 Diabetes B
APOA4 Diabetes B
APOA5 Triglyceride level
APOA5 Atherosclerosis, crown
APOB Hypercholesterolemia
Gene Phenotype
APOB Fat
APOB Cardiovascular
APOB Coronary artery disease
APOB Coronary heart disease
APOB Diabetes B
APOC1 Alzheimer
APOC3 Triglyceride level
APOC3 Diabetes B
APOE Alzheimer
APOE Diabetes B
APOE Multiple sclerosis
APOE Atherosclerosis, crown
APOE Parkinson's disease
APOE Coronary heart disease
APOE Myocardial infarction
APOE Apoplexy
APOE Alzheimer
APOE Coronary artery disease
APP Alzheimer
AR Prostate cancer
AR Mammary cancer
ATM Mammary cancer
ATP7B Hepatolenticular degeneration
ATXN8OS Spinocebellar ataxia
BACE1 Alzheimer
BCHE Alzheimer
BDKRB2 Hypertension
Gene Phenotype
BDNF Alzheimer
BDNF Bipolar disorder
BDNF Parkinson's disease
BDNF Schizophrenia
BDNF Memory
BGLAP Bone density
BRAF Thyroid carcinoma
BRCA1 Mammary cancer
BRCA1 Mammary cancer; Ovarian cancer
BRCA1 Ovarian cancer
BRCA2 Mammary cancer
BRCA2 Mammary cancer; Ovarian cancer
BRCA2 Ovarian cancer
BRIP1 Mammary cancer
C4A Systemic lupus erythematous
CALCR Bone density
CAMTA1 Episodic memory
CAPN10 Diabetes, 2 types
CAPN10 Diabetes B
CAPN3 Muscular dystrophy
CARD15 Crohn disease
CARD15 Crohn disease; Ulcerative colitis
CARD15 Inflammatory bowel
CART Fat
CASR Bone density
CCKAR Schizophrenia
CCL2 Systemic lupus erythematous
Gene Phenotype
CCL5 HIV
CCL5 Asthma
CCND1 Colorectal carcinoma
CCR2 HIV
CCR2 HIV infects
CCR2 Hepatitis C
CCR2 Myocardial infarction
CCR3 Asthma
CCR5 HIV
CCR5 HIV infects
CCR5 Hepatitis C
CCR5 Asthma
CCR5 Multiple sclerosis
CD14 Atopy (atopy)
CD14 Asthma
CD14 Crohn disease
CD14 Crohn disease; Ulcerative colitis
CD14 Periodontitis
CD14 Total IgE
CDH1 Prostate cancer
CDH1 Colorectal carcinoma
CDKN2A Melanoma
CDSN Psoriasis
CEBPA Leukemia, marrow
CETP Atherosclerosis, crown
CETP Coronary heart disease
CETP Hypercholesterolemia
Gene Phenotype
CFH Macular degeneration
CFTR Cystic fibrosis
CFTR Pancreatitis
CFTR Cystic fibrosis
CHAT Alzheimer
CHEK2 Mammary cancer
CHRNA7 Schizophrenia
CMA1 Atopic dermatitis
CNR1 Schizophrenia
COL1A1 Bone density
COL1A1 Osteoporosis
COL1A2 Bone density
COL2A1 Osteoarthritis
COMT Schizophrenia
COMT Mammary cancer
COMT Parkinson's disease
COMT Bipolar disorder
COMT Obsessive compulsive neurosis
COMT Alcoholism
CR1 Systemic lupus erythematous
CRP C reactive protein
CST3 Alzheimer
CTLA4 Type
1 diabetes
CTLA4 Graves' disease
CTLA4 Multiple sclerosis
CTLA4 Rheumatoid arthritis
CTLA4 Systemic lupus erythematous
Gene Phenotype
CTLA4 Lupus erythematosus
CTLA4 Celiac disease
CTSD Alzheimer
CX3CR1 HIV
CXCL12 HIV
CXCL12 HIV infects
CYBA Atherosclerosis, crown
CYBA Hypertension
CYP11B2 Hypertension
CYP11B2 Left ventricular hypertrophy
CYP17A1 Mammary cancer
CYP17A1 Prostate cancer
CYP17A1 Endometriosis
CYP17A1 Carcinoma of endometrium
CYP19A1 Mammary cancer
CYP19A1 Prostate cancer
CYP19A1 Endometriosis
CYP1A1 Lung cancer
CYP1A1 Mammary cancer
CYP1A1 Colorectal carcinoma
CYP1A1 Prostate cancer
CYP1A1 The esophageal carcinoma
CYP1A1 Endometriosis
CYP1A1 Cell is studied
CYP1A2 Schizophrenia
CYP1A2 Colorectal carcinoma
CYP1B1 Mammary cancer
Gene Phenotype
CYP1B1 Glaucoma
CYP1B1 Prostate cancer
CYP21A2 21-hydroxylase disappearance
CYP21A2 Adrenal,congenital hyperplasia
CYP21A2 Adrenal hyperplasia, inborn
CYP2A6 Cigarette smoking
CYP2A6 Nicotine
CYP2A6 Lung cancer
CYP2C19 Helicobacter pylori infection
CYP2C19 Phenytoin Sodium Salt
CYP2C19 Stomach trouble
CYP2C8 Malaria, plasmodium falciparum
CYP2C9 Anti-coagulant complication
CYP2C9 Method China makes susceptibility
CYP2C9 Fa Hualin treatment, its reaction
CYP2C9 Colorectal carcinoma
CYP2C9 Phenytoin Sodium Salt
CYP2C9 Acenocoumarol reaction
CYP2C9 Blood coagulation disorders
CYP2C9 Hypertension
CYP2D6 Colorectal carcinoma
CYP2D6 Parkinson's disease
CYP2D6 The bad metabolizer phenotype of CYP2D6
CYP2E1 Lung cancer
CYP2E1 Colorectal carcinoma
CYP3A4 Prostate cancer
CYP3A5 Prostate cancer
Gene Phenotype
CYP3A5 The esophageal carcinoma
CYP46A1 Alzheimer
DBH Schizophrenia
DHCR7 Shi-Lun-Ao tri-syndromes
DISC1 Schizophrenia
DLST Alzheimer
DMD Muscular dystrophy
DRD2 Alcoholism
DRD2 Schizophrenia
DRD2 Cigarette smoking
DRD2 Parkinson's disease
DRD2 Tardive dyskinesia
DRD3 Schizophrenia
DRD3 Tardive dyskinesia
DRD3 Bipolar disorder
DRD4 Attention deficit disorder (ADD) [companion is how moving]
DRD4 Schizophrenia
DRD4 Strange seeking (novelty seeking)
DRD4 ADHD
DRD4 Individual character
DRD4 Heroine abuse
DRD4 Alcohol abuse
DRD4 Alcoholism
DRD4 Personality disorder
DTNBP1 Schizophrenia
EDN1 Hypertension
EGFR Lung cancer
Gene Phenotype
ELAC2 Prostate cancer
ENPP1 Diabetes B
EPHB2 Prostate cancer
EPHX1 Lung cancer
EPHX1 Colorectal carcinoma
EPHX1 Hemapoiesis research
EPHX1 Chronic obstructive pulmonary disease/COPD
ERBB2 Mammary cancer
ERCC1 Lung cancer
ERCC1 Colorectal carcinoma
ERCC2 Lung cancer
ERCC2 Hemapoiesis research
ERCC2 Bladder cancer
ERCC2 Colorectal carcinoma
ESR1 Bone density
ESR1 Bone mineral density
ESR1 Mammary cancer
ESR1 Endometriosis
ESR1 Osteoporosis
ESR2 Bone density
ESR2 Mammary cancer
Estrogen receptor Bone mineral density
F2 Coronary heart disease
F2 Apoplexy
F2 Thromboembolism, vein
F2 Preeclampsia
F2 Thrombosis
Gene Phenotype
F5 Thromboembolism, vein
F5 Preeclampsia
F5 Myocardial infarction
F5 Apoplexy
F5 Apoplexy, ischemic
F7 Atherosclerosis, crown
F7 Myocardial infarction
F8 Hemophilia
F9 Hemophilia
FABP2 Diabetes B
FAS Alzheimer
FASLG Multiple sclerosis
FCGR2A Systemic lupus erythematous
FCGR2A Lupus erythematosus
FCGR2A Periodontitis
FCGR2A Rheumatoid arthritis
FCGR2B Lupus erythematosus
FCGR2B Systemic lupus erythematous
FCGR3A Systemic lupus erythematous
FCGR3A Lupus erythematosus
FCGR3A Periodontitis
FCGR3A Sacroiliitis
FCGR3A Rheumatoid arthritis
FCGR3B Periodontitis
FCGR3B Periodontopathy
FCGR3B Lupus erythematosus
FGB Fibrinogen
Gene Phenotype
FGB Myocardial infarction
FGB Coronary heart disease
FLT3 Leukemia, marrow
FLT3 Leukemia
FMR1 Fragile X chromosome syndromes
FRAXA Fragile X chromosome syndromes
FUT2 Helicobacter pylori infection
FVL Factor V Leiden
G6PD G6PD disappearance
G6PD Hyperbilirubinemia
GABRA5 Bipolar disorder
GBA Gaucher disease
GBA Parkinson's disease
GCGR(FAAH，ML4R，UCP2) Body weight/obesity
GCK Diabetes B
GCLM(F12，TLR4) Atherosclerosis, myocardial infarction
GDNF Schizophrenia
GHRL Fat
GJB1 Charcot Marie Tooth
GJB2 Deaf
GJB2 Hearing disability, sensory nerve non-syndrome
GJB2 Hearing disability, sensorineural
GJB2 Hearing disability/deafness
GJB6 Hearing disability, sensory nerve non-syndrome
GJB6 Hearing disability/deafness
GNAS Hypertension
GNB3 Hypertension
Gene Phenotype
GPX1 Lung cancer
GRIN1 Schizophrenia
GRIN2B Schizophrenia
GSK3B Bipolar disorder
GSTM1 Lung cancer
GSTM1 Colorectal carcinoma
GSTM1 Mammary cancer
GSTM1 Prostate cancer
GSTM1 Hemapoiesis research
GSTM1 Bladder cancer
GSTM1 The esophageal carcinoma
GSTM1 Head and neck cancer
GSTM1 Leukemia
GSTM1 Parkinson's disease
GSTM1 Cancer of the stomach
GSTP1 Lung cancer
GSTP1 Colorectal carcinoma
GSTP1 Mammary cancer
GSTP1 Hemapoiesis research
GSTP1 Prostate cancer
GSTT1 Lung cancer
GSTT1 Colorectal carcinoma
GSTT1 Mammary cancer
GSTT1 Prostate cancer
GSTT1 Bladder cancer
GSTT1 Hemapoiesis research
GSTT1 Asthma
Gene Phenotype
GSTT1 Benzene toxicity
GSTT1 The esophageal carcinoma
GSTT1 Head and neck cancer
GYS1 Diabetes B
HBB Thalassemia
HBB Thalassemia, β-
HD Heng Yandunshi tarantism
HFE Hemochromatosis disease
HFE Iron level
HFE Colorectal carcinoma
HK2 Diabetes B
HLA Rheumatoid arthritis
HLA Type
1 diabetes
HLA Behcet's disease
HLA Celiac disease
HLA Psoriasis
HLA Graves disease
HLA Multiple sclerosis
HLA Schizophrenia
HLA Asthma
HLA Diabetes
HLA Lupus
HLA—A Leukemia
HLA—A HIV
HLA—A Diabetes, 1 type
HLA—A Graft versus host disease (GVH disease)
HLA—A Multiple sclerosis
Gene Phenotype
HLA—B Leukemia
HLA—B Behcet's disease
HLA—B Celiac disease
HLA—B Diabetes, 1 type
HLA—B Graft versus host disease (GVH disease)
HLA—B Sarcoidosis
HLA—C Psoriasis
HLA—DPA1 Measles
HLA—DPB1 Diabetes, 1 type
HLA—DPB1 Asthma
HLA—DQA1 Diabetes, 1 type
HLA—DQA1 Celiac disease
HLA—DQA1 Cervical cancer
HLA—DQA1 Asthma
HLA—DQA1 Multiple sclerosis
HLA—DQA1 Diabetes, 2 types; Diabetes, 1 type
HLA—DQA1 Lupus erythematosus
HLA—DQA1 Gestation is lost, recurrence
HLA—DQA1 Psoriasis
HLA—DQB1 Diabetes, 1 type
HLA—DQB1 Celiac disease
HLA—DQB1 Multiple sclerosis
HLA—DQB1 Cervical cancer
HLA—DQB1 Lupus erythematosus
HLA—DQB1 Gestation is lost, recurrence
HLA—DQB1 Sacroiliitis
HLA—DQB1 Asthma
Gene Phenotype
HLA-DQB1 HIV
HLA—DQB1 Lymphoma
HLA—DQB1 Tuberculosis
HLA—DQB1 Rheumatoid arthritis
HLA—DQB1 Diabetes, 2 types
HLA—DQB1 Graft versus host disease (GVH disease)
HLA—DQB1 Hypnolepsy
HLA—DQB1 Sacroiliitis, rheumatoid
HLA—DQB1 Cholangitis, indurative
HLA—DQB1 Diabetes, 2 types; Diabetes, 1 type
HLA—DQB1 Graves' disease
HLA—DQB1 Hepatitis C
HLA—DQB1 Hepatitis C, chronic
HLA—DQB1 Malaria
HLA—DQB1 Malaria, plasmodium falciparum
HLA—DQB1 Melanoma
HLA—DQB1 Psoriasis
HLA—DQB1 Sjogren syndrome
HLA—DQB1 Systemic lupus erythematous
HLA—DRB1 Diabetes, 1 type
HLA—DRB1 Multiple sclerosis
HLA—DRB1 Systemic lupus erythematous
HLA—DRB1 Rheumatoid arthritis
HLA—DRB1 Cervical cancer
HLA—DRB1 Sacroiliitis
HLA—DRB1 Celiac disease
HLA—DRB1 Lupus erythematosus
Gene Phenotype
HLA—DRB1 Sarcoidosis
HLA-DRB1 HIV
HLA—DRB1 Tuberculosis
HLA—DRB1 Graves' disease
HLA—DRB1 Lymphoma
HLA—DRB1 Psoriasis
HLA-DRB1 Asthma
HLA—DRB1 Crohn disease
HLA—DRB1 Graft versus host disease (GVH disease)
HLA—DRB1 Hepatitis C, chronic
HLA—DRB1 Hypnolepsy
HLA—DRB1 Sclerosis, whole body
HLA—DRB1 Sjogren syndrome
HLA—DRB1 Type 1 diabetes
HLA—DRB1 Sacroiliitis, rheumatoid
HLA—DRB1 Cholangitis, indurative
HLA—DRB1 Diabetes, 2 types; Diabetes, 1 type
HLA—DRB1 Helicobacter pylori infection
HLA—DRB1 Hepatitis C
HLA—DRB1 Adolescent arthritis
HLA—DRB1 Leukemia
HLA—DRB1 Malaria
HLA—DRB1 Melanoma
HLA—DRB1 Gestation is lost, recurrence
HLA—DRB3 Psoriasis
HLA—G Gestation is lost, recurrence
HMOX1 Atherosclerosis, crown
Gene Phenotype
HNF4A Diabetes, 2 types
HNF4A Diabetes B
HSD11B2 Hypertension
HSD17B1 Mammary cancer
HTR1A Dysthymia disorders, heavy
HTR1B Alcohol dependence
HTR1B Alcoholism
HTR2A Memory
HTR2A Schizophrenia
HTR2A Bipolar disorder
HTR2A Depressed
HTR2A Dysthymia disorders, heavy
HTR2A Commit suiside
HTR2A Alzheimer
HTR2A Anorexia nervosa
HTR2A Hypertension
HTR2A Obsessive compulsive neurosis
HTR2C Schizophrenia
HTR6 Alzheimer
HTR6 Schizophrenia
HTRA1 Wet age related macular degeneration
IAPP Diabetes B
IDE Alzheimer
IFNG Tuberculosis
IFNG Type
1 diabetes
IFNG Graft versus host disease (GVH disease)
IFNG Hepatitis B
Gene Phenotype
IFNG Multiple sclerosis
IFNG Asthma
IFNG Mammary cancer
IFNG Renal transplantation
IFNG Complication of transplanted kidney
IFNG Long-lived
IFNG Gestation is lost, recurrence
IGFBP3 Mammary cancer
IGFBP3 Prostate cancer
IL10 Systemic lupus erythematous
IL10 Asthma
IL10 Graft versus host disease (GVH disease)
IL10 HIV
IL10 Renal transplantation
IL10 Complication of transplanted kidney
IL10 Hepatitis B
IL10 Adolescent arthritis
IL10 Long-lived
IL10 Multiple sclerosis
IL10 Gestation is lost, recurrence
IL10 Rheumatoid arthritis
IL10 Tuberculosis
IL12B Type
1 diabetes
IL12B Asthma
IL13 Asthma
IL13 Atopy
IL13 Chronic obstructive pulmonary disease/COPD
Gene Phenotype
IL13 Graves' disease
IL1A Periodontitis
IL1A Alzheimer
IL1B Periodontitis
IL1B Alzheimer
IL1B Cancer of the stomach
IL1R1 Type
1 diabetes
IL1RN Cancer of the stomach
IL2 Asthma; Eczema; Allergic disease
IL4 Asthma
IL4 Atopy
IL4 HIV
IL4R Asthma
IL4R Atopy
IL4R Total serum IgE
IL6 Bone mineralising
IL6 Renal transplantation
IL6 Complication of transplanted kidney
IL6 Long-lived
IL6 Multiple sclerosis
IL6 Bone density
IL6 Bone mineral density
IL6 Colorectal carcinoma
IL6 Adolescent arthritis
IL6 Rheumatoid arthritis
IL9 Asthma
INHA Premature ovarian failure
Gene Phenotype
INS Type
1 diabetes
INS Diabetes B
INS Diabetes, 1 type
INS Fat
INS Prostate cancer
INSIG2 Fat
INSR Diabetes B
INSR Hypertension
INSR Polycystic ovary syndrome
IPF1 Diabetes, 2 types
IRS1 Diabetes B
IRS1 Diabetes, 2 types
IRS2 Diabetes, 2 types
ITGB3 Myocardial infarction
ITGB3 Atherosclerosis, crown
ITGB3 Coronary heart disease
ITGB3 Myocardial infarction
KCNE1 EKG is abnormal
KCNE2 EKG is abnormal
KCNH2 EKG is abnormal
KCNH2 QT interval prolongation syndromes
KCNJ11 Diabetes, 2 types
KCNJ11 Diabetes B
KCNN3 Schizophrenia
KCNQ1 EKG is abnormal
KCNQ1 QT interval prolongation syndromes
KIBRA Episodic memory
Gene Phenotype
KLK1 Hypertension
KLK3 Prostate cancer
KRAS Colorectal carcinoma
LDLR Hypercholesterolemia
LDLR Hypertension
LEP Fat
LEPR Fat
LIG4 Mammary cancer
LIPC Atherosclerosis, crown
LPL Coronary artery disease
LPL Hyperlipidaemia
LPL Triglyceride level
LRP1 Alzheimer
LRP5 Bone density
LRRK2 Parkinson's disease
LRRK2 Parkinson's disease
LTA Type
1 diabetes
LTA Asthma
LTA Systemic lupus erythematous
LTA Septicemia
LTC4S Asthma
MAOA Alcoholism
MAOA Schizophrenia
MAOA Bipolar disorder
MAOA Cigarette smoking
MAOA Personality disorder
MAOB Parkinson's disease
Gene Phenotype
MAOB Cigarette smoking
MAPT Parkinson's disease
MAPT Alzheimer
MAPT Dull-witted
MAPT Frontotemporal dementia
MAPT Stein-leventhal syndrome
MC1R Melanoma
MC3R Fat
MC4R Fat
MECP2 Rett syndrome
MEFV Familial Mediterranean fever
MEFV Amyloidosis
MICA Type
1 diabetes
MICA Behcet's disease
MICA Celiac disease
MICA Rheumatoid arthritis
MICA Systemic lupus erythematous
MLH1 Colorectal carcinoma
MME Alzheimer
MMP1 Lung cancer
MMP1 Ovarian cancer
MMP1 Periodontitis
MMP3 Myocardial infarction
MMP3 Ovarian cancer
MMP3 Rheumatoid arthritis
MPO Lung cancer
MPO Alzheimer
Gene Phenotype
MPO Mammary cancer
MPZ Charcot Marie Tooth
MS4A2 Asthma
MS4A2 Atopy
MSH2 Colorectal carcinoma
MSH6 Colorectal carcinoma
MSR1 Prostate cancer
MTHFR Colorectal carcinoma
MTHFR Diabetes B
MTHFR Neural tube defect
MTHFR Homocysteine
MTHFR Thromboembolism, vein
MTHFR Atherosclerosis, crown
MTHFR Alzheimer
MTHFR The esophageal carcinoma
MTHFR Preeclampsia
MTHFR Gestation is lost, recurrence
MTHFR Apoplexy
MTHFR Thrombosis, dark vein
MT—ND1 Diabetes, 2 types
MTR Colorectal carcinoma
MT—RNR1 Hearing disability, sensory nerve non-syndrome
MTRR Neural tube defect
MTRR Homocysteine
MT—TL1 Diabetes, 2 types
MUTYH Colorectal carcinoma
MYBPC3 Myocardosis
Gene Phenotype
MYH7 Myocardosis
MYOC Glaucoma, former angle of release
MYOC Glaucoma
NAT1 Colorectal carcinoma
NAT1 Mammary cancer
NAT1 Bladder cancer
NAT2 Colorectal carcinoma
NAT2 Bladder cancer
NAT2 Mammary cancer
NAT2 Lung cancer
NBN Mammary cancer
NCOA3 Mammary cancer
NCSTN Alzheimer
NEUROD1 Type
1 diabetes
NF1 Neurofibromatosis
1
NOS1 Asthma
NOS2A Multiple sclerosis
NOS3 Hypertension
NOS3 Coronary heart disease
NOS3 Atherosclerosis, crown
NOS3 Coronary artery disease
NOS3 Myocardial infarction
NOS3 Acute coronary syndrome
NOS3 Blood pressure, artery
NOS3 Preeclampsia
NOS3 Nitrogen protoxide
NOS3 Alzheimer
Gene Phenotype
NOS3 Asthma
NOS3 Diabetes B
NOS3 Cardiovascular diseases
NOS3 Behcet's disease
NOS3 Erectile dysfunction
NOS3 Renal failure, chronic
NOS3 Toxicity of Lead
NOS3 Left ventricular hypertrophy
NOS3 Gestation is lost, recurrence
NOS3 Retinopathy, diabetes
NOS3 Apoplexy
NOTCH4 Schizophrenia
NPY Alcohol abuse
NQO1 Lung cancer
NQO1 Colorectal carcinoma
NQO1 Benzene toxicity
NQO1 Bladder cancer
NQO1 Parkinson's disease
NR3C2 Hypertension
NR4A2 Parkinson's disease
NRG1 Schizophrenia
NTF3 Schizophrenia
OGG1 Lung cancer
OGG1 Colorectal carcinoma
OLR1 Alzheimer
OPA1 Glaucoma
OPRM1 Alcohol abuse
Gene Phenotype
OPRM1 Pharmacological dependence
OPTN Glaucoma, former angle of release
P450 Drug metabolism
PADI4 Rheumatoid arthritis
PAH Phenylketonuria/PKU
PAI1 Coronary heart disease
PAI1 Asthma
PALB2 Mammary cancer
PARK2 Parkinson's disease
PARK7 Parkinson's disease
PDCD1 Lupus erythematosus
PINK1 Parkinson's disease
PKA Memory
PKC Memory
PLA2G4A Schizophrenia
PNOC Schizophrenia
POMC Fat
PON1 Atherosclerosis, crown
PON1 Parkinson's disease
PON1 Diabetes B
PON1 Atherosclerosis
PON1 Coronary artery disease
PON1 Coronary heart disease
PON1 Alzheimer
PON1 Long-lived
PON2 Atherosclerosis, crown
PON2 Premature labor
Gene Phenotype
PPARG Diabetes B
PPARG Fat
PPARG Diabetes, 2 types
PPARG Colorectal carcinoma
PPARG Hypertension
PPARGC1A Diabetes, 2 types
PRKCZ Diabetes B
PRL Systemic lupus erythematous
PRNP Alzheimer
PRNP Creutzfeldt-Jacob disease
PRNP Jakob-Creutzfeldt disease
PRODH Schizophrenia
PRSS1 Pancreatitis
PSEN1 Alzheimer
PSEN2 Alzheimer
PSMB8 Type
1 diabetes
PSMB9 Type
1 diabetes
PTCH Skin carcinoma, non-melanoma
PTGIS Hypertension
PTGS2 Colorectal carcinoma
PTH Bone density
PTPN11 Exert southern syndromes
PTPN22 Rheumatoid arthritis
PTPRC Multiple sclerosis
PVT1 End stagerenaldisease
RAD51 Mammary cancer
RAGE Retinopathy, diabetes
Gene Phenotype
RB1 Retinoblastoma
RELN Schizophrenia
REN Hypertension
RET Thyroid carcinoma
RET Hirschsprung's disease
RFC1 Neural tube defect
RGS4 Schizophrenia
RHO Retinitis pigmentosa
RNASEL Prostate cancer
RYR1 Pernicious hyperpyrexia
SAA1 Amyloidosis
SCG2 Hypertension
SCG3 Fat
SCGB1A1 Asthma
SCN5A Brugada syndromes
SCN5A EKG is abnormal
SCN5A QT interval prolongation syndromes
SCNN1B Hypertension
SCNN1G Hypertension
SERPINA1 COPD
SERPINA3 Alzheimer
SERPINA3 COPD
SERPINA3 Parkinson's disease
SERPINE1 Myocardial infarction
SERPINE1 Diabetes B
SERPINE1 Atherosclerosis, crown
SERPINE1 Fat
Gene Phenotype
SERPINE1 Preeclampsia
SERPINE1 Apoplexy
SERPINE1 Hypertension
SERPINE1 Gestation is lost, recurrence
SERPINE1 Thromboembolism, vein
SLC11A1 Tuberculosis
SLC22A4 Crohn disease; Ulcerative colitis
SLC22A5 Crohn disease; Ulcerative colitis
SLC2A1 Diabetes B
SLC2A2 Diabetes B
SLC2A4 Diabetes B
SLC3A1 Cystinuria
SLC6A3 Attention deficit disorder (ADD) [companion is how moving]
SLC6A3 Parkinson's disease
SLC6A3 Cigarette smoking
SLC6A3 Alcoholism
SLC6A3 Schizophrenia
SLC6A4 Depressed
SLC6A4 Dysthymia disorders, heavy
SLC6A4 Schizophrenia
SLC6A4 Commit suiside
SLC6A4 Alcoholism
SLC6A4 Bipolar disorder
SLC6A4 Individual character
SLC6A4 Attention deficit disorder (ADD) [companion is how moving]
SLC6A4 Alzheimer
SLC6A4 Personality disorder
Gene Phenotype
SLC6A4 Panic-stricken
SLC6A4 Alcohol abuse
SLC6A4 Affective disorder
SLC6A4 Anxiety disorder
SLC6A4 Cigarette smoking
SLC6A4 Dysthymia disorders, heavy; Bipolar disorder
SLC6A4 Heroine abuse
SLC6A4 Irritable bowel syndrome
SLC6A4 Migraine
SLC6A4 Obsessive compulsive neurosis
SLC6A4 Suicide
SLC7A9 Cystinuria
SNAP25 ADHD
SNCA Parkinson's disease
SOD1 ALS/ amyotrophic lateral sclerosis
SOD2 Mammary cancer
SOD2 Lung cancer
SOD2 Prostate cancer
SPINK1 Pancreatitis
SPP1 Multiple sclerosis
SRD5A2 Prostate cancer
STAT6 Asthma
STAT6 Total IgE
SULT1A1 Mammary cancer
SULT1A1 Colorectal carcinoma
TAP1 Type
1 diabetes
TAP1 Lupus erythematosus
Gene Phenotype
TAP2 Type
1 diabetes
TAP2 Diabetes, 1 type
TBX21 Asthma
TBXA2R Asthma
TCF1 Diabetes, 2 types
TCF1 Diabetes B
TF Alzheimer
TGFB1 Mammary cancer
TGFB1 Renal transplantation
TGFB1 Complication of transplanted kidney
TH Schizophrenia
THBD Myocardial infarction
TLR4 Asthma
TLR4 Crohn disease; Ulcerative colitis
TLR4 Septicemia
TNF Asthma
TNFA Cerebrovascular disease
TNF Type
1 diabetes
TNF Rheumatoid arthritis
TNF Systemic lupus erythematous
TNF Renal transplantation
TNF Psoriasis
TNF Septicemia
TNF Diabetes B
TNF Alzheimer
TNF Crohn disease
TNF Diabetes, 1 type
Gene Phenotype
TNF Hepatitis B
TNF Complication of transplanted kidney
TNF Multiple sclerosis
TNF Schizophrenia
TNF Celiac disease
TNF Fat
TNF Gestation is lost, recurrence
TNFRSF11B Bone density
TNFRSF1A Rheumatoid arthritis
TNFRSF1B Rheumatoid arthritis
TNFRSF1B Systemic lupus erythematous
TNFRSF1B Sacroiliitis
TNNT2 Myocardosis
TP53 Lung cancer
TP53 Mammary cancer
TP53 Colorectal carcinoma
TP53 Prostate cancer
TP53 Cervical cancer
TP53 Ovarian cancer
TP53 Smoking
TP53 The esophageal carcinoma
TP73 Lung cancer
TPH1 Commit suiside
TPH1 Dysthymia disorders, heavy
TPH1 Suicide
TPH1 Schizophrenia
TPMT Thiopurine methyltransferase is active
Gene Phenotype
TPMT Leukemia
TPMT Inflammatory bowel
TPMT Thio-purine S-methyltransgerase phenotype
TSC1 Tuberous sclerosis
TSC2 Tuberous sclerosis
TSHR Graves' disease
TYMS Colorectal carcinoma
TYMS Cancer of the stomach
TYMS The esophageal carcinoma
UCHL1 Parkinson's disease
UCP1 Fat
UCP2 Fat
UCP3 Fat
UGT1A1 Hyperbilirubinemia
UGT1A1 Er Bei syndromes
UGT1A6 Colorectal carcinoma
UGT1A7 Colorectal carcinoma
UTS2 Diabetes, 2 types
VDR Bone density
VDR Prostate cancer
VDR Bone mineral density
VDR Type
1 diabetes
VDR Osteoporosis
VDR Bone amount
VDR Mammary cancer
VDR Toxicity of Lead
VDR Tuberculosis
Gene Phenotype
VDR Diabetes B
VEGF Mammary cancer
Vit?D?rec Idiopathic short stature
VKORC1 Warfarin therapy, its reaction
WNK4 Hypertension
XPA Lung cancer
XPC Lung cancer
XPC Hemapoiesis research
XRCC1 Lung cancer
XRCC1 Hemapoiesis research
XRCC1 Mammary cancer
XRCC1 Bladder cancer
XRCC2 Mammary cancer
XRCC3 Mammary cancer
XRCC3 Hemapoiesis research
XRCC3 Lung cancer
XRCC3 Bladder cancer
ZDHHC8 Schizophrenia
Heredity aggregative index (GCI)
The etiology of many states or disease is owing to h and E factor.The latest developments of genotyping technique have offered an opportunity to identify new associated between disease and whole genomic genetic marker.In fact, much research recently has been found that these associations, and wherein specific allelotrope or genotype are relevant with the disease risks of increase.Some in these researchs comprise collects one group of test case and one group of allele distributions that contrasts and compare genetic marker between Liang Ge colony.In some researchs of these researchs, being associated in the situation of isolating with other genetic marker between specific genetic marker and disease measured, and other genetic marker is processed as a setting and in statistical study, do not worked.
Genetic marker and modification can comprise that SNP, Nucleotide repetition, Nucleotide insertion, nucleotide deletion, chromosome translocation, karyomit(e) repeat or copy number variation.Copy number variation can comprise that micro-satellite repeats, Nucleotide repeats, repeat in kinetochore or telomere repeats.
In one aspect of the invention, in conjunction with the associated information about many genetic markers and one or more diseases or state and analyze to obtain GCI scoring.GCI scoring can be used for providing to the people who was not subject to genetics training based on current scientific research reliable (that is, firm), intelligible and/or be familiar with intuitively of the disease individual risk of comparing them with Reference Group.In one embodiment, the method for reliable GCI scoring that generates the combined effect of different genes seat is that the report of the locus studied individual dangerous based on each.For example, identify interested disease or state, then Query Information source (include, but are not limited to database, patent is open and scientific literature) is to find the associated information of diseases related or state and one or more genetic locis.These information sources are assessed through checking functional quality standard.In some embodiments, evaluation process comprises a plurality of steps.In other embodiments, with a plurality of quality standard sources of assessments.The information that is derived from information resources is for identifying odds ratio or the relative risk of one or more genetic locis for interested each disease or state.
In alternative embodiment, for odds ratio (OR) or the relative risk (RR) of at least one genetic loci, can not from available information source, obtain.Then a plurality of allelic report OR, (2) that uses (1) homologous genes seat for example, from the gene frequency of data set (HapMap data set) and/or (3) for example, from disease/state popularity computation RR of available stock (, CDC, National Center for Health Statistics etc.) to draw all interested allelic RR.In one embodiment, assess respectively or independently a plurality of allelic OR of homologous genes seat.In a preferred embodiment, in conjunction with a plurality of allelic OR of homologous genes seat with explanation the dependence (dependency) between not homoallelic OR.In some embodiments, the disease model of setting up (including, but are not limited to as long-pending property (multiplicative), additivity (additive), Harvard model improvement, dominant effect) is for generating according to scoring in the middle of selected model representation individual risk.
In another embodiment, use the method for a plurality of models of analyzing interested disease or state, and the method is interrelated by the result being obtained by these different models; This makes to minimize by the probable error of selecting specified disease model to introduce.Reasonable error in popularity, gene frequency and OR assessment that this method makes to be obtained by information source minimizes the impact of the calculating of relative risk.Because popularity assessment is on " linearity " of the impact of RR or monotonicity feature, estimate that improperly popularity only has seldom final scoring or not impact; Suppose that identical model is as one man applied to generate all individualities of report.
In another embodiment, use the method that environment/behavior/demographic data is considered as additional " locus ".In relevant embodiment, these data can be obtained by information source, for example medical science or scientific literature or database (for example, smoking w/ lung cancer associated or from insurance industry health risk assessment).In one embodiment, for one or more complex diseases, produce GCI scoring.Complex disease can be affected by a plurality of genes, environmental factors and their interaction.When research complex disease, need to analyze a large amount of possible interactions.In one embodiment, the program that for example Bonferroni proofreaies and correct is used for proofreading and correct multiple comparisons.In alternative embodiment, when test is independently or shows the dependence of special type, use Simes check to control whole significance level (also referred to as " family specific inaccuracy ") (Sarkar S. (1998)).Proof (Ann Stat26:494-504) for some probability inequality: Simes hypothesis of orderly MTP2 stochastic variable.If 1, ..., in K for any k, p (k)≤α k/K, all Kappa test specificity null hypothesiss of Simes check refusal are genuine overall null hypothesis (Simes RJ (1986) An improved Bonferroni procedure for multiple tests of significance.Biometrika73:751-754) so.
Other embodiment that can use in the situation of polygene and many Environmental factor analysis is controlled false discovery rate (false-discovery rate), i.e. the expectation ratio of the refusal null hypothesis of False Rejects.As in microarray research, when a part for null hypothesis can be assumed to mistake, this method is useful especially.The people such as Devlin (2003, Analysis of multilocus models of association.Genet Epidemiol25:36-47) Benjamini of false discovery rate and the modification that Hochberg (1995, Controlling the false discovery rate:a practical and powerful approach to multiple testing.J R Stat Soc Ser B57:289-300) increases progressively program have been proposed to control when a large amount of possible gene * gene interaction of test in polygene seat association study.Benjamini is relevant with Simes check with Hochberg program; Set k *=maxk so that p (k)≤α k/K, its refusal is all corresponding to p (1) ..., p (k *) k *null hypothesis.In fact, when all null hypothesiss are true time, Benjamini and Hochberg program simplification are Simes check (Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency.Ann Stat29:1165-1188).
In some embodiments, individual based on wherein asking that scoring and individual colony relatively carry out ranking to produce final scoring, this can be expressed as the ranking in colony, for example the 99th minute position or the 99th, 98,97,96,95,94,93,92,91,90,89,88,87,86,85,84,83,82,81,80,79,78,77,76,75,74,73,72,71,70,69,65,60,55,50,45,40,40,35,30,25,20,15,10,5 or 0 minutes positions.In another embodiment, scoring can be shown as scope, for example the 100th to 95 minutes positions, the 95th to 85 minutes positions, the 85th to 60 minutes positions or any subrange between the 100th to 0 minute position.In another embodiment again, individually by quartile, carry out ranking, for example the 75th the highest quartile or the 25th minimum quartile.In further embodiment, ranking is relatively carried out in the average or meta scoring in individual and group.
In one embodiment, the colony comparing from individuality comprises a large amount of people from different geography and ethnic background, for example global colony.In other embodiments, the colony comparing with individuality be limited to specific geographic, family, race, sex, age (fetus, newborn infant, children, teenager, youth, grownup, the elderly are individual), morbid state (for example, Symptomatic, asymptomatic, carrier, early send out, tardy).In some embodiments, the colony comparing with individuality is derived from information open and/or personal information source report.
In one embodiment, use display unit to make individual GCI scoring or GCIPlus scoring visual.In some embodiments, display screen (for example, computer monitor or TV screen) for visual demonstration, for example, has the individual entrance of relevant information.In another embodiment, display unit is static status display device, for example printer page.In one embodiment, demonstration can comprise, but be not limited to one or more with lower device: (bin) (for example, 1-5,6-10,11-15,16-20,21-25,26-30,31-35,36-40,41-45,46-50,51-55,56-60,61-65,66-70,71-75,76-80,81-85,86-90,91-95, the 96-100) of case unit, colour or shade of gray, thermometer, scale, pie chart, column diagram or rod figure.For example, the difference that Figure 18 and 19 is MS shows and Figure 20 is for Crohn disease.In another embodiment, thermometer is used for showing GCI scoring and disease/state popularity.In another embodiment, thermometer shows the level along with the GCI scoring variation of report, for example, Figure 15 to 17, color is corresponding with risk.Thermometer can show that the colourity increasing with GCI scoring changes (for example, from the blueness of lower GCI scoring, gradually changing to the redness of higher GCI scoring).In related embodiment, thermometer shows that the level changing with the GCI scoring of reporting and the colourity increasing with risk class change.
In alternative embodiment, use audio feedback to transmit individual GCI scoring to individuality.In one embodiment, audio feedback is that danger classes is high or low verbal communication.In another embodiment, audio feedback is the narration that special GCI marks, for example the comparison of numeral, hundredths, scope, quartile or or middle GCI scoring average with colony.In one embodiment, lived people in person or by communicator, for example phone (landline telephone, portable phone or satellite phone) transmits audio feedback, or transmit audio feedback by individual entrance.In another embodiment, audio feedback for example, is transmitted by automatic system (computer).In one embodiment, audio feedback is as the part transmission of interactive sound reaction (IVR) system, and this system is a kind of technology that allows computer to use normal telephone calling detection voice and keypad tone.In another embodiment, individuality can be interactive by IVR system and central server.IVR system can be to recording or the audio frequency of Dynamic Generation is reacted with individual interactive and the audio feedback of its risk class is provided to them in advance.In one embodiment, individuality can be called out the number of being answered by IVR.For example, at optionally input authentication code, security code or after speech recognition program, IVR system allows object select option from menu, keypad tone or voice menu.One in these options can provide his or her risk class to individuality.
In another embodiment, individual GCI scoring use display unit is visual and use audio feedback transmission, for example, by individual entrance.This combination can comprise visual display and the audio feedback of GCI scoring, and it discusses GCI scoring to the dependency of individual holistic health and the possible preventive measures that can propose.
In one embodiment, use multistep processes to generate GCI scoring.Start, for each state that will study, calculate the relative risk of the odds ratio that is derived from each genetic marker.For p=0.01,0.02 ..., each popularity value of 0.5, the GCI scoring of HapMap CEU colony is calculated based on popularity and HapMap gene frequency.If GCI scoring is constant under the popularity changing, there is long-pending property model in unique being assumed to of considering.In addition, can determine that this model pop degree is responsive.For any combination of never call value, obtain relative risk and the distribution of scoring in HapMap colony.For each new individuality, individual score and HapMap distribution comparison and gained scoring are individual ranking in this colony.Due to the hypothesis of doing in process, the resolving power of the scoring of report may be lower.Colony will be divided into percentage point (3-6 case unit), and the case unit of report will be one that wherein individual ranking falls into.Based on for example, for the consideration of the resolving power of the scoring of each disease, the quantity of case unit can be different to various disease.In the situation that link between the scoring of different HapMap individualities, will use average ranking.
In one embodiment, higher GCI scoring is interpreted as representing to obtain or had by diagnosis the increase risk of state or disease.In another embodiment, use mathematical model to draw GCI scoring.In some embodiments, the mathematical model of GCI scoring based on illustrating as the basic incomplete feature of the information about colony and/or disease or state.In some embodiments, mathematical model comprises that wherein said hypothesis includes, but are not limited to: the hypothesis of given advantage ratio as specific at least one hypothesis of calculating the basic part of GCI scoring; The known hypothesis of popularity of state; The known hypothesis of genotype frequency in colony; With human consumer from the colony using with institute and with HapMap the hypothesis of identical family background; Merging risk is the long-pending hypothesis of the different risk factors of idiogenetics mark.In some embodiments, GCI also can comprise that genotypic polygene type frequency for example, for the long-pending hypothesis of the gene frequency of each SNP or idiogenetics mark (, different SNP or genetic marker are independently in whole colony).
long-pending property model
In one embodiment, in the risk owing to genetic marker set, be to calculate GCI scoring under the long-pending hypothesis owing to the risk of indivedual genetic markers.This means that different genetic markers and other genetic marker are independently owing to the risk of disease.In form, exist and there is risk allelotrope r 1..., r kwith non-risk allelotrope n 1..., n kk genetic marker.In SNPi, we represent that three possible genotype values are r ir i, n ir iand n in i.Individual genotype information can be by vector (g 1..., g k) describe, wherein according to the allelic number of risk on i position, g ican be 0,1 or 2.We pass through
Figure BSA0000097585230000951
the relative risk of heterozygous genes type in the same position that expression is compared with the non-risk allelotrope that isozygotys on i position.In other words, we define similarly, we represent r ir igenotypical relative risk is
Figure BSA0000097585230000962
under long-pending property model, we have genotype (g at supposition 1..., g k) individual risk be long-pending property model before this for document with Model case comparative study or for visual object.
assessment relative risk
In another embodiment, for the relative risk of different genetic markers, be known, and long-pending property model can be for risk assessment.But, at some, comprise that, in the embodiment of association study, research and design prevents from reporting relative risk.In some case control studies, relative risk can not directly be calculated by data in the situation that further not supposing.Replace report relative risk, common mode is the odds ratio (OR) of reporter gene type, and it is to carry given risk genes type disease (r ir ior n ir i) probability to not carrying the ratio of the probability of given risk genes type disease.In form,
OR i 1 = P ( D | n i r i | ) P ( D | n i r i | ) · 1 - P ( D | n i n i | ) 1 - P ( D | n i r i | )
OR i 2 = P ( D | r i r i | ) P ( D | n i n i | ) · 1 - P ( D | n i n i | ) 1 - P ( D | r i r i | )
By odds ratio, find relative risk may require extra hypothesis.For example, suppose the gene frequency in whole population
Figure BSA0000097585230000966
with
Figure BSA0000097585230000967
known or for example, through assessment (these can be by existing data set, comprise 120 chromosomal HapMap data sets assess), and/or the popularity p=p (D) of hypothesis disease is known.By aforementioned three equatioies, can be obtained:
p=a·P(D|n in i)+b·P(D|n ir i)+c·P(D|r ir i)
OR i 1 = P ( D | n i r i | ) P ( D | n i r i | ) · 1 - P ( D | n i n i | ) 1 - P ( D | n i r i | )
OR i 2 = P ( D | r i r i | ) P ( D | n i n i | ) · 1 - P ( D | n i n i | ) 1 - P ( D | r i r i | )
By the definition of relative risk, divided by pP (D|n in i) after, the first equation can be rewritten as:
1 P ( D | n i n i ) = a + bλ 1 i + cλ 2 i p ,
And therefore, latter two equation can be rewritten as:
OR 1 i = λ 1 i · ( a - p ) + bλ 1 i + cλ 2 i a + ( b - p ) λ 1 i + cλ 2 i
(1)
OR i 2 = λ 2 i · ( a - p ) + bλ 1 i + cλ 2 i a + bλ 1 i + ( c - p ) λ 2 i
It should be noted that, when a=1 (non-risk gene frequency is 1), equation system 1 be equal to Zhang in Zhang J and Yu K. and Yu formula (What ' s the relative risk A method of correcting the odds ratio in cohort studies of common outcomes.JAMA, 280:1690-1,1998, its full content is incorporated herein by reference).Contrary with Yu formula with Zhang, some embodiments of the present invention are considered the gene frequency in colony, and it may affect relative risk.Other embodiment is considered the interdependent property of relative risk.This is with to calculate independently each relative risk contrary.
Equation system 1 can be rewritten as has two quadratic equations of four feasible solutions at the most.Gradient descent algorithm (gradient descent algorithm) can be for solving these equations, and wherein starting point is set to odds ratio, for example, with
Figure BSA0000097585230000977
.
For example:
f 1 ( λ 1 , λ 2 ) = OR i 1 ( a + ( b - p ) λ 1 i + cλ 2 i ) - λ 1 i · ( ( a - p ) + bλ 1 i + cλ 2 i )
f 2 ( λ 1 , λ 2 ) = OR i 2 ( a + bλ 1 i + ( c - p ) λ 2 i ) - λ 2 i · ( ( a - p ) + bλ 1 i + cλ 2 i )
Find the solution of these equations to be equivalent to find function g (λ 1, λ 2)=f 1(λ 1, λ 2) 2+ f 2(λ 1, λ 2) 2minimum value.
Therefore,
dg dλ 1 = 2 f 1 ( λ 1 , λ 2 ) · b · ( λ 2 - OR 2 ) + 2 f 2 ( λ 1 , λ 2 ) ( 2 bλ 1 + cλ 2 + a - OR 1 b - p + OR 1 p )
dg dλ 2 = 2 f 2 ( λ 1 , λ 2 ) · c · ( λ 1 - OR 1 ) + 2 f 1 ( λ 1 , λ 2 ) ( 2 cλ 2 + bλ 1 + a - OR 2 c - p + OR 2 p )
In this example, we are by setting x 0=OR 1, y 0=OR 2start.We will be worth [ε]=10 -10be set as the tolerance constant (tolerance constant) of whole algorithm.In iteration i, we define γ = min { 0.001 , x i - 1 [ epsilon ] + 10 | dg dλ 1 ( x i - 1 , y i - 1 ) | , y i - 1 [ epsilon ] + 10 | dg dλ 2 ( x i - 1 , y i - 1 ) | } . Then, we set
x i = x i - 1 - γ dg dλ 1 ( x i - 1 , y i - 1 )
y i = y i - 1 - γ dg dλ 2 ( x i - 1 , y i - 1 )
Repeat these iteration until g (x i, y i) < tolerance, wherein in the code providing, tolerance is set as 10 -7.
In this embodiment, these equations have provided a, b, c, p, OR 1and OR 2the normal solution of different value.Figure 10
the steadiness of relative risk assessment
In some embodiments, measured the impact of different parameters (popularity, gene frequency and odds ratio error) on the estimated value of relative risk.In order to measure gene frequency and the impact of popularity estimated value on Relative risk value, calculating is from the relative risk (under HWE) of the value of one group of different odds ratio and different gene frequencies, and the result of these calculating is drawn for the popularity value in 0 to 1 scope.Figure 10.In addition, for fixing popularity value, the relative risk of gained can be used as the function plotting of risk gene frequency.Figure 11.When p=0, λ 1=OR 1, and λ 2=OR 2, and when p=1, λ 1=λ 2=0.This can directly calculate from described equation.In addition, in some embodiments, when risk gene frequency is high, λ 1closer to linear function, and λ 2closer to the concave function with bounded second derivative.Under limiting case, when c=1, λ 2=OR 2+ p (1-OR 2), and if OR 1≈ OR 2, the latter is equally close to linear function.When risk gene frequency is low, λ 1and λ 2approach the behavior of function 1/p.Under limiting case, when c=0, &lambda; 1 = O R 1 1 - p + pO R 1 , &lambda; 2 = O R 2 1 - p + pO R 2 . This shows, for high risk gene frequency, incorrect popularity estimated value will can not affect the relative risk of gained significantly.In addition, for low risk gene frequency, if substitute correct popularity p with popularity value p '=α p, the relative risk of gained will be eliminated at the most so
Figure BSA0000097585230000994
coefficient.This be illustrated in Figure 11's (c) and (d) in drawing.It should be noted that, for high risk gene frequency, two width drawings are quite similar, and for low gene frequency, have higher deviation in the difference of Relative risk value, and this deviation is less than coefficient 2.
calculate GCI scoring
In one embodiment, use and represent that the reference set of Reference Group calculates hereditary aggregative index.This reference set can be one of colony in HapMap or another genotype data collection.
In this embodiment, GCI is calculated as follows.For each in k risk genes seat, use equation system 1 to calculate relative risk by odds ratio.Then, calculate the long-pending property scoring of each individuality in reference set.The individual GCI with long-pending property scoring s is the mark that reference data is concentrated all individualities of the scoring with s '≤s.For example, if 50% individuality has the long-pending property scoring that is less than s in reference set, the final GCI scoring of this individuality will be 0.5 so.
other model
In one embodiment, use long-pending property model.In alternative embodiment, can be by other model for determining the object of GCI scoring.Other suitable model includes, but are not limited to:
Additive model.Under additive model, there is genotype (g 1... g k) individual risk be assumed to be GCI ( g 1 , . . . , g k ) = &Sigma; i = 1 k f ( &lambda; g i i ) .
Generalized Additive Models.In Generalized Additive Models, suppose existence function f so that there is genotype (g 1... g k) individual risk be
Harvard improvement scoring (Het).This scoring is drawn by people such as G.A Colditz, thereby this scoring is applied to genetic marker (Harvard report on cancer prevention volume4:Harvard cancer risk index.Cancer Causes and Controls, 11:477-488,2000, be incorporated herein its full content).Although function f is carried out computing with advantage ratio rather than relative risk, Het scoring is the scoring of broad sense additivity in essence.In this situation that is difficult to assess in relative risk, be useful.For defined function f, intermediate function g is defined as:
g ( x ) = 0 1 < x &le; 1.09 5 1.09 < x &le; 1.49 10 1.49 < x &le; 2.99 25 2.99 < x &le; 6.99 50 6.99 < x
Then calculate
Figure BSA0000097585230001004
amount, wherein
Figure BSA0000097585230001005
frequency for SNP i heterozygous individual in whole reference group.Then function f is defined as to f (x)=g (x)/het, and Harvard improvement scoring (Het) is defined as simply
Figure BSA0000097585230001006
Harvard improvement scoring (Hom).Except value het is worth
Figure BSA0000097585230001007
replace beyond, this scoring and Het scoring is similar, wherein,
Figure BSA0000097585230001008
for thering is the frequency of the allelic individuality of risk that isozygotys.
Sharpest edges ratio.In this model, suppose that one of genetic marker (have sharpest edges ratio) has provided the lower bound of the constitution's risk of whole group of objects.In form, there is genotype (g 1... g k) individual scoring be
Figure BSA0000097585230001009
comparison between scoring
In one embodiment, for 10 SNPs relevant to T2D, in whole HapMap CEU colony, based on a plurality of models, calculate GCI scoring.Related SNP is rs7754840, rs4506565, rs7756992, rs10811661, rs12804210, rs8050136, rs1111875, rs4402960, rs5215, rs1801282.For each in these SNP, three possible genotypic odds ratios are reported in the literature.CEU colony is comprised of three people's groups of 30 mother-father-children.For fear of dependence, adopt 60 father and mother from this colony.Eliminating has without the body one by one that calls in one of 10 SNP, obtains 59 individual one group.Then use several different models to calculate each individual GCI grade.
Can observe, for this data set, different models produce the result of height correlation.Figure 12 and 13.Therefore between each is to model, calculate Spearman dependency (table 2), it demonstrates long-pending property and additive model has 0.97 relation conefficient, and GCI scoring is firm while using additivity or long-pending property model.Similarly, the dependency between Harvard improvement scoring and long-pending property model is 0.83, and the relation conefficient between Harvard scoring and additive model is 0.7.But, use sharpest edges to be compared to hereditary score and produce the scorings (dichotomous score) in two minutes that defined by a SNP.Generally speaking, these results show, scoring ranking provides and made the minimized stable framework of model dependency.
Table 2: model between the Spearman dependency that distributes of the scoring of CEU data.
Figure BSA0000097585230001011
The impact that the variation of mensuration T2D popularity distributes on gained.Popularity value changes (Figure 14) between 0.001～0.512.For the situation of T2D, can find out, different popularity values causes individual identical sequence (Spearman dependency >0.99), therefore can suppose the artificial fixed value 0.001 of popularity.
modification by model extension to any amount
In another embodiment, can be by model extension to the situation that the possible modification of any amount occurs.Previous consideration relates to the situation that has three possible modification (nn, nr, rr).Conventionally, when known many SNP are associated, can in colony, find the modification of any amount.For example, when the interaction between two genetic markers is associated with state, there are nine kinds of possible modification.This has caused eight different advantage ratios.
In order to summarize prime formula, can suppose the modification a that exists k+1 kind possible 0..., a k, there is frequency f 0, f 1..., f k, the odds ratio of mensuration is 1, OR 1..., OR kand unknown Relative risk value is 1, λ 1..., λ k.Can further suppose, with respect to a 0measure all relative risks and odds ratio, and therefore, &lambda; i = P ( D | a i ) P ( D | a o ) With OR i = P ( D | a i ) P ( D | a o ) &CenterDot; 1 - P ( D | a i ) 1 - P ( D | a o ) . Based on:
p = &Sigma; i = 0 k f i P ( D | a i ) ,
Can determine
OR i = &lambda; i &Sigma; i = 0 k f i &lambda; i - p &Sigma; i = 0 k f i &lambda; i - &lambda; i p .
And, if set
Figure BSA0000097585230001025
this causes following equation:
&lambda; i = C &CenterDot; OR i C - p + OR i p ,
And therefore,
C = &Sigma; i = 0 k f i &lambda; i = &Sigma; i = 0 k C &CenterDot; OR i f i C - p + OR i p ,
Or
1 = &Sigma; i = 0 k OR i f i C - p + OR i p .
The latter is the equation with a variable (C).This equation can produce many different solutions (as many as k+1 different solution substantially).Criteria optimization instrument (for example Gradient Descent) can approach C most for finding 0=∑ f it isolution.
The present invention has used for the quantitative stable scoring framework of risk factor.Although different genetic models can cause different scorings, result is normally correlated with.Therefore, risk factor does not quantitatively rely on used model conventionally.
the case control study of assessment relative risk
The method of being evaluated relative risk in case control study by multiallelic odds ratio is also provided in the present invention.Contrary with previous method, the method has been considered popularity and the dependence between not homoallelic relative risk of gene frequency, disease.Measured the performance of the method to the case control study of simulation, find it be the utmost point accurately.
method
In the situation that the cognation of the specific SNP of test and disease D, R and N represent risk and the non-risk allelotrope of this specific SNP.P (RR|D), P (RN|D) and P (NN|D) represent hypothesis individual respectively for risk allelotrope be isozygoty, for non-risk allelotrope, be in situation heterozygosis or that isozygoty, to be subject to the probability of sickness influence.F rR, f rNand f nNbe used for representing three genotypic frequencies of colony.Use these definition, relative risk is defined as
&lambda; RR = P ( D | RR ) P ( D | NN )
&lambda; RN = P ( D | RN ) P ( D | NN )
In case control study, can assess P (RR|D), P (RR|～D) value (being the frequency of RR in case and contrast), and P (RN|D), P (RN|～D), P (NN|D) and P (NN|～D), the i.e. frequency of RN and NN in case and contrast.In order to estimate relative risk, can use Bayes (Bayes) law to obtain:
&lambda; RR = P ( RR | D ) f NN P ( NN | D ) f RR
&lambda; RN = P ( D | RN ) f NN P ( D | NN ) f RR
Therefore,, if the frequency of known type, people can use them to calculate relative risk.In colony, genotypic frequency can not be calculated from case-control study itself, because they depend on the popularity of disease in colony.Particularly, if the popularity of disease is p (D):
f RR=P(RR|D)p(D)+P(RR|～D)(1-p(D))
f RN=P(RN|D)p(D)+P(RN|～D)(1-p(D))
f NN=P(NN|D)p(D)+P(NN|～D)(1-p(D))。
As enough hour of p (D), genotypic frequency can approach the genotype frequency in control population, but when popularity is high, and this will can not be estimated value accurately.But for example, if provide comparable data collection (, HapMap[cite]), people can estimate genotype frequency based on comparable data collection.
Great majority research recently is not used comparable data collection to estimate relative risk, and only reports odds ratio.Odds ratio can be write
OR RR = P ( RR | D ) P ( NN | ~ D ) P ( NN | D ) P ( RR | ~ D )
OR RN = P ( RN | D ) P ( NN | ~ D ) P ( NN | D ) P ( RN | ~ D )
Owing to conventionally not needing to have the estimated value of gene frequency in colony, so odds ratio is normally favourable; In order to calculate odds ratio, conventionally needed is genotype frequency in case and contrast.
In some cases, genotype data itself is unavailable, but summary data (for example odds ratio) is available.This is the situation when the result of the case control study based on from previous is carried out meta (meta-analysis).In this case, confirmed how from odds ratio, to find relative risk.The fact of using following equation to show:
p(D)=f RRP(D|RR)+f RNP(D|RN)+f NNP(D|NN)
If this equation is divided by P (D|NN), we obtain
p ( D ) p ( D | NN ) = f RR &lambda; RR + f RN &lambda; RN + f NN
This makes odds ratio can be write as following form:
OR RR = P ( D | RR ) ( 1 - P ( D | NN ) ) P ( D | NN ) ( 1 - P ( D | RR ) ) = &lambda; RR p ( D ) p ( D | NN ) - p ( D ) p ( D ) p ( D | NN ) - p ( D ) &lambda; RR = &lambda; RR f RR &lambda; RR + f RN &lambda; RN + f NN - p ( D ) f RR &lambda; RR + f RN &lambda; RN + f NN - p ( D ) &lambda; RR
By similar calculating, obtain following equation system:
OR RR = &lambda; RR f RR &lambda; RR + f RN &lambda; RN + f NN - p ( D ) f RR &lambda; RR + f RN &lambda; RN + f NN - p ( D ) &lambda; RR
OR RN = &lambda; RN f RR &lambda; RR + f RN &lambda; RN + f NN - p ( D ) f RR &lambda; RR + f RN &lambda; RN + f NN - p ( D ) &lambda; RN .
Equation 1
If the genotype frequency in known advantages ratio, colony and the popularity of disease, can obtain relative risk by solving this system of equations.
It should be noted that, have two quadratic equations, so they have maximum four solutions.But, as shown below, for this equation, conventionally there is a possible solution.
It should be noted that, work as f nN=1 o'clock, equation system 1 was equal to Zhang and Yu formula; But, considered the gene frequency in colony here.And our method has been considered the following fact: two relative risks rely on each other, and previous method proposes to calculate independently each relative risk.
The relative risk of multiple alleles locus.If consider multiple labeling or other multiple alleles modification, calculate slightly complicated.A 0, a 1..., a kthe k+1 an expressing possibility allelotrope, wherein a 0for non-risk allelotrope.Supposed the gene frequency f in colony for k+1 possible allelotrope 0, f 1, f 2..., f k.For allelotrope i, relative risk and odds ratio are defined as
&lambda; i = P ( D | a i ) P ( D | a 0 )
OR i = P ( D | a i ) ( 1 - P ( D | a 0 ) ) P ( D | a 0 ) ( 1 - P ( D | a i ) ) = &lambda; i 1 - P ( D | a 0 ) 1 - P ( D | a i )
Following equation is applicable to the popularity of disease:
p ( D ) = &Sigma; i = 0 k f i P ( D | a i )
Therefore, by by equation both sides all divided by p (D|a 0), we obtain:
p ( D ) P ( D | a 0 ) = &Sigma; i = 0 k f i &lambda; i
Obtain:
OR i = &lambda; i &Sigma; i = 0 k f i &lambda; i - p ( D ) &Sigma; i = 0 k f i &lambda; i - &lambda; i p ( D ) ,
By setting C = &Sigma; i = 0 k f i &lambda; i , Obtain &lambda; i = C &CenterDot; OR i p ( D ) OR i + C - p ( D ) . Therefore, the definition by C, draws:
1 = &Sigma; i = 0 k f i &lambda; i C = &Sigma; i = 0 k f i OR i p ( D ) OR i + C - p ( D ) .
This is the polynomial equation with a variable C.Once determine C, just determined relative risk.Polynomial expression is k+1 degree, and therefore we estimate to have k+1 solution at the most.But, because the right side of equation strictly simplifies the function into C, for this equation, may conventionally only there is a solution so.Use binary search easily to find this to separate because Gai Xie circle in C=1 and C = &Sigma; i = 0 k O R i Between.
The stability of relative risk assessment.Measure variant parameter (popularity, gene frequency and odds ratio error) for the impact of the estimated value of relative risk.In order to measure gene frequency and the impact of popularity estimated value on Relative risk value, value (under HWE) by one group of different odds ratio, different gene frequencies is calculated relative risk, and for the popularity value in 0 to 1 scope, the result of these calculating is drawn.
In addition, for fixing popularity value, the relative risk of gained is as the function plotting of risk gene frequency.Clearly, all p (D)=0 in the situation that, λ rR=OR rRand λ rN=OR rN, and when p (D)=1, λ rR=λ rN=0.This can directly be calculated by equation 1.In addition, when risk gene frequency is high, λ rRclose to linearity performance, and λ rNclose to the concave function with bounded second derivative.When risk gene frequency is low, λ rRand λ rNperformance close to function 1/p (D).This means for high risk gene frequency, the erroneous estimate of popularity will can not affect the relative risk of gained greatly.
Following examples illustrate and have explained the present invention.Scope of the present invention is not limited to these embodiment.
example I
sNP distribution map generalization and analysis
To individuality, provide for example, sample hose test kit (buying from DNA Genotek), individuality leaves saliva sample (approximately 4ml) in this stopple coupon in, will from saliva sample, extract genomic dna.Saliva sample is delivered to the laboratory of the CLIA authentication of processing and analyzing.Conventionally, sample is delivered to mechanism for testing by mailing overnight offer easily individual transport container in gathering test kit in.
In a preferred embodiment, genomic dna is separated from saliva.For example, use the DNA being provided by DNA Genotek from gathering test kit technology, the individual about 4ml saliva sample gathering for Clinical Processing.Sample is delivered to suitable for the treatment of laboratory after, the thermally denature by sample and protease digestion (conventionally use by the reagent that gathers test kit supplier and provide and process at least one hour at 50 ℃) DNA isolation.Subsequently, sample is carried out centrifugal, and supernatant liquid is carried out to ethanol precipitation.DNA throw out is suspended in the damping fluid that is suitable for subsequent analysis.
According to known program and/or by the program that gathers test kit manufacturers and provide, separated individual genomic dna from saliva sample.Conventionally, first sample is carried out to thermally denature and protease digestion.Then, sample is carried out to centrifugation, and retain supernatant liquid.Then supernatant liquid is carried out to ethanol precipitation with the precipitation of the genomic dna that obtains comprising about 5～16ug.DNA throw out is suspended in the EDTA (TE) of Tris (pH7.6), 1mM of 10mM.The instrument that use is provided by array manufacturers and operation instruction, by for example, hybridizing to generate SNP distribution plan by genomic dna and the high-density SNP array (the high-density SNP array being provided by Affymetrix or Illumina) being purchased.Individual SNP distribution plan is stored in encrypting database or strong room.
By comparing with clinical database that established, medical science related SNP (its existence in genome is relevant with given disease or state), inquiry patient's data structure is to find the SNP that gives risk.This database comprises the information of the statistics dependency of specific SNP and SNP haplotype and specified disease or state.For example, as shown in EXAMPLE III, the polymorphism in apolipoprotein E gene causes the different isotype of protein, and this is relevant with the statistics likelihood that Alzheimer occurs again.As another embodiment, the individuality with the modification of the blood coagulating protein prime factor V that is called factor VLeiden has the blood coagulation trend of increase.Wherein many genes of SNP and disease or state phenotypic correlation are shown in Table 1.By research/clinical board of consultants, check and approve science accuracy and the importance of the information in database, and can be checked by the government organs that supervise.Can be continuously new database more because more SNP-disease-related Xing Cong scientific circles occur.
By online entrance or mail to patient safety the analytical results of individual SNP distribution plan is provided.To patient, provide and explain and supportive information, the information about factor V Leiden for example showing in EXAMPLE IV.The doctor of being convenient to patient is discussed in the secure access of individual SNP profile information (for example, by online entrance), and give the ability of selecting for individualized medical treatment.
Example II
the renewal of genotypic correlation
In response to the initial request of determining idiotype dependency, generate Genome Atlas, obtain genotypic correlation, and to individuality, provide result as described in example I.After individual genotypic correlation initial determined, subsequently when known additional genotypic correlation, definitely maybe can determine the dependency of renewal.Registered user has advanced resistry and its gene type spectrum is kept in encrypting database.The dependency of upgrading is carried out on the gene type spectrum of storage.
For example, as described in above example I, initial gene type dependency has determined that particular individual does not have ApoE4, and is therefore difficult for suffering from early hair style Alzheimer, and determines that this individuality does not have factor V Leiden.After this is initially determined, new dependency becomes known and through checking, consequently the polymorphism in given gene (being assumed to be gene XYZ) is relevant to given state (being assumed to be state 321).This new genotypic correlation is joined in the master data base of Human genome type dependency.Then by first obtain the data of genes involved XYZ from be stored in the Genome Atlas of the particular individual encrypting database, to particular individual, provide renewal.The genes involved XYZ data of particular individual are compared with the gene XYZ information of the master data base of renewal.From this contrast, determine specific individual susceptibility or ill physique for state 321.This definite result is joined in the genotypic correlation of particular individual.By whether particular individual renewal result responsive to state 321 or the upper susceptible of heredity offers particular individual together with explanatory and supportive information.
EXAMPLE III
the dependency of ApoE4 locus and Alzheimer
The risk that has shown Alzheimer (AD) is relevant to the polymorphism in apo E (APOE) gene, and this polymorphism causes being called three kinds of isotypes of the APOE of ApoE2, ApoE3 and ApoE4.These isotypes one or two amino acid on the residue 112 and 158 of APOE albumen is mutually different.Halfcystine/halfcystine that ApoE2 comprises 112/158; Halfcystine/arginine that ApoE3 comprises 112/158; Arginine/the arginine that comprises 112/158 with ApoE4.As shown in table 3, Alzheimer is increasing with APOE ε 4 gene copy numbers compared with the danger of outbreak in age in off year.Equally, as shown in table 3, the relative risk of AD increases with APOE ε 4 gene copy numbers.
The table allelic popularity of 3:AD risk (Corder etc., Science:261:921-3,1993)
Table 4: the AD relative risk (Farrer etc., JAMA:278:1349-56,1997) with ApoE4
APOE genotype Odds ratio
ε2ε2 0.6
ε2ε3 0.6
ε3ε3 1.0
ε2ε4 2.6
ε3ε4 3.2
ε4ε4 14.9
EXAMPLE IV
the information of factor V Leiden positive patient
Following information is possible offer the example with the individual information that demonstrates the genome SNP distribution plan that has factor V Leiden gene.This individuality can have can provide the registration of the basis of information in Initial Report.
what is factor V Leiden
Factor V Leiden is not disease, and it refers to the specific gene existing by a people's direct heredity.Factor V Leiden is the modification of the rho factor V (5) of blood coagulation needs.The people with factor V disappearance more may seriously bleed, and has the people's of factor V Leiden blood coagulation trend increase.
The people who carries factor V Leiden gene has than the risk of high 5 times of others's in colony appearance blood clot (thrombosis).But never there is blood clot in many people with this gene.At UK and USA, one or more factor V Leiden genes carry in 5% of colony, and this is far more than reality being suffered to the people's of thrombosis quantity.
how you obtain factor V Leiden
Factor V gene is by a people's direct heredity.As all heredity features, gene genetic from mother and a heredity from father.Thus, may heredity: two normal genes or factor V Leiden gene and a normal gene or two factor VLeiden genes.Having a factor V Leiden gene will cause the risk of slightly high generation thrombosis, but having two genes causes much bigger risk.
what the symptom of factor V Leiden is
There is no symptom, unless you have blood clot (thrombosis).
what does is danger signal?
Modal problem is the blood clot at shank.Shank swelling, pain and rubescent this problem that demonstrates.In rarer case, may there is lung's blood clot (lung thrombosis), it causes expiratory dyspnea.According to the size of blood clot, there is serious expiratory dyspnea from not almost being aware patient in the severity of this illness.In even rarer case, blood clot may occur in arm or other body part.Because these grumeleuses are formed on pumping blood to the vein of heart rather than are formed on artery (it exports blood from heart), factor VLeiden can not make the risk of Coronary thrombosis increase.
what is done and can avoid blood clot
Factor V Leiden only slightly increases the risk that causes blood clot, and thrombosis occurs many people with this state never.A people can do many things and avoid causing blood clot.Avoid with station or the sitting for a long time of same posture.When long-distance travel, importantly take exercise regularly---must make blood not " standing motionless ".Stay up late or smoking will be greatly increases the risk that occurs blood clot.The women who carries factor V Leiden gene should not take Contraceptive pill, because this will enlarge markedly the chance of suffering from thrombosis.The women who carries factor V Leiden gene also should seek advice from its doctor before gestation, because this also can increase thrombotic risk.
how doctor finds whether you have factor V Leiden
The gene of factor V Leiden can be found in blood sample.
Blood clot at shank or arm is determined by ultrasonic examination conventionally.
Blood clot a kind of material injected to blood so that after blood clot manifests, also can be detected by X ray.Clot in lung is more difficult to find, but doctor is by the distribution of using radioactive substance to go to test the distribution of intrapulmonary blood flow and flow to the air in lung conventionally.These two kinds of distribution patterns should match---and unmatch list shows and has blood clot.
how factor V Ieiden processes
The people with factor V Leiden does not need treatment, unless their blood starts to condense, in this case, doctor will output dilute blood (anticoagulant) medicine, for example warfarin (for example, tintorane) or heparin are to prevent further blood clot.Treatment will continue three to six months conventionally, if but there are several blood clots, may need the longer time.The in the situation that of severe, the process of pharmacological agent may continue indefinitely; Extremely rare in the situation that, blood clot may need operation to remove.
at pregnancy duration factor V Leiden, how to process
The women who carries two factor V Leiden genes need to accept the treatment of the solidifying medicine of heparin promoting at pregnancy duration.Identical treatment is applicable to itself previously there had been blood clot or has the women who only carries a factor V Leiden gene of blood clotting family history.
All women that carry factor V Leiden gene may need to wear special stocking in case hemostasis grumeleuse in the gestation second half section.After child's birth, can open anticoagulation medicine heparin to them.
prognosis
The risk that occurs blood clot increased with the age, but in the investigation with the age of carrying out the people who 100 is carried to this gene, found that only minority was once suffered from thrombosis.Genetic consultant association of country (The National Society for Genetic Counselors (NSGC)) can provide the list of genetic consultant in your location and about setting up the information of family history.On www.nsgc.org/consumer, search their online database.
Although shown and described the preferred embodiment of the present invention at this, very clear to those skilled in the art, these embodiments only provide in the mode of embodiment.Many modification, change and the replacement that those skilled in the art can expect now and do not depart from the present invention.Should be appreciated that, can be for realizing the present invention for many alternative of embodiments of the present invention described herein.Anticipation, following claim limits scope of the present invention, and the present invention covers method and structure and equivalent thereof in the scope of these claims.

Claims (10)

1. a method of assessing individual genotypic correlation, the method comprises:
A) obtain the hereditary sample of described individuality;
B) generate the Genome Atlas of described individuality;
C) by the Genome Atlas of described individuality is compared and is determined the genotype of described individuality and the dependency of phenotype with the correlation data storehouse of phenotype with current mankind genotype;
D) to the health care management person report of described individuality or described individuality by step c) the described result that obtains;
E), when knowing additional Human genome type dependency, by described additional Human genome type dependency, upgrade described Human genome type correlation data storehouse; With
F) by by step c) the Genome Atlas of described individuality or its part compare with described additional Human genome type dependency and upgrade the genotypic correlation of described individuality the episome type dependency of definite described individuality; With
G) to the health care management person report of described individuality or described individuality by step f) the described result that obtains.
2. method claimed in claim 1, wherein, third party obtains described hereditary sample.
3. method claimed in claim 1, wherein, described generation Genome Atlas is undertaken by third party.
4. method claimed in claim 1, wherein, described result is based on GCI or GCIPlus scoring.
5. method claimed in claim 1, wherein, described report comprises by result described in Internet Transmission.
6. method claimed in claim 1, wherein, the described report of described result is by online entrance.
7. method claimed in claim 1, wherein, the described report of described result is by paper part or passes through e-mail.
8. method claimed in claim 1, wherein, the mode that described report comprises encrypting is reported described result.
9. method claimed in claim 1, wherein, described report comprises in unencrypted mode reports described result.
10. method claimed in claim 1, wherein, the Genome Atlas of described individuality is stored in encrypting database or strong room.
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