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Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

Sequencing ancient DNA can offer direct probing of population history. Yet, such data are commonly analyzed with standard tools that assume DNA samples are all contemporary. We present DyStruct, a model and inference algorithm for inferring shared ancestry from temporally sampled genotype data. DyStruct explicitly incorporates temporal dynamics by modeling individuals as mixtures of unobserved populations whose allele frequencies drift over time. We develop an efficient inference algorithm for our model using stochastic variational inference. On simulated data, we show that DyStruct outperforms the current state of the art when individuals are sampled over time. Using a dataset of 296 modern and 80 ancient samples, we demonstrate DyStruct is able to capture a well-supported admixture event of steppe ancestry into modern Europe. We further apply DyStruct to a genome-wide dataset of 2,067 modern and 262 ancient samples used to study the origin of farming in the Near East. We show that DyStruct provides new insight into population history when compared with alternate approaches, within feasible run time.

Motivation The human leukocyte antigen (HLA) locus plays a critical role in tissue compatibility and regulates the host response to many diseases, including cancers and autoimmune di3orders. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional modifications and bias due to amplification. Results Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA-sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two-field resolution for Class I genes, and over 99.7% for Class II. Furthermore, we evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. Availability and implementation arcasHLA is available at https://github.com/RabadanLab/arcasHLA. Supplementary information Supplementary data are available at Bioinformatics online.

Topographic factor models separate overlapping signals into latent spatial functions to identify correlation structure across observations. These methods require the underlying structure to be held fixed and are not robust to deviations commonly found across images. We present autoencoding topographic factors, a novel variational inference scheme, to decompose irregular observations on a lattice into a superposition of low-rank sources. By exploiting recent developments in variational autoencoders, we replace fixed sources with a nonlinear mapping that parameterizes an unnormalized distribution on the lattice. In doing so, we permit sources to drift dynamically, filtering residual differences in location across comparable areas of interest. This gives an implicit mapping to a unique latent representation while simultaneously forcing the posterior to model group variability in spatial structure. Simulation results and applications to functional imaging demonstrate the effectiveness of our method and its ability to outperform existing spatial factor models.

Despite impressive improvement in the next-generation sequencing technology, reliable detection of indels is still a difficult endeavour. Recognition of true indels is of prime importance in many applications, such as personalized health care, disease genomics and population genetics. Recently, advanced machine learning techniques have been successfully applied to classification problems with large-scale data. In this paper, we present SICaRiO, a gradient boosting classifier for the reliable detection of true indels, trained with the gold-standard dataset from ‘Genome in a Bottle’ (GIAB) consortium. Our filtering scheme significantly improves the performance of each variant calling pipeline used in GIAB and beyond. SICaRiO uses genomic features that can be computed from publicly available resources, i.e. it does not require sequencing pipeline-specific information (e.g. read depth). This study also sheds lights on prior genomic contexts responsible for the erroneous calling of indels made by sequencing pipelines. We have compared prediction difficulty for three categories of indels over different sequencing pipelines. We have also ranked genomic features according to their predictivity in determining false positives.

Evidence from both GWAS and clinical observation has suggested that certain psychiatric, metabolic, and autoimmune diseases are heterogeneous, comprising multiple subtypes with distinct genomic etiologies and Polygenic Risk Scores (PRS). However, the presence of subtypes within many phenotypes is frequently unknown. We present CLiP (Correlated Liability Predictors), a method to detect heterogeneity in single GWAS cohorts. CLiP calculates a weighted sum of correlations between SNPs contributing to a PRS on the case/control liability scale. We demonstrate mathematically and through simulation that among i.i.d. homogeneous cases generated by a liability threshold model, significant anti-correlations are expected between otherwise independent predictors due to ascertainment on the hidden liability score. In the presence of heterogeneity from distinct etiologies, confounding by covariates, or mislabeling, these correlation patterns are altered predictably. We further extend our method to two additional association study designs: CLiP-X for quantitative predictors in applications such as transcriptome-wide association, and CLiP-Y for quantitative phenotypes, where there is no clear distinction between cases and controls. Through simulations, we demonstrate that CLiP and its extensions reliably distinguish between homogeneous and heterogeneous cohorts when the PRS explains as low as 3% of variance on the liability scale and cohorts comprise 50, 000 - 100, 000 samples, an increasingly practical size for modern GWAS. We apply CLiP to heterogeneity detection in schizophrenia cohorts totaling > 50, 000 cases and controls collected by the Psychiatric Genomics Consortium. We observe significant heterogeneity in mega-analysis of the combined PGC data (p-value 8.54 × 0-4), as well as in individual cohorts meta-analyzed using Fisher's method (p-value 0.03), based on significantly associated variants. We also apply CLiP-Y to detect heterogeneity in neuroticism in over 10, 000 individuals from the UK Biobank and detect heterogeneity with a p-value of 1.68 × 10-9. Scores were not significantly reduced when partitioning by known subclusters ("Depression" and "Worry"), suggesting that these factors are not the primary source of observed heterogeneity.

IMPORTANCE Schizophrenia is a serious mental illness with high heritability. While common genetic variants account for a portion of the heritability, identification of rare variants associated with the disorder has proven challenging. OBJECTIVE To identify genes and gene sets associated with schizophrenia in a founder population (Ashkenazi Jewish), and to determine the relative power of this population for rare variant discovery. DESIGN, SETTING, AND PARTICIPANTS Data on exonic variants were extracted from whole genome sequences drawn from 786 patients with schizophrenia and 463 healthy control subjects, all drawn from the Ashkenazi Jewish population. Variants observed in two large publicly available datasets (total n≈153,000, excluding neuropsychiatric patients) were filtered out, and novel ultra-rare variants (URVs) were compared in cases and controls. MAIN OUTCOMES AND MEASURES The number of novel URVs and genes carrying them were compared across cases and controls. Genes in which only cases or only controls carried novel, functional URVs were examined using gene set analyses. RESULTS Cases had a higher frequency of novel missense or loss of function (MisLoF) variants compared to controls, as well as a greater number of genes impacted by MisLoF variants. Characterizing 141 “case-only” genes (in which ≥ 3 AJ cases in our dataset had MisLoF URVs with none found in our AJ controls), we replicated prior findings of both enrichment for synaptic gene sets, as well as specific genes such as SETD1A and TRIO. Additionally, we identified cadherins as a novel gene set associated with schizophrenia including a recurrent mutation in PCDHA3. Several genes associated with autism and other neurodevelopmental disorders including CACNA1E, ASXL3, SETBP1, and WDFY3, were also identified in our case-only gene list, as was TSC2, which is linked to tuberous sclerosis. Modeling the effects of purifying selection demonstrated that deleterious rare variants are greatly over-represented in a founder population with a tight bottleneck and rapidly expanding census, resulting in enhanced power for rare variant association studies. CONCLUSIONS AND RELEVANCE Identification of cell adhesion genes in the cadherin/protocadherin family is consistent with evidence from large-scale GWAS in schizophrenia, helps specify the synaptic abnormalities that may be central to the disorder, and suggests novel potential treatment strategies (e.g., inhibition of protein kinase C). Study of founder populations may serve as a cost-effective way to rapidly increase gene discovery in schizophrenia and other complex disorders.

Dynamic changes in microbial communities play an important role in human health and disease. Specifically, deciphering how microbial species in a community interact with each other and their environment can elucidate mechanisms of disease, a problem typically investigated using tools from community ecology. Yet, such methods require measurements of absolute densities, whereas typical datasets only provide estimates of relative abundances. Here, we systematically investigate models of microbial dynamics in the simplex of relative abundances. We derive a new nonlinear dynamical system for microbial dynamics, termed “compositional” Lotka-Volterra (cLV), unifying approaches using generalized Lotka-Volterra (gLV) equations from community ecology and compositional data analysis. On three real datasets, we demonstrate that cLV recapitulates interactions between relative abundances implied by gLV. Moreover, we show that cLV is as accurate as gLV in forecasting microbial trajectories in terms of relative abundances. We further compare cLV to two other models of relative abundance dynamics motivated by common assumptions in the literature—a linear model in a log-ratio transformed space, and a linear model in the space of relative abundances—and provide evidence that cLV more accurately describes community trajectories over time. Finally, we investigate when information about direct effects can be recovered from relative data that naively provide information about only indirect effects. Our results suggest that strong effects may be recoverable from relative data, but more subtle effects are challenging to identify., Author summary Dynamic changes in microbial communities play an important role in human health and disease. Specifically, deciphering how microbial species in a community interact with each other and their environment can elucidate mechanisms of disease, a problem typically investigated using tools from community ecology. Yet, such methods require measurements of absolute densities, whereas typical only provide estimates of relative abundances. We investigate methods for describing microbial dynamics in terms of relative abundances using approaches from machine learning and dynamical systems. Across three real datasets, we show that relative abundances are sufficient to describe compositional dynamics. Additionally, we show that models trained on relative abundances alone predict future compositions as well models trained on absolute abundances. Finally, we provide criteria for when direct effects, which typically can only be learned from absolute abundances, are recoverable for relative data. As a proof of concept, we recapitulate a previously proposed interaction network for C. difficile colonization.

Summary The recently completed second phase of the Human Microbiome Project has highlighted the relationship between dynamic changes in the microbiome and disease, motivating new microbiome study designs based on longitudinal sampling. Yet, analysis of such data is hindered by presence of technical noise, high dimensionality, and data sparsity. Here, we introduce LUMINATE (longitudinal microbiome inference and zero detection), a fast and accurate method for inferring relative abundances from noisy read count data. We demonstrate that LUMINATE is orders of magnitude faster than current approaches, with better or similar accuracy. We further show that LUMINATE can accurately distinguish biological zeros, when a taxon is absent from the community, from technical zeros, when a taxon is below the detection threshold. We conclude by demonstrating the utility of LUMINATE on a real dataset, showing that LUMINATE smooths trajectories observed from noisy data. LUMINATE is freely available from https://github.com/tyjo/luminate .

The human body is home to trillions of microbial cells that play an essential role in health and disease [2]. The gut microbiome, for instance, is responsible for a variety of normal physiological processes such as the regulation of immune response and breakdown of xenobiotics [3]. Disturbances in gut communities have been associated with several diseases, notably obesity [7] and colitis [8]. Moreover, changes to the vaginal microbiome during pregnancy are associated with risk of preterm birth [4]. Consequently, investigating the human microbiome can provide insight into biological processes and the etiology of disease.

Evidence from both GWAS and clinical observation has suggested that certain psychiatric, metabolic, and autoimmune diseases are heterogeneous, comprising multiple subtypes with distinct genomic etiologies and Polygenic Risk Scores (PRS). However, the presence of subtypes within many phenotypes is frequently unknown. We present CLiP (Correlated Liability Predictors), a method to detect heterogeneity in single GWAS cohorts. CLiP calculates a weighted sum of correlations between SNPs contributing to a PRS on the case/control liability scale. We demonstrate mathematically and through simulation that among i.i.d. homogeneous cases, significant anti-correlations are expected between otherwise independent predictors due to ascertainment on the hidden liability score. In the presence of heterogeneity from distinct etiologies, confounding by covariates, or mislabeling, these correlation patterns are altered predictably. We further extend our method to two additional association study designs: CLiP-X for quantitative predictors in applications such as transcriptome-wide association, and CLiP-Y for quantitative phenotypes, where there is no clear distinction between cases and controls. Through simulations, we demonstrate that CLiP and its extensions reliably distinguish between homogeneous and heterogeneous cohorts when the PRS explains as low as 5% of variance on the liability scale and cohorts comprise 50, 000 − 100, 000 samples, an increasingly practical size for modern GWAS. We apply CLiP to heterogeneity detection in schizophrenia cohorts totaling > 50, 000 cases and controls collected by the Psychiatric Genomics Consortium. We observe significant heterogeneity in mega-analysis of the combined PGC data (p-value 8.54e-4), as well as in individual cohorts meta-analyzed using Fisher’s method (p-value 0.03), based on significantly associated variants.

Human leukocyte antigen (HLA) locus makes up the major compatibility complex (MHC) and plays a critical role in host response to disease, including cancers and autoimmune disorders. In the clinical setting, HLA typing is necessary for determining tissue compatibility. Recent improvements in the quality and accessibility of next-generation sequencing have made HLA typing from standard short-read data practical. However, this task remains challenging given the high level of polymorphism and homology between the HLA genes. HLA typing from RNA sequencing is further complicated by post-transcriptional splicing and bias due to amplification. Here, we present arcasHLA: a fast and accurate in silico tool that infers HLA genotypes from RNA sequencing data. Our tool outperforms established tools on the gold-standard benchmark dataset for HLA typing in terms of both accuracy and speed, with an accuracy rate of 100% at two field precision for MHC class I genes, and over 99.7% for MHC class II. Importantly, arcasHLA takes as its input pre-aligned BAM files, and outputs three-field resolution for all HLA genes in less than 2 minutes. Finally, we discuss evaluate the performance of our tool on a new biological dataset of 447 single-end total RNA samples from nasopharyngeal swabs, and establish the applicability of arcasHLA in metatranscriptome studies. arcasHLA is available at https://github.com/RabadanLab/arcasHLA.

While increasingly large reference panels for genome-wide imputation have been recently made available, the degree to which imputation accuracy can be enhanced by population-specific reference panels remains an open question. Here, we sequenced at full-depth (≥ 30×), across two platforms (Illumina X Ten and Complete Genomics, Inc.), a moderately large (n = 738) cohort of samples drawn from the Ashkenazi Jewish population. We developed a series of quality control steps to optimize sensitivity, specificity, and comprehensiveness of variant calls in the reference panel, and then tested the accuracy of imputation against target cohorts drawn from the same population. Quality control (QC) thresholds for the Illumina X Ten platform were identified that permitted highly accurate calling of single nucleotide variants across 94% of the genome. QC procedures also identified numerous regions that are poorly mapped using current reference or alternate assemblies. After stringent QC, the population-specific reference panel produced more accurate and comprehensive imputation results relative to publicly available, large cosmopolitan reference panels, especially in the range of rare variants that may be most critical to further progress in mapping of complex phenotypes. The population-specific reference panel also permitted enhanced filtering of clinically irrelevant variants from personal genomes.

BackgroundWhile increasingly large reference panels for genome-wide imputation have been recently made available, the degree to which imputation accuracy can be enhanced by population-specific reference panels remains an open question. In the present study, we sequenced at full-depth (≥30x) a moderately large (n=738) cohort of samples drawn from the Ashkenazi Jewish population across two platforms (Illumina X Ten and Complete Genomics, Inc.). We developed and refined a series of quality control steps to optimize sensitivity, specificity, and comprehensiveness of variant calls in the reference panel, and then tested the accuracy of imputation against target cohorts drawn from the same population.ResultsFor samples sequenced on the Illumina X Ten platform, quality thresholds were identified that permitted highly accurate calling of single nucleotide variants across 94% of the genome. The Complete Genomics, Inc. platform was more conservative (fewer variants called) compared to the Illumina platform, but also demonstrated relatively greater numbers of false positives that needed to be filtered. Quality control procedures also permitted detection of novel genome reads that are not mapped to current reference or alternate assemblies. After stringent quality control, the population-specific reference panel produced more accurate and comprehensive imputation results relative to publicly available, large cosmopolitan reference panels. The population-specific reference panel also permitted enhanced filtering of clinically irrelevant variants from personal genomes.ConclusionsOur primary results demonstrate enhanced accuracy of a population-specific imputation panel relative to cosmopolitan panels, especially in the range of infrequent (

SummaryGermline determinants of gene expression in tumors are infrequently studied due to the complexity of transcript regulation caused by somatically acquired alterations. We performed expression quantitative trait locus (eQTL)-based analyses using the multi-level information provided in The Cancer Genome Atlas (TCGA). Of the factors we measured, cis-acting eQTLs accounted for 1.2% of the total variation of tumor gene expression, while somatic copy-number alteration and CpG methylation accounted for 7.3% and 3.3%, respectively. eQTL analyses of 15 previously reported breast cancer risk loci resulted in the discovery of three variants that are significantly associated with transcript levels (false discovery rate [FDR] < 0.1). Our trans-based analysis identified an additional three risk loci to act through ESR1, MYC, and KLF4. These findings provide a more comprehensive picture of gene expression determinants in breast cancer as well as insights into the underlying biology of breast cancer risk loci.

Background & Aims Crohn's disease (CD) has the highest prevalence in Ashkenazi Jewish populations. We sought to identify rare, CD-associated frameshift variants of high functional and statistical effects. Methods We performed exome sequencing and array-based genotype analyses of 1477 Ashkenazi Jewish individuals with CD and 2614 Ashkenazi Jewish individuals without CD (controls). To validate our findings, we performed genotype analyses of an additional 1515 CD cases and 7052 controls for frameshift mutations in the colony-stimulating factor 2–receptor β common subunit gene ( CSF2RB ). Intestinal tissues and blood samples were collected from patients with CD; lamina propria leukocytes were isolated and expression of CSF2RB and granulocyte-macrophage colony–stimulating factor–responsive cells were defined by adenomatous polyposis coli (APC) time-of-flight mass cytometry (CyTOF analysis). Variants of CSF2RB were transfected into HEK293 cells and the expression and functions of gene products were compared. Results In the discovery cohort, we associated CD with a frameshift mutation in CSF2RB ( P = 8.52 × 10 -4 ); the finding was validated in the replication cohort (combined P = 3.42 × 10 -6 ). Incubation of intestinal lamina propria leukocytes with granulocyte-macrophage colony–stimulating factor resulted in high levels of phosphorylation of signal transducer and activator of transcription (STAT5) and lesser increases in phosphorylation of extracellular signal–regulated kinase and AK straining transforming (AKT). Cells co-transfected with full-length and mutant forms of CSF2RB had reduced pSTAT5 after stimulation with granulocyte-macrophage colony–stimulating factor, compared with cells transfected with control CSF2RB, indicating a dominant-negative effect of the mutant gene. Monocytes from patients with CD who were heterozygous for the frameshift mutation (6% of CD cases analyzed) had reduced responses to granulocyte-macrophage colony–stimulating factor and markedly decreased activity of aldehyde dehydrogenase; activity of this enzyme has been associated with immune tolerance. Conclusions In a genetic analysis of Ashkenazi Jewish individuals, we associated CD with a frameshift mutation in CSF2RB . Intestinal monocytes from carriers of this mutation had reduced responses to granulocyte-macrophage colony–stimulating factor, providing an additional mechanism for alterations to the innate immune response in individuals with CD.

The Ashkenazi Jewish (AJ) population is important in genetics due to its high rate of Mendelian disorders. AJ appeared in Europe in the 10th century, and their ancestry is thought to comprise European (EU) and Middle-Eastern (ME) components. However, both the time and place of admixture are subject to debate. Here, we attempt to characterize the AJ admixture history using a careful application of new and existing methods on a large AJ sample. Our main approach was based on local ancestry inference, in which we first classified each AJ genomic segment as EU or ME, and then compared allele frequencies along the EU segments to those of different EU populations. The contribution of each EU source was also estimated using GLOBETROTTER and haplotype sharing. The time of admixture was inferred based on multiple statistics, including ME segment lengths, the total EU ancestry per chromosome, and the correlation of ancestries along the chromosome. The major source of EU ancestry in AJ was found to be Southern Europe (≈60–80% of EU ancestry), with the rest being likely Eastern European. The inferred admixture time was ≈30 generations ago, but multiple lines of evidence suggest that it represents an average over two or more events, pre- and post-dating the founder event experienced by AJ in late medieval times. The time of the pre-bottleneck admixture event, which was likely Southern European, was estimated to ≈25–50 generations ago., Author summary The Ashkenazi Jewish population has resided in Europe for much of its 1000-year existence. However, its ethnic and geographic origins are controversial, due to the scarcity of reliable historical records. Previous genetic studies have found links to Middle-Eastern and European ancestries, but the admixture history has not been studied in detail yet, partly due to technical difficulties in disentangling signals from multiple admixture events. Here, we present an in-depth analysis of the sources of European gene flow and the time of admixture events by using multiple new and existing methods and extensive simulations. Our results suggest a model of at least two events of European admixture. One event slightly pre-dated a late medieval founder event and was likely from a Southern European source. Another event post-dated the founder event and likely occurred in Eastern Europe. These results, as well as the methods introduced, will be highly valuable for geneticists and other researchers interested in Ashkenazi Jewish origins.

Missing data are an unavoidable component of modern statistical genetics. Different array or sequencing technologies cover different single nucleotide polymorphisms (SNPs), leading to a complicated mosaic pattern of missingness where both individual genotypes and entire SNPs are sporadically absent. Such missing data patterns cannot be ignored without introducing bias, yet cannot be inferred exclusively from nonmissing data. In genome-wide association studies, the accepted solution to missingness is to impute missing data using external reference haplotypes. The resulting probabilistic genotypes may be analyzed in the place of genotype calls. A general-purpose paradigm, called Multiple Imputation (MI), is known to model uncertainty in many contexts, yet it is not widely used in association studies. Here, we undertake a systematic evaluation of existing imputed data analysis methods and MI. We characterize biases related to uncertainty in association studies, and find that bias is introduced both at the imputation level, when imputation algorithms generate inconsistent genotype probabilities, and at the association level, when analysis methods inadequately model genotype uncertainty. We find that MI performs at least as well as existing methods or in some cases much better, and provides a straightforward paradigm for adapting existing genotype association methods to uncertain data., Author Summary Genetic research has been focused at analysis of datapoints that are assumed to be deterministically known. However, the majority of current, high throughput data is only probabilistically known, and proper methods for handing such uncertain genotypes are limited. Here, we build on existing theory from the field of statistics to introduce a general framework for handling probabilistic genotype data obtained through genotype imputation. This framework, called Multiple Imputation, matches or improves upon existing methods for handling uncertainty in basic analysis of genetic association. As opposed to such methods, our work furthermore extends to more advanced analysis, such as mixed-effects models, with no additional complication. Importantly, it generates posterior probabilities of association that are intrinsically weighted by the certainty of the underlying data, a feature unmatched by other existing methods. Multiple Imputation is also fully compatible with meta-analysis. Finally, our analysis of probabilistic genotype data brings into focus the accuracy and unreliability of imputation’s estimated probabilities. Taken together, these results substantially increase the utility of imputed genotypes in statistical genetics, and may have strong implications for analysis of sequencing data moving forward.

Producción Científica, Background & Aims Drug-induced liver injury (DILI), especially from antimicrobial agents, is an important cause of serious liver disease. Amoxicillin-clavulanate (AC) is a leading cause of idiosyncratic DILI, but little is understood about genetic susceptibility to this adverse reaction. Methods We performed a genome-wide association study using 822,927 single-nucleotide polymorphism (SNP) markers from 201 White European and US cases of AC-DILI and 532 population controls, matched for genetic background. Results AC-DILI was associated with many loci in the major histocompatibility complex. The strongest effect was with a human leukocyte antigen (HLA) class II SNP (rs9274407, P=4.8×10−14), which correlated with rs3135388, a tag SNP of HLA-DRB1*1501-DQB1*0602 that was previously associated with AC-DILI. Conditioned on rs3135388, rs9274407 is still significant (P=1.1×10−4). An independent association was observed in the class I region (rs2523822, P=1.8×10−10), related to HLA-A*0201. The most significant class I and II SNPs showed statistical interaction (P=0.0015). High-resolution HLA genotyping (177 cases and 219 controls) confirmed associations of HLA-A*0201 (P=2×10−6) and HLA-DQB1*0602 (P=5×10−10), and their interaction (P=0.005). Additional, population-dependent effects were observed in HLA alleles with nominal significance. In an analysis of auto-immunerelated genes, rs2476601 in the gene PTPN22 was associated (P=1.3×10−4). Conclusions Class I and II HLA genotypes affect susceptibility to AC-DILI, indicating the importance of the adaptive immune response in pathogenesis. The HLA genotypes identified will be useful in studies of the pathogenesis of AC-DILI, but have limited utility as predictive or diagnostic biomarkers because of the low positive-predictive values.

The potential benefits of using population isolates in genetic mapping, such as reduced genetic, phenotypic and environmental heterogeneity, are offset by the challenges posed by the large amounts of direct and cryptic relatedness in these populations confounding basic assumptions of independence. We have evaluated four representative specialized methods for association testing in the presence of relatedness; (i) within-family (ii) within- and between-family and (iii) mixed-models methods, using simulated traits for 2906 subjects with known genome-wide genotype data from an extremely isolated population, the Island of Kosrae, Federated States of Micronesia. We report that mixed models optimally extract association information from such samples, demonstrating 88% power to rank the true variant as among the top 10 genome-wide with 56% achieving genome-wide significance, a >80% improvement over the other methods, and demonstrate that population isolates have similar power to non-isolate populations for observing variants of known effects. We then used the mixed-model method to reanalyze data for 17 published phenotypes relating to metabolic traits and electrocardiographic measures, along with another 8 previously unreported. We replicate nine genome-wide significant associations with known loci of plasma cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, thyroid stimulating hormone, homocysteine, C-reactive protein and uric acid, with only one detected in the previous analysis of the same traits. Further, we leveraged shared identity-by-descent genetic segments in the region of the uric acid locus to fine-map the signal, refining the known locus by a factor of 4. Finally, we report a novel associations for height (rs17629022, P< 2.1 × 10−8).

The rate at which human genomes mutate is a central biological parameter that has many implications for our ability to understand demographic and evolutionary phenomena. We present a method for inferring mutation and gene-conversion rates by using the number of sequence differences observed in identical-by-descent (IBD) segments together with a reconstructed model of recent population-size history. This approach is robust to, and can quantify, the presence of substantial genotyping error, as validated in coalescent simulations. We applied the method to 498 trio-phased sequenced Dutch individuals and inferred a point mutation rate of 1.66 × 10(-8) per base per generation and a rate of 1.26 × 10(-9) for

The rate at which human genomes mutate is a central biological parameter that has many implications for our ability to understand demographic and evolutionary phenomena. We present a method for inferring mutation and gene conversion rates using the number of sequence differences observed in identical-by-descent (IBD) segments together with a reconstructed model of recent population size history. This approach is robust to, and can quantify, the presence of substantial genotyping error, as validated in coalescent simulations. We applied the method to 498 trio-phased Dutch individuals from the Genome of the Netherlands (GoNL) project, sequenced at an average depth of 13×. We infer a point mutation rate of 1.66 ± 0.04 × 10-8per base per generation, and a rate of 1.26 ± 0.06 × 10-9for -6, consistent with recent reports. We find that recombination does not have observable mutagenic effects after gene conversion is accounted for, and that local gene conversion rates reflect recombination rates. We detect a strong enrichment for recent deleterious variation among mismatching variants found within IBD regions, and observe summary statistics of local IBD sharing to closely match previously proposed metrics of background selection, but find no significant effects of selection on our estimates of mutation rate. We detect no evidence for strong variation of mutation rates in a number of genomic annotations obtained from several recent studies.

Genome-wide association studies (GWAS) have identified hundreds of SNPs responsible for variation in human quantitative traits. However, genome-wide-significant associations often fail to replicate across independent cohorts, in apparent inconsistency with their apparent strong effects in discovery cohorts. This limited success of replication raises pervasive questions about the utility of the GWAS field. We identify all 332 studies of quantitative traits from the NHGRI-EBI GWAS Database with attempted replication. We find that the majority of studies provide insufficient data to evaluate replication rates. The remaining papers replicate significantly worse than expected (p < 10−14), even when adjusting for regression-to-the-mean of effect size between discovery- and replication-cohorts termed the Winner’s Curse (p < 10−16). We show this is due in part to misreporting replication cohort-size as a maximum number, rather than per-locus one. In 39 studies accurately reporting per-locus cohort-size for attempted replication of 707 loci in samples with similar ancestry, replication rate matched expectation (predicted 458, observed 457, p = 0.94). In contrast, ancestry differences between replication and discovery (13 studies, 385 loci) cause the most highly-powered decile of loci to replicate worse than expected, due to difference in linkage disequilibrium., Author summary The majority of associations between common genetic variation and human traits come from genome-wide association studies, which have analyzed millions of single-nucleotide polymorphisms in millions of samples. These kinds of studies pose serious statistical challenges to discovering new associations. Finite resources restrict the number of candidate associations that can brought forward into validation samples, introducing the need for a significance threshold. This threshold creates a phenomenon called the Winner’s Curse, in which candidate associations close to the discovery threshold are more likely to have biased overestimates of the variant’s true association in the sampled population. We survey all human quantitative trait association studies that validated at least one signal. We find the majority of these studies do not publish sufficient information to actually support their claims of replication. For studies that did, we computationally correct the Winner’s Curse and evaluate replication performance. While all variants combined replicate significantly less than expected, we find that the subset of studies that (1) perform both discovery and replication in samples of the same ancestry; and (2) report accurate per-variant sample sizes, replicate as expected. This study provides strong, rigorous evidence for the broad reliability of genome-wide association studies. We furthermore provide a model for more efficient selection of variants as candidates for replication, as selecting variants using cursed discovery data enriches for variants with little real evidence for trait association.

Parkinson's disease (PD) is a common neurodegenerative disease whose pathological hallmark is the accumulation of intracellular α-synuclein aggregates in Lewy bodies. Lipid metabolism dysregulation may play a significant role in PD pathogenesis; however, large plasma lipidomic studies in PD are lacking. In the current study, we analyzed the lipidomic profile of plasma obtained from 150 idiopathic PD patients and 100 controls, taken from the 'Spot' study at Columbia University Medical Center in New York. Our mass spectrometry based analytical panel consisted of 520 lipid species from 39 lipid subclasses including all major classes of glycerophospholipids, sphingolipids, glycerolipids and sterols. Each lipid species was analyzed using a logistic regression model. The plasma concentrations of two lipid subclasses, triglycerides and monosialodihexosylganglioside (GM3), were different between PD and control participants. GM3 ganglioside concentration had the most significant difference between PD and controls (1.531±0.037 pmol/μl versus 1.337±0.040 pmol/μl respectively; p-value = 5.96E-04; q-value = 0.048; when normalized to total lipid: p-value = 2.890E-05; q-value = 2.933E-03). Next, we used a collection of 20 GM3 and glucosylceramide (GlcCer) species concentrations normalized to total lipid to perform a ROC curve analysis, and found that these lipids compare favorably with biomarkers reported in previous studies (AUC = 0.742 for males, AUC = 0.644 for females). Our results suggest that higher plasma GM3 levels are associated with PD. GM3 lies in the same glycosphingolipid metabolic pathway as GlcCer, a substrate of the enzyme glucocerebrosidase, which has been associated with PD. These findings are consistent with previous reports implicating lower glucocerebrosidase activity with PD risk.

Stevens-Johnson syndrome (SJS) and Toxic Epidermal Necrolysis (TEN) represent rare but serious adverse drug reactions (ADRs). Both are characterized by distinctive blistering lesions and significant mortality rates. While there is evidence for strong drug-specific genetic predisposition related to HLA alleles, recent genome wide association studies (GWAS) on European and Asian populations have failed to identify genetic susceptibility alleles that are common across multiple drugs. We hypothesize that this is a consequence of the low to moderate effect size of individual genetic risk factors. To test this hypothesis we developed Pointer, a new algorithm that assesses the aggregate effect of multiple low risk variants on a pathway using a gene set enrichment approach. A key advantage of our method is the capability to associate SNPs with genes by exploiting physical proximity as well as by using expression quantitative trait loci (eQTLs) that capture information about both cis- and trans-acting regulatory effects. We control for known bias-inducing aspects of enrichment based analyses, such as: 1) gene length, 2) gene set size, 3) presence of biologically related genes within the same linkage disequilibrium (LD) region, and, 4) genes shared among multiple gene sets. We applied this approach to publicly available SJS/TEN genome-wide genotype data and identified the ABC transporter and Proteasome pathways as potentially implicated in the genetic susceptibility of non-drug-specific SJS/TEN. We demonstrated that the innovative SNP-to-gene mapping phase of the method was essential in detecting the significant enrichment for those pathways. Analysis of an independent gene expression dataset provides supportive functional evidence for the involvement of Proteasome pathways in SJS/TEN cutaneous lesions. These results suggest that Pointer provides a useful framework for the integrative analysis of pharmacogenetic GWAS data, by increasing the power to detect aggregate effects of multiple low risk variants. The software is available for download at https://sourceforge.net/projects/pointergsa/.

Schizophrenia and bipolar disorder are major psychiatric disorders with high heritability and overlapping genetic variance. Here we perform a genome-wide association study in an ethnically homogeneous cohort of 904 schizophrenia cases and 1,640 controls drawn from the Ashkenazi Jewish population. We identify a novel genome-wide significant risk locus at chromosome 4q26, demonstrating the potential advantages of this founder population for gene discovery. The top single-nucleotide polymorphism (SNP; rs11098403) demonstrates consistent effects across 11 replication and extension cohorts, totalling 23, 191 samples across multiple ethnicities, regardless of diagnosis (schizophrenia or bipolar disorder), resulting in Pmeta=9.49 × 10−12 (odds ratio (OR)=1.13, 95% confidence interval (CI): 1.08–1.17) across both disorders and Pmeta=2.67 × 10−8 (OR=1.15, 95% CI: 1.08–1.21) for schizophrenia alone. In addition, this intergenic SNP significantly predicts postmortem cerebellar gene expression of NDST3, which encodes an enzyme critical to heparan sulphate metabolism. Heparan sulphate binding is critical to neurite outgrowth, axon formation and synaptic processes thought to be aberrant in these disorders., Schizophrenia and bipolar disorder are important psychiatric disorders with overlapping genetic components. Here, the authors identify and replicate a genome-wide significant risk locus for the two disorders, and suggest a role for NDST3 in severe psychiatric disease.

Widespread sharing of long, identical-by-descent (IBD) genetic segments is a hallmark of populations that have experienced recent genetic drift. Detection of these IBD segments has recently become feasible, enabling a wide range of applications from phasing and imputation to demographic inference. Here, we study the distribution of IBD sharing in the Wright-Fisher model. Specifically, using coalescent theory, we calculate the variance of the total sharing between random pairs of individuals. We then investigate the cohort-averaged sharing: the average total sharing between one individual and the rest of the cohort. We find that for large cohorts, the cohort-averaged sharing is distributed approximately normally. Surprisingly, the variance of this distribution does not vanish even for large cohorts, implying the existence of "hyper-sharing" individuals. The presence of such individuals has consequences for the design of sequencing studies, since, if they are selected for whole-genome sequencing, a larger fraction of the cohort can be subsequently imputed. We calculate the expected gain in power of imputation by IBD, and subsequently, in power to detect an association, when individuals are either randomly selected or specifically chosen to be the hyper-sharing individuals. Using our framework, we also compute the variance of an estimator of the population size that is based on the mean IBD sharing and the variance in the sharing between inbred siblings. Finally, we study IBD sharing in an admixture pulse model, and show that in the Ashkenazi Jewish population the admixture fraction is correlated with the cohort-averaged sharing., Includes Supplementary Material

Exome sequencing of patients with congenital heart disease (CHD) and their unaffected parents reveals an excess of strong-effect, protein-altering de novo mutations in genes expressed in the developing heart, many of which regulate chromatin modification in key developmental genes; collectively, these mutations are predicted to account for approximately 10% of severe CHD cases. This paper demonstrates that de novo mutations with large effect have a role in the pathogenesis of at least 10% of cases of congenital heart disease (CHD). Using exome sequence analysis in parent–offspring trios Richard Lifton and colleagues compared the frequency of de novo mutations, identified by exome sequencing, in 362 CHD parent–offspring trios and 264 control trios. Gene ontology analysis demonstrated significant enrichment of de novo protein-altering mutation of genes involved in chromatin modification, notably a marked enrichment of genes involved in the production, removal and reading of methylation of histone H3K4 and H3K27. Congenital heart disease (CHD) is the most frequent birth defect, affecting 0.8% of live births1. Many cases occur sporadically and impair reproductive fitness, suggesting a role for de novo mutations. Here we compare the incidence of de novo mutations in 362 severe CHD cases and 264 controls by analysing exome sequencing of parent–offspring trios. CHD cases show a significant excess of protein-altering de novo mutations in genes expressed in the developing heart, with an odds ratio of 7.5 for damaging (premature termination, frameshift, splice site) mutations. Similar odds ratios are seen across the main classes of severe CHD. We find a marked excess of de novo mutations in genes involved in the production, removal or reading of histone 3 lysine 4 (H3K4) methylation, or ubiquitination of H2BK120, which is required for H3K4 methylation2,3,4. There are also two de novo mutations in SMAD2, which regulates H3K27 methylation in the embryonic left–right organizer5. The combination of both activating (H3K4 methylation) and inactivating (H3K27 methylation) chromatin marks characterizes ‘poised’ promoters and enhancers, which regulate expression of key developmental genes6. These findings implicate de novo point mutations in several hundreds of genes that collectively contribute to approximately 10% of severe CHD.

The Ashkenazi Jewish population has a several-fold higher prevalence of Crohn’s disease compared to non-Jewish European ancestry populations and has a unique genetic history. Haplotype association is critical to Crohn’s disease etiology in this population, most notably at NOD2, in which three causal, uncommon, and conditionally independent NOD2 variants reside on a shared background haplotype. We present an analysis of extended haplotypes which showed significantly greater association to Crohn’s disease in the Ashkenazi Jewish population compared to a non-Jewish population (145 haplotypes and no haplotypes with P-value < 10−3, respectively). Two haplotype regions, one each on chromosomes 16 and 21, conferred increased disease risk within established Crohn’s disease loci. We performed exome sequencing of 55 Ashkenazi Jewish individuals and follow-up genotyping focused on variants in these two regions. We observed Ashkenazi Jewish-specific nominal association at R755C in TRPM2 on chromosome 21. Within the chromosome 16 region, R642S of HEATR3 and rs9922362 of BRD7 showed genome-wide significance. Expression studies of HEATR3 demonstrated a positive role in NOD2-mediated NF-κB signaling. The BRD7 signal showed conditional dependence with only the downstream rare Crohn’s disease-causal variants in NOD2, but not with the background haplotype; this elaborates NOD2 as a key illustration of synthetic association.

Crohn's disease (CD) is a complex disorder resulting from the interaction of intestinal microbiota with the host immune system in genetically susceptible individuals. The largest meta-analysis of genome-wide association to date identified 71 CD–susceptibility loci in individuals of European ancestry. An important epidemiological feature of CD is that it is 2–4 times more prevalent among individuals of Ashkenazi Jewish (AJ) descent compared to non-Jewish Europeans (NJ). To explore genetic variation associated with CD in AJs, we conducted a genome-wide association study (GWAS) by combining raw genotype data across 10 AJ cohorts consisting of 907 cases and 2,345 controls in the discovery stage, followed up by a replication study in 971 cases and 2,124 controls. We confirmed genome-wide significant associations of 9 known CD loci in AJs and replicated 3 additional loci with strong signal (p, Author Summary Crohn's disease causes inflammation of the digestive tract resulting from the interaction of normal gut bacteria with the host immune system in genetically predisposed individuals. People of Jewish heritage have an increased risk of developing Crohn's disease compared to non-Jewish Europeans. So far, 71 genetic variants that increase the risk of Crohn's disease have been identified in individuals of European ancestry. Here, we take advantage of recent technical and methodological advances to explore Crohn's diseases-related genetic variants specific to the Ashkenazi Jewish population. We examined 6,347 individuals whose Jewish ancestry was confirmed by a large number of genetic markers and detected several variants associated with the increased risk of Crohn' disease. We confirmed the involvement of 12 known Crohn's disease risk variants in Ashkenazi Jews and identified novel genetic regions not previously found in non-Jewish European populations. Further studies of these regions may help discover biological pathways affecting susceptibility to Crohn's disease and lead to the development of novel treatments. This study also demonstrates the complementary value of genetic studies in isolated populations, like the Ashkenazim.

Rare variants affecting phenotype pose a unique challenge for human genetics. Although genome-wide association studies have successfully detected many common causal variants, they are underpowered in identifying disease variants that are too rare or population-specific to be imputed from a general reference panel and thus are poorly represented on commercial SNP arrays. We set out to overcome these challenges and detect association between disease and rare alleles using SNP arrays by relying on long stretches of genomic sharing that are identical by descent. We have developed an algorithm, DASH, which builds upon pairwise identical-by-descent shared segments to infer clusters of individuals likely to be sharing a single haplotype. DASH constructs a graph with nodes representing individuals and links on the basis of such segments spanning a locus and uses an iterative minimum cut algorithm to identify densely connected components. We have applied DASH to simulated data and diverse GWAS data sets by constructing haplotype clusters and testing them for association. In simulations we show this approach to be significantly more powerful than single-marker testing in an isolated population that is from Kosrae, Federated States of Micronesia and has abundant IBD, and we provide orthogonal information for rare, recent variants in the outbred Wellcome Trust Case-Control Consortium (WTCCC) data. In both cohorts, we identified a number of haplotype associations, five such loci in the WTCCC data and ten in the isolated, that were conditionally significant beyond any individual nearby markers. We have replicated one of these loci in an independent European cohort and identified putative structural changes in low-pass whole-genome sequence of the cluster carriers.

Cancer is a disease driven by a combination of inherited risk alleles coupled with the acquisition of somatic mutations, including amplification and deletion of genomic DNA. Potential relationships between the inherited and somatic aspects of the disease have only rarely been examined on a genome-wide level. Applying a novel integrative analysis of SNP and copy number measurements, we queried the tumor and normal-tissue genomes of 178 glioblastoma patients from the Cancer Genome Atlas project for preferentially amplified alleles, under the hypothesis that oncogenic germline variants will be selectively amplified in the tumor environment. Selected alleles are revealed by allelic imbalance in amplification across samples. This general approach is based on genetic principles and provides a method for identifying important tumor-related alleles. We find that SNP alleles that are most significantly overrepresented in amplicons tend to occur in genes involved with regulation of kinase and transferase activity, and many of these genes are known contributors to gliomagenesis. The analysis also implicates variants in synapse genes. By incorporating gene expression data, we demonstrate synergy between preferential allelic amplification and expression in DOCK4 and EGFR. Our results support the notion that combining germline and tumor genetic data can identify regions relevant to cancer biology., Author Summary Cancer is a disease of two distinct, but related, genomes: the inherited genome and the tumor genome. Despite the fact that the tumor genome arises from the germline, the genomes are typically studied as separate entities. For example, germline genetic studies focus on how inherited variation is related to a particular trait such as disease risk, whereas tumor genetic studies focus on areas of recurrent aberrations such as amplifications to identify genes involved in tumor biology. In this study, we integrated both germline and tumor genetic information to pinpoint areas of the human genome that are likely undergoing selection during the evolution of the tumor. Our results support the notion that combining germline and tumor genetic data can identify regions relevant to cancer biology.

Despite the recent rapid growth in genome-wide data, much of human variation remains entirely unexplained. A significant challenge in the pursuit of the genetic basis for variation in common human traits is the efficient, coordinated collection of genotype and phenotype data. We have developed a novel research framework that facilitates the parallel study of a wide assortment of traits within a single cohort. The approach takes advantage of the interactivity of the Web both to gather data and to present genetic information to research participants, while taking care to correct for the population structure inherent to this study design. Here we report initial results from a participant-driven study of 22 traits. Replications of associations (in the genes OCA2, HERC2, SLC45A2, SLC24A4, IRF4, TYR, TYRP1, ASIP, and MC1R) for hair color, eye color, and freckling validate the Web-based, self-reporting paradigm. The identification of novel associations for hair morphology (rs17646946, near TCHH; rs7349332, near WNT10A; and rs1556547, near OFCC1), freckling (rs2153271, in BNC2), the ability to smell the methanethiol produced after eating asparagus (rs4481887, near OR2M7), and photic sneeze reflex (rs10427255, near ZEB2, and rs11856995, near NR2F2) illustrates the power of the approach., Author Summary Twin studies have shown that many human physical characteristics, such as hair curl, earlobe shape, and pigmentation are at least partly heritable. In order to identify the genes involved in such traits, we administered Web-based surveys to the customer base of 23andMe, a personal genetics company. Upon completion of surveys, participants were able to see how their answers compared to those of other customers. Our examination of 22 different common traits in nearly 10,000 participants revealed associations among several single-nucleotide polymorphisms (SNPs, a type of common DNA sequence variation) and freckling, hair curl, asparagus anosmia (the inability to detect certain urinary metabolites produced after eating asparagus), and photic sneeze reflex (the tendency to sneeze when entering bright light). Additionally our analysis verified the association of a large number of previously identified genes with variation in hair color, eye color, and freckling. Our analysis not only identified new genetic associations, but also showed that our novel way of doing research—collecting self-reported data over the Web from involved participants who also receive interpretations of their genetic data—is a viable alternative to traditional methods.

It has been argued that the limited genetic diversity and reduced allelic heterogeneity observed in isolated founder populations facilitates discovery of loci contributing to both Mendelian and complex disease. A strong founder effect, severe isolation, and substantial inbreeding have dramatically reduced genetic diversity in natives from the island of Kosrae, Federated States of Micronesia, who exhibit a high prevalence of obesity and other metabolic disorders. We hypothesized that genetic drift and possibly natural selection on Kosrae might have increased the frequency of previously rare genetic variants with relatively large effects, making these alleles readily detectable in genome-wide association analysis. However, mapping in large, inbred cohorts introduces analytic challenges, as extensive relatedness between subjects violates the assumptions of independence upon which traditional association test statistics are based. We performed genome-wide association analysis for 15 quantitative traits in 2,906 members of the Kosrae population, using novel approaches to manage the extreme relatedness in the sample. As positive controls, we observe association to known loci for plasma cholesterol, triglycerides, and C-reactive protein and to a compelling candidate loci for thyroid stimulating hormone and fasting plasma glucose. We show that our study is well powered to detect common alleles explaining ≥5% phenotypic variance. However, no such large effects were observed with genome-wide significance, arguing that even in such a severely inbred population, common alleles typically have modest effects. Finally, we show that a majority of common variants discovered in Caucasians have indistinguishable effect sizes on Kosrae, despite the major differences in population genetics and environment., Starr Foundation, Howard Hughes Medical Institute, Deutsche Forschungsgemeinschaft (DFG Bu2263/1-1), National Institutes of Health (U.S.) (1F32DK070527), National Institutes of Health (U.S.) (5 U54 CA121852), Massachusetts Biomedical Research Corporation (Tosteson Postdoctoral Fellowship), National Institutes of Health (U.S.) (NIH/NIDDK grant (5R01 DK60089))

Background Whole-genome sequencing represents a promising approach to pinpoint chemically induced mutations in genetic model organisms, thereby short-cutting time-consuming genetic mapping efforts. Principal Findings We compare here the ability of two leading high-throughput platforms for paired-end deep sequencing, SOLiD (ABI) and Genome Analyzer (Illumina; “Solexa”), to achieve the goal of mutant detection. As a test case we used a mutant C. elegans strain that harbors a mutation in the lsy-12 locus which we compare to the reference wild-type genome sequence. We analyzed the accuracy, sensitivity, and depth-coverage characteristics of the two platforms. Both platforms were able to identify the mutation that causes the phenotype of the mutant C. elegans strain, lsy-12. Based on a 4 MB genomic region in which individual variants were validated by Sanger sequencing, we observe tradeoffs between rates of false positives and false negatives when using both platforms under similar coverage and mapping criteria. Significance In conclusion, whole-genome sequencing conducted by either platform is a viable approach for the identification of single-nucleotide variations in the C. elegans genome.

Genetic association studies of common disease often rely on linkage disequilibrium (LD) along the human genome and in the population under study. Although understanding the characteristics of this correlation has been the focus of many large-scale surveys (culminating in genomewide haplotype maps), the results of different studies have yielded wide-ranging estimates. Since understanding these differences (and whether they can be reconciled) has important implications for whole-genome association studies, in this article we dissect biases in these estimations that are due to known aspects of study design and analytic methodology. In particular, we document in the empirical data that the long-known complicating effects of allele frequency, marker density, and sample size largely reconcile all large-scale surveys. Two exceptions are an underappraisal of redundancy among single-nucleotide polymorphisms (SNPs) when evaluation is limited to short regions (as in candidate-gene resequencing studies) and an inflation in the extent of LD in HapMap phase I, which is likely due to oversampling of specific haplotypes in the creation of the public SNP map. Understanding these factors can guide the understanding of empirical LD surveys and has implications for genetic association studies.

Associations between the level of single transcripts and single corresponding genetic variants, expression single nucleotide polymorphisms (eSNPs), have been extensively studied and reported. However, most expression traits are complex, involving the cooperative action of multiple SNPs at different loci affecting multiple genes. Finding these cooperating eSNPs by exhaustive search has proven to be statistically challenging. In this paper we utilized availability of sequencing data with transcriptional profiles in the same cohorts to identify two kinds of usual suspects: eSNPs that alter coding sequences or eSNPs within the span of transcription factors (TFs). We utilize a computational framework for considering triplets, each comprised of a SNP and two associated genes. We examine pairs of triplets with such cooperating source eSNPs that are both associated with the same pair of target genes. We characterize such quartets through their genomic, topological and functional properties. We establish that this regulatory structure of cooperating quartets is frequent in real data, but is rarely observed in permutations. eSNP sources are mostly located on different chromosomes and away from their targets. In the majority of quartets, SNPs affect the expression of the two gene targets independently of one another, suggesting a mutually independent rather than a directionally dependent effect. Furthermore, the directions in which the minor allele count of the SNP affects gene expression within quartets are consistent, so that the two source eSNPs either both have the same effect on the target genes or both affect one gene in the opposite direction to the other. Same-effect eSNPs are observed more often than expected by chance. Cooperating quartets reported here in a human system might correspond to bi-fans, a known network motif of four nodes previously described in model organisms. Overall, our analysis offers insights regarding the fine motif structure of human regulatory networks., Author Summary Previous analysis charts ubiquitous associations between the level of single transcripts and single corresponding genetic variants, eSNPs. However, most expression traits are complex, involving the cooperative action of multiple SNPs at different loci affecting multiple genes. The basic structure of variants within two source genes that affect the expression of two different target genes is compelling in terms of its potential to divulge information regarding the features of more complex interactions. We therefore devised a computational framework for the analysis of such variants, as ascertained from genomic sequencing data along with gene expression profiling of the same cohort. We focus on genetic markers that reside within interpretable regions of the genome: exon sequences, or transcription factors. Such cooperating eSNP sources are both associated with the same pair of target transcripts. We characterize such quartets through their genomic, topological and functional properties. Our findings suggest that this regulatory structure of quartets exhibits distinct characteristics and is frequent in real data, but is rarely observed in permutations.

The detection of genetic segments of Identical by Descent (IBD) in Genome-Wide Association Studies has proven successful in pinpointing genetic relatedness between reportedly unrelated individuals and leveraging such regions to shortlist candidate genes. These techniques depend on high-density genotyping arrays and their effectiveness in diverse sequence data is largely unknown. Due to decreasing costs and increasing effectiveness of high throughput techniques for whole-exome sequencing, an influx of exome sequencing data has become available. Studies using exomes and IBD-detection methods within known pedigrees have shown that IBD can be useful in finding hidden genetic candidates where known relatives are available. We set out to examine the viability of using IBD-detection in whole exome sequencing data in population-wide studies. In doing so, we extend GERMLINE, a method to detect IBD from exome sequencing data by finding small slices of matching alleles between pairs of individuals and extending them into full IBD segments. This algorithm allows for efficient population-wide detection in dense data. We apply this algorithm to a cohort of Crohn's Disease cases where whole-exome and GWAS array data is available. We confirm that GWAS-based detected segments are highly accurate and predictive of underlying shared variation. Where segments inferred from GWAS are expected to be of high accuracy, we compare exome-based detection accuracy of multiple detection strategies. We find detection accuracy to be prohibitively low in all assessments, both in terms of segment sensitivity and specificity. Even after isolating relatively long segments beyond 10cM, exome-based detection continued to offer poor specificity/sensitivity tradeoffs. We hypothesize that the variable coverage and platform biases of exome capture account for this decreased accuracy and look toward whole genome sequencing data as a higher quality source for detecting population-wide IBD.

Cataloging the association of transcripts to genetic variants in recent years holds the promise for functional dissection of regulatory structure of human transcription. Here, we present a novel approach, which aims at elucidating the joint relationships between transcripts and single-nucleotide polymorphisms (SNPs). This entails detection and analysis of modules of transcripts, each weakly associated to a single genetic variant, together exposing a high-confidence association signal between the module and this 'main' SNP. To explore how transcripts in a module are related to causative loci for that module, we represent such dependencies by a graphical model. We applied our method to the existing data on genetics of gene expression in the liver. The modules are significantly more, larger and denser than found in permuted data. Quantification of the confidence in a module as a likelihood score, allows us to detect transcripts that do not reach genome-wide significance level. Topological analysis of each module identifies novel insights regarding the flow of causality between the main SNP and transcripts. We observe similar annotations of modules from two sources of information: the enrichment of a module in gene subsets and locus annotation of the genetic variants. This and further phenotypic analysis provide a validation for our methodology.

Motivation: Copy Number Variants (CNVs) are important genetic factors for studying human diseases. While high-throughput whole genome re-sequencing provides multiple lines of evidence for detecting CNVs, computational algorithms need to be tailored for different type or size of CNVs under different experimental designs. Results: To achieve optimal power and resolution of detecting CNVs at low depth of coverage, we implemented a Hidden Markov Model that integrates both depth of coverage and mate-pair relationship. The novelty of our algorithm is that we infer the likelihood of carrying a deletion jointly from multiple mate pairs in a region without the requirement of a single mate pairs being obvious outliers. By integrating all useful information in a comprehensive model, our method is able to detect medium-size deletions (200-2000bp) at low depth (

Background Relatively small, reproductively isolated populations with reduced genetic diversity may have advantages for genomewide association mapping in disease genetics. The Ashkenazi Jewish population represents a unique population for study based on its recent (< 1,000 year) history of a limited number of founders, population bottlenecks and tradition of marriage within the community. We genotyped more than 1,300 Ashkenazi Jewish healthy volunteers from the Hebrew University Genetic Resource with the Illumina HumanOmni1-Quad platform. Comparison of the genotyping data with that of neighboring European and Asian populations enabled the Ashkenazi Jewish-specific component of the variance to be characterized with respect to disease-relevant alleles and pathways. Results Using clustering, principal components, and pairwise genetic distance as converging approaches, we identified an Ashkenazi Jewish-specific genetic signature that differentiated these subjects from both European and Middle Eastern samples. Most notably, gene ontology analysis of the Ashkenazi Jewish genetic signature revealed an enrichment of genes functioning in transepithelial chloride transport, such as CFTR, and in equilibrioception, potentially shedding light on cystic fibrosis, Usher syndrome and other diseases over-represented in the Ashkenazi Jewish population. Results also impact risk profiles for autoimmune and metabolic disorders in this population. Finally, residual intra-Ashkenazi population structure was minimal, primarily determined by class 1 MHC alleles, and not related to host country of origin. Conclusions The Ashkenazi Jewish population is of potential utility in disease-mapping studies due to its relative homogeneity and distinct genomic signature. Results suggest that Ashkenazi-associated disease genes may be components of population-specific genomic differences in key functional pathways.