Biology subjects

Schork, A.

Publications and source records attributed to Schork, A..

7 recordsLinked to original sources

bioRxiv2022

Accuracy of haplotype estimation and whole genome imputation affects complex trait analyses in complex biobanks

Sample recruitment for research consortia, hospitals, biobanks, and personal genomics companies span years, necessitating genotyping in batches, using different technologies. As marker content on genotyping arrays varies systematically, integrating such datasets is non-trivial and its impact on haplotype estimation (phasing) and whole genome imputation, necessary steps for complex trait analysis, remains under-evaluated. Using the iPSYCH consortium dataset, comprising 130,438 individuals, genotyped in two stages, on different arrays, we evaluated phasing and imputation performance across multiple phasing methods and data integration protocols. While phasing accuracy varied both by choice of method and data integration protocol, imputation accuracy varied mostly between data integration protocols. We demonstrate an attenuation in imputation accuracy within samples of non-European origin, highlighting challenges to studying complex traits in diverse populations. Finally, imputation errors can modestly bias association tests and reduce predictive utility of polygenic scores. This is the largest, most comprehensive comparison of data integration approaches in the context of a large psychiatric biobank.

bioinformatics↗

bioRxiv2022

Cross-trait assortative mating is widespread and inflates genetic correlation estimates

The observation of genetic correlations between disparate traits has been interpreted as evidence of widespread pleiotropy, altered theories of human genetic architecture, and spurred considerable research activity across the natural and social sciences. Here, we introduce cross-trait assortative mating (xAM) as an alternative explanation for observed genetic correlations. We observe that xAM is common across a broad array of phenotypes and that phenotypic cross-mate correlation estimates are strongly associated with genetic correlation estimates (R2 = 76%). Then, we present theoretical and simulation-based results demonstrating that, under xAM, genetic correlation estimators yield significant estimates even for traits with entirely distinct genetic bases. We demonstrate that existing xAM plausibly accounts for substantial fractions of genetic correlation estimates in two large samples (N = 827,960). For example, previously reported genetic correlation estimates between many pairs of psychiatric disorders are fully consistent with xAM alone. Finally, we provide evidence for a history of xAM at the genetic level using a novel approach based on cross-trait even/odd chromosome polygenic score correlations. Together, our results demonstrate that previous reports have likely overestimated the true genetic similarity between many phenotypes.

genetics↗

bioRxiv2021

Accounting for age-of-onset and family history improves power in genome-wide association studies

Genome-wide association studies (GWAS) have revolutionized human genetics, allowing researchers to identify thousands of disease-related genes and possible drug targets. However, case-control status does not account for the fact that not all controls may have lived through their period of risk for the disorder of interest. This can be quantified by examining the age-of-onset distribution and the age of the controls or the age-of-onset for cases. The age-of-onset distribution may also depend on information such as sex and birth year. In addition, family history is not routinely included in the assessment of control status. Here we present LT-FH++, an extension of the liability threshold model conditioned on family history (LT-FH), that jointly accounts for age-of-onset and sex, as well as family history. Using simulations, we show that, when family history and the age-of-onset distribution are available, the proposed approach yields large power gains over both LT-FH and genome-wide association study by proxy (GWAX). We applied our method to four psychiatric disorders available in the iPSYCH data, and to mortality in the UK Biobank, finding 20 genome-wide significant associations with LT-FH++, compared to 10 for LT-FH and 8 for a standard case-control GWAS. As more genetic data with linked electronic health records become available to researchers, we expect methods that account for additional health information, such as LT-FH++, to become even more beneficial.

bioinformatics↗

bioRxiv2020

Genome-wide association study of school grades identifies a genetic overlap between language ability, psychopathology and creativity

Individuals with psychiatric disorders perform differently in school compared to the general population. Genetic factors contribute substantially to such differences. It is however unclear if differential performance is seen across all cognitive domains such as math and language. Here we report a genome-wide association study (GWAS) of school grades in 30,982 individuals (18,495 with and 12,487 without one or more of six major psychiatric disorders) and a replication study in 4,547 individuals. GWAS of overall school performance yielded results that were highly similar to the results of a previous GWAS of educational attainment. Analyzing subject specific grades, we observed that math performance was severely affected whereas language performance (Danish and English) was relatively unaffected or enhanced in those with psychiatric disorders compared to controls. We found that the genetic variants associated with poor math performance, but better language performance were also associated with increased risk for multiple psychiatric disorders. The same variants were also associated with creativity, which we show through a polygenic score analysis of 2953 creative professionals and 164,622 controls. The results overall suggest that risk for psychiatric disorders, language ability and creativity might have overlapping genetic roots.

genomics↗

bioRxiv2020

Genomic evaluation of circulating proteins for drug target characterisation and precision medicine

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. By mapping and replicating protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, we identified 467 pQTLs for 85 proteins. The pQTLs were used in combination with other sources of information to evaluate known drug targets, and suggest new target candidates or repositioning opportunities, underpinned by a) causality assessment using Mendelian randomization, b) pathway mapping using trans-pQTL gene assignments, and c) protein-centric polygenic risk scores enabling matching of plausible target mechanisms to sub-groups of individuals enabling precision medicine.

genetics↗

bioRxiv2018

Exploring contributors to variability in estimates of SNP-heritability and genetic correlations from the iPSYCH case-cohort and published meta-studies of major psychiatric disorders.

As more and more large psychiatric genetic cohorts are becoming available, more and more independent investigations into the underlying genetic architecture are performed, and an expanding set of replicates for estimates of key genetic parameters, namely, liability scale SNP heritability and genetic correlations - is amassing in the literature. In recent work, we published a set of SNP-heritability and genetic correlation estimates for major psychiatric disorders using data from the iPSYCH case-cohort study, and presented them alongside estimates gleaned from large, independently collected, analyzed and published meta-studies of the same disorders. Although in the broadest sense the estimates from iPSYCH and external meta-studies were concordant, and requiring strict statistical significance could reject the null hypothesis for few pairs, there were enough subtle trends in the differences to warrant further investigation. In this work, we consider a set of factors related to sample ascertainment, including the lifetime risks for disorders for the sampled populations, the use of age censored or partially screened controls, the sampling of extreme cases and controls, and diagnostic error rates, and attempt to assess their potential contributions to estimates of genetic parameters in the context of the difference trends observed in our previous work.

genetics↗

bioRxiv2017

Semi-Parametric Covariate-Modulated Local False Discovery Rate For Genome-Wide Association Studies

While genome-wide association studies (GWAS) have discovered thousands of risk loci for heritable disorders, so far even very large meta-analyses have recovered only a fraction of the heritability of most complex traits. Recent work utilizing variance components models has demonstrated that a larger fraction of the heritability of complex phenotypes is captured by the additive effects of SNPs than is evident only in loci surpassing genome-wide significance thresholds, typically set at a Bonferroni-inspired p [≤] 5 x 10-8. Procedures that control false discovery rate can be more powerful, yet these are still under-powered to detect the majority of non-null effects from GWAS. The current work proposes a novel Bayesian semi-parametric two-group mixture model and develops a Markov Chain Monte Carlo (MCMC) algorithm for a covariate-modulated local false discovery rate (cmfdr). The probability of being non-null depends on a set of covariates via a logistic function, and the non-null distribution is approximated as a linear combination of B-spline densities, where the weight of each B-spline density depends on a multinomial function of the covariates. The proposed methods were motivated by work on a large meta-analysis of schizophrenia GWAS performed by the Psychiatric Genetics Consortium (PGC). We show that the new cmfdr model fits the PGC schizophrenia GWAS test statistics well, performing better than our previously proposed parametric gamma model for estimating the non-null density and substantially improving power over usual fdr. Using loci declared significant at cmfdr [≤] 0.20, we perform follow-up pathway analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) homo sapiens pathways database. We demonstrate that the increased yield from the cmfdr model results in an improved ability to test for pathways associated with schizophrenia compared to using those SNPs selected according to usual fdr.

bioinformatics↗