Biology subjects

Alexander Gusev

Publications and source records attributed to Alexander Gusev.

15 recordsLinked to original sources

bioRxiv2016

Correcting subtle stratification in summary association statistics

Population stratification is a well-documented confounder in GWASes, and is often addressed by including principal component (PC) covariates computed from common SNPs (SNP-PCs). In our analyses of summary statistics from 36 GWASes (mean n=88k), including 20 GWASes using 23andMe data that included SNP-PC covariates, we observed a significantly inflated LD score regression (LDSC) intercept for several traits--suggesting that residual stratification remains a concern, even when SNPPC covariates are included.\n\nHere we propose a new method, PC loading regression, to correct for stratification in summary statistics by leveraging SNP loadings for PCs computed in a large reference panel. In addition to SNP-PCs, the method can be applied to haploSNP-PCs, i.e. PCs computed from a larger number of rare haplotype variants that better capture subtle structure. Using simulations based on real genotypes from 54,000 individuals of diverse European ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort, we show that PC loading regression effectively corrects for stratification along top PCs.\n\nWe applied PC loading regression to several traits with inflated LDSC intercepts. Correcting for the top four SNP-PCs in GERA data, we observe a significant reduction in LDSC intercept height summary statistics from the Genetic Investigation of ANthropometric Traits (GIANT) consortium, but not for 23andMe summary statistics, which already included SNP-PC covariates. However, when correcting for additional haploSNP-PCs in 23andMe GWASes, inflation in the LDSC intercept was eliminated for eye color, hair color, and skin color and substantially reduced for height (1.41 to 1.16; n=430k). Correcting for haploSNP-PCs in GIANT height summary statistics eliminated inflation in the LDSC intercept (from 1.35 to 1.00; n=250k), eliminating 27 significant association signals including one at the LCT locus, which is highly differentiated among European populations and widely known to produce spurious signals. Overall, our results suggest that uncorrected population stratification is a concern in GWASes of large sample size and that PC loading regression can correct for this stratification.

Genetics↗

bioRxiv2016

Integrating gene expression with summary association statistics to identify susceptibility genes for 30 complex traits

Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. We leverage recently introduced methods to integrate gene expression measurements from 45 expression panels with summary GWAS data to perform 30 transcriptome-wide association studies (TWASs). We identify 1,196 susceptibility genes whose expression is associated with these traits; of these, 168 reside more than 0.5Mb away from any previously reported GWAS significant variant, thus providing new risk loci. Second, we find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, 8 are not found through genetic correlation at the SNP level. Third, we use bi-directional regression to find evidence for BMI causally influencing triglyceride levels, and triglyceride levels causally influencing LDL. Taken together, our results provide insights into the role of expression to susceptibility of complex traits and diseases.

Genetics↗

bioRxiv2016

Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights

Genome-wide association studies (GWAS) have identified over 100 risk loci for schizophrenia, but the causal mechanisms remain largely unknown. We performed a transcriptome-wide association study (TWAS) integrating expression data from brain, blood, and adipose tissues across 3,693 individuals with schizophrenia GWAS of 79,845 individuals from the Psychiatric Genomics Consortium. We identified 157 genes with a transcriptome-wide significant association, of which 35 did not overlap a known GWAS locus; the largest number involved alternative splicing in brain. 42/157 genes were also associated to specific chromatin phenotypes measured in 121 independent samples (a 4-fold enrichment over background genes). This high-throughput connection of GWAS findings to specific genes, tissues, and regulatory mechanisms is an essential step toward understanding the biology of schizophrenia and moving towards therapeutic interventions.

Genomics↗

bioRxiv2016

Subtle stratification confounds estimates of heritability from rare variants

Genome-wide significant associations generally explain only a small proportion of the narrow-sense heritability of complex disease (h2). While considerably more heritability is explained by all genotyped SNPs (hg2), for most traits, much heritability remains missing (hg2 < h2). Rare variants, poorly tagged by genotyped SNPs, are a major potential source of the gap between hg2 and h2. Recent efforts to assess the contribution of both sequenced and imputed rare variants to phenotypes suggest that substantial heritability may lie in these variants. Here we analyze sequenced SNPs, imputed SNPs and haploSNPs-- haplotype variants constructed from within a sample, without using a reference panel-- and show that studies of heritability from these variants may be strongly confounded by subtle population stratification. For example, when meta-analyzing heritability estimates from 22 randomly ascertained case-control traits from the GERA cohort, we observe a statistically significant increase in heritability explained by imputed SNPs even after correcting for principal components (PCs) from genotyped (or imputed) SNPs. However, this increase is eliminated when correcting for stratification using PCs from a larger number of haploSNPs. We note that subtle stratification may also impact estimates of heritability from array SNPs, although we find that this is generally a less severe problem. Overall, our results suggest that estimating the heritability explained by rare variants for case-control traits requires exquisite control for population stratification, but current methods may not provide this level of control.

Genetics↗

bioRxiv2016

Mixed Model Association with Family-Biased Case-Control Ascertainment

Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where cases and controls are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average{chi} 2 = 1.00), whereas Armitage Trend Test (ATT) and standard mixed model association (MLM) were mis-calibrated (e.g. average{chi} 2 = 0.50-0.67 for MLM). LT-Fam also attained higher power in these simulations, with increases of up to 8% vs. ATT and 3% vs. MLM after correcting for mis-calibration. In 1,269 type 2 diabetes cases and 5,819 controls from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT and MLM were again mis-calibrated (e.g. average{chi} 2 = 0.60-0.82 for MLM). Our results highlight the importance of modeling family sampling bias in case-control data sets with related samples.

Genetics↗

bioRxiv2016

Functional partitioning of local and distal gene expression regulation in multiple human tissues

Studies of the genetics of gene expression have served as a key tool for linking genetic variants to phenotypes. Large-scale eQTL mapping studies have identified a large number of local eQTLs, but the molecular mechanism of how genetic variants regulate expression is still unclear, particularly for distal eQTLs, which these studies are not well-powered to detect. In this study, we use a heritability partitioning approach to dissect the functional components of gene regulation. We make use of an existing method, stratified LD score regression, that leverages all variants (not just those that pass stringent significance thresholds) to partition heritability across functional categories, and we extend this method to partition local and distal gene expression heritability in 15 human tissues. The top enriched functional categories in local regulation of peripheral blood gene expression included super enhancers (5.18x), coding regions (3.73x), conserved regions (2.33x) and four histone marks (p<3x10-7 for all enrichments); local enrichments were similar across the 15 tissues. We also observed substantial enrichments for distal regulation of peripheral blood gene expression: super enhancers (1.91x), coding regions (4.47x), conserved regions (4.51x) and two histone marks (p<3x10-7 for all enrichments). Analyses of the genetic correlation of gene expression across tissues showed that local gene expression regulation is largely shared across tissues, but distal gene expression regulation is highly tissue-specific. Our results elucidate the functional components of the genetic architecture of local and distal gene expression regulation.

Genomics↗

bioRxiv2015

Integrative approaches for large-scale transcriptome-wide association studies

Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance levels of one or multiple proteins. In this work we introduce a powerful strategy that integrates gene expression measurements with large-scale genome-wide association data to identify genes whose cis-regulated expression is associated to complex traits. We use a relatively small reference panel of individuals for which both genetic variation and gene expression have been measured to impute gene expression into large cohorts of individuals and identify expression-trait associations. We extend our methods to allow for indirect imputation of the expression-trait association from summary association statistics of large-scale GWAS1-3. We applied our approaches to expression data from blood and adipose tissue measured in [~]3,000 individuals overall. We then imputed gene expression into GWAS data from over 900,000 phenotype measurements4-6 to identify 69 novel genes significantly associated to obesity-related traits (BMI, lipids, and height). Many of the novel genes were associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Overall our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.

Genetics↗

bioRxiv2015

Haplotypes of common SNPs can explain missing heritability of complex diseases

While genome-wide significant associations generally explain only a small proportion of the narrow-sense heritability of complex disease (h2), recent work has shown that more heritability is explained by all genotyped SNPs (hg2). However, much of the heritability is still missing (hg2 < h2). For example, for schizophrenia, h2 is estimated at 0.7-0.8 but hg2 is estimated at [~]0.3. Efforts at increasing coverage through accurately imputed variants have yielded only small increases in the heritability explained, and poorly imputed variants can lead to assay artifacts for case-control traits. We propose to estimate the heritability explained by a set of haplotype variants (haploSNPs) constructed directly from the study sample (hhap2). Our method constructs a set of haplotypes from phased genotypes by extending shared haplotypes subject to the 4-gamete test. In a large schizophrenia data set (PGC2-SCZ), haploSNPs with MAF > 0.1% explained substantially more phenotypic variance (hhap2 = 0.64 (S.E. 0.084)) than genotyped SNPs alone (hg2 = 0.32 (S.E. 0.029)). These estimates were based on cross-cohort comparisons, ensuring that cohort-specific assay artifacts did not contribute to our estimates. In a large multiple sclerosis data set (WTCCC2-MS), we observed an even larger difference between hhap2 and hg2, though data from other cohorts will be required to validate this result. Overall, our results suggest that haplotypes of common SNPs can explain a large fraction of missing heritability of complex disease, shedding light on genetic architecture and informing disease mapping strategies.

Genetics↗

bioRxiv2015

Leveraging distant relatedness to quantify human mutation and gene conversion rates

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 13x. We infer a point mutation rate of 1.66 {+/-} 0.04 x 10-8 per base per generation, and a rate of 1.26 {+/-} 0.06 x 10-9 for < 20 bp indels. Our estimated average genome-wide mutation rate is higher than most pedigree-based estimates reported thus far, but lower than estimates obtained using substitution rates across primates. By quantifying how estimates vary as a function of allele frequency, we infer the probability that a site is involved in non-crossover gene conversion as 5.99 {+/-} 0.69 x 10-6, 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.

Genetics↗

bioRxiv2015

Contrasting regional architectures of schizophrenia and other complex diseases using fast variance components analysis

Heritability analyses of GWAS cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here, we analyze the genetic architecture of schizophrenia in 49,806 samples from the PGC, and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which [≥]71% of 1Mb genomic regions harbor at least one variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe significant genetic correlations (ranging from 0.18 to 0.85) among several pairs of GERA diseases; genetic correlations were on average 1.3x stronger than correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multi-component, multi-trait variance components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.

Genetics↗

bioRxiv2015

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk, and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves LD-pruning markers and applying a P-value threshold to association statistics, but this discards information and may reduce predictive accuracy. We introduce a new method, LDpred, which infers the posterior mean causal effect size of each marker using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the pruning/thresholding approach, particularly at large sample sizes. Accordingly, prediction R2 increased from 20.1% to 25.3% in a large schizophrenia data set and from 9.8% to 12.0% in a large multiple sclerosis data set. A similar relative improvement in accuracy was observed for three additional large disease data sets and when predicting in non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Bioinformatics↗

bioRxiv2015

An Atlas of Genetic Correlations across Human Diseases and Traits

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use our method to estimate 300 genetic correlations among 25 traits, totaling more than 1.5 million unique phenotype measurements. Our results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no genome-wide significant SNPs for anorexia nervosa and only three for educational attainment.

Genomics↗

bioRxiv2015

Partitioning heritability by functional category using GWAS summary statistics

Recent work has demonstrated that some functional categories of the genome contribute disproportionately to the heritability of complex diseases. Here, we analyze a broad set of functional elements, including cell-type-specific elements, to estimate their polygenic contributions to heritability in genome-wide association studies (GWAS) of 17 complex diseases and traits spanning a total of 1.3 million phenotype measurements. To enable this analysis, we introduce a new method for partitioning heritability from GWAS summary statistics while controlling for linked markers. This new method is computationally tractable at very large sample sizes, and leverages genome-wide information. Our results include a large enrichment of heritability in conserved regions across many traits; a very large immunological disease-specific enrichment of heritability in FANTOM5 enhancers; and many cell-type-specific enrichments including significant enrichment of central nervous system cell types in body mass index, age at menarche, educational attainment, and smoking behavior. These results demonstrate that GWAS can aid in understanding the biological basis of disease and provide direction for functional follow-up.

Genetics↗

bioRxiv2014

Mixed Model with Correction for Case-Control Ascertainment Increases Association Power

We introduce a Liability Threshold Mixed Linear Model (LTMLM) association statistic for ascertained case-control studies that increases power vs. existing mixed model methods, with a well-controlled false-positive rate. Recent work has shown that existing mixed model methods suffer a loss in power under case-control ascertainment, but no solution has been proposed. Here, we solve this problem using a chi-square score statistic computed from posterior mean liabilities (PML) under the liability threshold model. Each individuals PML is conditional not only on that individuals case-control status, but also on every individuals case-control status and on the genetic relationship matrix obtained from the data. The PML are estimated using a multivariate Gibbs sampler, with the liability-scale phenotypic covariance matrix based on the genetic relationship matrix (GRM) and a heritability parameter estimated via Haseman-Elston regression on case-control phenotypes followed by transformation to liability scale. In simulations of unrelated individuals, the LTMLM statistic was correctly calibrated and achieved higher power than existing mixed model methods in all scenarios tested, with the magnitude of the improvement depending on sample size and severity of case-control ascertainment. In a WTCCC2 multiple sclerosis data set with >10,000 samples, LTMLM was correctly calibrated and attained a 4.1% improvement (P = 0.007) in chi-square statistics (vs. existing mixed model methods) at 75 known associated SNPs, consistent with simulations. Larger increases in power are expected at larger sample sizes. In conclusion, an increase in power over existing mixed model methods is available for ascertained case-control studies of diseases with low prevalence.

Genetics↗

bioRxiv2014

Regulatory variants explain much more heritability than coding variants across 11 common diseases

Common variants implicated by genome-wide association studies (GWAS) of complex diseases are known to be enriched for coding and regulatory variants. We applied methods to partition the heritability explained by genotyped SNPs [Formula] across functional categories (while accounting for shared variance due to linkage disequilibrium) to genotype and imputed data for 11 common diseases. DNaseI Hypersensitivity Sites (DHS) from 218 cell-types, spanning 16% of the genome, explained an average of 79% of [Formula] (5.1x enrichment; P < 10-20); further enrichment was observed at enhancer and cell-type specific DHS elements. The enrichments were much smaller in analyses that did not use imputed data or were restricted to GWAS-associated SNPs. In contrast, coding variants, spanning 1% of the genome, explained only 8% of [Formula] enrichment; P = 5 x 10-4). We replicated these findings but found no significant contribution from rare coding variants in an independent schizophrenia cohort genotyped on GWAS and exome chips.

Genetics↗