CN118647717A - Non-invasive prenatal sample preparation and related methods and uses - Google Patents

Abstract

The present disclosure relates to methods for preparing cell-free DNA samples from pregnant women or pregnant women, and related methods for analyzing such samples.

Description

Non-invasive prenatal sample preparation and related methods and uses

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 63/298,593, filed on January 11, 2022, and U.S. Provisional Application No. 63/357,915, filed on July 1, 2022, and the entire contents of each application are incorporated herein by reference.

Technical Field

This article describes methods for preparing samples from pregnant women and associated methods for analyzing such samples.

Background Art

The following description of the background of the present technology is provided only to help understand the present technology, and is not admitted to describe or constitute the prior art of the present technology.

Non-invasive pre-natal screening (NIPS) has become a routine part of maternal care. NIPS can involve screening for aneuploidy (such as Down syndrome) and other genetic abnormalities in the mother or fetus. Many of these screens utilize cell-free DNA (cfDNA); however, the use of cfDNA encounters many challenges because only a small portion of cfDNA in maternal plasma comes from the fetus.

In addition, prenatal screening for certain genetic conditions traditionally requires obtaining DNA samples from both the mother and the father. For example, traditional methods for detecting aneuploidy and various genetic conditions require obtaining genomic DNA (gDNA) samples from both the mother and the father of the fetus, as well as cfDNA from the mother. Therefore, such tests require at least three samples, each of which can be processed and evaluated in different ways.

The present disclosure addresses those challenges by providing methods to selectively enrich the fetal portion of a maternal sample so that NIPS for both aneuploidy and other genetic variants/mutations can be performed in parallel with only a single maternal sample.

Summary of the invention

The present disclosure is generally directed to novel sample preparation and parallel screening for aneuploidy and other genetic variations (such as pathogenic SNPs, insertions or deletions (INDELs), and single gene copy number variations) from a single sample. These compositions and methods improve non-invasive prenatal screening (NIPS) by streamlining and simplifying the required analysis, using less sample, and reducing background noise, all with less complexity and requiring less time than conventional prenatal screening analysis.

In one aspect, the present disclosure provides a method of preparing a biological sample having an enriched fetal portion, comprising:

(a-1) obtaining a biological sample including cell-free DNA (cfDNA) from a pregnant woman;

(b-1) extracting cfDNA from the biological sample;

(c-1) preparing a cfDNA fragment library to obtain a cfDNA library;

(d-1) separating the cfDNA fragments in the cfDNA pool according to size to retain only cfDNA fragments of less than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, about 180 nucleotides in length, about 185 nucleotides in length, about 190 nucleotides in length, about 195 nucleotides in length, or about 200 nucleotides in length;

(e-1) sequencing the retained cfDNA fragments to obtain a first sequence library;

(f-1) identifying (i) cell-free fetal DNA (cffDNA) sequences and (ii) cell-free maternal DNA (cfmDNA) sequences present in at least two windows of the first sequence library based on the length of the reads; and

(g-1) separating the cffDNA sequence from each of the at least two windows of the sequence library, thereby obtaining at least two sequence libraries enriched in the fetal portion;

or

(a-2) obtaining biological samples including cell-free DNA (cfDNA) from pregnant women;

(b-2) extracting cfDNA from the biological sample;

(c-2) separating the cfDNA fragments in the extracted sample from (b-2) to retain only cfDNA fragments of less than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, about 180 nucleotides in length, about 185 nucleotides in length, about 190 nucleotides in length, about 195 nucleotides in length, or about 200 nucleotides in length;

(d-2) preparing a cfDNA library from the isolated cfDNA fragments from (c-2);

(e-2) sequencing the cfDNA library to obtain a first sequence library;

(f-2) identifying (i) cell-free fetal DNA (cffDNA) sequences and (ii) cell-free maternal DNA (cfmDNA) sequences present in at least two windows of the first sequence library based on the length of the reads; and

(g-2) isolating the cffDNA sequence from each of the at least two windows of the sequence library, thereby obtaining at least two sequence libraries enriched in the fetal part.

In some embodiments, isolating the cfDNA fragments enriches the fetal portion of the biological sample by about 1.1 times, about 1.2 times, about 1.3 times, about 1.4 times, about 1.5 times, about 1.6 times, about 1.7 times, about 1.8 times, about 1.9 times, or about 2.0 times.

In some embodiments, separation of the cffDNA sequences from the at least two windows of the first sequence library enriches the fetal portion in the biological sample by about 1.1 times, about 1.2 times, about 1.3 times, about 1.4 times, about 1.5 times, about 1.6 times, about 1.7 times, about 1.8 times, about 1.9 times, about 2.0 times, about 2.1 times, about 2.2 times, about 2.3 times, about 2.4 times, about 2.5 times, about 2.6 times, about 2.7 times, about 2.8 times, about 2.9 times, about 3.0 times, about 3.1 times, about 3.2 times, about 3.3 times, about 3.4 times, or about 3.5 times.

In some embodiments, separating the cfDNA fragments comprises electrophoresis.

In some embodiments, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 windows of the first sequence library are evaluated to identify and isolate cffDNA sequences, thereby obtaining at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 sequence libraries enriched in fetal portions, respectively.

In some embodiments, the method may further include identifying and separating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA in the first sequence library to a reference genome, demultiplexing sequence reads from the first library, removing repetitive sequences from the first sequence library, or a combination thereof.

In some embodiments, the method may further include evaluating the presence of one or more genetic mutations in the sequence libraries of the at least two enriched fetal portions. In some embodiments, the one or more genetic mutations result in at least one condition selected from the following: 21-hydroxylase deficiency, ABCC8-related hyperinsulinism, ARSACS, achondroplasia, achromatopsia, adenosine monophosphate deaminase 1, corpus callosum agenesis with neuronopathy, alkaptonuria, alpha-1-antitrypsin deficiency, alpha-mannose storage disease, alpha-sarcoglycan disease, alpha-thalassemia; Alzheimer's disease, angiotensin II receptor type I, lipoprotein E genotyping; Argininosuccinic aciduria, aspartate glucosamineuria, ataxia with vitamin E deficiency, ataxia telangiectasia dystrophia, autoimmune polyendocrinopathy syndrome type 1, BRCA1 hereditary breast/ovarian cancer, BRCA2 hereditary breast/ovarian cancer, Bardet-Biedl syndrome, Best yolk sac macular dystrophy, beta-sarcoglycanosis, beta-thalassemia, biotinidase deficiency, Blau syndrome, Bloom syndrome, CFTR-related disorders, CLN3-related neurogenic cerolipofuscinosis, CLN5-related neurogenic cerolipofuscinosis, CLN8-related neurogenic cerolipofuscinosis, Canavan disease, carnitine palmitoyltransferase IA deficiency, carnitine palmitoyltransferase II deficiency, chondro-hair dysplasia, cerebral cavernous malformation (Cerebral cavernous malformation Cavernous Malformation), Choroiderosis, Cohen's Syndrome, Congenital Cataract, Facial Dysmorphism and Neuropathy, Congenital Disorder of Glycosylation la, Congenital Disorder of Glycosylation Ib, Congenital Finnish Nephrosis, Crohn Disease, Cystinosis, DFNA 9 (COCH), Diabetes and Hearing Loss, Early-Onset Primary Dystonia (DYTI), Epidermolysis Bullosa Junctional, Herlitz-Pearson Type, FANCC-associated Fanconi Anemia, FGFR1-associated Premature Suture Closure, FGFR2-associated Premature Suture Closure, FGFR3-associated Premature Suture Closure, Factor V Leiden Thrombophilia Thrombophilia), Factor V R2 Mutation Thrombophilia, Factor 11 Deficiency, Factor 13 Deficiency, Familial Adenomatous Polyposis, Familial Dysautonomia, Familial Hypercholesterolemia Type B, Familial Mediterranean Fever, Free Sialic Acid Storage Disorder, Frontotemporal Dementia with Parkinsonism-17, Fumarase Deficiency, GJB2-Related DFNA Type 3 Nonsyndromic Hearing Loss and Deafness, GJB2-Related DFNB 1 Nonsyndromic Hearing Loss and Deafness, GNE-Related Myopathy, Galactosemia, Gaucher's Disease, Glucose-6-Phosphate Dehydrogenase Deficiency, Glutaric Acidemia Type 1, Glycogen Storage Disease Type 1a 1a), Glycogen Storage Disease Type Ib, Glycogen Storage Disease Type II, Glycogen Storage Disease Type III, Glycogen Storage Disease Type V, Gracile Syndrome, HFE-Associated Hereditary Hemochromatosis, Halder AIMs, Hemoglobin Sβ-thalassemia, Hereditary Fructose Intolerance, Hereditary Pancreatitis, Hereditary Thymine-Uraciluria, Hexosaminidase A Deficiency, Hidrotic Ectodermal Dysplasia 2 2), Homocystinuria due to cystathionine β-synthase deficiency, Hyperkalemic periodic paralysis type 1, Hyperornithinemia-hyperammonemia-homocitrullinuria syndrome, Primary hyperoxaluria type 1, Primary hyperoxaluria type 2, Hypochondrogenosis, Hypokalemic periodic paralysis type 1, Hypokalemic periodic paralysis type 2, Hypophosphatasia, Infantile myopathy and lactic acidosis (fatal and non-fatal), Isovaleric acidemia, Krabbe disease, LGMD2I, Leber hereditary optic neuropathy, French-Canadian Leigh syndrome, Long Chain 3-Hydroxyacyl-CoA Dehydrogenase deficiency (Long Chain 3-Hydroxyacyl-CoA Dehydrogenase deficiency Deficiency), MELAS, MERRF, MTHFR Deficiency, MTHFR Thermolabile Variants, MTRNR1-Associated Hearing Loss and Deafness, MTTS1-Associated Hearing Loss and Deafness, MYH-Associated Polyposis, Maple Syrup Urine Disease Type 1A, Maple Syrup Urine Disease Type 1B, McCune-Albright Syndrome, Medium Chain Acyl-CoA Dehydrogenase Deficiency, Megalencephalic Leukoencephalopathy with Subcortical Cyst, Metachromatic Leukodystrophy, Mitochondrial Cardiomyopathy, Mitochondrial DNA-Associated Leigh Syndrome and NARP, Mucolipidosis IV, Mucopolysaccharidosis Type I I), Mucopolysaccharidosis Type IIIA, Mucopolysaccharidosis Type VII, Multiple Endocrine Neoplasia Type 2, Muscle-Eye-Brain Disease, Nemaline Myopathy, Neurological Phenotype, Niemann-Pick Disease Due to Sphingomyelinase Deficiency, Niemann-Pick Disease Type C1, Nijmegen Breakage Syndrome, PPT1-Related Neurological Cerofuscinosis, PROP1-Related Pituitary Hormone Deficiency, Pallister-Hall Syndrome, Paramyotonia Congenita, Pendred Syndrome, Peroxisome Bifunctional Enzyme Deficiency, Pervasive Developmental Disorder, Phenylalanine Hydroxylase Deficiency, Plasminogen Activator Inhibitor I I), autosomal recessive polycystic kidney disease, prothrombin G20210A thrombophilia, pseudovitamin D deficiency rickets, pycnodysostosis, autosomal recessive pigmented retinitis Bothnia, Rett syndrome, Rhizomelic Chondrodysplasia Punctata Type 1, short-chain acyl-CoA dehydrogenase deficiency, Shwachman-Diamond syndrome, Sjogren-Larsson syndrome, Smith-Lemli-Opitz syndrome, spastic paraplegia 13, sulfate transporter-related osteochondrodysplasia, TFR2-related hereditary hemochromatosis, TPP1-related neurological cerolipofuscinosis, lethal achondroplasia, transthyretin amyloidosis Amyloidosis), trifunctional protein deficiency, tyrosine hydroxylase deficiency DRD, tyrosinemia type I, Wilson's disease, X-linked juvenile retinoschisis, cystic fibrosis, spinal muscular atrophy (SMA), heme diseases, and Zellweger syndrome spectrum.

In some embodiments, the method may further include assessing the presence of aneuploidy in a biological sample comprising cfDNA. In some embodiments, the aneuploidy is selected from a single chromosome, a trisomy, a tetrasome, a pentasome, a microdeletion, a microduplication, and a mosaic form of a single chromosome, a trisomy, a tetrasome, and a pentasome.

In another aspect, the present disclosure provides a method for concurrently detecting the presence or absence of aneuploidy and the presence or absence of at least one genetic variant in a single maternal sample, comprising

(i) obtaining a biological sample from a pregnant woman, wherein the biological sample comprises cell-free DNA (cfDNA);

(ii) preparing a cfDNA library;

(iii) sequencing the cfDNA library to generate a sequence library; and

(iv) detecting the presence or absence of aneuploidy and the presence or absence of at least one genetic variant in the single maternal sample;

Wherein (a) the cfDNA library is enriched to increase the fetal portion, (b) the sequence library is enriched to increase the fetal portion, or (c) a combination thereof, such that the fetal portion of the single maternal sample is increased by at least 1.1 times, at least 1.2 times, at least 1.3 times, at least 1.4 times, or at least 1.5 times prior to detecting the presence or absence of aneuploidy and the presence or absence of at least one genetic variant in the single maternal sample.

In some embodiments, the biological sample is blood, serum, or plasma.

In some embodiments, the cfDNA library is enriched to increase the fetal portion, and the sequence library is enriched to increase the fetal portion.

In some embodiments, enriching the fetal portion of the cfDNA pool comprises removing any DNA fragments greater than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, or about 180 nucleotides in length from the cfDNA pool. In some embodiments, removing the DNA fragments from the cfDNA pool comprises electrophoresis.

In some embodiments, enriching the fetal portion of the sequence library comprises performing size exclusion based on read length on sequences in at least two windows of the sequence library, thereby obtaining at least two sequence libraries enriched in the fetal portion. In some embodiments, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 windows of the first sequence library are evaluated to identify and isolate cffDNA sequences, thereby obtaining at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 sequence libraries enriched in the fetal portion, respectively. In some embodiments, at least two windows of the sequence library are selected from (i) sequences of 0-145 nucleotides, (ii) sequences of 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-168 nucleotides, (vii) 0-170 nucleotides, (viii) 0-175 nucleotides, (ix) 0-180 nucleotides, (x) 0-185 nucleotides, (xi) 0-190 nucleotides, (xii) 0-195 nucleotides, (xiii) 0-200 nucleotides, and (xiv) ungated.

In some embodiments, enriching the fetal portion of the sequence library further includes identifying and separating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA in the first sequence library to a reference genome, demultiplexing sequence reads from the first library, removing repetitive sequences from the first sequence library, or a combination thereof.

In some embodiments, detecting the presence or absence of at least one genetic variant includes determining the allelic balance of each allele encoding the at least one genetic variant in the sample in each of the at least two sequence libraries enriched for the fetal portion, and generating an allelic balance trajectory for each allele based on the allelic balance in each of the at least two sequence libraries enriched for the fetal portion, generating a depth trajectory based on the depth of the at least two sequence libraries enriched for the fetal portion, or generating a combination of an allelic balance trajectory and a depth trajectory.

In some embodiments, detecting the presence or absence of aneuploidy includes analyzing the sequence depth of at least one sequence corresponding to a chromosome of interest in the sequence library. In some embodiments, the sequence depth of at least one sequence corresponding to the chromosome of interest is adapted to the expected depth model of the chromosome of interest. In some embodiments, the sequence depth is calculated by the following formula:

in:

d _p is the depth of pregnancy

f is the fetus

c _m is the maternal copy number

d _b is the background depth

c _f is the fetal copy number.

In some embodiments, the sequence depth is normalized to control for GC bias, sample background, hybridization probe capture, or a combination thereof.

In some embodiments, the method comprises detecting the presence or absence of an aneuploidy selected from a monosomy, a trisomy, a tetrasomy, polysomy X, polysomy Y, a microdeletion, a microduplication, a pentasome, and combinations thereof.

In some embodiments, the at least one genetic variant is associated with a disease selected from the group consisting of: 21-hydroxylase deficiency, ABCC8-related hyperinsulinism, ARSACS, achondroplasia, achromatopsia, adenosine monophosphate deaminase 1, corpus callosum agenesis with neuronopathy, alkaptonuria, alpha-1-antitrypsin deficiency, alpha-mannose storage disease, alpha-sarcoglycanosis, alpha-thalassemia; Alzheimer's disease, angiotensin II receptor type I, lipoprotein E genotyping; sperminosuccinic aciduria, aspartaminuria, ataxia with vitamin E deficiency, ataxia telangiectasia, autoimmune polyendocrinopathy syndrome type 1, BRCA1 hereditary breast cancer/ovarian cancer, BRCA2 hereditary breast cancer/ovarian cancer, Bardet -Biedl syndrome, Best yolk sac macular dystrophy, beta-sarcoglycanosis, beta-thalassemia, biotinidase deficiency, Blau syndrome, Bloom syndrome, CFTR-related disorders, CLN3-related neurogenic cerolipofuscinosis, CLN5-related neurogenic cerolipofuscinosis, CLN8-related neurogenic cerolipofuscinosis, Canavan disease, carnitine palmitoyltransferase IA deficiency, carnitine palmitoyltransferase II deficiency, chondro-hair dysplasia, spongiomatous malformation, choroidergic malformation, Cohen syndrome, congenital cataract, facial dysmorphism and neuropathy, congenital glycosylation disorder la, congenital glycosylation disorder Ib, congenital Finnish nephropathy, Crohn's disease, cystinosis, DFNA 9(COCH), diabetes mellitus and hearing loss, early-onset primary atonia (DYTI), junctional epidermolysis bullosa Herlitz-Pearson type, FANCC-related Fanconi anemia, FGFR1-related premature cranial suture closure, FGFR2-related premature cranial suture closure, FGFR3-related premature cranial suture closure, factor V Leiden thrombophilia, factor V R2 mutation thrombophilia, factor 11 deficiency, factor 13 deficiency, familial adenomatous polyposis, familial dysautonomia, familial hypercholesterolemia type B, familial Mediterranean fever, free sialic acid storage disorder, frontotemporal dementia with Parkinson's disease 17, fumarase deficiency, GJB2-related DFNA type 3 non-syndromic hearing loss and deafness, GJB2-related DFNB 1Nonsyndromic hearing loss and deafness, GNE-related myopathy, galactosemia, Gaucher's disease, glucose-6-phosphate dehydrogenase deficiency, glutaric acidemia type 1, glycogen storage disease type 1a, glycogen storage disease type Ib, glycogen storage disease type II, glycogen storage disease type III, glycogen storage disease type V, Gracile syndrome, HFE-related hereditary hemosideroscopy, Halder AIMs, hemoglobin Sβ-thalassemia, hereditary fructose intolerance, hereditary pancreatitis, hereditary thymine-uraciluria, hexosaminidase A deficiency, hidrotic ectodermal dysplasia 2, homocystinuria due to cystathionine β-synthase deficiency, hyperkalemic periodic paralysis type 1, hyperornithinemia-hyperammonemia-homocitrullinuria syndrome, primary hyperoxaluria type 1, primary hyperoxaluria type 2, hypochondrogenosis, hypokalemic periodic paralysis type 1, hypokalemic periodic paralysis type 2, hypophosphatasia, infantile myopathy and lactic acidosis (fatal and nonfatal), isovaleric acidemia, Krabbe disease, LGMD2I, Leber hereditary optic neuropathy, French-Canadian Leigh syndrome syndrome, long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency, MELAS, MERRF, MTHFR deficiency, MTHFR thermolabile variants, MTRNR1-related hearing loss and deafness, MTTS1-related hearing loss and deafness, MYH-associated polyposis, maple syrup urine disease type 1A, maple syrup urine disease type 1B, Markon-Subbatt syndrome, medium-chain acyl-CoA dehydrogenase deficiency, megalencephaly with subcortical cysts, metachromatic leukodystrophy, mitochondrial cardiomyopathy, mitochondrial DNA-associated Leigh syndrome and NARP, mucolipidosis IV, mucopolysaccharidosis type I, mucopolysaccharidosis type IIIA, mucopolysaccharidosis type VII, multiple endocrine neoplasia type 2, muscle-eye-brain disease, nematode myopathy, neuromyopathy Phenotype, Niemann-Pick disease due to phospholipase deficiency, Niemann-Pick disease type C1, Nijmegen chromosome breakage syndrome, PPT1-related neurological cerolipofuscinosis, PROP1-related pituitary hormone deficiency, Pallister-Hall syndrome, myospasm congenita, Pendred syndrome, peroxisome bifunctional enzyme deficiency, pervasive developmental disorder, phenylalanine hydroxylase deficiency, plasma proteinogen activator inhibitor I, autosomal recessive polycystic kidney disease, prothrombin G20210A thrombophilia, pseudovitamin D deficiency rickets, pycnodystrophy, autosomal recessive pigmented retinitis Bothnia type, Rett syndrome, chondrodysplasia punctata abnormality type 1, short-chain acyl-CoA dehydrogenase deficiency, Shwachman-Diamond syndrome, Sjogren-Larsson syndrome, Smith-Lemli-Opitz syndrome, spastic paraplegia 13, sulfate transporter-related osteochondrodysplasia, TFR2-related hereditary hemochromatosis, TPP1-related neuropathic cerolipofuscinosis, lethal achondroplasia, transthyretin amyloidosis, trifunctional protein deficiency, tyrosine hydroxylase deficiency DRD, tyrosinemia type I, Wilson's disease, X-linked juvenile retinoschisis, cystic fibrosis, spinal muscular atrophy (SMA), heme diseases, and Zellweger syndrome spectrum.

In another aspect, the present disclosure provides a method for enriching free fetal DNA (cffDNA) in a biological sample, comprising obtaining a biological sample comprising free DNA (cfDNA) from a pregnant woman, wherein the cfDNA comprises cffDNA and free maternal DNA (cfmDNA); extracting the cfDNA from the biological sample; and subjecting the extracted cfDNA to a size exclusion process, wherein the size exclusion process has a cutoff size of about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, or about 180 nucleotides in length, thereby producing a sample enriched in cffDNA.

In another aspect, the present disclosure provides a method for computer simulation processing of free DNA (cfDNA), which includes sequencing a cfDNA sample including free fetal DNA (cffDNA) and free maternal DNA (cfmDNA) to prepare a sequence library; performing a read length-based analysis, wherein an allelic balance of a nucleic acid sequence of interest is established in at least two windows of the sequence library; and establishing a trajectory based on the allelic balance of the at least two windows.

In another aspect, the present disclosure provides a method for reducing background noise from excess genetic material in non-invasive prenatal screening (NIPS), comprising

(i) obtaining a biological sample from a pregnant woman, wherein the biological sample comprises cell-free DNA (cfDNA); and

(ii) processing the cfDNA for NIPS, wherein the processing comprises enriching the free fetal DNA (cffDNA) in the biological sample, performing computer simulation processing on the cfDNA, or a combination thereof.

In some embodiments, processing includes both enriching cell-free fetal DNA (cffDNA) in said biological sample and performing computer simulation processing on said cfDNA.

In some embodiments, enriching the cell-free fetal DNA (cffDNA) in the biological sample comprises any of the methods disclosed herein for enriching the cell-free fetal DNA (cffDNA) in a biological sample.

In some embodiments, performing computer simulation processing on the cfDNA includes any of the methods for performing computer simulation processing on free DNA (cfDNA) disclosed herein.

In some embodiments, the method may further include normalization to control for GC bias, sample background, hybridization probe capture, or a combination thereof.

The following embodiments are exemplary and explanatory but not intended to be limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a graph comparing conventional size exclusion techniques to the disclosed size exclusion method, which is more forgiving and retains more cffDNA.

FIG. 2 provides a visualization of the disclosed in silico enrichment method, which relies on a moving window analysis to closely observe changes in allelic balance as the amount of fetal and maternal cfDNA changes.

FIG. 3 shows two visualizations of allelic balance observed from the disclosed moving window analysis.

FIG. 4 shows an overview of an exemplary computational flow of one embodiment of the disclosed method and system.

Fig. 5 shows several visual representations of how deep calling is used to establish the presence of aneuploidy. The upper figure compares the depth reads of conventional karyotype and chromosome 21 in normal pregnancy and pregnancy in which the fetus has three chromosomes 21. The middle figure represents the type of deep offset expected when three chromosomes are observed. The lower figure shows the expected fit of four known copy number (CN) curves (e.g., CN=1, CN=2, CN=3, and CN=4) representing various ploidy, where the shaded area indicates how the depth of the reads from the sample containing the three chromosomes will fit within the expected fitting curve.

FIG6 shows an exemplary improvement in data plots that can be achieved by employing a triple normalization control for variations caused by 1) GC bias, 2) sample background, and 3) hybridization probe capture.

Fig. 7 shows the fitting of the depth reads relative to the expected fitting curve for several chromosomes with different fitting samples. The shaded area in each figure represents the depth of a given sample of a specified chromosome. The fitting curve from left to right in each figure is the expected fitting of 1, 2, or 3 chromosomes in the fitting model.

FIG. 8 shows a deep trajectory graph for a gene (SMN2) where the mother has one copy of the gene and the fetus has zero.

DETAILED DESCRIPTION

The sample preparation and methods disclosed herein generally involve new methods for collecting biological samples (e.g., blood or other DNA-containing samples) from a biological mother and then performing screening, such as through a non-invasive prenatal screening to detect aneuploidy and genetic mutations in parallel (e.g., a recessive monitoring program). That is, the present disclosure provides a single test (e.g., a parallel test) to discover two sets of detectable genetic conditions (e.g., aneuploidy and genetic variant screening) using only samples from one individual, the biological mother. Combining these two monitoring tests into a single test that does not involve the biological father provides efficiency and convenience compared to conventional tests and methods, which typically require a father's sample and perform aneuploidy screening and genetic variant screening separately. In addition, sample preparation can improve sensitivity, specificity, and minimize the noise of excess genetic material that is not required for the detection of various causal genetic variants.

The following will more fully describe embodiments according to the present disclosure. However, aspects of the present disclosure can be implemented in different forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the present invention to those with ordinary knowledge in the art. The terms used in the description herein are only for the purpose of describing specific embodiments and are not intended to be limiting.

Unless expressly stated otherwise, all specified embodiments, features, and terms are intended to include the referenced embodiments, features, or terms and their equivalents.

I. Definitions

As used herein, the singular forms "a," "an," and "the" refer to both the singular and the plural, unless explicitly stated to refer to the singular only.

As used herein, the term "about" should be understood as a relative term including the stated value and a range of +/-10%. For example, the phrase "about 10" should be understood to mean both "10" and "9 to 11".

Furthermore, as used herein, "and/or" refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as no combination when interpreted in the alternative ("or").

As used herein, "optional" or "optionally" means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where said event or circumstance occurs and instances where it does not.

As used herein, "DNA binding particles" refers to any conventional solid phase material that interacts with DNA fragments such as cfDNA fragments, or conventional solid phase materials that are modified to interact with the DNA fragments. For example, a solid phase material is any type of insoluble, generally rigid material, matrix, or stationary phase material that interacts directly or indirectly with DNA in a reaction solution. In certain exemplary embodiments, the DNA binding particles are beads.

As used herein, "beads" refer to solid phase particles of any convenient size and may have irregular or regular shapes. In certain exemplary embodiments, the surface of the beads is modified to bind directly and/or indirectly to DNA. For example, the beads may contain silanol groups, carboxyl groups, or other groups that promote direct interaction and/or interaction between the beads and DNA. In certain exemplary embodiments, the silica beads (and gels) may be functionalized by adding primary amines, thiols, mercaptos, propyl groups, octyl groups, and other derivatives to the hydroxyl groups (silanols) attached to the silica. The beads may be made of any number of known materials, including cellulose, cellulose derivatives, acrylic resins, glass, silica gel, polystyrene, gelatin, polyvinyl pyrrolidone, copolymers of vinyl and acrylamide, polystyrene cross-linked with divinylbenzene or the like, polyacrylamide, latex gel, polystyrene, dextran, rubber, silicon, plastic, nitrocellulose, natural sponges, silica gel, controlled pore glass (CPG), metals, cross-linked dextran (e.g. ), agarose gel and other solid phase bead supports known to those of ordinary skill in the art. In certain exemplary embodiments, the beads can be packed together to form a column that can be used with conventional column chromatography.

As used herein, the term "gene variant" refers to a change that is considered as a non-pathogenic or wild-type gene sequence when used to refer to screening, calling, or processes described herein. Therefore, the term "gene variant" includes pathogenic single nucleotide polymorphisms (SNPs), insertions or deletions (INDELs) of bases in the subject's genome, substitution mutations, single gene copy number variations, etc. In addition, it should be noted that the term "gene variant" used herein is different from aneuploidy, and the term "gene variant" does not involve deletions or extra chromosomes. On the contrary, the term "gene variant" should be understood to be related to features or changes (pathogenic or other) in the subject's genome sequence, rather than chromosomal abnormalities.

As used herein, the terms "cfDNA library" or "nucleic acid library" are used interchangeably to refer to a collection of nucleic acids, for example, a collection of free nucleic acids derived from a biological sample. In some embodiments, the cfDNA library or nucleic acid library is produced by amplifying nucleic acids in a sample or otherwise using a PCR-free method to prepare a library. In some embodiments, the cfDNA library or nucleic acid library is produced by amplifying specific target fragments within a sample, as described in detail below. In some embodiments, some or all of the nucleic acids in the cfDNA library or nucleic acid library include a transfer subsequence. The transfer subsequence can be located at one end or both ends. The transfer subsequence can be used, for example, for sequencing methods (e.g., NGS methods), amplification, reverse transcription, or cloning into a vector.

A cfDNA library or nucleic acid library may comprise a collection of nucleic acid fragments, which may comprise a target nucleic acid sequence (e.g., a nucleic acid sequence in which a gene variant associated with a disease may be detected), a reference nucleic acid sequence, or a combination thereof. In some embodiments, two or more cfDNA or nucleic acid libraries from the same subject may be combined.

As used herein, a "sequence library" is a collection of nucleic acid sequences that have been prepared by sequencing a cfDNA library or a nucleic acid library, for example using a massively parallel method such as next generation sequencing or NGS. NGS generally refers to a sequencing method that allows massively parallel sequencing of clonally amplified and single nucleic acid molecules, during which multiple, for example millions of nucleic acid fragments from a single sample or multiple different samples are sequenced in unison. Non-limiting examples of NGS include synthetic sequencing, ligation sequencing, real-time sequencing, and nanopore sequencing.

II. Sample Preparation

Free DNA (cfDNA) is a mixture of DNA with different properties (e.g., size, sequence, abundance) and source tissues (e.g., maternal versus fetal). For example, cfDNA obtained from pregnant women contains DNA from both maternal and fetal sources. When cfDNA is used in a given maternal plasma sample, the main driver of NIPS sensitivity is the fetal fraction (FF). The fetal fraction includes the total free DNA fraction from the fetus or derived from free fetal DNA (cffDNA). For most samples, the FF value is between 1% and 30%, but in many cases, the number may be even lower.

The present disclosure provides sample preparation and methods for preparing samples from pregnant women (i.e., pregnant women or biological mothers) that can be used to improve sensitivity, specificity, and minimize noise when performing NIPS. Specifically, sample preparation may rely on physical processing of cfDNA samples obtained from pregnant women, computer simulation processing of sequencing reads generated from cfDNA samples obtained from pregnant women, or a combination thereof.

A. Physical enrichment of fetal parts

Physical treatment of cfDNA samples (e.g., blood) obtained from pregnant women by the methods disclosed herein can enrich the fetal portion of the cfDNA sample by up to 3 times. Specifically, size selection can be performed by using a size cutoff that retains most of the fetal free DNA fragments and removes some of the large free maternal DNA fragments, and the fetal portion can be enriched in the sample. For example, a cutoff value can be set to retain cfDNA fragments less than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, or about 180 nucleotides in length.

In some embodiments, the methods can be used to select and isolate fragments of less than 75 nucleotides, less than 80 nucleotides, less than 85 nucleotides, less than 90 nucleotides, less than 95 nucleotides, less than 100 nucleotides, less than 105 nucleotides, less than 110 nucleotides, less than 115 nucleotides, less than 120 nucleotides, less than 125 nucleotides, less than 130 nucleotides, less than 135 nucleotides, less than 140 nucleotides, less than 145 nucleotides, less than 150 nucleotides, less than 155 nucleotides, less than 160 nucleotides, less than 165 nucleotides, less than 170 nucleotides, less than 175 nucleotides, less than 180 nucleotides, less than 195 nucleotides, less than 200 nucleotides, less than 205 nucleotides, less than 206 nucleotides, less than 210 nucleotides, less than 215 nucleotides, less than 220 nucleotides, less than 225 nucleotides, less than 230 nucleotides, less than 235 nucleotides, less than 240 nucleotides, less than 245 nucleotides, less than 250 nucleotides, less than 255 nucleotides, less than 260 nucleotides, less than 265 nucleotides, less than 270 nucleotides, less than 275 nucleotides, less than 280 nucleotides, less than 285 nucleotides, less than 290 nucleotides, less than 295 nucleotides, less than 300 nucleotides, less than 305 nucleotides, less than 310 nucleotides, less than 311 nucleotides, less than 315 nucleotides, less than 320 nucleotides, or less than 325 nucleotides. In some embodiments, the target size can be less than 125 nucleotides, less than 130 nucleotides, less than 135 nucleotides, less than 140 nucleotides, less than 145 nucleotides, less than 150 nucleotides, less than 155 nucleotides, less than 160 nucleotides, less than 165 nucleotides, less than 170 nucleotides, less than 175 nucleotides, less than 180 nucleotides, less than 195 nucleotides, or less than 200 nucleotides. Regardless of the precise cutoff or target size, the goal of the process is to retain cffDNA with little or no loss and minimize or deplete cfmDNA.

This type of size-based exclusion can be performed using electrophoresis (e.g., gel electrophoresis or capillary electrophoresis) and other known methods, which can utilize, for example, DNA-bound particles, such as beads (e.g., AMPURE ^™ beads). In one embodiment, nucleic acid electrophoresis is used to separate and then the required fragment length is recovered. Various known electrophoretic processes can be used for this purpose, but in one embodiment, the NIMBUS Select ^™ workstation with Ranger Technology ^™ for high-throughput nucleic acid size selection can be used. Other strategies for fragment size selection include following the manufacturer's instructions for "range" mode and performing electrophoresis on agarose boxes (BluePippin, Sage Science). Short fragments are eluted from the gel until the required target size of the eluted DNA is obtained. Other methods include, but are not limited to, solid support capture (e.g., affinity columns), such as antibody-coated spin columns; synchronous (or non-synchronous) coefficient of drag alteration sizing (SCODA); solid phase reversible immobilization sizing (e.g., using carboxylated magnetic beads); affinity chromatography processes, or a combination of PCR amplification with amplicons of different lengths and microchip separation.

The disclosed size-based exclusion methods can enrich the fetal portion in a cfDNA sample by at least 1.1X, 1.2X, 1.25X, 1.5X, 1.75X, 2X, 2.25X, 2.5X, 2.75X, 3X, 3.25X, 3.5X, 3.75X, 4X, 4.25X, 4.5X, 4.75X, 5X, 5.5X, 6X, 6.5X, 7X, 7.5X, 8X, 8.5X, 9X, 9.5X, 10X, 15X, 20X, 25X, or more.

Accordingly, the present disclosure provides a method for size selection of cell-free fetal DNA (cffDNA), which comprises subjecting a cell-free DNA (cfDNA) sample comprising cffDNA and cell-free maternal DNA (cfmDNA) to a size exclusion process to enrich the fetal portion in a DNA sample obtained from a pregnant woman.

B. In silico enrichment of fetal fractions

The present disclosure also provides computer simulation enrichment of cfDNA samples (e.g., blood, plasma, serum) obtained from pregnant women, which can further enrich the fetal part of cfDNA samples. Specifically, the disclosed computer simulation enrichment includes size analysis based on read length. For the purpose of the present disclosure, "size analysis based on read length" is a computer simulation process that establishes a track from a series of windows, and the track is applied to sequence the read data. The established track is based on the allele balance (AB) observed at a set of FF levels. Therefore, the FF level is determined by computer simulation size selection from different windows, thereby allowing maternal and fetal DNA (cfmDNA and cffDNA, respectively) to be distinguished. For example, the track can show that AB at 10% FF is 55%, AB at 15% FF is 60%, and AB at 20% FF is 65%. This is an upward sloping track because AB increases as FF increases. Both the slope and offset (or intercept) of this track are useful. For example, if cfmDNA is mainly selected by a given window so that FF is as low as possible, the resulting AB mainly reflects the maternal genotype. As windows with smaller fragments pick up more FF, the deflection of AB is indicative of the fetal genotype. Thus, if the intercept is about 50% (meaning the mother is heterozygous for the variant), then a negatively sloped trajectory indicates that the fetus did not inherit the particular maternal variant.

Understanding the allelic balance in cfDNA samples improves the ability to focus on the desired sample portion (e.g., FF for aneuploidy and genetic variant analysis, or the maternal portion for carrier analysis). In some embodiments, in vitro moderate size selection (i.e., physical processing/size exclusion) followed by size-based moving window analysis can provide optimal results.

Once a sequence library has been prepared, the fetal portion of the sequence library can be further processed or enriched using a computer simulated moving window analysis. For the purposes of the disclosed methods, a "window" is a selection or sub-portion of a sequence library that contains sequences of a particular size range. For example, a "window" can include all sequences in a sequence library that are 0-145 nucleotides, 0-150 nucleotides, 0-155 nucleotides, 0-160 nucleotides, 0-165 nucleotides, 0-170 nucleotides, 0-175 nucleotides, 0-180 nucleotides, 0-185 nucleotides, 0-190 nucleotides, 0-195 nucleotides, 0-200 nucleotides, 0-205 nucleotides, 0-210 nucleotides, 0-215 nucleotides, 0-220 nucleotides, 0-225 nucleotides, 25-145 nucleotides, 25-150 nucleotides, 25-155 nucleotides, 25-160 nucleotides, 25 ... 165 nucleotides, 25-170 nucleotides, 25-175 nucleotides, 25-180 nucleotides, 25-185 nucleotides, 25-190 nucleotides, 25-195 nucleotides, 25-200 nucleotides, 25-205 nucleotides, 25-210 nucleotides, 25-215 nucleotides, 25-220 nucleotides, 25-225 nucleotides, 50-145 nucleotides, 50-150 nucleotides, 50-155 nucleotides, 50-160 nucleotides, 50-165 nucleotides, 50-170 nucleotides, 50-175 nucleotides, 50-180 nucleotides, 50-185 nucleotides, 50-1 90 nucleotides, 50-195 nucleotides, 50-200 nucleotides, 50-205 nucleotides, 50-210 nucleotides, 50-215 nucleotides, 50-220 nucleotides, 50-225 nucleotides, 75-145 nucleotides, 75-150 nucleotides, 75-155 nucleotides, 75-160 nucleotides, 75-165 nucleotides, 75-170 nucleotides, 75-175 nucleotides, 75-180 nucleotides, 75-185 nucleotides, 75-190 nucleotides, 75-195 nucleotides, 75-200 nucleotides, 75-205 nucleotides, 75-210 nucleotides, 75-215 In some embodiments, the window is divided into four windows. The window is a plurality of nucleotides, 5 nucleotides, 75-220 nucleotides, 75-225 nucleotides, 100-145 nucleotides, 100-150 nucleotides, 100-155 nucleotides, 100-160 nucleotides, 100-165 nucleotides, 100-170 nucleotides, 100-175 nucleotides, 100-180 nucleotides, 100-185 nucleotides, 100-190 nucleotides, 100-195 nucleotides, 100-200 nucleotides, 100-205 nucleotides, 100-210 nucleotides, 100-215 nucleotides, 100-220 nucleotides, 100-225 nucleotides, or any range therebetween. If a specific maximum and minimum values are not set, the window can be considered as "ungated", and on the contrary the window comprises the entire sequence library. Fig. 2 illustrates an example in which the sequence in the sequence library is divided into four windows.

Therefore, disclosed computer simulation enrichment method can include the sequence in at least two windows of sequence library being excluded based on the size of read length, thereby obtains the sequence library of at least two enrichment fetal parts.In certain embodiments, 3,4,5,6,7,8,9,10 or more windows can be evaluated.In certain embodiments, at least 5, at least 6, at least 7 or at least 8 windows can be evaluated.In certain embodiments, window size is identical (for example, each window comprises the nucleotide of setting range, such as 0-100,5-105,10-110 etc.).In certain embodiments, window has different sizes.For example, the size of each additional window can increase, and minimum value keeps the same (for example, the size cutoff value of a group of windows is 0-145,0-150,0-155,0-160,0-165,0-170 etc.).Comparing the allele balance in each window allows calculating the allele balance track between the sequence library of each enrichment fetal part.Described track is the variation of the allele balance percentage of any given gene sequence interested across the observed window. The allelic balance trajectory can be calculated as the slope of the allelic balance in each observed window, and it can be visualized in a variety of ways, as shown in Figure 3.

In addition, the cfmDNA sequence library can be enriched by focusing the analysis between two fragment sizes, such as 100-200 nucleotides, 105-200 nucleotides, 110-200 nucleotides, 115-200 nucleotides, 120-200 nucleotides, 125-200 nucleotides, 130-200 nucleotides, 135-200 nucleotides, 140-200 nucleotides, 140-200 nucleotides, 145-200 nucleotides, 150-200 nucleotides, 155-200 nucleotides, 160-200 nucleotides, 165-200 nucleotides, 170-200 nucleotides, or 175-200 nucleotides, or any size range therebetween. In some embodiments, the size range selected for enrichment can be about 155 to about 200 nucleotides.

In some embodiments, at least two windows of the sequence library are selected from (i) sequences of 0-145 nucleotides, (ii) sequences of 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-168 nucleotides, (vii) 0-170 nucleotides, (viii) 0-175 nucleotides, (ix) 0-180 nucleotides, (x) 0-185 nucleotides, (xi) 0-190 nucleotides, (xii) 0-195 nucleotides, (xiii) 0-200 nucleotides, and (xiv) ungated. In some embodiments, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 windows are selected from (i) a sequence of 0-145 nucleotides, (ii) a sequence of 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-168 nucleotides, (vii) 0-170 nucleotides, (viii) 0-175 nucleotides, (ix) 0-180 nucleotides, (x) 0-185 nucleotides, (xi) 0-190 nucleotides, (xii) 0-195 nucleotides, (xiii) 0-200 nucleotides, and (xiv) ungated.

Computer simulation of enriching the fetal portion of the sequence library can further include identifying and separating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA in the first sequence library to a reference genome, demultiplexing sequence reads from the first library, removing repetitive sequences from the first sequence library, or a combination thereof.

For example, sample preparation may include computer simulation binary comparison processing, wherein the collected DNA samples can be reconstructed by using the overlap between short sequencing reads. If a reference genome with which sequencing reads can be compared is available, the reconstruction of the genome can be facilitated. A sequence alignment tool can be used to map the short reads stored in a file to a reference genome. Subsequently, depth and variant processing can be used to identify and separate specific gene sequences to inform subsequent analysis, which can be directed to, for example, the identification of specific aneuploidies and/or gene variants. In this way, only a limited amount of initially collected cfDNA can be used, i.e., specific portions of the collected DNA can be plotted and assembled for specific assay detection (specific assay detection).

The collected DNA samples can be computationally reconstructed using the overlap between short sequencing reads. Thus, a demultiplexer (e.g., demux) can be used to plot the DNA samples in the first pass, which allows the determination of unique molecular identifiers that may be needed to assess specific screening (e.g., carriers, prenatal, etc.). Unique molecular identifiers (UMIs), sometimes referred to as molecular barcodes (MBCs), are short sequences (e.g., tags) added to DNA fragments during sequencing library preparation protocols to identify desired DNA molecules that may be targeted by specific screening. These tags are added before any amplification and can be used to reduce errors and quantitative bias introduced by amplification.

Once labeled, the specifically labeled DNA sequences can initially be aligned using an alignment process to map the desired DNA sequences to each other. Repeat reduction (e.g., "de-replication") can then clean up any erroneous identifications and/or misalignments, which can include retaining the consensus sequence of the overlapping portions of paired end reads. Thereafter, a re-alignment process can be performed to produce a more robust mapping between the desired DNA sequence and the labeled DNA sequence.

Amplification can be used to isolate specific nucleic acid sequences of interest or subsequent screening. For example, computational tools can be used to calculate theoretical polymerase chain reaction (PCR) results, using a given set of primers (probes) to amplify DNA sequences from sequenced DNA samples, thereby completing computer simulation amplification. After amplification, the quality of specific read sequences can be improved by removing (e.g., trimming) partial (e.g., incomplete) sequences located at the beginning and end of the sequence. An exemplary but non-limiting method to achieve this is called paired end (PE) trimming, which can include two input files (for forward and reverse reads) and four output files (for forward paired, forward unpaired, reverse paired, and reverse unpaired reads) to identify and remove partial sequences. The reconstruction of useful DNA samples can be facilitated and stored in spare files. In addition, files can be plotted as different binary numbers according to fragment length (according to the number of nucleotides).

As part of depth and variant processing, specific gene sequences stored in files can be identified and isolated to inform subsequent analysis of specific aneuploidy and/or causal gene variants. The files can be used in specific programs to mitigate bias in the samples initially collected. The aforementioned computer simulation steps and calculation preparation can optimize DNA samples for specific DNA sequences for specific targets of a given test or screening.

The disclosed computer simulation processing can enrich the fetal portion in the cfDNA sample by at least 1.1X, 1.2X, 1.25X, 1.5X, 1.75X, 2X, 2.25X, 2.5X, 2.75X, 3X, 3.25X, 3.5X, 3.75X, 4X, 4.25X, 4.5X, 5.75X, 5X, 5.5X, 6X, 6.5X, 7X, 7.5X, 8X, 8.5X, 9X, 9.5X, 10X, 15X, 20X, 25X, or more times.

Alternatively, if desired, the disclosed computer simulation process can also be used to enrich the maternal portion of the sample by selecting larger fragments. In some embodiments, the disclosed computer simulation process can enrich the maternal portion in the cfDNA sample by at least 1.1X, 1.2X, 1.25X, 1.5X, 1.75X, 2X, 2.25X, 2.5X, 2.75X, 3X, 3.25X, 3.5X, 3.75X, 4X, 4.25X, 4.5X, 5.75X, 5X, 5.5X, 6X, 6.5X, 7X, 7.5X, 8X, 8.5X, 9X, 9.5X, 10X, 15X, 20X, 25X, or more times.

Therefore, the present disclosure provides a method for computer simulation sorting and enrichment of cffDNA, which includes sequencing a cell-free DNA (cfDNA) sample including cffDNA and cell-free DNA maternal (cfmDNA), and performing a size analysis based on the length of the read segment, wherein a size-based moving window is used to establish a trajectory based on the allelic balance between cfmDNA and cffDNA, thereby clarifying the genotype of cfmDNA or cffDNA in a given sample. In some embodiments, such methods may further include identifying and separating cffDNA from cfmDNA by comparing the sequence reads of cffDNA and cfmDNA to a reference genome, demultiplexing the sequence reads, and removing repetitive sequences.

C. Combination of physical enrichment and computer simulation enrichment

The aforementioned sample preparation methods can be performed alone or in combination to enrich the fetal portion of a given sample. Before physical enrichment or computer simulation enrichment, total cfDNA can be separated from maternal samples (e.g., blood, plasma, serum) by conventional means. For example, APOSTLE ^TM free DNA extraction kit can be used to extract total cfDNA from clarified plasma obtained from a sample. Other known methods and commercially available kits for cfDNA extraction can also be used, including but not limited to Molzym GmbH & Co KG (Bremen, DE, Germany), Qiagen (Hilden, DE, Germany), MN (Macherey-Nagel, Germany) (Düren, DE, Germany), Roche (Basel, CH, Switzerland) and Sigma (Sigma) (Deisenhofen, DE, Germany) produced kits.

After physical enrichment and computer simulation enrichment, the fetal portion can be 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 100%, 101%, 102%, 103%, 104%, 105%, 106%, 107%, 108%, 109%, 110%, 0%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 85%, 90%, 95%, 99%, or 100%. Additionally or alternatively, after physical enrichment and computer simulation enrichment, the fetal portion can be about 5% to 100%, about 5% to about 95%, about 5% to about 90%, about 5% to about 85%, about 5% to about 80%, about 5% to about 75%, about 10% to 100%, about 10% to about 95%, about 10% to about 90%, about 10% to about 85%, about 10% to about 80%, about 10% to about 75%, about 15% to 100%, about 15% to about ...0% to about 95%, about 10% to about 90%, about 10% to about 85%, about 10% to about 80%, about 10% to about 75%, about 15% to 100%, about 15% to about 100 % to about 95%, about 15% to about 90%, about 15% to about 85%, about 15% to about 80%, about 15% to about 75%, about 20% to 100%, about 20% to about 95%, about 20% to about 90%, about 20% to about 85%, about 20% to about 80%, about 20% to about 75%, about 25% to 100%, about 25% to about 95%, about 25% to about 90%, about 20% to about 85%, about 20% to about 80%, about 20% to about 75%, about 25% to 100%, about 25% to about 95%, about 25% to about 90%, about 25% to about 85%, about 25% to about 80% , about 25% to about 75%, about 30% to 100%, about 30% to about 95%, about 30% to about 90%, about 30% to about 85%, about 30% to about 80%, about 30% to about 75%, about 35% to 100%, about 35% to about 95%, about 35% to about 90%, about 35% to about 85%, about 35% to about 80%, about 35% to about 75%, about 40% to 100%, about 40% to about 95%, about 40% to About 90%, about 40% to about 85%, about 40% to about 80%, about 40% to about 75%, about 45% to 100%, about 45% to about 95%, about 45% to about 90%, about 45% to about 85%, about 45% to about 80%, about 45% to about 75%, about 50% to 100%, about 50% to about 95%, about 50% to about 90%, about 50% to about 85%, about 50% to about 80%, and about 50% to about 75%.

Accordingly, the present disclosure provides a method for preparing a cell-free DNA sample having an enriched fetal portion, comprising treating the cfDNA sample using size exclusion to retain cell-free fetal DNA (cffDNA) and remove cell-free maternal DNA (cfmDNA), performing computer simulation processing to identify and separate cffDNA from cfmDNA, or a combination thereof.

III. Parallel Screening Methods

The present disclosure provides a method for evaluating or screening aneuploidy and gene variants in a fetus using only a single biological sample (e.g., blood, plasma, serum) from the fetus's biological mother. Conventionally, aneuploidy testing and gene variant testing are performed separately and require multiple samples. In fact, screening for certain conditions even requires obtaining a biological sample from the biological father. The disclosed method overcomes these problems and is used to provide a new useful method for improving conventional non-invasive prenatal screening (NIPS).

The disclosed method may include two parallel screens utilizing the same single cfDNA sample from the biological mother: a first screen for detecting aneuploidy and a second screen for detecting genetic variants.

In the first screening, a specific sub-portion of the collected sample (e.g., a sub-portion of a smaller cfDNA fragment) can be used to optimize the fetal portion to assess the presence or absence of an aneuploidy condition. The presence or absence of an aneuploidy can be determined by determining a trajectory that allows the distinction between maternal and fetal DNA. In this way, the disclosed screening can simultaneously assess fetal aneuploidy and maternal aneuploidy, which was not possible before. The first screening can additionally or alternatively rely on sequencing depth to determine whether aneuploidy is present or absent in a given sample.

In the second screening, the fetal part can be optimized and the noise from the redundant genetic material can be minimized using a specific sub-portion of the collected sample (e.g., a sub-portion of a smaller cfDNA fragment). Then, by, for example, establishing a trajectory to plot the relevant sample material from the redundant sample material, the sub-portion can be used to detect various gene variants. In each screening, the most preferred crop of a gene sample containing free maternal DNA (cfmDNA) and free fetal cffDNA at an appropriate ratio is used, allowing the presence or absence of known aneuploidies and gene variants to be detected with reasonable certainty, without having to resort to individual focus of customized parallel screening for one method or another.

The method can start with collecting a sample from the biological mother, typically by drawing blood, although other biological samples (e.g., plasma, serum, etc.) are also considered. This sample contains free DNA (cfDNA). cfDNA can contain various free-circulating DNA, including circulating tumor DNA (ctDNA), free mitochondrial DNA (cf mtDNA), free maternal DNA (cfmDNA), and free fetal DNA (cffDNA). Since the subject is a pregnant woman, there will also be a certain level of fetal DNA in the cfDNA sample. In addition, targeted DNA capture suitable for specific gene sequences can also be performed. Therefore, aspects of cfDNA and targeted capture can be used for the purposes of the disclosed method.

In one aspect, the present disclosure provides a method for concurrently detecting the presence or absence of aneuploidy and at least one genetic mutation in a single maternal sample, comprising (i) obtaining a biological sample from a pregnant woman, wherein the biological sample comprises cell-free DNA (cfDNA); (ii) preparing a cfDNA library (e.g., by amplifying a target population of cfDNA fragments); (iii) sequencing the cfDNA library to prepare a sequence library; and (iv) detecting the presence or absence of aneuploidy and at least one genetic variant in the single maternal sample;

Wherein (a) cfDNA library is enriched to increase the fetal part, (b) sequence library is enriched to increase the fetal part, or (c) its combination, so that before detecting the presence or absence of aneuploidy and at least one gene variant in a single maternal sample, the fetal part of a single maternal sample is increased by at least 1.5 times. In some embodiments, the cfDNA library is enriched to increase the fetal part, and the sequence library is enriched to increase the fetal part. The following sections provide more details on the relevant processes of each enrichment form.

(i) Biological samples

For the purposes of the disclosed methods, biological samples need to contain cfDNA, including cffDNA. Examples of samples that can be obtained from the biological mother for the disclosed methods include, but are not limited to, blood, serum, and plasma.

In some embodiments, nucleic acid extraction will be performed prior to amplifying the cfDNA in the sample and preparing the one or more cfDNA pools. Various protocols for nucleic acid extraction can be used in the methods of the present technology. Examples of commercially available nucleic acid purification kits include Apostle MiniMax kits, Molzym GmbH (Bremen, Germany), Qiagen (Hilden, Germany), MN (Düren, Germany), Roche (Basel, Switzerland), or Sigma (Deisenhofen, Germany). Other nucleic acid purification systems based on the use of polystyrene beads and the like as support materials can also be used. Automated DNA extraction platforms, such as Automation, or EZ1 ^TM automation system.

(ii) cfDNA library preparation

cfDNA library preparation can be performed using known amplification methods (e.g., xGen Prism Library Preparation Kit (IDT ^™ )) as well as PCR-free library preparation methods, such as COLLIBRI ^™ , and TRUSEQ ^TM kit, KAPA ^TM HyperPrep kit produced by Roche, and MGIEasy kit produced by MG Tech. Optionally, the preparation of the cfDNA library may include an end repair step. cfDNA may include overhangs of other damage to the ends of a given nucleic acid sequence, and end repair may convert such damaged or sheared DNA into a blunt-ended molecule that is more easily connected to a transferor, a tag, or a barcode. One or more ligation reactions may be performed to connect the transferor to the nucleic acid sequence of the sample. The transferor is used to promote amplification by providing a consistent sequence to which the primer can anneal, and is used to separate sequences of interest. The transferor may be a unique length (to allow separation and separation by electrophoresis), a unique sequence, or include other features to help separate the target nucleic acid sequence after amplification.

PCR-based methods are often used to generate amplified libraries prior to sequencing or analysis of a given nucleic acid sample; however, PCR is not required, and one of ordinary skill in the art will be aware of methods for library preparation that do not involve PCR. Various PCR methods utilizing commercially available reagents and polymerases can be used for the nucleic acid amplification portion of library preparation (e.g., KAPA ^™ HiFiHotStart ReadyMix).

A cfDNA library can be prepared from a maternal sample using any method described herein or otherwise known to one of ordinary skill in the art. Optionally, the cfDNA library can be cleaned up using known methods, such as separating the amplified fragments in the library using AMPURE beads or other similar methods to remove salts, unwanted macromolecules, and other debris from the sample.

Before cfDNA library sequencing, the fetal part can be enriched as described herein. In addition or alternatively, before preparing the cfDNA library, the fetal part can be enriched from the maternal sample. In simple terms, the fetal part of the enriched cfDNA library or maternal sample is a physical treatment of the sample, which may include removing any DNA fragment greater than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, about 180 nucleotides in length, about 185 nucleotides in length, about 190 nucleotides in length, about 195 nucleotides in length, or about 200 nucleotides in length from the cfDNA library.

This type of size-based exclusion can be performed using electrophoresis (e.g., gel electrophoresis or capillary electrophoresis) and other known methods, which can utilize, for example, DNA-bound particles, such as beads (e.g., AMPURE ^™ beads). In one embodiment, nucleic acid electrophoresis is used to separate and then the required fragment length is recovered. Various known electrophoretic processes can be used for this purpose. For example, in one embodiment, the NIMBUS Select ^™ workstation with RangerTechnology ^™ for high-throughput nucleic acid size selection can be used. In another embodiment, the BluePippin electrophoresis system can be used.

Previous size-based exclusion methods have been used to enrich the fetal portion of the cfDNA library, but unlike those previous methods, the inventors found that, as described herein, the use of a higher cutoff value can improve the noise reduction effect when combined with further computer simulation selection. In short, although not bound by theory, the noise can be reduced because a higher total number of cffDNA molecules are retained by a more tolerant size selection. It is conventionally believed that using a lower cutoff value is superior because it excludes more maternal cfDNA. Figure 1 shows a comparison of the disclosed size exclusion process with traditional methods. As shown in Figure 1, these more restrictive traditional methods also discard a large amount of cffDNA. Therefore, the disclosed method of combining a more "tolerant" size exclusion technique with further computer simulation enrichment is an improvement that specifically solves a key problem in the field of prenatal screening: enrichment of the fetal portion without inadvertently or unnecessarily discarding the extremely limited supply of cffDNA in a given sample.

The disclosed size-based exclusion methods can enrich the fetal portion in a cfDNA sample by at least 1.1X, 1.2X, 1.25X, 1.5X, 1.75X, 2X, 2.25X, 2.5X, 2.75X, 3X, 3.25X, 3.5X, 3.75X, 4X, 4.25X, 4.5X, 5.75X, 5X, 5.5X, 6X, 6.5X, 7X, 7.5X, 8X, 8.5X, 9X, 9.5X, 10X, 15X, 20X, 25X, or more.

(iii) Sequencing of nucleic acid library

The nucleic acid library that can be enriched for the fetal part can be sequenced using known sequencing methods (e.g., NovaSeq sequencer and flow cell, Illumina sequencer, pyrosequencing, reversible dye-terminator sequencing, SOLiD sequencing, ion semiconductor sequencing, Helioscope single molecule sequencing, Ion Torrent ^TM (Life Technologies, Carlsbad, CA) amplicon sequencing system, 454 ^TM GS FLX ^TM sequencing system, SMRT ^TM sequencing, etc.). In some embodiments, the cfDNA fragments in the nucleic acid library are sequenced from both ends (i.e., paired end mode). In some embodiments, the cfDNA fragments in the nucleic acid library are sequenced at one end (i.e., single end mode). In some embodiments, the cfDNA fragments in the nucleic acid library can be separated or combined using a targeted capture method such as hybrid capture. Sequencing from the two ends of each fragment allows the length of the fragment to be determined. In some embodiments, the resulting sequence can be used to draw cfDNA fragments.

In some embodiments, the disclosed methods can utilize a targeted capture method to sequence only specific fragments of interest. The fragments of interest may, for example, correspond to cfDNA encoding a gene associated with a genetic disease, condition, or trait (i.e., a genetic variant of interest) or cfDNA corresponding to a specific chromosome.

Once the cfDNA fragments in the nucleic acid library have been sequenced, the fetal portion of the sequence library can be further enriched using a computer simulated moving window analysis as described herein. For the purposes of the disclosed methods, a "window" is a selection or sub-portion of a sequence library that contains sequences of a particular size range. For example, a "window" can include all sequences in a sequence library that are 0-145 nucleotides, 0-150 nucleotides, 0-155 nucleotides, 0-160 nucleotides, 0-165 nucleotides, 0-170 nucleotides, 0-175 nucleotides, 0-180 nucleotides, 0-185 nucleotides, 0-190 nucleotides, 0-195 nucleotides, 0-200 nucleotides, 25-145 nucleotides, 25-150 nucleotides, 25-155 nucleotides, 25-160 nucleotides, 25-165 nucleotides, 25-170 nucleotides, 25-175 nucleotides, 25-180 nucleotides, 25-185 nucleotides, 25-190 nucleotides, 25-195 nucleotides, 25-200 nucleotides, 50-145 nucleotides, 50-150 nucleotides, 50-155 nucleotides, 50-160 nucleotides, 50-165 nucleotides, 50-170 nucleotides, 50-1 75 nucleotides, 50-180 nucleotides, 50-185 nucleotides, 50-190 nucleotides, 50-195 nucleotides, 50-200 nucleotides, 75-145 nucleotides, 75-150 nucleotides, 75-155 nucleotides, 75-160 nucleotides, 75-165 nucleotides, 75-170 nucleotides, 75-175 nucleotides, 75-180 nucleotides, 75-185 nucleotides, 75-190 nucleotides, 75 -195 nucleotides, 75-200 nucleotides, 100-145 nucleotides, 100-150 nucleotides, 100-155 nucleotides, 100-160 nucleotides, 100-165 nucleotides, 100-170 nucleotides, 100-175 nucleotides, 100-180 nucleotides, 100-185 nucleotides, 100-190 nucleotides, 100-195 nucleotides, 100-200 nucleotides, or any range therebetween. In some embodiments, the disclosed methods can utilize two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10, or more) windows comprising fragments in two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10, or more) size ranges selected from 0-145 nucleotides, 0-146 nucleotides, 0-147 nucleotides, 0-148 nucleotides, 0-149 nucleotides, 0-150 nucleotides, 0-151 nucleotides, 0-152 nucleotides, 0-153 nucleotides, 0-154 nucleotides, 0-155 nucleotides, 0-156 nucleotides, -157 nucleotides, 0-158 nucleotides, 0-159 nucleotides, 0-160 nucleotides, 0-161 nucleotides, 0-162 nucleotides, 0-163 nucleotides, 0-164 nucleotides, 0-165 nucleotides, 0-166 nucleotides, 0-167 nucleotides, 0-168 nucleotides, 0-169 nucleotides, 0-170 nucleotides, 0-171 nucleotides, 0-172 nucleotides, 0-173 nucleotides, 0-174 nucleotides, 0-175 nucleotides, 0-176 nucleotides, 0-177 nucleotides, 0-178 nucleotides, 0-179 nucleotides, 0-18 0 nucleotides, 0-181 nucleotides, 0-182 nucleotides, 0-183 nucleotides, 0-184 nucleotides, 0-185 nucleotides, 0-186 nucleotides, 0-187 nucleotides, 0-188 nucleotides, 0-189 nucleotides, 0-190 nucleotides, 0-191 nucleotides, 0-192 nucleotides, 0-193 nucleotides, 0-194 nucleotides, 0-195 nucleotides Acid, 0-196 nucleotides, 0-197 nucleotides, 0-198 nucleotides, 0-199 nucleotides, 0-200 nucleotides, 5-145 nucleotides, 5-146 nucleotides, 5-147 nucleotides, 5-148 nucleotides, 5-149 nucleotides, 5-150 nucleotides, 5-151 nucleotides, 5-152 nucleotides, 5-153 nucleotides, 5-154 nucleotides, 5- 155 nucleotides, 5-156 nucleotides, -157 nucleotides, 5-158 nucleotides, 5-159 nucleotides, 5-160 nucleotides, 5-161 nucleotides, 5-162 nucleotides, 5-163 nucleotides, 5-164 nucleotides, 5-165 nucleotides, 5-166 nucleotides, 5-167 nucleotides, 5-168 nucleotides, 5-169 nucleotides, 5-170 nucleotides nucleotides, 5-171 nucleotides, 5-172 nucleotides, 5-173 nucleotides, 5-174 nucleotides, 5-175 nucleotides, 5-176 nucleotides, 5-177 nucleotides, 5-178 nucleotides, 5-179 nucleotides, 5-180 nucleotides, 5-181 nucleotides, 5-182 nucleotides, 5-183 nucleotides, 5-184 nucleotides, 5-185 nucleotides, 5 -186 nucleotides, 5-187 nucleotides, 5-188 nucleotides, 5-189 nucleotides, 5-190 nucleotides, 5-191 nucleotides, 5-192 nucleotides, 5-193 nucleotides, 5-194 nucleotides, 5-195 nucleotides, 5-196 nucleotides, 5-197 nucleotides, 5-198 nucleotides, 5-199 nucleotides, 5-200 nucleotides, 10-14 5 nucleotides, 10-146 nucleotides, 10-147 nucleotides, 10-148 nucleotides, 10-149 nucleotides, 10-150 nucleotides, 10-151 nucleotides, 10-152 nucleotides, 10-153 nucleotides, 10-154 nucleotides, 10-155 nucleotides, 10-156 nucleotides, -157 nucleotides, 10-158 nucleotides, 10-159 nucleotides, 10-160 nucleotides, 10-161 nucleotides, 10-162 nucleotides, 10-163 nucleotides, 10-164 nucleotides, 10-165 nucleotides, 10-166 nucleotides, 10-167 nucleotides, 10-168 nucleotides, 10-169 nucleotides, 10-170 nucleotides, 10-171 nucleotides, 10-172 nucleotides, 10-173 nucleotides, 10-174 nucleotides, 10-175 nucleotides, 10-176 nucleotides, 10-177 nucleotides, 10-178 nucleotides, 10-179 nucleotides, 10-180 nucleotides, 10-181 nucleotides, 10-182 nucleotides, 10-183 nucleotides, 10-184 nucleotides, 10-185 nucleotides, 10-186 nucleotides, 10-187 nucleotides, 10-188 nucleotides, 10-189 nucleotides, 10-190 nucleotides, 10-191 nucleotides, 10-192 nucleotides, 10-193 nucleotides, 10-194 nucleotides, 10-195 nucleotides, 10-196 nucleotides, 10-197 nucleotides, 10-198 nucleotides, 10-199 nucleotides, 10-200 nucleotides, 15-145 nucleotides, 15-146 nucleotides, 15-147 nucleotides, 15-148 nucleotides, 15-149 nucleotides, 15-150 nucleotides, 15-151 nucleotides, 15-152 nucleotides, 15-153 nucleotides, 15-154 nucleotides, 15-155 nucleotides, 15-156 nucleotides, -157 nucleotides, 15-158 nucleotides, 15-159 nucleotides Acid, 15-160 nucleotides, 15-161 nucleotides, 15-162 nucleotides, 15-163 nucleotides, 15-164 nucleotides, 15-165 nucleotides, 15-166 nucleotides, 15-167 nucleotides, 15-168 nucleotides, 15-169 nucleotides, 15-170 nucleotides, 15-171 nucleotides, 15-172 nucleotides, 15-173 nucleotides Acid, 15-174 nucleotides, 15-175 nucleotides, 15-176 nucleotides, 15-177 nucleotides, 15-178 nucleotides, 15-179 nucleotides, 15-180 nucleotides, 15-181 nucleotides, 15-182 nucleotides, 15-183 nucleotides, 15-184 nucleotides, 15-185 nucleotides, 15-186 nucleotides, 15-187 nucleotides In some embodiments, the disclosed method can utilize at least eight windows, and the size ranges included in the windows include 0 to about 145 nucleotides, 0 to about 150 nucleotides, 0 to about 155 nucleotides, 0 to about 160 nucleotides, 0 to about 165 nucleotides, 0 to about 168 nucleotides, 0 to about 175 nucleotides, and 0 to about 190 nucleotides. In some embodiments, the disclosed methods may utilize eight windows including size ranges of 0-145 nucleotides, 0-150 nucleotides, 0-155 nucleotides, 0-160 nucleotides, 0-165 nucleotides, 0-168 nucleotides, 0-175 nucleotides, and 0-190 nucleotides.

In some embodiments, the disclosed methods can utilize two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10, or more) windows comprising fragments in two or more (e.g., 2, 3, 4, 5, 6, 7, 8, 9, or 10, or more) size ranges selected from about 20 to about 145 nucleotides, about 20 to about 150 nucleotides, about 20 to about 155 nucleotides, about 20 to about 160 nucleotides, about 20 to about 165 nucleotides, about 20 to about 170 nucleotides, about 20 to about 175 nucleotides, about 20 to about 180 nucleotides, about 20 to about 185 nucleotides, about 20 to about 190 nucleotides, about 20 to about 195 nucleotides, about 20 to about 196 nucleotides, about 20 to about 197 nucleotides, about 20 to about 198 nucleotides, about 20 to about 210 nucleotides, about 20 to about 211 nucleotides, about 20 to about 212 nucleotides, about 20 to about 213 nucleotides, about 20 to about 214 nucleotides, about 20 to about 215 nucleotides, about 20 to about 216 nucleotides, about 20 to about 218 190 nucleotides, about 20 to about 195 nucleotides, about 20 to about 200 nucleotides, about 25 to about 145 nucleotides, about 25 to about 150 nucleotides, about 25 to about 155 nucleotides, about 25 to about 160 nucleotides, about 25 to about 165 nucleotides, about 25 to about 170 nucleotides, about 25 to about 175 nucleotides, about 25 to about 180 nucleotides, about 25 to about 185 nucleotides, about 25 to about 190 nucleotides, about 25 to about 195 nucleotides, about 25 to about 200 nucleotides, about 50 to about 145 nucleotides, about 50 to about 150 nucleotides, about 50 ... nucleotides, about 50 to about 160 nucleotides, about 50 to about 165 nucleotides, about 50 to about 170 nucleotides, about 50 to about 175 nucleotides, about 50 to about 180 nucleotides, about 50 to about 185 nucleotides, about 50 to about 190 nucleotides, about 50 to about 195 nucleotides, about 50 to about 200 nucleotides, about 75 to about 145 nucleotides, about 75 to about 150 nucleotides, about 75 to about 155 nucleotides, about 75 to about 160 nucleotides, about 75 to about 165 nucleotides, about 75 to about 170 nucleotides, about 75 to about 175 nucleotides, about 75 to about 180 nucleotides, about 75 to about 185 nucleotides, about 50 to about 190 nucleotides, about 50 to about 195 nucleotides, about 50 to about 200 nucleotides. In some embodiments, the present invention relates to a polypeptide having a plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of nucleotides, the plurality of

For the purposes of the disclosed method, the windows used for subsequent analysis and trajectory calculation can be different sizes (i.e., each window contains a different range of fragment sizes, such as 0-145, 0-150, 0-155, etc.) or the windows can be the same size (i.e., each window contains different fragments, but spans a set size range, such as 0-145, 5-150, 10-155, etc.). The windows can be considered "ungated", i.e., no specific maximum and minimum values are set, and instead the window contains the entire sequence library. Figure 2 shows an example of how the sequences in the sequence library can be divided into six different windows.

As mentioned above, the fetal part of enrichment sequence library is the form of computer simulation enrichment, which may include the sequence in at least two windows of sequence library being excluded based on the size of read length, thereby obtaining the sequence library of at least two enrichment fetal parts.Comparing the allele balance in each window allows calculating the allele balance track between the sequence library of each enrichment fetal part.The allele balance track is the variation of the allele balance percentage across the observed window of any given gene sequence of interest.The allele balance track can be calculated as the slope of allele balance in each observed window, and it can be visualized in a variety of ways, as shown in Figure 3.For example, the banding pattern (banding pattern) in the figure above Fig. 3 shows the difference of allele balance in multiple observed windows, or the allele balance track can be visualized as Gaussian mixture model (Gaussian mixture model; GMM).It should be understood that each window (for example, 0-145, 0-150, 0-155 etc.) will have the fetal part associated with different from other windows, and this fetal part value can be used as the X-axis of trajectory diagram, as shown in Figure 3 (upper figure). In other words, a trajectory plot of the type shown in FIG3 (top) provides a visualization of allelic balance relative to fetal fraction, where points along the X-axis (ie, the fetal fraction axis) are provided by selections of different windows.

Regardless of how the allelic balance data is visualized, it can be used to identify heterozygous and homozygous mutations or markers of interest in a library of cfDNA sequences. For example, allelic balance can be converted into a strip chart where the y-axis shows the percentage of cfDNA in a sample with a given allele for a particular gene or nucleic acid sequence of interest (e.g., 0%, 10%, 20%, 30%, 40%, 50%, 60%), and the x-axis shows different alternatives for the gene or nucleic acid of interest corresponding to the wild-type sequence or a mutation/variant associated with a particular disease, condition, or trait (e.g., different known mutations within the CFTR gene associated with cystic fibrosis).

For example, in a sample or window with a 20% fetal portion, the band at 10% on the y-axis may be shown to correspond to a fetus that is a carrier of DNA from the biological father (or, in some cases, it may represent a denovo mutation in the fetus). The band at 40% on the y-axis within the window corresponds to a fetus that is negative (i.e., homozygous reference) for a mutation/variant in the gene or sequence of interest. The band at 50% on the y-axis corresponds to a fetus that is a carrier of DNA from the biological mother, or, if the mother and father carry the same mutation/variant (i.e., alt allele), the fetus may have the father's alt allele and the mother's reference allele. Therefore, the band at 50% may indicate that the fetus and mother each have an alt allele. The band at 60% on the y-axis corresponds to a fetus that is homozygous alt for a mutation/variant in the gene or sequence of interest (i.e., the fetus is positive). Therefore, analyzing the allele balance of multiple windows across the sequence library (i.e., multiple sequence libraries enriched with fetal parts) provides a new and useful method to determine the presence or absence of gene variants/mutations from maternal samples containing cfDNA without the need for any additional samples. In addition, due to the enrichment provided by the moving window analysis, noise and background are significantly reduced, which enables robust detection even in samples with very small amounts of cffDNA (e.g., <5% of total cfDNA). In addition, it should be noted that if the window or sample does not have a 20% fetal part, the aforementioned bands may be offset or moved, and they may not be exactly at 10%, 40%, 50%, and 60%, respectively.

Although at least two windows are required to determine the allelic balance trajectory, the number of windows that can be evaluated for the purposes of the disclosed method is not particularly limited, and may include multiple additional windows. Therefore, in some embodiments, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 windows of the first sequence library are evaluated, thereby obtaining at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 sequence libraries enriched in the fetal portion, respectively, from which cffDNA sequences can be identified and isolated. In some embodiments, at least two windows of the sequence library are selected from (i) sequences of 0-145 nucleotides, (ii) sequences of 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-170 nucleotides, (vii) 0-175 nucleotides, (viii) 0-180 nucleotides, (ix) 0-190 nucleotides, (x) 0-195 nucleotides, (xi) 0-200 nucleotides, and (xii) ungated. In some embodiments, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 windows are selected from (i) a sequence of 0-145 nucleotides, (ii) a sequence of 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-170 nucleotides, (vii) 0-175 nucleotides, (viii) 0-180 nucleotides, (ix) 0-190 nucleotides, (x) 0-195 nucleotides, (xi) 0-200 nucleotides, and (xii) ungated.

FIG. 4 provides an overview of an exemplary embodiment of a process for in silico enrichment of fetal fractions within a sequence library, followed by use of a bioinformatics algorithm (also referred to herein as a "caller") and post-processing to identify aneuploidies and genetic variants in parallel from a single sample.

A. Sample Processing and Computational Pipeline

In general, the sample processing steps of the disclosed methods for concurrent assessment of aneuploidy and genetic variants can be performed as described above in Section II ("Sample Preparation"). Expand more features here.

The disclosed method may include converting the sequencing data from the sequence library into a computational pipeline of useful output, which includes determining whether aneuploidy or any gene variant exists in the cffDNA. The additional useful output that may be optionally provided includes but is not limited to the determination of fetal gender and other basic fetal statistics.

The computational pipeline may include a binary alignment map (BAM) process, in which the collected DNA samples may be reconstructed using short sequencing reads. If a reference genome to which sequencing reads can be compared is available, the reconstruction of the genome may be facilitated. A sequence alignment tool may be used to map the short reads stored in the file to the reference genome. This approach may produce a BAM file, in which the specific gene sequence may be processed in the next step.

The computing pipeline may also include depth and variant processing, during which time, specific gene sequences may be identified and separated to notify subsequent analysis of specific aneuploidy and/or gene variants. Based on the amount of the initial DNA collected, the specific portion of the DNA collected may be plotted, and optionally, it may be brought together for analysis and detection of specific sequences of interest. Once plotted in depth and variant processing steps, specific call routines and post-processing may be used to identify and bring together output information about aneuploidy, gene variants, and any other outputs in the result report. Usually the result report, delivery, or transmission to mother, father, the doctor supervising pregnancy (i.e., mother's obstetrician and gynecologist) or a combination thereof.

A specific bioinformatics algorithm (i.e., a "caller"; described below) can be used to assess the depth of a DNA sample in a BAM file. The caller used can determine the presence or absence of both aneuploidy and a gene variant of interest. That is, these two goals can be accomplished together (e.g., in parallel) using the same prepared and processed BAM file. Therefore, an aneuploid caller can be used to detect aneuploidy, while other gene variants using a dedicated caller can be run in parallel. The specific scope of these calculation steps will be discussed in more detail below.

It should be understood that the present disclosure as described above can be implemented in the form of control logic using computer software in a modular or integrated manner. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will know and understand other ways and/or methods of implementing the present disclosure using hardware and a combination of hardware and software.

Any of the software components, processes, or functions described in this application may be implemented as software code executed by a processor using any suitable computer language (such as, for example, Python, R, assembly language Java, JavaScript, C, C++, or Perl), using, for example, conventional or object-oriented techniques. The software code may be stored as a series of instructions or commands on a computer-readable medium, such as a random access memory (RAM), a read-only memory (ROM), a magnetic medium (such as a hard disk or a floppy disk), or an optical medium (such as a CD-ROM). Any such computer-readable medium may reside on or within a single computing device, and may be present on or within different computing devices within a system or network.

B. Detection of Aneuploidy

For the purposes of this disclosure, aneuploidies that may be assessed or detected using the disclosed methods include, but are not limited to, single chromosomes (e.g., Turner syndrome), trisomy (e.g., Down syndrome, Edward syndrome, Patau syndrome, trisomy 13, trisomy 18, trisomy 21), quadrisomes, polysomes X and/or Y, microdeletions and microduplications (e.g., chromosome 22q11.2 deletion syndrome), and pentasomes.

The present disclosure provides systems and methods for detecting aneuploidy alone or in parallel with a gene variant/mutation of interest, which rely on sequencing depth to determine whether aneuploidy is present or absent in a given sample. For the purposes of the present disclosure, "depth" is defined as the ratio of the number of reads obtained by sequencing that overlap with a site of interest and the size of the library or the average number of times each base in the library is measured.

The depth observed in any given library prepared from a maternal cfDNA sample is a function of the fetal portion, maternal copy number, and fetal copy number. If aneuploidy is present (e.g., trisomy), the depth of the target chromosome should be different from a sample with 23 chromosomes in a determined, predictable manner. For example, in a trisomy, the depth of the target chromosome (e.g., chromosome 21) will be increased compared to the background. Figure 5 illustrates the principle behind this measure.

In general, when detecting aneuploidy (whether it is fetal aneuploidy or maternal aneuploidy) in a maternal sample containing some parts (e.g., fetal part) of fetal cfDNA, the presence of aneuploidy can be identified based on the offset of detectable aneuploid regions or aneuploid autosomes compared to known non-aneuploid regions or chromosomes. That is, according to the actual fetal part, the analysis of each fragment (e.g., Formula 1 below) will produce a plottable result of cfDNA pregnancy depth to cfDNA density. As shown in the middle figure of Figure 5, this offset can be calculated or visualized in a statistical manner, wherein the background depth represents a comparator or set of samples without aneuploidy, and the offset target depth represents a sample including three chromosomes, thus indicating that there is aneuploidy in the fetus (assuming that the pregnant woman does not express the aneuploidy). This deviation is detectable using a normalized distribution curve, and as the fetal part of the sample increases via the enrichment process described herein, this deviation will be more obvious.

In some embodiments, the offset can be visualized and quantified using a depth call map (shown in the lower figure of FIG. 5 ). As shown in the lower figure of FIG. 5 , the depth (i.e., shaded area) of a given sample can be determined to fit within one of four known copy number (CN) curves (e.g., CN=1, CN=2, CN=3, and CN=4). In this exemplary figure, the shaded distribution fits within the CN=3 curve, thus indicating the presence of an aneuploidy (i.e., trisomy) with three chromosome copies. Various processing steps can be used to enhance the distribution map results and eliminate noise in the data during analysis.

In certain embodiments, the presence or absence of detecting aneuploidy may include calculating depth track. Depth track is the variation of the depth of the read of any given gene sequence of interest across the observed window. Depth track can be calculated as the slope of the depth across the observed window to the fetal part, and can be visualized in a variety of ways, as shown in Figure 8. The depth track that decreases when the fetal part increases will indicate that the fetus has fewer genes (or chromosomes) copies than the mother. The depth track that keeps constant as the fetal part increases will indicate that the fetus has the same gene (or chromosome) copy number as the mother. And the depth track that increases as the fetal part increases will indicate that the fetus has more genes (or chromosomes) copies than the mother. Although the depth track can be used to determine the chromosome number to detect the presence or absence of aneuploidy, it should be noted that the depth track can also be used to detect the presence or absence of certain gene variants (such as, abnormal copy number).

When analyzing the chromosome depth of any given sample, it may be necessary to account for and normalize GC bias. GC content (or guanine-cytosine content) is the percentage of nitrogenous bases on a DNA molecule that are guanine or cytosine (from the possibility of four different bases, also including adenine and thymine). High GC content can distort the results and lead to high levels of noise. For example, in the context of Figure 5C, the increased noise will widen the width of the data band and the corresponding copy number hypothesis (black line), and as these distributions become wider, it becomes more difficult to accurately interpret the true copy number level. Correct normalization will reduce the difference in depth in high-noise samples, thereby reducing the impact of GC bias and improving aneuploidy calls.

In addition, a triple normalization control of the changes caused by 1) GC bias, 2) sample background, and 3) hybridization probe capture (when appropriate; that is, in embodiments utilizing hybrid probes) can be adopted across the sampled data to improve the distribution map of the sampled data, as shown in Figure 6. As provided in Figure 6, the top group of distribution maps shows the original depth data without any normalization, the middle group of distribution maps shows the improved distribution map after adopting GC bias normalization, and the bottom group of distribution maps is even more improved after the second (sample background) and third (hybridization probe capture) normalization data processing steps are completed. Therefore, the triple normalization control can improve the distribution map of the sampled data, and is useful in some disclosed embodiments or for some samples. Once normalized, these distribution maps can be compared with model expectations to draw conclusions about the presence or absence of aneuploidy, as illustrated in Figure 7.

Fig. 7 shows the figure of the normalized depth fitting model expectation of aneuploidy incidence, which can be used for interpreting the sample distribution of collection and alternatively normalization. The depth fitting model can be collected using conventional known aneuploidy distribution, for comparison step, to interpret whether the actual collection and alternatively normalized distribution match one or more of the known models collected. As shown in Figure 7, the normalized depth distribution shown in gray can be set relative to a known distribution curve, and the distribution curve reflects 1, 2 or 3 copies (in order from left to right) of chromosome 13, 18, 21 and X. Maximum likelihood can be used to select the most likely fetal copy number to determine specific curve fitting. Because maximum likelihood fitting produces a matching with a specific call, it is possible to draw a conclusion about the presence or absence of aneuploidy in the analyzed sample.

Based on the predicted differences in depth that will be observed when aneuploidy is present in a sample, an aneuploidy caller can be designed to select a set of maternal and fetal copy numbers that have the highest likelihood of producing an aneuploidy on a normal distribution. To this end, the following equation was developed to determine the depth of a given aneuploidy:

[Formula 1]

in:

d _p is the plasma depth

f is the fetus

c _m is the maternal copy number

d _b is the background depth

c _f is the fetal copy number

This calling program shows high sensitivity and specificity for detecting autosomal and sex chromosome aneuploidies as well as fetal sex calling. The following example provides further details on the performance of the aneuploidy calling program.

After completing the screening disclosed herein, the physician may choose to perform further evaluation, such as an extended aneuploidy analysis (EAA), which analyzes even more numbered chromosome pairs to provide additional insights into the health of the pregnancy. Thus, in some embodiments, the disclosed methods of determining the presence or absence of aneuploidy may further include an EAA.

C. Detection of Gene Variants

In general, the genetic variants (e.g., genetic mutations) detected as part of the disclosed methods are genetic variants, markers, or mutations associated with specific hereditary or heritable diseases, conditions, or traits. Genetic variants may include single nucleotide variations (SNVs), pathogenic or non-pathogenic single nucleotide polymorphisms (SNPs), insertions and deletions (indels), substitution mutations, or single gene copy number variants.

Gene variants may be associated with more than one disease, condition, or trait. Gene variants may be expressed as variations in polynucleotides, such as at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, or more sequence differences between wild-type (i.e., non-mutated or unrelated to a disease or condition) genes or loci. Non-limiting examples of types of gene variants that can be detected using the disclosed methods include, but are not limited to, single nucleotide polymorphisms (SNPs), deletion/insertion polymorphisms (DIPs), microcopy number variants (CNVs), short tandem repeats (STRs), restriction fragment length polymorphisms (RFLPs), single sequence repeats (SSRs), variable tandem repeats (VNTRs), randomly amplified polymorphic DNA (RAPDs), amplified fragment length polymorphisms, retrotransposon-based insertion polymorphisms, sequence-specific amplified polymorphisms, and heritable epigenetic modifications (e.g., DNA methylation).

For purposes of the disclosed methods, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375, 400, 425, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540, 550, 560, 570, 580, 590, 601 The presence or absence of 50, 475, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, or 1000, or more different genetic variants can be detected in a single assay and performed in parallel with the detection of the presence or absence of aneuploidy. In some embodiments, the methods can detect in parallel the presence or absence of gene variants associated with at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, or 200, or more diseases, conditions, or traits.

In general, the presence of a gene variant type detected by the disclosed methods is associated with an increased risk of having or developing a disease, condition, or trait by about, less than about, or greater than about 1%, 5%, 10%, 15%, 20%, 25%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 200%, 300%, 400%, 500%, or more. In some embodiments, the presence of a gene variant increases the risk of having or developing a disease, condition, or trait by about, less than about, or greater than about 1-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 25-fold, 50-fold, 100-fold, 500-fold, 1000-fold, 10000-fold, or more. In some embodiments, the presence of a genetic variant increases the risk of having or developing a disease, condition, or trait by any statistically significant amount, such as having a p-value of about or less than about 0.1, 0.05, ^10-3 , ^10-4 , ^10-5 , ^10-6 , ^10-7 , ^10-8 , ^10-9 , 10-10, ^10-11 , ^10-12 , ^10-13 , ^10-14 , ^10-15 , or ^an increase of less than about.

For the purposes of this disclosure, genetic diseases that can be evaluated or detected by determining the presence or absence of gene variants include, but are not limited to, 21-hydroxylase deficiency, ABCC8-related hyperinsulinism, ARSACS, achondroplasia, achromatopsia, adenosine monophosphate deaminase 1, agenesis of the corpus callosum with neuronopathy, alkaptonuria, alpha-1-antitrypsin deficiency, alpha-mannose storage disease, alpha-sarcoglycanosis, alpha-thalassemia; Alzheimer's disease, angiotensin II receptor type I, lipoprotein E genotyping; sperminosuccinic aciduria, asparaginuria, exercise disorders with vitamin E deficiency, ataxia-telangiectasia, autoimmune polyendocrinopathy type 1, BRCA1 hereditary breast/ovarian cancer, BRCA2 hereditary breast/ovarian cancer, Bardet-Biedl syndrome, Best yolk sac macular dystrophy, beta-sarcoglycanopathy, beta-thalassemia, biotinidase deficiency, Blau syndrome, Bloom syndrome, CFTR-related disorders, CLN3-related neurogenic cerolipofuscinosis, CLN5-related neurogenic cerolipofuscinosis, CLN8-related neurogenic cerolipofuscinosis disease, Canavan disease, carnitine palmitoyltransferase IA deficiency, carnitine palmitoyltransferase II deficiency, chondro-hair dysplasia, cerebral cavernous malformation, choroidergic malformation, Cohen syndrome, congenital cataract, facial dysmorphism and neuropathy, congenital glycosylation disorder la, congenital glycosylation disorder Ib, congenital Finnish nephropathy, Crohn's disease, cystinosis, DFNA9 (COCH), diabetes mellitus and hearing loss, early-onset primary myotonia insufficiency (DYTI), Herlitz-Pearson type junctional epidermolysis bullosa anemia, FANCC-associated Fanconi anemia, FGFR1-associated premature suture closure, FGFR2-associated premature suture closure, FGFR3-associated premature suture closure, factor V Leiden thrombophilia, factor V R2 mutation thrombophilia, factor 11 deficiency, factor 13 deficiency, familial adenomatous polyposis, familial dysautonomia, familial hypercholesterolemia type B, familial Mediterranean fever, free sialic acid storage disorder, frontotemporal dementia with Parkinson's disease 17, fumarase deficiency, GJB2-associated DFNA Type 3 nonsyndromic hearing loss and deafness, GJB2-related DFNB 1 nonsyndromic hearing loss and deafness, GNE-related myopathy, galactosemia, Gaucher's disease, glucose-6-phosphate dehydrogenase deficiency, glutaric acidemia type 1, glycogen storage disease type 1a, glycogen storage disease type Ib, glycogen storage disease type II, glycogen storage disease type III, glycogen storage disease type V, Gracile syndrome, HFE-related hereditary hemosiderinosis, Halder AIMs, hemoglobin Sβ-thalassemia, hereditary fructose intolerance, hereditary pancreatitis, hereditary thymine-uraciluria, hexosaminidase A deficiency, hidrotic ectodermal dysplasia 2, homocystinuria due to cystathionine β-synthase deficiency, hyperkalemic periodic paralysis type 1, hyperornithinemia-hyperammonemia-homocitrullinuria syndrome, primary hyperoxaluria type 1, primary hyperoxaluria type 2, hypochondrogenosis, hypokalemic periodic paralysis type 1, hypokalemic periodic paralysis type 2, hypophosphatasia, infantile myopathy and lactic acidosis (fatal and nonfatal), isovaleric acidemia, Krabbe disease, LGMD2I, Leber hereditary optic neuropathy, French-Canadian Leigh syndrome syndrome, long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency, MELAS, MERRF, MTHFR deficiency, MTHFR thermolabile variants, MTRNR1-related hearing loss and deafness, MTTS1-related hearing loss and deafness, MYH-associated polyposis, maple syrup urine disease type 1A, maple syrup urine disease type 1B, Markon-Subbatt syndrome, medium-chain acyl-CoA dehydrogenase deficiency, megalencephaly with subcortical cysts, metachromatic leukodystrophy, mitochondrial cardiomyopathy, mitochondrial DNA-associated Leigh syndrome and NARP, mucolipidosis IV, mucopolysaccharidosis type I, mucopolysaccharidosis type IIIA, mucopolysaccharidosis type VII, multiple endocrine neoplasia type 2, muscle-eye-brain disease, nematode myopathy, neuromyopathy Phenotype, Niemann-Pick disease due to phospholipase deficiency, Niemann-Pick disease type C1, Nijmegen chromosome breakage syndrome, PPT1-related neurogenic cerolipofuscinosis, PROP1-related pituitary hormone deficiency, Pallister-Hall syndrome, myospasm congenita, Pendred syndrome, peroxisome bifunctional enzyme deficiency, pervasive developmental disorder, phenylalanine hydroxylase deficiency, plasma proteinogen activator inhibitor I, autosomal recessive polycystic kidney disease, prothrombin G20210A thrombophilia, pseudovitamin D deficiency rickets, pycnodystrophy, autosomal recessive pigmented retinitis Bothnia type, Rett syndrome, chondrodysplasia punctata abnormality type 1, short-chain acyl-CoA dehydrogenase deficiency, Shwachman-Diamond syndrome, Sjogren-Larsson syndrome, Smith-Lemli-Opitz syndrome, spastic paraplegia 13, sulfate transporter-related osteochondrodysplasia, TFR2-related hereditary hemochromatosis, TPP1-related neuropathic cerolipofuscinosis, lethal achondroplasia, transthyretin amyloidosis, trifunctional protein deficiency, tyrosine hydroxylase deficiency DRD, tyrosinemia type I, Wilson's disease, X-linked juvenile retinoschisis, cystic fibrosis, spinal muscular atrophy (SMA), heme diseases, and Zellweger syndrome spectrum.

For the purpose of disclosed method, the identification or detection of gene variant can be carried out using computer simulation moving window analysis, to establish track based on the allele balance (when assessing the gene variant of SNP, insertion and deletion or other point mutation) across the analyzed window described herein or based on depth (when assessing the gene variant of copy number variation). This analysis may be particularly useful for detecting recessive conditions, traits or diseases. As mentioned above, this process may include the sequence in at least two windows of the sequence library being excluded based on the size of the read length, thereby obtaining the sequence library of at least two enriched fetal parts. The allele balance in each window is compared to allow the allele balance track between the sequence library of each enriched fetal part to be calculated. The allele balance track is the variation of the allele balance percentage of any given gene sequence of interest across the observed window. The allele balance track can be calculated as the slope of the allele balance across the observed window relative to the fetal part, and it can be visualized in a variety of ways, as shown in Figure 3.

Allelic balance tracks can be used to identify heterozygous and homozygous mutations within a cfDNA library. For example, a single point in the track is based on allelic balance in a given window and can be converted into a strip chart, where the y-axis shows the percentage of cfDNA in a sample with a given allele for a particular gene or nucleic acid sequence of interest (e.g., 0%, 10%, 20%, 30%, 40%, 50%, 60%), and the x-axis shows different alleles of the gene or nucleic acid of interest (i.e., reference alleles or alt alleles), which correspond to wild-type sequences or mutations/variants associated with a particular disease, condition, or trait (e.g., different known mutations in the CFTR gene associated with cystic fibrosis). If the fetal portion in the window or sample is, for example, 20%, then the band at 10% on the y-axis corresponds to a primary mutation in a fetus or fetus that is a carrier of DNA from the biological father. The band at 40% on the y-axis corresponds to a fetus that is negative for mutations/variants of the gene or sequence of interest (i.e., homozygous reference), while the mother is heterozygous (i.e., carrier). The band at 50% on the y-axis corresponds to the fetus from the carrier of the biological mother's DNA, or when the mother and the father are both carriers with the same alt allele, corresponds to the fetus for the carrier of the biological father's DNA. The band at 60% on the y-axis corresponds to the fetus that the mutation/variant of the gene or sequence interested is homozygous positive. As mentioned above, the band discussed above (that is, at 10%, 40%, 50%, and 60%) is not fixed, and their position will change based on the fetal part. For example, if the fetal part is changed to 10% (compared with 20% in the above example), the value of the band changes to 5%, 45%, 50%, and 55% from 10%, 40%, 50%, and 60% respectively.

The allele balance track combines this static information from each observed window, which will necessarily have a different fetal portion. Thus, the track can rely on a window with the above-mentioned 20% fetal portion, a second window with a 10% fetal portion, and optionally, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 more windows with different fetal portions.

Additionally or alternatively, specific calling routines for gene variants of interest may rely on an assessment of copy number, deep analysis (as described above with respect to aneuploidy), or other forms of detection known in the art. For example, a deep track may be used to detect the presence or absence of copy number variants, such as those of SMA1, RHD, HBA1, and HBA 2, which are all associated with specific genetic diseases. In some embodiments, a deep track may have a negative slope (indicating fewer copies in the fetus), an approximately flat slope (indicating the same number of copies between the fetus and the mother), or a positive slope (indicating more copies in the fetus, and such slope may be based on 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 more windows with different fetal portions.

In some embodiments, the calling program for certain conditions may rely on the presence or absence of detecting "difference bases". In some embodiments, the calling program for certain conditions may rely on detecting the presence or absence of substitutions in wild-type sequences (e.g., SNVs). In some embodiments, the calling program for certain conditions may rely on detecting the presence or absence of single nucleotide polymorphisms (SNPs). In some embodiments, the calling program for certain conditions may rely on detecting the presence or absence of one or more insertions or deletions (INDELs). In the case where multiple SNVs, difference bases, SNPs, or combinations thereof are associated with a given condition, focusing or merging even a small amount of SNVs, difference bases, SNPs, insertions or deletions, or combinations thereof (e.g., <3, <4, <5, <6, <7, <8, <9, <10, <11, <12, <13, <14, <15) detection signals can provide improved separation between genotypes. Thus, in some embodiments, a call for a condition may rely on detection of the presence or absence of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15, or more SNVs, differential bases, SNPs, insertions or deletions, or a combination thereof.

For example, in detecting alpha thalassemia, the disclosed methods can utilize a caller that detects the presence or absence of a double cis mutation of HBA1 and HBA2, which is the most common cause of the condition. Thus, the caller can detect, for example, a shared copy number signal obtained from multiple probes in a region of interest, such as a double deletion region.

Therefore, utilizing the disclosed method allows calling the gene variant of interest with a single sample, and is performed in parallel with the detection of aneuploidy. In fact, this method can even determine whether the fetus is homozygous or heterozygous for a given gene variant of interest. In addition, in some embodiments where the mother and the father have different alt alleles, it can be determined whether the fetus has obtained a specific variant from the mother, the father, or both. This is a new and useful way to determine the presence or absence of gene variants/mutations in maternal samples.

D. Noise reduction

As explained above and further shown in the examples, the disclosed methods and systems can significantly reduce the noise in cfDNA data, which will improve the performance of assays for detecting genetic variants and aneuploidy. Since the cffDNA levels are low in most biological samples obtained from pregnant women, the high level of background noise generated from conventional processing and detection methods can cause samples to be unusable, uninterpretable, or both. Therefore, the disclosed noise reduction method represents a new and useful method for improving conventional non-invasive prenatal screening (NIPS).

In addition, the present disclosure provides a method for reducing background noise from excess genetic material in non-invasive prenatal screening (NIPS), which comprises (i) obtaining a biological sample from a pregnant woman, wherein the biological sample comprises cell-free DNA (cfDNA); and (ii) processing the cfDNA for NIPS, wherein the processing comprises enriching free fetal DNA (cffDNA) in the biological sample, performing computer simulation processing on the cfDNA, or a combination thereof.

In some embodiments, the method of reducing noise will include enriching free fetal DNA (cffDNA) in a biological sample and performing computer simulation processing on the cfDNA.

To reduce noise, enriching cffDNA in a biological sample may include a disclosed method of physical separation or enrichment of a fetal portion. For example, in some embodiments, enriching cffDNA in a biological sample may include obtaining a biological sample including cell-free DNA (cfDNA) from a pregnant woman, wherein the cfDNA includes cffDNA and cell-free DNA maternal (cfmDNA); extracting cfDNA from the biological sample; and subjecting the extracted cfDNA to a size exclusion process, wherein the size exclusion process has a cutoff size of about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, or about 180 nucleotides in length, thereby generating nucleic acids enriched in cffDNA.

Similarly, to reduce noise, the computer simulation processing may include any of the disclosed methods for analyzing a sequence library or sequence library data to focus any analysis of genetic variants or aneuploidies on the fetal portion of the sample. For example, in some embodiments, the computer simulation processing may include sequencing a cfDNA sample including free fetal DNA (cffDNA) and free maternal DNA (cfmDNA) to prepare a sequence library; performing a read length-based analysis, wherein an allelic balance of a nucleic acid sequence of interest is established in at least two windows of the sequence library; and establishing a trajectory based on the allelic balance of the at least two windows, wherein the trajectory indicates the percentage of alleles present in the sample including the nucleic acid sequence of interest.

Noise reduction can further include normalization to control for GC bias, sample background, hybridization probe capture, or a combination thereof. In general, normalization can be "median normalization." In other words, the probe read depth can be divided by the median across probes with similar GC content, and then divided by the interquartile range mean across samples and probes with an inferred copy number of 2 in the mother and fetus.

Hybridization probe capture can be problematic because DNA fragments containing variants and overlapping capture probes are captured with lower efficiency, thereby reducing the allelic balance of alternative alleles. However, capture bias is often reproducible and in such cases can be learned and corrected using the following formula:

[Formula 2]

Correction and normalization of hybridization probe capture is particularly useful for ensuring correct insertion or deletion calls, although it can aid variant calling more generally.

The following examples are given to illustrate the disclosed sample preparation and methods. It should be understood, however, that the invention is not limited to the specific embodiments or details described in these examples.

Examples

Example 1 - Aneuploidy caller performance

The disclosed aneuploidy calling program is based on the sequence read depth as described above. In order to establish the feasibility of this method, 110 feasibility samples were analyzed and compared with the standard recognized aneuploidy detection system (MyriadPREQUEL ^TM prenatal screening). The detectable aneuploids in the samples and the control calls for each sample are shown in the following table:

The disclosed depth-based analysis method provides the following results:

●For autosome +22q

○Sensitivity = 100% (CI: 89.95%-100%)

○Specificity = 99.75% (CI: 98.59%-99.96%)

○ A false positive mosaic single chromosome 21 call

● For sex chromosome aneuploidy

○Sensitivity = 100% (CI: 63.06%-100%)

○Specificity = 100% (CI: 96.41%-100%)

●Calling the sex of the fetus

○100% consistent with control test

Only one sample out of 110 failed due to low depth (0.9% repeat run rate).

Example 2 - SNV/Indel (i.e., Gene Variant) Calling Program Performance

Fifteen (15) designed mixtures from 5 prenatal pairs were used to validate the performance of the SNV/indel caller system disclosed herein. The sensitivity and specificity of the gene region of interest (ROI) alone and in combination with a set of SNVs known to have high variability within the population (i.e., dbSNP) are shown in the following table:

This initial performance was established without using the physical enrichment process described herein. It is expected that enrichment of the fetal fraction and optimization of different filter parameters will further improve the performance.

In addition, the disclosed single gene SNV/insertion or deletion calling program met the performance requirements on 5 unique cfDNA samples with FF of 5.8%-16%. The results are shown in the following table.

For this performance evaluation, the best performance was observed when both enrichment by physical size-based exclusion and bias correction were performed simultaneously.

Example 3 - SMA caller performance analysis

Spinal muscular atrophy (SMA) is a genetic condition that is often included in prenatal screening. However, SMA calling is difficult due to the high homology between the SMN1 and SMN2 genes. These genes differ in a few positions (most notably exon 7), and the SMA carrier/affected status depends only on the number of SMN1 copies.

The disclosed system evaluates the presence or absence of multiple bases (up to 44 differential bases) to ensure correct calling. As shown in the table below, the SMA calling program is very accurate, sensitive, and specific.

For the purpose of this evaluation, carrier fetuses were considered healthy.

Example 4 - Alpha Thalassemia Caller Performance Analysis

Alpha thalassemia is a blood disorder that reduces hemoglobin production. It is a genetically inherited condition that is often included in prenatal screening. The disclosed system evaluates the presence or absence of double cis mutations of HBA1 and HBA2, which are the most common cause of the condition. More specifically, a common copy number signal is obtained from multiple probes in the double deleted region. As shown in the table below, the alpha thalassemia caller is highly accurate, sensitive, and specific.

For the purpose of this evaluation, carrier fetuses were considered healthy.

Example 5 - RhD caller performance analysis

If the pregnant mother is D(-) and the fetus is D(+), a hemolytic disease can occur when the mother's blood comes into contact with the fetus's blood. This condition is often included in prenatal screening. The most common cause of RhD(-) is a deletion of the entire gene for RHD. Therefore, a caller based on 221 reliable differential bases was developed to estimate copy number. As shown in the table below, the RhD caller is highly accurate, sensitive, and specific.

*****

All patents and publications mentioned in the specification are indicative of the levels of ordinary skill in the art to which the present disclosure pertains. All patents and publications are incorporated herein by reference to the same extent as if each individual publication was specifically and individually indicated to be incorporated by reference.

The present technology is not limited to the specific embodiments described in this application, and these specific embodiments are intended to be a single description of the individual scope of the present technology. Without departing from the spirit and scope of the present technology, many modifications and changes can be made to the present technology, which is obvious to those with common knowledge in the art. In addition to those listed herein, those with common knowledge in the art will be obvious from the foregoing description that the functionally equivalent methods and equipment within the scope of the present technology. Such modifications and changes are intended to fall within the scope of the present technology. It should be understood that the present technology is not limited to ad hoc methods, reagents, compounds, compositions, or systems, which can certainly be changed. It should also be understood that the terms used herein are only used to describe the purpose of specific embodiments and are not intended to be limited.

Claims (33)

1. A method of preparing a biological sample having an enriched fetal portion, comprising:

(a-1) obtaining a biological sample comprising free DNA (cfDNA) from a pregnant woman;

(b-1) extracting cfDNA from the biological sample;

(c-1) preparing a cfDNA fragment library to obtain a cfDNA library;

(d-1) isolating the cfDNA fragments in the cfDNA library according to size to retain cfDNA fragments of less than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, about 180 nucleotides in length, about 185 nucleotides in length, about 190 nucleotides in length, about 195 nucleotides in length, or about 200 nucleotides in length;

(e-1) sequencing the retained cfDNA fragments to obtain a first sequence pool;

(f-1) identifying (i) free fetal DNA (cffDNA) sequences and (ii) free maternal DNA (cfmDNA) sequences present in at least two windows of the first sequence pool based on the length of the read length; and

(G-1) isolating the cffDNA sequences from each of the at least two windows of the sequence library, thereby obtaining at least two fetal fraction-enriched sequence libraries;

Or alternatively

(A-2) obtaining a biological sample comprising free DNA (cfDNA) from a pregnant woman;

(b-2) extracting cfDNA from the biological sample;

(c-2) isolating cfDNA fragments in the extracted sample from (b-2) to retain cfDNA fragments of only less than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, about 180 nucleotides in length, about 185 nucleotides in length, about 190 nucleotides in length, about 195 nucleotides in length, or about 200 nucleotides in length;

(d-2) preparing a cfDNA library from the isolated cfDNA fragments from (c-2);

(e-2) sequencing the cfDNA library to obtain a first sequence library;

(f-2) identifying (i) free fetal DNA (cffDNA) sequences and (ii) free maternal DNA (cfmDNA) sequences present in at least two windows of the first sequence pool based on the length of the read length; and

(G-2) isolating the cffDNA sequences from each of the at least two windows of the sequence pool, thereby obtaining at least two sequence pools enriched for fetal parts.

2. The method of claim 1, wherein isolating the cfDNA fragments enriches the fetal portion in the biological sample about 1.1-fold, about 1.2-fold, about 1.3-fold, about 1.4-fold, about 1.5-fold, about 1.6-fold, about 1.7-fold, about 1.8-fold, about 1.9-fold, or about 2.0-fold.

3. The method of claim 1 or 2, wherein isolating the cffDNA sequences from the at least two windows of the first sequence library enriches the fetal portion in the biological sample by about 1.1-fold, about 1.2-fold, about 1.3-fold, about 1.4-fold, about 1.5-fold, about 1.6-fold, about 1.7-fold, about 1.8-fold, about 1.9-fold, about 2.0-fold, about 2.1-fold, about 2.2-fold, about 2.3-fold, about 2.4-fold, about 2.5-fold, about 2.6-fold, about 2.7-fold, about 2.8-fold, about 2.9-fold, about 3.0-fold, about 3.1-fold, about 3.2-fold, about 3.3-fold, about 3.4-fold, or about 3.5-fold.

4. A method according to any one of claims 1 to 3, wherein isolating the cfDNA fragments comprises electrophoresis.

5. The method of any one of claims 1 to 4, wherein at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 windows of the first sequence pool are evaluated to identify and isolate cffDNA sequences to obtain at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 fetal fraction enriched sequence pools, respectively.

6. The method of any one of claims 1 to 5, further comprising identifying and isolating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA in the first sequence pool to a reference genome, demultiplexing sequence reads from the first pool, removing repetitive sequences from the first sequence pool, or a combination thereof.

7. The method of any one of claims 1 to 6, further comprising assessing the presence of one or more genetic mutations in the sequence pool of the at least two fetal-enriched parts.

8. The method of claim 7, wherein the one or more genetic mutations result in at least one condition selected from the group consisting of: 21-hydroxylase deficiency, ABCC 8-associated hyperinsulinemia, ARSACS, achondroplasia, holoceanopia, adenosine monophosphate deaminase 1, corpus callosum dysplasia with neuronal disease, black urine, alpha-1-antitrypsin deficiency, alpha-mannose storage disorder, alpha-sarcoidosis, alpha-thalassemia; alzheimer's disease (Alzheimers), angiotensinamide II receptor type I, lipoprotein E genotyping; Spermine succinuria (Argininosuccinicaciduria), asparagi glucosaminuria, movement disorders with vitamin E deficiency, movement disorder telangiectasia, autoimmune endocrine disorder syndrome type 1, BRCA1 hereditary breast/ovarian cancer, BRCA2 hereditary breast/ovarian cancer, bardet-Biedl two's syndrome, best yolk vesicular dystrophy, beta-sarcoidosis, beta-thalassemia, biotin enzyme deficiency, blau syndrome, bloom syndrome, CFTR-related disorders, CLN 3-related neuroid lipofuscinosis, CLN 5-related neuroid lipofuscinosis, CLN 8-related neuroid lipofuscinosis, canavan-related carnitine palmitoyl transferase IA deficiency, carnitine palmitoyl transferase II deficiency, cartilage-hair dysplasia (CerebralCavernous Malformation), brain spongiform malformation (CerebralCavernous Malformation), pulse-free vein abnormality, cohen syndrome, congenital cataract, facial dysmorphism (Facial Dysmorphism) and neuropathy, congenital glycosylation disorder la (Congenital Disorder of Glycosylationla), Congenital glycosylation disorder Ib, congenital finnish nephropathy (CongenitalFinnish Nephrosis), crohn's Disease (Crohn's Disease), cystine Disease, DFNA9 (COCH), diabetes mellitus, hearing loss, primary deficiency of muscle tone in Early stage (Early-Onset Primary Dystonia; DYTI), herlitz-Pearson Type boundary vesicular epidermolysis (Epidermolysis Bullosa Junctional, herlitz-Pearson Type), FANCC-related Fanconi anemia, FGFR 1-related cranial suture closure prematurely, FGFR 2-related cranial suture closure prematurely, FGFR 3-related cranial suture closure prematurely, leiden thrombotic complications of the fifth factor (Factor V Leiden Thrombophilia), R2-mutant thrombotic complications of the fifth factor, Deficiency of the eleventh factor, deficiency of the thirteenth factor, familial adenomatous polyposis (FamilialAdenomatous Polyposis), familial autonomic dysfunction (Familial Dysautonomia), familial hypercholesterolemia type B, familial mediterranean fever (FAMILIAL MEDITERRANEAN FEVER), free sialic acid Storage dysfunction (FREE SIALIC ACID Storage dispenser), frontotemporal dementia with Parkinsonism 17 (Frontotemporal DEMENTIA WITH Parkinsonism-17), and, Fumarase deficiency, GJB 2-related DFNA 3-type non-syndrome hearing loss and deafness, GJB 2-related DFNB-type non-syndrome hearing loss and deafness, GNE-related myopathy, galactosylation, gaucher's disease, glucose-6-phosphate dehydrogenase deficiency, glutarate 1 type, glycogen Storage disease type 1a (Glycogen Storage DISEASE TYPE a), glycogen Storage disease type Ib, glycogen Storage disease type II, glycogen Storage disease type III, glycogen Storage disease type V, gracile syndrome, HFE-associated hereditary blood iron deposition (HFE-Associated Hereditary Hemochromatosis), glucose-6-phosphate dehydrogenase deficiency, glutarate 1 type, HALDER AIMS, hemoglobin S beta-thalassemia, hereditary fructose intolerance, hereditary pancreatitis, hereditary thymine-uracil urine disease (HEREDITARY THYMINE-Uraciluria), hexosaminidase A deficiency, sweat ectodermal dysplasia 2 (Hidrotic Ectodermal Dysplasia 2), homocystinuria due to cystathionine beta-synthase deficiency, hyperkalemia periodic paralysis type 1, hyperornithine blood-hyperaminomia-homocystinuria syndrome, primary hyperoxaluria type 1, Primary hyperoxaluria type 2, hypochondrogenesis, hypokalemia type 1, hypokalemia type 2, hypophosphatase, infantile myopathy, lactic acidosis (dying and non-dying), isovaleric acidemia, krabbe disease, LGMD2I, leber hereditary optic neuropathy, french-Canadian Leigh syndrome, long-Chain 3-hydroxyacyl-CoA dehydrogenase deficiency (Long Chain 3-Hydroxyacyl-CoADehydrogenase Deficiency), MELAS, MERRF, MTHFR deficiency, MTHFR thermolabile mutation, MTRNR 1-related hearing loss and deafness, MTTS 1-related hearing loss and deafness, MYH-related polyposis, maple syrup urine disease type 1A, maple syrup urine disease type 1B, equine Cohn-sub-baite syndrome (McCune-Albright Syndrome), medium chain acyl-CoA dehydrogenase deficiency, megawhite encephalopathy with subcortical cyst (MEGALENCEPHALIC LEUKOENCEPHALOPATHY WITH SUBCORTICAL CYST), Metachromatic white matter malnutrition (Metachromatic Leukodystrophy), mitochondrial cardiomyopathy (Mitochondrial Cardiomyopathy), mitochondrial DNA-associated Leigh syndrome and NARP, mucolipidosis IV (Mucolipidosis IV), mucopolysaccharidosis type I (Mucopolysaccharidosis Type I), mucopolysaccharidosis type IIIA, mucopolysaccharidosis type VII, multiple endocrine tumor type 2, myo-ocular-brain disease, sarcoidosis (Nemaline Myopathy), neuro-phenotype, niemann-pick disease due to neurotrypsin deficiency (Niemann-PICK DISEASE Due to Sphingomyelinase Deficiency), niemann-pick disease type C1, nemehenne chromosome fracture syndrome (Nijmegen Breakage Syndrome), PPT 1-related neuro-ceroid lipofuscinosis, PROP1-related hypophysin hormone deficiency (PROP 1-related pituitary hormone deficiency), and, PALLISTER-Hall syndrome, congenital myorigid spasticity (Paramyotonia Congenita), pendred syndrome, peroxisome bifunctional enzyme deficiency, widespread dysfunction (PERVASIVE DEVELOPMENTALDISORDER), phenylalanine hydroxylase deficiency, plasma protein activation factor inhibitor I (Plasminogen ActivatorInhibitorI), autosomal recessive polycystic kidney disease, prothrombin G20210A thrombotic complications, prothrombin G20210A, Pseudo-vitamin D deficiency rickets, compact osteogenesis imperfecta, bothnia autosomal recessive pigmentation retinitis, ratty Syndrome (Rett Syndrome), acrophnodic cartilage dysplasia type 1 (Rhizomelic Chondrodysplasia Punctata Type 1), short chain acyl-CoA dehydrogenase deficiency, SHWACHMAN-Diamond Syndrome, sjogren-Larsson Syndrome, smith-Lemli-Opitz Syndrome, spastic paraplegia 13, Sulfate transporter-related osteochondral dysplasia, TFR 2-related hereditary hemochromatosis, TPP 1-related neurotype ceruloplasmin brown disease, lethal cartilage hypoplasia, transthyretin amyloidosis (TRANSTHYRETIN AMYLOIDOSIS), trifunctional protein deficiency, tyrosine hydroxylase deficiency DRD, tyrosinemia type I, wilson's disease, X-Linked Juvenile Retinoschisis, cystic fibrosis (cystic fibrosis), X-bijuveniles retinal cleavage disease, Spinal Muscular Atrophy (SMA), heme disease, and Zellweger syndrome lineages.

9. The method of any one of claims 1 to 8, further comprising assessing the presence of aneuploidy in the biological sample comprising cfDNA.

10. The method of claim 9, wherein the aneuploidy is selected from the group consisting of a single chromosome, a trisomy, a tetrasomy, a pentachromosome, a microdeletion, a microreplication, and chimeric forms of a single chromosome, a trisomy, a tetrasomy, and a pentachromosome.

11. A method of detecting in parallel the presence or absence of aneuploidy and the presence or absence of at least one gene variant in a single maternal sample comprising:

(i) Obtaining a biological sample from a pregnant woman, wherein the biological sample comprises free DNA (cfDNA);

(ii) Preparing a cfDNA library;

(iii) Sequencing the cfDNA library to generate a sequence library; and

(Iv) Detecting the presence or absence of aneuploidy and the presence or absence of at least one gene variant in the single maternal sample;

Wherein (a) enriching the cfDNA pool to increase fetal fraction, (b) enriching the sequence pool to increase fetal fraction, or (c) a combination thereof, such that the fetal fraction of the single maternal sample is increased by at least 1.1-fold, at least 1.2-fold, at least 1.3-fold, at least 1.4-fold, or at least 1.5-fold prior to detecting the presence or absence of aneuploidy and the presence or absence of at least one gene variant in the single maternal sample.

12. The method of claim 11, wherein the biological sample is blood or plasma.

13. The method of claim 11 or 12, wherein the cfDNA pool is enriched to increase the fetal portion and the sequence pool is enriched to increase the fetal portion.

14. The method of any one of claims 11 to 13, wherein enriching the fetal portion of the cfDNA library comprises removing from the cfDNA library any DNA fragment greater than about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, or about 180 nucleotides in length.

15. The method of claim 14, wherein removing the DNA fragments from the cfDNA library comprises electrophoresis.

16. The method of any one of claims 11 to 15, wherein enriching the fetal portions of the pool of sequences comprises size exclusion based on read length of sequences in at least two windows of the pool of sequences to obtain at least two pools of sequences enriched in fetal portions.

17. The method of claim 16, wherein at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 windows of the first sequence pool are evaluated to identify and isolate cffDNA sequences to obtain at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 fetal fraction enriched sequence pools, respectively.

18. The method of claim 16 or 17, wherein the at least two windows of the sequence library are selected from (i) a sequence of 0-145 nucleotides, (ii) a sequence of 0-150 nucleotides, (iii) 0-155 nucleotides, (iv) 0-160 nucleotides, (v) 0-165 nucleotides, (vi) 0-168 nucleotides, (vii) 0-170 nucleotides, (viii) 0-175 nucleotides, (ix) 0-180 nucleotides, (x) 0-185 nucleotides, (xi) 0-190 nucleotides, (xii) 0-195 nucleotides, (xiii) 0-200 nucleotides, and (xiv) an unchecked.

19. The method of any one of claims 16 to 18, wherein enriching the fetal portion of the library of sequences further comprises identifying and isolating cffDNA from cfmDNA by comparing sequence reads of cffDNA and cfmDNA in the first library of sequences to a reference genome, demultiplexing sequence reads from the first library, removing repetitive sequences from the first library of sequences, or a combination thereof.

20. The method of any one of claims 11 to 19, wherein detecting the presence or absence of at least one gene variant comprises determining in each of the at least two fetal portion-enriched sequence libraries the allelic balance of each allele encoding the at least one gene variant in the sample, and generating an allelic balance locus for each allele based on the allelic balance in each of the at least two fetal portion-enriched sequence libraries, a depth locus based on the depth of the at least two fetal portion-enriched sequence libraries, or a combination of an allelic balance locus and a depth locus.

21. The method of any one of claims 11 to 20, wherein detecting the presence or absence of aneuploidy comprises analyzing the sequence library for sequence depth corresponding to at least one sequence of a chromosome of interest.

22. The method of claim 21, wherein the sequence depth corresponding to the at least one sequence of the chromosome of interest adapts an expected depth model of the chromosome of interest.

23. The method of claim 21 or 22, wherein the sequence depth is calculated by:

wherein:

d _p is the depth of pregnancy

F is fetal part

C _m is the parent copy number

D _b is the background depth

C _f is fetal copy number.

24. The method of any one of claims 21 to 23, wherein the sequence depth is normalized to control GC bias, sample background, hybridization probe capture, or a combination thereof.

25. The method of any one of claims 11 to 24, wherein the method comprises detecting the presence or absence of an aneuploidy selected from the group consisting of a single chromosome, a trisomy, a tetrasomy, a polysomy, a microdeletion, a microreplication, a pentachromosome, and combinations thereof.

26. The method of any one of claims 11 to 24, wherein the at least one gene variant is associated with a disease selected from the group consisting of: 21-hydroxylase deficiency, ABCC 8-associated hyperinsulinemia, ARSACS, achondroplasia, holoceanopia, adenosine monophosphate deaminase 1, corpus callosum dysplasia with neuronal disease, black urine, alpha-1-antitrypsin deficiency, alpha-mannose storage disorder, alpha-sarcoidosis, alpha-thalassemia; alzheimer's disease, angiotensinamide II receptor type I, lipoprotein E genotyping; Spermine succinuria, asparagi glucosaminuria, movement disorders with vitamin E deficiency, movement disorder telangiectasia, autoimmune endocrine disorder syndrome type 1, BRCA1 hereditary breast/ovarian cancer, BRCA2 hereditary breast/ovarian cancer, bardet-Biedl two's syndrome, best yolk vesicular dystrophy, beta-sarcoidosis, beta-thalassemia, biotin enzyme deficiency, blau syndrome, bloom syndrome, CFTR related disorders, CLN3 related neurotype ceroid lipofuscinosis, CLN5 related neurotype ceroid lipofuscinosis, and, CLN 8-related neuroid lipofuscinosis, canavan disease, carnitine palmitoyl transferase IA deficiency, carnitine palmitoyl transferase II deficiency, cartilage-hair dysplasia, cerebral spongiform malformation, pulse-free vein abnormality, cohen's syndrome, congenital cataract, facial dysmorphism and neuropathy, congenital glycosylation disorder la, congenital glycosylation disorder Ib, congenital Finnish nephropathy, crohn's disease, cystine disease, DFNA 9 (COCH), diabetes mellitus and hearing loss, early primary muscular tension Deficiency (DYTI), herlitz-Pearson-type interface-type epidermolysis bullosa, FANCC-related Fanconi anemia, FGFR 1-related cranial suture closure prematurely, FGFR 2-related cranial suture closure prematurely, FGFR 3-related cranial suture closure prematurely, leiden thrombotic complications of the fifth factor, R2 mutant thrombotic complications of the fifth factor, deficiency of the eleventh factor, deficiency of the thirteenth factor, familial adenomatous polyposis, familial autonomic dysfunction, familial hypercholesterolemia type B, familial mediterranean fever, free sialic acid storage disorders, frontotemporal dementia with Parkinson's disease 17, fumarase deficiency, GJB 2-related DFNA 3-type non-symptomatic hearing loss, deafness, GJB 2-related DFNB non-syndrome hearing loss, deafness, GNE-related myopathy, galactosylemia, gaucher' S disease, glucose-6-phosphate dehydrogenase deficiency, glutarate 1, glycogen storage disease type 1a, glycogen storage disease type Ib, glycogen storage disease type II, glycogen storage disease type III, glycogen storage disease type V, gracile syndrome, HFE-related hereditary iron deposition, HALDER AIMS, hemoglobin S beta-thalassemia, hereditary fructose intolerance, hereditary pancreatitis, hereditary thymine-uracil uracratia, hexosaminidase A deficiency, Hyperhidrosis ectodermal dysplasia 2, cystathionine beta-synthase deficiency induced homocystinuria, hyperkalemia periodic paralysis type 1, hypercornithine-hyperchlorhydria syndrome, primary hyperoxaluria type 1, primary hyperoxaluria type 2, hypochondrus, hypokalemia periodic paralysis type 1, hypokalemia periodic paralysis type 2, hypophosphatase, infantile myopathy and lactic acidosis (dying and non-lethal), isovaleria, krabbe disease, LGMD2I, leber hereditary optic neuropathy, french-Canadian Leigh syndrome, long-chain 3-hydroxyacyl-CoA dehydrogenase deficiency, MELAS, MERRF, MTHFR deficiency, MTHFR thermolabile mutation, MTRNR 1-related hearing loss and deafness, MTTS 1-related hearing loss and deafness, MYH-related polyposis, maple syrup urine disease type 1A, maple syrup urine disease type 1B, equine Cohn-multiple-hundred's syndrome, medium chain acyl-coa dehydrogenase deficiency, megawhite matter disease with subcortical cyst, metachromatic white matter malnutrition, mitochondrial cardiomyopathy, mitochondrial DNA-related Leigh syndrome and NARP, mucopolysaccharidosis IV, mucopolysaccharidosis type I, mucopolysaccharidosis IIIA, mucopolysaccharidosis type VII, Multiple endocrine tumor type 2, myo-eye-brain disease, linear myopathy, neurophenogenesis, niman-pick disease due to neurotrypsin deficiency, niman-pick disease type C1, nemeheng chromosome breakage syndrome, PPT 1-related neuroceroid lipofuscinosis, PROP 1-related hypophysin hormone deficiency, PALLISTER-Hall syndrome, congenital myospasticity, pendred syndrome, peroxisome bifunctional enzyme deficiency, extensive development disorder, phenylalanine hydroxylase deficiency, plasma protein activating factor inhibitor I, autosomal recessive genetic polycystic kidney disease, prothrombin G20210A thrombotic complications, Pseudo-vitamin D deficiency rickets, compact osteogenesis imperfecta, bothnia autosomal recessive pigmentation retinitis, rattsia syndrome, punctate cartilage dysplasia type 1, short chain acyl-coa dehydrogenase deficiency, SHWACHMAN-Diamond syndrome, sjogren-Larsson syndrome, smith-Lemli-Opitz syndrome, spastic paraplegia 13, sulfate transporter-related osteochondral dysplasia, TFR 2-related hereditary hemochromatosis, TPP 1-related neurotype ceroid lipofuscinosis, lethal cartilage hypoplasia, transthyretin amyloidosis, trifunctional protein deficiency, tyrosine hydroxylase deficiency DRD, tyrosinemia type I, wilson's disease, bipolar X retinal splitting, cystic fibrosis, spinal Muscular Atrophy (SMA), heme disease, and Zellweger syndrome lineages.

27. A method of enriching a biological sample for free fetal DNA (cfDNA), comprising obtaining a biological sample comprising free DNA (cfDNA) from a pregnant woman, wherein the cfDNA comprises cfDNA and free maternal DNA (cfmDNA); extracting the cfDNA from the biological sample; and subjecting the extracted cfDNA to a size exclusion process, wherein the size exclusion process has a cutoff size of about 150 nucleotides in length, about 155 nucleotides in length, about 160 nucleotides in length, about 165 nucleotides in length, about 170 nucleotides in length, about 175 nucleotides in length, or about 180 nucleotides in length, thereby producing a cfDNA enriched sample.

28. A method of computer-simulated processing of free DNA (cfDNA), comprising sequencing cfDNA samples comprising free fetal DNA (cffDNA) and free maternal DNA (cfmDNA) to prepare a sequence pool; performing a read length-based analysis, wherein an allelic balance of the nucleic acid sequence of interest is established in at least two windows of the sequence library; and establishing a trajectory based on the allelic balance of the at least two windows.

29. A method of reducing background noise from unwanted genetic material in non-invasive prenatal screening (NIPS), comprising

(I) Obtaining a biological sample from a pregnant woman, wherein the biological sample comprises free DNA (cfDNA); and

(Ii) Treating cfDNA for NIPS, wherein treating comprises enriching free fetal DNA (cffDNA) in the biological sample, subjecting the cfDNA to a computer-simulated treatment, or a combination thereof.

30. The method of claim 29, wherein processing comprises both enriching free fetal DNA (cffDNA) in the biological sample and subjecting the cfDNA to a computer-simulated process.

31. The method of claim 29 or 30, wherein enriching free fetal DNA (cffDNA) in the biological sample comprises the method of claim 27.

32. The method of any one of claims 29 to 31, wherein computer-simulated processing of the cfDNA comprises the method of claim 28.

33. The method of any one of claims 29 to 32, further comprising normalization to control GC bias, sample background, hybridization probe capture, or a combination thereof.