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Volume 5
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Genes, Volume 5, Issue 2 (June 2014) – 13 articles , Pages 254-496

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364 KiB  
Article
Imprinted Genes and the Environment: Links to the Toxic Metals Arsenic, Cadmium and Lead
by Lisa Smeester, Andrew E. Yosim, Monica D. Nye, Cathrine Hoyo, Susan K. Murphy and Rebecca C. Fry
Genes 2014, 5(2), 477-496; https://doi.org/10.3390/genes5020477 - 11 Jun 2014
Cited by 24 | Viewed by 10667
Abstract
Imprinted genes defy rules of Mendelian genetics with their expression tied to the parent from whom each allele was inherited. They are known to play a role in various diseases/disorders including fetal growth disruption, lower birth weight, obesity, and cancer. There is increasing [...] Read more.
Imprinted genes defy rules of Mendelian genetics with their expression tied to the parent from whom each allele was inherited. They are known to play a role in various diseases/disorders including fetal growth disruption, lower birth weight, obesity, and cancer. There is increasing interest in understanding their influence on environmentally-induced disease. The environment can be thought of broadly as including chemicals present in air, water and soil, as well as food. According to the Agency for Toxic Substances and Disease Registry (ATSDR), some of the highest ranking environmental chemicals of concern include metals/metalloids such as arsenic, cadmium, lead and mercury. The complex relationships between toxic metal exposure, imprinted gene regulation/expression and health outcomes are understudied. Herein we examine trends in imprinted gene biology, including an assessment of the imprinted genes and their known functional roles in the cell, particularly as they relate to toxic metals exposure and disease. The data highlight that many of the imprinted genes have known associations to developmental diseases and are enriched for their role in the TP53 and AhR pathways. Assessment of the promoter regions of the imprinted genes resulted in the identification of an enrichment of binding sites for two transcription factor families, namely the zinc finger family II and PLAG transcription factors. Taken together these data contribute insight into the complex relationships between toxic metals in the environment and imprinted gene biology. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Top canonical pathways and their relationships to toxic metals. The aryl-hydrocarbon receptor (AhR) (<span class="html-italic">p</span> = 0.001) and TP53 (<span class="html-italic">p</span> = 0.007) networks display known interactions between pathway genes and priority metals (arsenic, cadmium, lead, and mercury). Imprinted status is also noted; abbreviations are shown in the figure legend.</p> Full article ">
766 KiB  
Article
GWAS to Sequencing: Divergence in Study Design and Analysis
by Christopher Ryan King and Dan L. Nicolae
Genes 2014, 5(2), 460-476; https://doi.org/10.3390/genes5020460 - 28 May 2014
Cited by 14 | Viewed by 7471
Abstract
The success of genome-wide association studies (GWAS) in uncovering genetic risk factors for complex traits has generated great promise for the complete data generated by sequencing. The bumpy transition from GWAS to whole-exome or whole-genome association studies (WGAS) based on sequencing investigations has [...] Read more.
The success of genome-wide association studies (GWAS) in uncovering genetic risk factors for complex traits has generated great promise for the complete data generated by sequencing. The bumpy transition from GWAS to whole-exome or whole-genome association studies (WGAS) based on sequencing investigations has highlighted important differences in analysis and interpretation. We show how the loss in power due to the allele frequency spectrum targeted by sequencing is difficult to compensate for with realistic effect sizes and point to study designs that may help. We discuss several issues in interpreting the results, including a special case of the winner’s curse. Extrapolation and prediction using rare SNPs is complex, because of the selective ascertainment of SNPs in case-control studies and the low amount of information at each SNP, and naive procedures are biased under the alternative. We also discuss the challenges in tuning gene-based tests and accounting for multiple testing when genes have very different sets of SNPs. The examples we emphasize in this paper highlight the difficult road we must travel for a two-letter switch. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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<p>The plot shows the sample sizes (on the y-axis, in thousands) needed to achieve 80% power at the 10<sup>−6</sup> significance level as a function of “sparsity”, <math display="inline"> <semantics id="sm3"> <mrow> <msub> <mi>k</mi> <mn>1</mn></msub> <mo>/</mo> <msqrt> <mi>k</mi></msqrt></mrow></semantics></math> (on the x-axis), with <span class="html-italic">k</span> and <span class="html-italic">k</span><sub>1</sub> as defined in the text. It is assumed for these calculations that the <span class="html-italic">k</span> SNPs are independent (no linkage disequilibrium), with the minor allele frequency (MAF) sampled from a beta distribution with parameters selected to match allele frequencies from the CEU of the 1000 Genomes Project, <span class="html-italic">B</span>(0.14,0.73); the distribution is truncated at 0.01 (so SNPs have MAF &lt; 1%) and only polymorphic SNPs when sequencing 10,000 subjects are selected. Calculations are based on the NCP in <a href="#FD1" class="html-disp-formula">Equation (1)</a>. OR, odds ratio.</p> Full article ">Figure 2
<p>Sampling probability by MAF, log odds-ratio. The contour plot has on the x-axis the allelic expected count in a population sample the same size as the control group (sample sizes times MAF) and, on the y-axis, the log-odds ratio. Contours are the absolute probability of being sampled in a case-control study of 100 cases and 100 controls when prevalence equals 1%.</p> Full article ">Figure 3
<p>Observed data probabilities by MAF. X-axis N<span class="html-italic">·</span>MAF. The y-axis shows the probability of each special data type conditional on the SNP being polymorphic: occurring only in cases (red), once in controls and zero times in cases (blue) and all other (black). Green = expected log-odds-ratio (OR) of sampled SNPs (same numeric scale). The log-ORs are assumed to be distributed left: <span class="html-italic">N</span>(0, 1); center: <span class="html-italic">N</span>(0, 0.5<sup>2</sup>); right: <span class="html-italic">N</span>(0, 0.25<sup>2</sup>); other settings are as in <a href="#f2-genes-05-00460" class="html-fig">Figure 2</a>.</p> Full article ">Figure 4
<p>Implied alternative OR (on the y-axis, logarithmic scale) as a function of MAF (x-axis) for three burden weighting schemes. The black line corresponds to the Madsen– Browning weight [<a href="#b10-genes-05-00460" class="html-bibr">10</a>]; the red line corresponds to the attributable risk weight [<a href="#b41-genes-05-00460" class="html-bibr">41</a>], and the blue line corresponds to the default in SKAT, Beta(25,1) [<a href="#b42-genes-05-00460" class="html-bibr">42</a>]; the green line is for equal weights. For the <b>left</b> panel, the MAF is truncated at 5%, and for the <b>right</b> panel at 1%. We assume that the OR of the SNP with the largest MAF is 1.2.</p> Full article ">Figure 5
<p>Density of latent trait before SNP effects. The dotted line indicates the case threshold. The blue area corresponds to controls that become cases if possessing an SNP with OR = 1.6. The red area indicates cases that become controls if possessing an SNP with lOR = 1/1.6.</p> Full article ">
820 KiB  
Review
Changing Histopathological Diagnostics by Genome-Based Tumor Classification
by Michael Kloth and Reinhard Buettner
Genes 2014, 5(2), 444-459; https://doi.org/10.3390/genes5020444 - 28 May 2014
Cited by 13 | Viewed by 8059
Abstract
Traditionally, tumors are classified by histopathological criteria, i.e., based on their specific morphological appearances. Consequently, current therapeutic decisions in oncology are strongly influenced by histology rather than underlying molecular or genomic aberrations. The increase of information on molecular changes however, enabled by [...] Read more.
Traditionally, tumors are classified by histopathological criteria, i.e., based on their specific morphological appearances. Consequently, current therapeutic decisions in oncology are strongly influenced by histology rather than underlying molecular or genomic aberrations. The increase of information on molecular changes however, enabled by the Human Genome Project and the International Cancer Genome Consortium as well as the manifold advances in molecular biology and high-throughput sequencing techniques, inaugurated the integration of genomic information into disease classification. Furthermore, in some cases it became evident that former classifications needed major revision and adaption. Such adaptations are often required by understanding the pathogenesis of a disease from a specific molecular alteration, using this molecular driver for targeted and highly effective therapies. Altogether, reclassifications should lead to higher information content of the underlying diagnoses, reflecting their molecular pathogenesis and resulting in optimized and individual therapeutic decisions. The objective of this article is to summarize some particularly important examples of genome-based classification approaches and associated therapeutic concepts. In addition to reviewing disease specific markers, we focus on potentially therapeutic or predictive markers and the relevance of molecular diagnostics in disease monitoring. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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<p>Timeline of the elucidation of genomic alterations in myeloproliferative neoplasms. Major breakthroughs in the understanding of the Ph+ neoplasm CML are depicted below the line, those in the understanding of Ph- neoplasms above. Note the significant impact of the human genome project on the elucidation on Ph- specific genomic alterations.</p> Full article ">Figure 2
<p>Diagnostic algorithm of classic myeloproliferative neoplasms using specific molecular aberrations. Detection of the molecular aberrations depicted above is highly suggestive for the suspected myeloproliferative disorder. Nevertheless, at least in the case of absence of these specific aberrations, a bone marrow biopsy should be performed.</p> Full article ">Figure 3
<p>Frequencies of significant genomic alterations in histological subgroups of lung cancer. Colors of histological subtypes are encoded as follows: green—squamous cell lung cancer (SQ), purple—carcinoid tumor, light blue—large cell lung cancer (LC), red—adenocarcinoma of the lung (AD), dark blue—small cell lung cancer (SCLC). Data adapted from [<a href="#B33-genes-05-00444" class="html-bibr">33</a>].</p> Full article ">Figure 4
<p>Predictive Biomarkers for Targeted and Selective Therapies. Signaling of EGFR-family receptors is characterized by homo-/heterodimerization and subsequent activation of the targetable downstream signaling pathways RAS/RAF and PI3K/AKT. Present therapeutic approaches focus on the inhibition of ligand-dependent activation, dimerization and receptor tyrosine kinases. Immunoconjugates, e.g., T-DM1, specifically deliver chemotherapeutic agents by the process of receptor internalization. As described in the text in more detail, ongoing efforts investigate the effectiveness of combined or dual approaches.</p> Full article ">
420 KiB  
Review
Pharmacogenomics: Current State-of-the-Art
by Daniel F. Carr, Ana Alfirevic and Munir Pirmohamed
Genes 2014, 5(2), 430-443; https://doi.org/10.3390/genes5020430 - 26 May 2014
Cited by 49 | Viewed by 10118
Abstract
The completion of the human genome project 10 years ago was met with great optimism for improving drug therapy through personalized medicine approaches, with the anticipation that an era of genotype-guided patient prescribing was imminent. To some extent this has come to pass [...] Read more.
The completion of the human genome project 10 years ago was met with great optimism for improving drug therapy through personalized medicine approaches, with the anticipation that an era of genotype-guided patient prescribing was imminent. To some extent this has come to pass and a number of key pharmacogenomics markers of inter-individual drug response, for both safety and efficacy, have been identified and subsequently been adopted in clinical practice as pre-treatment genetic tests. However, the universal application of genetics in treatment guidance is still a long way off. This review will highlight important pharmacogenomic discoveries which have been facilitated by the human genome project and other milestone projects such as the International HapMap and 1000 genomes, and by the continued development of genotyping and sequencing technologies, including rapid point of care pre-treatment genetic testing. However, there are still many challenges to implementation for the many other reported biomarkers which continue to languish within the discovery phase. As technology advances over the next 10 years, and the costs fall, the field will see larger genetic data sets, including affordable whole genome sequences, which will, it is hoped, improve patient outcomes through better diagnostic, prognostic and predictive biomarkers. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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Article
Genome-Wide Analysis of Alpharetroviral Integration in Human Hematopoietic Stem/Progenitor Cells
by Arianna Moiani, Julia Debora Suerth, Francesco Gandolfi, Ermanno Rizzi, Marco Severgnini, Gianluca De Bellis, Axel Schambach and Fulvio Mavilio
Genes 2014, 5(2), 415-429; https://doi.org/10.3390/genes5020415 - 16 May 2014
Cited by 21 | Viewed by 8035
Abstract
Gene transfer vectors derived from gamma-retroviruses or lentiviruses are currently used for the gene therapy of genetic or acquired diseases. Retroviral vectors display a non-random integration pattern in the human genome, targeting either regulatory regions (gamma-retroviruses) or the transcribed portion of expressed genes [...] Read more.
Gene transfer vectors derived from gamma-retroviruses or lentiviruses are currently used for the gene therapy of genetic or acquired diseases. Retroviral vectors display a non-random integration pattern in the human genome, targeting either regulatory regions (gamma-retroviruses) or the transcribed portion of expressed genes (lentiviruses), and have the potential to deregulate gene expression at the transcriptional or post-transcriptional level. A recently developed alternative vector system derives from the avian sarcoma-leukosis alpha-retrovirus (ASLV) and shows favorable safety features compared to both gamma-retroviral and lentiviral vectors in preclinical models. We performed a high-throughput analysis of the integration pattern of self-inactivating (SIN) alpha-retroviral vectors in human CD34+ hematopoietic stem/progenitor cells (HSPCs) and compared it to previously reported gamma-retroviral and lentiviral vectors integration profiles obtained in the same experimental setting. Compared to gamma-retroviral and lentiviral vectors, the SIN-ASLV vector maintains a preference for open chromatin regions, but shows no bias for transcriptional regulatory elements or transcription units, as defined by genomic annotations and epigenetic markers (H3K4me1 and H3K4me3 histone modifications). Importantly, SIN-ASLV integrations do not cluster in hot spots and target potentially dangerous genomic loci, such as the EVI2A/B, RUNX1 and LMO2 proto-oncogenes at a virtually random frequency. These characteristics predict a safer profile for ASLV-derived vectors for clinical applications. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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<p>Genomic distribution of SIN-MLV, SIN-ASLV and SIN-HIV integrations in human HSPCs. The distribution of the distance of SIN-MLV (red bars), SIN-ASLV (yellow bars) and SIN-HIV (blue bars) integration sites from the TSS of targeted genes at 2500-bp (<b>a</b>) or 50-bp (<b>b</b>) resolution. The percentage of genes targeted at each position is plotted on the y-axis. The black line indicates the distribution of random control sites.</p> Full article ">Figure 2
<p>Association of vector integration sites with different epigenetically-defined chromatin states. (<b>a</b>) The percentage of integration sites associated with specific, epigenetically defined genomic regions for each vector type. Chromatin states are categorized on the basis of the combination of different epigenetic marks mapped by ChIP-seq in human HSPCs. Only integration sites that are unambiguously associated with one chromatin state were used for the analysis. (<b>b</b>) The mean densities of H3K4me1, H3K4me3, H3K36me3 and H3K27me3 ChIP-seq fragments in a 5-kb window around all SIN-MLV (red), SIN-ASLV (yellow) and SIN-HIV (light blue) integration sites and random sequences (black). ac: H3K27ac.</p> Full article ">Figure 3
<p>SIN-MLV, SIN-ASLV and SIN-HIV integration sites and clusters in CD34<sup>+</sup> HSPC-specific loci. Distribution of SIN-MLV (red), SIN-ASLV (green) and SIN-HIV (blue) integration clusters (horizontal solid bars) and integrations (vertical marks) in the NF1-EVI2A/B, RUNX1, LMO2 and PACS1 loci, as displayed by the UCSC Genome Browser.</p> Full article ">
1588 KiB  
Review
The Little Fly that Could: Wizardry and Artistry of Drosophila Genomics
by Radoslaw K. Ejsmont and Bassem A. Hassan
Genes 2014, 5(2), 385-414; https://doi.org/10.3390/genes5020385 - 13 May 2014
Cited by 8 | Viewed by 12662
Abstract
For more than 100 years now, the fruit fly Drosophila melanogaster has been at the forefront of our endeavors to unlock the secrets of the genome. From the pioneering studies of chromosomes and heredity by Morgan and his colleagues, to the generation of [...] Read more.
For more than 100 years now, the fruit fly Drosophila melanogaster has been at the forefront of our endeavors to unlock the secrets of the genome. From the pioneering studies of chromosomes and heredity by Morgan and his colleagues, to the generation of fly models for human disease, Drosophila research has been at the forefront of genetics and genomics. We present a broad overview of some of the most powerful genomics tools that keep Drosophila research at the cutting edge of modern biomedical research. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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<p>Mosaic analysis with a repressible cell marker (MARCM). (<b>A</b>) Gal4 transcription factor (blue oval) drives expression of green fluorescent protein (GFP) gene (green box) by binding the upstream activation sequence (UAS) (white box). This expression is repressed when Gal80 (red oval) is present. As a consequence cells that do not carry a gene encoding Gal80 but carry genes encoding Gal4 and UAS<span class="html-italic">-</span>GFP are marked green. (<b>B</b>) In MARCM, the <span class="html-italic">GAL80</span> repressor gene (red box) is carried on a chromosome that bears the wild-type allele of a gene (yellow box) of interest and a flippase recognition target (FRT) site (grey triangle) placed pericentricaly. The homologous chromosome carries a FRT site in exactly the same position and a mutant allele (orange box), but does not carry the <span class="html-italic">GAL80</span> gene. Cells also carry the <span class="html-italic">GAL4</span> gene and UAS-GFP on the other chromosomes. During G2 phase (after DNA replication), flippase mediates recombination between two FRT sites of homologous chromosomes, thus generating sister chromatids; one of which carries the wild-type allele and <span class="html-italic">GAL80</span> repressor and the other the mutant allele. During mitosis, sister chromatids are distributed to daughter cells, generating cells that are homozygous wild-type or homozygous mutant. Cells that are homozygous mutant are the only cells lacking the <span class="html-italic">GAL80</span> gene and thus are labeled with GFP. Reproduced with permission from MacMillan: Nature Protocols ©2007 [<a href="#B181-genes-05-00385" class="html-bibr">181</a>].</p> Full article ">Figure 2
<p>FlyFos and p[ACMAN] genomic libraries. (<b>A</b>) FlyFos library is cloned in a fosmid vector, pFlyFos. Genomic inserts were cloned into the <span class="html-italic">Pml</span>I site. pFlyFos features an inducible origin of replication (oriS for single copy and oriV for arabinose-inducible moderate copy maintenance), attB site for fly transgenesis, and 3xP3-dsRed as a fly-selectable marker. (<b>B</b>) p[ACMAN] libraries are cloned into the <span class="html-italic">Bam</span>HI site of a p[ACMAN] bacterial artificial chromosome (BAC) vector. This vector also features inducible oriS/oriV and attB site, but uses white as fly selectable marker. In addition to phiC31-mediated transgenesis, p[ACMAN] vector carrying small inserts can theoretically be used for P-element transformation. (<b>C</b>) Size distribution of FlyFos and p[ACMAN] <span class="html-italic">D. melanogaster</span> libraries. There are two p[ACMAN] libraries: CHORI-321 with average clone size of 83.3 kb and CHORI-322 with average clone size of 21 kb. The FlyFos library has an average clone size of 36 kb.</p> Full article ">Figure 3
<p>Recombineering principles. (<b>A</b>) The target sequence (orange box) is carried on a single copy fosmid or bacterial artificial chromosome (BAC) vector. The 50 bp fragments flanking the target sequence, called homology arms (HA), are depicted as yellow boxes. In this example, the target sequence will be replaced with the recombineering cassette. However, when homology arms are designed to directly follow each other, the cassette can be simply inserted into the target vector. (<b>B</b>) The PCR-amplified recombineering cassette harboring homology arms (introduced as primer overhangs) on its termini is electroporated into bacteria carrying the target vector. In the depicted example, the cassette contains a reporter (green arrow) and a flippase recognition target (FRT)-flanked (grey triangles) bacterial selectable marker (red arrow). Homologous recombinase, transiently expressed in bacteria mediates recombination between homology arms replacing the target sequence with the recombineering cassette. (<b>C</b>) Recombinant bacterial cells are selected using the selectable marker encoded in the recombineering cassette. If the selectable marker is flanked by FRT sites, it can now be removed (flipped-out) through transient expression of flippase. (<b>D</b>) The final recombineering product contains the desired sequence and a 34 bp FRT scar flanked by the homology arms.</p> Full article ">Figure 4
<p>Integrase-mediated approach for gene knock-out (IMAGO). (<b>A</b>) A targeting construct harboring <span class="html-italic">white</span> gene flanked by attP sites, 1 kb–5 kb homology arms, I-<span class="html-italic">Sce</span>I meganuclease site, and flippase recognition target (FRT) sites is inserted into the fly genome using transposition or site-specific integration. (<b>B</b>) The targeting cassette is mobilized into the circular episome by flippase and subsequently linearized by meganuclease. This linear fragment induces cellular double strand break repair mechanisms and (with certain frequency) replaces the genomic locus flanked by homology arms. (<b>C</b>) Recombinant progeny are selected for <span class="html-italic">white</span> dominant marker. The attP sites flanking the <span class="html-italic">white</span> gene can be used for recombinase-mediated cassette exchange. (<b>D</b>, <b>F</b>) A plasmid containing the attB-flanked construct (cKO or a mutant allele) is injected into phiC31-expressing fly embryos and exchanges the attP-flanked <span class="html-italic">white</span> gene. (<b>E</b>, <b>G</b>) Recombinant progeny carrying modified alleles are selected for loss of the <span class="html-italic">white</span> dominant marker.</p> Full article ">Figure 5
<p>Designer nucleases. (<b>A</b>) Zink finger nucleases (ZFNs) combine a zinc finger DNA binding domain with a FokI nickase. Each zinc finger recognizes a triplet of bases and usually three to six zinc fingers are present in the targeting domain. Cleavage occurs outside the target sequence and requires a pair of ZFNs, each binding one DNA strand. (<b>B</b>) Transcription activator-like effector nucleases (TALEN), similarly to ZFNs have two domains: a DNA binding domain and a FokI nickase domain. The targeting domain is composed of 33–35 amino acid repeats, each binding a single nucleotide. The cleavage mechanism of TALENs is identical to ZFNs. (<b>C</b>) Clustered regularly interspaced short palindromic repeats (CRISPR) is a RNA driven double-stranded DNA endonuclease system. Cleavage specificity is provided by crRNA (cyan) that hybridizes with the target sequence (green). Cleavage is performed by the Cas9 protein that, in addition to crRNA, requires tracrRNA for activity. The cleavage site (star) is located between the target sequence and NGG protospacer adjacent motif, complimentary to the sequence immediately downstream of the target. crRNA and tracrRNA can be fused to form guide RNA of similar activity.</p> Full article ">
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Article
Characterization of the Genomic Architecture and Mutational Spectrum of a Small Cell Prostate Carcinoma
by Alan F. Scott, David W. Mohr, Hua Ling, Robert B. Scharpf, Peng Zhang and Gregory S. Liptak
Genes 2014, 5(2), 366-384; https://doi.org/10.3390/genes5020366 - 12 May 2014
Cited by 7 | Viewed by 9037
Abstract
We present the use of a series of laboratory, analytical and interpretation methods to investigate personalized cancer care for a case of small cell prostate carcinoma (SCPC), a rare and aggressive tumor with poor prognosis, for which the underlying genomic architecture and mutational [...] Read more.
We present the use of a series of laboratory, analytical and interpretation methods to investigate personalized cancer care for a case of small cell prostate carcinoma (SCPC), a rare and aggressive tumor with poor prognosis, for which the underlying genomic architecture and mutational spectrum has not been well characterized. We performed both SNP genotyping and exome sequencing of a Virchow node metastasis from a patient with SCPC. A variety of methods were used to analyze and interpret the tumor genome for copy number variation, loss of heterozygosity (LOH), somatic mosaicism and mutations in genes from known cancer pathways. The combination of genotyping and exome sequencing approaches provided more information than either technique alone. The results showed widespread evidence of copy number changes involving most chromosomes including the possible loss of both alleles of CDKN1B (p27/Kip1). LOH was observed for the regions encompassing the tumor suppressors TP53, RB1, and CHD1. Predicted damaging somatic mutations were observed in the retained TP53 and RB1 alleles. Mutations in other genes that may be functionally relevant were noted, especially the recently reported high confidence cancer drivers FOXA1 and CCAR1. The disruption of multiple cancer drivers underscores why SCPC may be such a difficult cancer to manage. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Allele frequencies (<b>A</b>) and log R ratios (<b>B</b>) estimated by GenomeStudio. Autosomal log R ratios were segmented by circular binary segmentation [<a href="#B11-genes-05-00366" class="html-bibr">11</a>] as indicated in black; (<b>C</b>) Log R values from whole exome sequencing were obtained by the EXCAVATOR program [<a href="#B12-genes-05-00366" class="html-bibr">12</a>] and aligned to panels A and B, providing a qualitatively similar profile of the copy number alterations. Black lines depict the segmentation of the log R values.</p> Full article ">Figure 2
<p>The location of the key genes described in the text. CHD1 (Chr 5: 98.190–98.265 Mb) was not mutated but occurs in a block of LOH; CDKN1B (Chr 12: 12.870–12.875 Mb) has low copy number, RB1 (Chr 13: 48.878 Mb–49.056 Mb) occurs in a block of LOH, has reduced copy number and the retained allele is predicted to be damaging; TP53 (Chr 17: 75.712 Mb–75.909 Mb) occurs in a copy neutral block of LOH and the retained allele is predicted to be damaged. <a href="#genes-05-00366-f002" class="html-fig">Figure 2</a>b, magnified view of the CDKN1B region showing a likely deletion of the gene supported by both the array (top and middle panels) and exome-sequencing platforms (bottom).</p> Full article ">
307 KiB  
Article
Epigenetic Variation in Monozygotic Twins: A Genome-Wide Analysis of DNA Methylation in Buccal Cells
by Jenny Van Dongen, Erik A. Ehli, Roderick C. Slieker, Meike Bartels, Zachary M. Weber, Gareth E. Davies, P. Eline Slagboom, Bastiaan T. Heijmans and Dorret I. Boomsma
Genes 2014, 5(2), 347-365; https://doi.org/10.3390/genes5020347 - 5 May 2014
Cited by 49 | Viewed by 15079
Abstract
DNA methylation is one of the most extensively studied epigenetic marks in humans. Yet, it is largely unknown what causes variation in DNA methylation between individuals. The comparison of DNA methylation profiles of monozygotic (MZ) twins offers a unique experimental design to examine [...] Read more.
DNA methylation is one of the most extensively studied epigenetic marks in humans. Yet, it is largely unknown what causes variation in DNA methylation between individuals. The comparison of DNA methylation profiles of monozygotic (MZ) twins offers a unique experimental design to examine the extent to which such variation is related to individual-specific environmental influences and stochastic events or to familial factors (DNA sequence and shared environment). We measured genome-wide DNA methylation in buccal samples from ten MZ pairs (age 8–19) using the Illumina 450k array and examined twin correlations for methylation level at 420,921 CpGs after QC. After selecting CpGs showing the most variation in the methylation level between subjects, the mean genome-wide correlation (rho) was 0.54. The correlation was higher, on average, for CpGs within CpG islands (CGIs), compared to CGI shores, shelves and non-CGI regions, particularly at hypomethylated CpGs. This finding suggests that individual-specific environmental and stochastic influences account for more variation in DNA methylation in CpG-poor regions. Our findings also indicate that it is worthwhile to examine heritable and shared environmental influences on buccal DNA methylation in larger studies that also include dizygotic twins. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Illustration of a CpG island (CGI) with surrounding CGI shores, CGI-shelves and non-CGI regions.</p> Full article ">Figure 2
<p>Density of β-values after normalization for all twin samples.</p> Full article ">Figure 3
<p>Average methylation level of individual CpGs across gene regions (<b>a</b>), CpG islands (CGI) and non-CGI regions (<b>b</b>) and for each genomic feature separately (<b>c</b>).</p> Full article ">Figure 4
<p>Cluster dendrogram of all twin and control samples. From left to right, the first two branches separate the control samples (HapMap cell line DNA) from the buccal samples from twins.</p> Full article ">Figure 5
<p>Smooth scatterplot of DNA methylation levels (β-values) at 420,921 CpGs in buccal cells from a monozygotic twin pair.</p> Full article ">Figure 6
<p>MZ twin correlations for individual CpGs grouped by genomic region and average methylation level. Hypo = Hypomethylated. Inter = intermediate methylation. Hyper = Hypermethylated. Results are based on the most variable CpGs (N = 59,041).</p> Full article ">
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Article
Polygenic Scores Predict Alcohol Problems in an Independent Sample and Show Moderation by the Environment
by Jessica E. Salvatore, Fazil Aliev, Alexis C. Edwards, David M. Evans, John Macleod, Matthew Hickman, Glyn Lewis, Kenneth S. Kendler, Anu Loukola, Tellervo Korhonen, Antti Latvala, Richard J. Rose, Jaakko Kaprio and Danielle M. Dick
Genes 2014, 5(2), 330-346; https://doi.org/10.3390/genes5020330 - 10 Apr 2014
Cited by 67 | Viewed by 11731
Abstract
Alcohol problems represent a classic example of a complex behavioral outcome that is likely influenced by many genes of small effect. A polygenic approach, which examines aggregate measured genetic effects, can have predictive power in cases where individual genes or genetic variants do [...] Read more.
Alcohol problems represent a classic example of a complex behavioral outcome that is likely influenced by many genes of small effect. A polygenic approach, which examines aggregate measured genetic effects, can have predictive power in cases where individual genes or genetic variants do not. In the current study, we first tested whether polygenic risk for alcohol problems—derived from genome-wide association estimates of an alcohol problems factor score from the age 18 assessment of the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 4304 individuals of European descent; 57% female)—predicted alcohol problems earlier in development (age 14) in an independent sample (FinnTwin12; n = 1162; 53% female). We then tested whether environmental factors (parental knowledge and peer deviance) moderated polygenic risk to predict alcohol problems in the FinnTwin12 sample. We found evidence for both polygenic association and for additive polygene-environment interaction. Higher polygenic scores predicted a greater number of alcohol problems (range of Pearson partial correlations 0.07–0.08, all p-values ≤ 0.01). Moreover, genetic influences were significantly more pronounced under conditions of low parental knowledge or high peer deviance (unstandardized regression coefficients (b), p-values (p), and percent of variance (R2) accounted for by interaction terms: b = 1.54, p = 0.02, R2 = 0.33%; b = 0.94, p = 0.04, R2 = 0.30%, respectively). Supplementary set-based analyses indicated that the individual top single nucleotide polymorphisms (SNPs) contributing to the polygenic scores were not individually enriched for gene-environment interaction. Although the magnitude of the observed effects are small, this study illustrates the usefulness of polygenic approaches for understanding the pathways by which measured genetic predispositions come together with environmental factors to predict complex behavioral outcomes. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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<p>Pearson partial correlations (controlling for sex) between polygenic scores and age 14 alcohol problems (all <span class="html-italic">p-</span>values ≤ 0.01) in FinnTwin12 (n = 1161).</p> Full article ">Figure 2
<p>Parental knowledge (top) and peer deviance (bottom) moderate polygenic risk to predict age 14 alcohol problems in FinnTwin12. Interactions are plotted as predicted values based on the moderated multiple regression equation for age 14 alcohol problems. Illustrative low and high values (±1 SD of mean) for the polygenic scores, parental knowledge, and peer deviance are shown. The predicted values for high parental knowledge and low peer deviance were out of bounds (negative values) and were set to zero—the lowest possible value for the alcohol problems measure. Error bars are equal to the standard deviation of the model residuals divided by the square root of the sample size. We note that high scores on the parental knowledge scale indicate low parental knowledge (<span class="html-italic">i.e.</span>, more risk). For ease of interpretation, we have formatted the axis for each figure so that the riskier environment appears on the right.</p> Full article ">
376 KiB  
Review
The Epigenome View: An Effort towards Non-Invasive Prenatal Diagnosis
by Elisavet A. Papageorgiou, George Koumbaris, Elena Kypri, Michael Hadjidaniel and Philippos C. Patsalis
Genes 2014, 5(2), 310-329; https://doi.org/10.3390/genes5020310 - 9 Apr 2014
Cited by 16 | Viewed by 8209
Abstract
Epigenetic modifications have proven to play a significant role in cancer development, as well as fetal development. Taking advantage of the knowledge acquired during the last decade, great interest has been shown worldwide in deciphering the fetal epigenome towards the development of methylation-based [...] Read more.
Epigenetic modifications have proven to play a significant role in cancer development, as well as fetal development. Taking advantage of the knowledge acquired during the last decade, great interest has been shown worldwide in deciphering the fetal epigenome towards the development of methylation-based non-invasive prenatal tests (NIPT). In this review, we highlight the different approaches implemented, such as sodium bisulfite conversion, restriction enzyme digestion and methylated DNA immunoprecipitation, for the identification of differentially methylated regions (DMRs) between free fetal DNA found in maternal blood and DNA from maternal blood cells. Furthermore, we evaluate the use of selected DMRs identified towards the development of NIPT for fetal chromosomal aneuploidies. In addition, we perform a comparison analysis, evaluate the performance of each assay and provide a comprehensive discussion on the potential use of different methylation-based technologies in retrieving the fetal methylome, with the aim of further expanding the development of NIPT assays. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
198 KiB  
Review
Reading and Language Disorders: The Importance of Both Quantity and Quality
by Dianne F. Newbury, Anthony P. Monaco and Silvia Paracchini
Genes 2014, 5(2), 285-309; https://doi.org/10.3390/genes5020285 - 4 Apr 2014
Cited by 29 | Viewed by 11909
Abstract
Reading and language disorders are common childhood conditions that often co-occur with each other and with other neurodevelopmental impairments. There is strong evidence that disorders, such as dyslexia and Specific Language Impairment (SLI), have a genetic basis, but we expect the contributing genetic [...] Read more.
Reading and language disorders are common childhood conditions that often co-occur with each other and with other neurodevelopmental impairments. There is strong evidence that disorders, such as dyslexia and Specific Language Impairment (SLI), have a genetic basis, but we expect the contributing genetic factors to be complex in nature. To date, only a few genes have been implicated in these traits. Their functional characterization has provided novel insight into the biology of neurodevelopmental disorders. However, the lack of biological markers and clear diagnostic criteria have prevented the collection of the large sample sizes required for well-powered genome-wide screens. One of the main challenges of the field will be to combine careful clinical assessment with high throughput genetic technologies within multidisciplinary collaborations. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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<p>Study design for quantitative phenotypes (<b>a</b>) genome-wide association (GWA) studies for speech and language-related traits typically use phenotypes across the entire distribution (population-based quantitative GWA studies). Others might apply a binary affection status under which low language-performing individuals are defined as “cases” and individuals within the “normal” language range (usually performance above the mean) as “controls”. Under certain conditions, “super-controls” can provide more power, as they are selected to fall at the upper extreme of the distribution. If controls with phenotype data are not available, they may be derived from standard control populations under the knowledge that they might include a small proportion of cases. Quantitative GWA studies restricted to cases may be based on a phenotypic distribution restricted to the lower tail of the entire distribution or may be based on a phenotypic curve derived across cases samples, as denoted by the two normal distributions in (<b>a</b>) (note that in (<b>a</b>), the phenotype distribution may not necessarily be expected to be normal, although it is shown as such in the figure). (<b>b</b>) The pegboard test generates a quantitative measure for handedness (PegQ) that is normally distributed around a positive mean. PegQ strongly correlates with hand preference, so that individuals with positive scores are very likely to be right-handed (roughly 90% of the population), and individuals with negative scores are likely to be left handed. Typically genetic studies for handedness have used the categorical measures of hand-preference using a case-control (left <span class="html-italic">vs.</span> right) study design.</p> Full article ">
157 KiB  
Review
Architecture of Inherited Susceptibility to Colorectal Cancer: A Voyage of Discovery
by Nicola Whiffin and Richard S. Houlston
Genes 2014, 5(2), 270-284; https://doi.org/10.3390/genes5020270 - 27 Mar 2014
Cited by 9 | Viewed by 7777
Abstract
This review looks back at five decades of research into genetic susceptibility to colorectal cancer (CRC) and the insights these studies have provided. Initial evidence of a genetic basis of CRC stems from epidemiological studies in the 1950s and is further provided by [...] Read more.
This review looks back at five decades of research into genetic susceptibility to colorectal cancer (CRC) and the insights these studies have provided. Initial evidence of a genetic basis of CRC stems from epidemiological studies in the 1950s and is further provided by the existence of multiple dominant predisposition syndromes. Genetic linkage and positional cloning studies identified the first high-penetrance genes for CRC in the 1980s and 1990s. More recent genome-wide association studies have identified common low-penetrance susceptibility loci and provide support for a polygenic model of disease susceptibility. These observations suggest a high proportion of CRC may arise in a group of susceptible individuals as a consequence of the combined effects of common low-penetrance risk alleles and rare variants conferring moderate CRC risks. Despite these advances, however, currently identified loci explain only a small fraction of the estimated heritability to CRC. It is hoped that a new generation of sequencing projects will help explain this missing heritability. Full article
(This article belongs to the Special Issue Grand Celebration: 10th Anniversary of the Human Genome Project)
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<p>Polygenic model of disease susceptibility. The distribution of risk alleles in both cases and controls follows a normal distribution. However, cases have a shift towards a higher number of high risk alleles.</p> Full article ">Figure 2
<p>Plot showing the increase in odds ratio for colorectal cancer with an increasing number of risk alleles.</p> Full article ">
637 KiB  
Article
Single-Nucleotide Variations in Cardiac Arrhythmias: Prospects for Genomics and Proteomics Based Biomarker Discovery and Diagnostics
by Ayman Abunimer, Krista Smith, Tsung-Jung Wu, Phuc Lam, Vahan Simonyan and Raja Mazumder
Genes 2014, 5(2), 254-269; https://doi.org/10.3390/genes5020254 - 27 Mar 2014
Cited by 14 | Viewed by 7400
Abstract
Cardiovascular diseases are a large contributor to causes of early death in developed countries. Some of these conditions, such as sudden cardiac death and atrial fibrillation, stem from arrhythmias—a spectrum of conditions with abnormal electrical activity in the heart. Genome-wide association studies can [...] Read more.
Cardiovascular diseases are a large contributor to causes of early death in developed countries. Some of these conditions, such as sudden cardiac death and atrial fibrillation, stem from arrhythmias—a spectrum of conditions with abnormal electrical activity in the heart. Genome-wide association studies can identify single nucleotide variations (SNVs) that may predispose individuals to developing acquired forms of arrhythmias. Through manual curation of published genome-wide association studies, we have collected a comprehensive list of 75 SNVs associated with cardiac arrhythmias. Ten of the SNVs result in amino acid changes and can be used in proteomic-based detection methods. In an effort to identify additional non-synonymous mutations that affect the proteome, we analyzed the post-translational modification S-nitrosylation, which is known to affect cardiac arrhythmias. We identified loss of seven known S-nitrosylation sites due to non-synonymous single nucleotide variations (nsSNVs). For predicted nitrosylation sites we found 1429 proteins where the sites are modified due to nsSNV. Analysis of the predicted S-nitrosylation dataset for over- or under-representation (compared to the complete human proteome) of pathways and functional elements shows significant statistical over-representation of the blood coagulation pathway. Gene Ontology (GO) analysis displays statistically over-represented terms related to muscle contraction, receptor activity, motor activity, cystoskeleton components, and microtubule activity. Through the genomic and proteomic context of SNVs and S-nitrosylation sites presented in this study, researchers can look for variation that can predispose individuals to cardiac arrhythmias. Such attempts to elucidate mechanisms of arrhythmia thereby add yet another useful parameter in predicting susceptibility for cardiac diseases. Full article
(This article belongs to the Section Human Genomics and Genetic Diseases)
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<p>Percentage of predicted S-nitrosylated proteins and sites and the ones which are conserved across mouse, fly, plant or yeast and the nsSNVs mapped to these proteins and sites. Details are available in <a href="#genes-05-00254-s001" class="html-supplementary-material">Table S2</a>.</p> Full article ">Figure 2
<p>HIVE interface showing results obtained from SNV profiling of human exome reads mapped to FASTA sequence surrounding a SNV. (<b>A</b>) Overall coverage result with the 603 position showing variation. (<b>B</b>) Reads mapped to the reference with the yellow highlighting the column selected. (<b>C</b>) Only variations are shown in this panel.</p> Full article ">
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