scholarly journals A unifying framework for joint trait analysis under a non-infinitesimal model

2018 ◽  
Author(s):  
Ruth Johnson ◽  
Huwenbo Shi ◽  
Bogdan Pasaniuc ◽  
Sriram Sankararaman
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Posterior Density ◽  
Trait Analysis ◽  
Genome Wide ◽  
Genetic Overlap ◽  
Causal Variants ◽  
Supplementary Material

AbstractMotivationA large proportion of risk regions identified by genome-wide association studies (GWAS) are shared across multiple diseases and traits. Understanding whether this clustering is due to sharing of causal variants or chance colocalization can provide insights into shared etiology of complex traits and diseases.ResultsIn this work, we propose a flexible, unifying framework to quantify the overlap between a pair of traits called UNITY (Unifying Non-Infinitesimal Trait analYsis). We formulate a Bayesian generative model that relates the overlap between pairs of traits to GWAS summary statistic data under a non-infinitesimal genetic architecture underlying each trait. We propose a Metropolis-Hastings sampler to compute the posterior density of the genetic overlap parameters in this model. We validate our method through comprehensive simulations and analyze summary statistics from height and BMI GWAS to show that it produces estimates consistent with the known genetic makeup of both traits.AvailabilityThe UNITY software is made freely available to the research community at: https://github.com/bogdanlab/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Genetic overlap between idiopathic pulmonary fibrosis and COVID−19

2021 ◽  
Author(s):  
Richard J Allen ◽  
Beatriz Guillen-Guio ◽  
Emma Croot ◽  
Luke M Kraven ◽  
Samuel Moss ◽  
...  
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Genetic Loci ◽  
Genome Wide ◽  
Genetic Overlap ◽  
Causal Variants ◽  
Shared Genetic

AbstractGenome-wide association studies (GWAS) of coronavirus disease 2019 (COVID-19) and idiopathic pulmonary fibrosis (IPF) have identified genetic loci associated with both traits, suggesting possible shared biological mechanisms. Using updated GWAS of COVID-19 and IPF, we evaluated the genetic overlap between these two diseases and identified four genetic loci (including one novel) with likely shared causal variants between severe COVID-19 and IPF. Although there was a positive genetic correlation between COVID-19 and IPF, two of these four shared genetic loci had an opposite direction of effect. IPF-associated genetic variants related to telomere dysfunction and spindle assembly showed no association with COVID-19 phenotypes. Together, these results suggest there are both shared and distinct biological processes driving IPF and severe COVID-19 phenotypes.

scholarly journals Phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories revealed by AI-MiXeR

2020 ◽  
Vol 36 (18) ◽  
pp. 4749-4756 ◽  
Author(s):  
Alexey A Shadrin ◽  
Oleksandr Frei ◽  
Olav B Smeland ◽  
Francesco Bettella ◽  
Kevin S O'Connell ◽  
...  
Keyword(s):  
Functional Annotation ◽  
Association Studies ◽  
Effect Sizes ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Protein Coding ◽  
Genome Wide ◽  
Whole Exome ◽  
Causal Variants ◽  
Complex Human Traits

Abstract Motivation Determining the relative contributions of functional genetic categories is fundamental to understanding the genetic etiology of complex human traits and diseases. Here, we present Annotation Informed-MiXeR, a likelihood-based method for estimating the number of variants influencing a phenotype and their effect sizes across different functional annotation categories of the genome using summary statistics from genome-wide association studies. Results Extensive simulations demonstrate that the model is valid for a broad range of genetic architectures. The model suggests that complex human phenotypes substantially differ in the number of causal variants, their localization in the genome and their effect sizes. Specifically, the exons of protein-coding genes harbor more than 90% of variants influencing type 2 diabetes and inflammatory bowel disease, making them good candidates for whole-exome studies. In contrast, <10% of the causal variants for schizophrenia, bipolar disorder and attention-deficit/hyperactivity disorder are located in protein-coding exons, indicating a more substantial role of regulatory mechanisms in the pathogenesis of these disorders. Availability and implementation The software is available at: https://github.com/precimed/mixer. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals emeraLD: Rapid Linkage Disequilibrium Estimation with Massive Data Sets

2018 ◽  
Author(s):  
Corbin Quick ◽  
Christian Fuchsberger ◽  
Daniel Taliun ◽  
Gonçalo Abecasis ◽  
Michael Boehnke ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Association Studies ◽  
Random Access ◽  
Supplementary Information ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Wide Range ◽  
Supplementary Material

AbstractSummaryEstimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies (GWAS). Large genetic data sets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD orders of magnitude faster than existing tools.Availability and ImplementationemeraLD is implemented in C++, and is open source under GPLv3. Source code, documentation, an R interface, and utilities for analysis of summary statistics are freely available at http://github.com/statgen/[email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals Bayesian weighted Mendelian randomization for causal inference based on summary statistics

2019 ◽  
Author(s):  
Jia Zhao ◽  
Jingsi Ming ◽  
Xianghong Hu ◽  
Gang Chen ◽  
Jin Liu ◽  
...  
Keyword(s):  
Causal Inference ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Genome Wide ◽  
Complex Human Traits

Abstract Motivation The results from Genome-Wide Association Studies (GWAS) on thousands of phenotypes provide an unprecedented opportunity to infer the causal effect of one phenotype (exposure) on another (outcome). Mendelian randomization (MR), an instrumental variable (IV) method, has been introduced for causal inference using GWAS data. Due to the polygenic architecture of complex traits/diseases and the ubiquity of pleiotropy, however, MR has many unique challenges compared to conventional IV methods. Results We propose a Bayesian weighted Mendelian randomization (BWMR) for causal inference to address these challenges. In our BWMR model, the uncertainty of weak effects owing to polygenicity has been taken into account and the violation of IV assumption due to pleiotropy has been addressed through outlier detection by Bayesian weighting. To make the causal inference based on BWMR computationally stable and efficient, we developed a variational expectation-maximization (VEM) algorithm. Moreover, we have also derived an exact closed-form formula to correct the posterior covariance which is often underestimated in variational inference. Through comprehensive simulation studies, we evaluated the performance of BWMR, demonstrating the advantage of BWMR over its competitors. Then we applied BWMR to make causal inference between 130 metabolites and 93 complex human traits, uncovering novel causal relationship between exposure and outcome traits. Availability and implementation The BWMR software is available at https://github.com/jiazhao97/BWMR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SparsePro: an efficient genome-wide fine-mapping method integrating summary statistics and functional annotations

2021 ◽  
Author(s):  
Wenmin Zhang ◽  
Hamed S Najafabadi ◽  
Yue Li
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Association Studies ◽  
Computational Cost ◽  
Mapping Method ◽  
Genome Wide Association Studies ◽  
Functional Annotations ◽  
Genome Wide ◽  
Causal Variants

Identifying causal variants from genome-wide association studies (GWASs) is challenging due to widespread linkage disequilibrium (LD). Functional annotations of the genome may help prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. However, classical fine-mapping methods have a high computational cost, particularly when the underlying genetic architecture and LD patterns are complex. Here, we propose a novel approach, SparsePro, to efficiently conduct functionally informed statistical fine-mapping. Our method enjoys two major innovations: First, by creating a sparse low-dimensional projection of the high-dimensional genotype, we enable a linear search of causal variants instead of an exponential search of causal configurations used in existing methods; Second, we adopt a probabilistic framework with a highly efficient variational expectation-maximization algorithm to integrate statistical associations and functional priors. We evaluate SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved more accurate and well-calibrated posterior inference with greatly reduced computation time. We demonstrate the utility of SparsePro by investigating the genetic architecture of five functional biomarkers of vital organs. We identify potential causal variants contributing to the genetically encoded coordination mechanisms between vital organs and pinpoint target genes with potential pleiotropic effects. In summary, we have developed an efficient genome-wide fine-mapping method with the ability to integrate functional annotations. Our method may have wide utility in understanding the genetics of complex traits as well as in increasing the yield of functional follow-up studies of GWASs.

scholarly journals Widespread allelic heterogeneity in complex traits

2016 ◽  
Author(s):  
Farhad Hormozdiari ◽  
Anthony Zhu ◽  
Gleb Kichaev ◽  
Ayellet V. Segrè ◽  
Chelsea J.-T. Ju ◽  
...  
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Computational Method ◽  
Allelic Heterogeneity ◽  
Genome Wide Association Studies ◽  
New Methods ◽  
Genome Wide ◽  
Causal Variants

AbstractRecent successes in genome-wide association studies (GWASs) make it possible to address important questions about the genetic architecture of complex traits, such as allele frequency and effect size. One lesser-known aspect of complex traits is the extent of allelic heterogeneity (AH) arising from multiple causal variants at a locus. We developed a computational method to infer the probability of AH and applied it to three GWAS and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4152 loci with strong evidence of AH. The proportion of all loci with identified AH is 4-23% in eQTLs, 35% in GWAS of High-Density Lipoprotein (HDL), and 23% in schizophrenia. For eQTLs, we observed a strong correlation between sample size and the proportion of loci with AH (R2=0.85, P = 2.2e-16), indicating that statistical power prevents identification of AH in other loci. Understanding the extent of AH may guide the development of new methods for fine mapping and association mapping of complex traits.

scholarly journals Identifying causal variants by fine mapping across multiple studies

PLoS Genetics ◽  
2021 ◽  
Vol 17 (9) ◽  
pp. e1009733
Author(s):  
Nathan LaPierre ◽  
Kodi Taraszka ◽  
Helen Huang ◽  
Rosemary He ◽  
Farhad Hormozdiari ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Genome Wide Association Studies ◽  
Multivariate Normal ◽  
Multiple Study ◽  
Genome Wide ◽  
Causal Variants ◽  
Different Populations

Increasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. We demonstrate the efficacy of MsCAVIAR in both a simulation study and a trans-ethnic, trans-biobank fine mapping analysis of High Density Lipoprotein (HDL).

scholarly journals JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts

2017 ◽  
Author(s):  
Chris Chatzinakos ◽  
Donghyung Lee ◽  
Bradley T Webb ◽  
Vladimir I Vladimirov ◽  
Kenneth S Kendler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Supplementary Information ◽  
Joint Analysis ◽  
Genome Wide Association Studies ◽  
Expression Studies ◽  
Genome Wide ◽  
Gene Level ◽  
Supplementary Material ◽  
Gene Expression Studies

AbstractMotivationTo increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on i) summary statistics from genome-wide association studies (GWAS) and ii) linkage disequilibrium (LD) patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals.ResultsWe propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses i) cis-eQTL SNPs from the latest expression studies and ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and ii), to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of [email protected] informationSupplementary material is available at Bioinformatics online.

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

2016 ◽  
Author(s):  
Nicholas Mancuso ◽  
Huwenbo Shi ◽  
Pagé Goddard ◽  
Gleb Kichaev ◽  
Alexander Gusev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Correlation ◽  
Complex Traits ◽  
Association Studies ◽  
Susceptibility Genes ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Causal Variants

AbstractAlthough 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.

scholarly journals Identifying Causal Variants by Fine Mapping Across Multiple Studies

2020 ◽  
Author(s):  
Nathan LaPierre ◽  
Kodi Taraszka ◽  
Helen Huang ◽  
Rosemary He ◽  
Farhad Hormozdiari ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Multiple Study ◽  
Current State ◽  
Genome Wide ◽  
Causal Variants ◽  
Different Populations

AbstractIncreasingly large Genome-Wide Association Studies (GWAS) have yielded numerous variants associated with many complex traits, motivating the development of “fine mapping” methods to identify which of the associated variants are causal. Additionally, GWAS of the same trait for different populations are increasingly available, raising the possibility of refining fine mapping results further by leveraging different linkage disequilibrium (LD) structures across studies. Here, we introduce multiple study causal variants identification in associated regions (MsCAVIAR), a method that extends the popular CAVIAR fine mapping framework to a multiple study setting using a random effects model. MsCAVIAR only requires summary statistics and LD as input, accounts for uncertainty in association statistics using a multivariate normal model, allows for multiple causal variants at a locus, and explicitly models the possibility of different SNP effect sizes in different populations. In a trans-ethnic, trans-biobank Type 2 Diabetes analysis, we show that MsCAVIAR returns causal set sizes that are over 20% smaller than those given by current state of the art methods for trans-ethnic fine-mapping.

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