Finding associated variants in genome-wide association studies on multiple traits

AbstractGenome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is de-spite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.1Author SummaryGenome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time (‘univariate’); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously (‘multivariate’). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected.

Download Full-text

Pleiotropic Mapping and Annotation Selection in Genome-wide Association Studies with Penalized Gaussian Mixture Models

10.1101/256461 ◽

2018 ◽

Author(s):

Ping Zeng ◽

Xinjie Hao ◽

Xiang Zhou

Keyword(s):

Association Mapping ◽

Complex Traits ◽

Association Studies ◽

Penalized Regression ◽

Genome Wide Association ◽

Accurate Estimation ◽

Genome Wide Association Studies ◽

Multiple Traits ◽

Snp Association ◽

Genome Wide

AbstractMotivationGenome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci are associated with multiple traits – a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide SNPs together.ResultsWe develop a joint modeling method, which we refer to as the integrative MApping of Pleiotropic association (iMAP). iMAP models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation, simultaneously infers genome-wide SNP association pattern using mixture modeling, and has the potential to reveal causal relationship between traits. Importantly, iMAP integrates a large number of SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty, is capable of selecting informative annotations from a large, potentially noninformative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we develop an efficient expectation maximization algorithm based on an approximate penalized regression algorithm. With simulations and comparisons to existing methods, we illustrate the benefits of iMAP both in terms of high association mapping power and in terms of accurate estimation of genome-wide SNP association patterns. Finally, we apply iMAP to perform a joint analysis of 48 traits from 31 GWAS consortia together with 40 tissue-specific SNP annotations generated from the Roadmap Project. iMAP is freely available at www.xzlab.org/software.html.

Download Full-text

Multi-trait genome-wide association study identifies new loci associated with optic disc parameters

Communications Biology ◽

10.1038/s42003-019-0634-9 ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 2

Author(s):

Pieter W. M. Bonnemaijer ◽

◽

Elisabeth M. van Leeuwen ◽

Adriana I. Iglesias ◽

Puya Gharahkhani ◽

...

Keyword(s):

Genome Wide Association Study ◽

Open Angle Glaucoma ◽

Association Studies ◽

Corneal Thickness ◽

Genome Wide Association ◽

Joint Analysis ◽

Genome Wide Association Studies ◽

Multiple Traits ◽

Trait Analysis ◽

Genome Wide

AbstractA new avenue of mining published genome-wide association studies includes the joint analysis of related traits. The power of this approach depends on the genetic correlation of traits, which reflects the number of pleiotropic loci, i.e. genetic loci influencing multiple traits. Here, we applied new meta-analyses of optic nerve head (ONH) related traits implicated in primary open-angle glaucoma (POAG); intraocular pressure and central corneal thickness using Haplotype reference consortium imputations. We performed a multi-trait analysis of ONH parameters cup area, disc area and vertical cup-disc ratio. We uncover new variants; rs11158547 in PPP1R36-PLEKHG3 and rs1028727 near SERPINE3 at genome-wide significance that replicate in independent Asian cohorts imputed to 1000 Genomes. At this point, validation of these variants in POAG cohorts is hampered by the high degree of heterogeneity. Our results show that multi-trait analysis is a valid approach to identify novel pleiotropic variants for ONH.

Download Full-text

Integrative functional linear model for genome-wide association studies with multiple traits

Biostatistics ◽

10.1093/biostatistics/kxaa043 ◽

2020 ◽

Author(s):

Yang Li ◽

Fan Wang ◽

Mengyun Wu ◽

Shuangge Ma

Keyword(s):

Linear Model ◽

Association Studies ◽

Genome Wide Association ◽

High Dimensionality ◽

Sufficient Information ◽

Genome Wide Association Studies ◽

Multiple Traits ◽

Functional Linear Model ◽

Genome Wide ◽

Good Prediction Accuracy

Summary In recent biomedical research, genome-wide association studies (GWAS) have demonstrated great success in investigating the genetic architecture of human diseases. For many complex diseases, multiple correlated traits have been collected. However, most of the existing GWAS are still limited because they analyze each trait separately without considering their correlations and suffer from a lack of sufficient information. Moreover, the high dimensionality of single nucleotide polymorphism (SNP) data still poses tremendous challenges to statistical methods, in both theoretical and practical aspects. In this article, we innovatively propose an integrative functional linear model for GWAS with multiple traits. This study is the first to approximate SNPs as functional objects in a joint model of multiple traits with penalization techniques. It effectively accommodates the high dimensionality of SNPs and correlations among multiple traits to facilitate information borrowing. Our extensive simulation studies demonstrate the satisfactory performance of the proposed method in the identification and estimation of disease-associated genetic variants, compared to four alternatives. The analysis of type 2 diabetes data leads to biologically meaningful findings with good prediction accuracy and selection stability.

Download Full-text

High-Performance Mixed Models Based Genome-Wide Association Analysis with omicABEL software

F1000Research ◽

10.12688/f1000research.4867.1 ◽

2014 ◽

Vol 3 ◽

pp. 200 ◽

Cited By ~ 11

Author(s):

Diego Fabregat-Traver ◽

Sodbo Zh. Sharapov ◽

Caroline Hayward ◽

Igor Rudan ◽

Harry Campbell ◽

...

Keyword(s):

High Performance ◽

Mixed Model ◽

Association Studies ◽

Structured Populations ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Multiple Traits ◽

Multiple Trait ◽

Model Based ◽

Genome Wide

To raise the power of genome-wide association studies (GWAS) and avoid false-positive results in structured populations, one can rely on mixed model based tests. When large samples are used, and when multiple traits are to be studied in the ’omics’ context, this approach becomes computationally challenging. Here we consider the problem of mixed-model based GWAS for arbitrary number of traits, and demonstrate that for the analysis of single-trait and multiple-trait scenarios different computational algorithms are optimal. We implement these optimal algorithms in a high-performance computing framework that uses state-of-the-art linear algebra kernels, incorporates optimizations, and avoids redundant computations,increasing throughput while reducing memory usage and energy consumption. We show that, compared to existing libraries, our algorithms and software achieve considerable speed-ups. The OmicABEL software described in this manuscript is available under the GNUGPL v. 3 license as part of the GenABEL project for statistical genomics at http: //www.genabel.org/packages/OmicABEL.

Download Full-text

Fast and Accurate Genome-Wide Association Test of Multiple Quantitative Traits

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/2564531 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Baolin Wu ◽

James S. Pankow

Keyword(s):

Quantitative Traits ◽

Association Studies ◽

R Package ◽

Genome Wide Association ◽

Type I ◽

Genome Wide Association Studies ◽

Competitive Performance ◽

Multiple Traits ◽

Atherosclerosis Risk In Communities ◽

Genome Wide

Multiple correlated traits are often collected in genetic studies. By jointly analyzing multiple traits, we can increase power by aggregating multiple weak effects and reveal additional insights into the genetic architecture of complex human diseases. In this article, we propose a multivariate linear regression-based method to test the joint association of multiple quantitative traits. It is flexible to accommodate any covariates, has very accurate control of type I errors, and offers very competitive performance. We also discuss fast and accurate significance p value computation especially for genome-wide association studies with small-to-medium sample sizes. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to genome-wide association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) study. We found some very interesting associations with diabetes traits which have not been reported before. We implemented the proposed methods in a publicly available R package.

Download Full-text

Multi-Trait Genome-Wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture

Plant and Cell Physiology ◽

10.1093/pcp/pcaa039 ◽

2020 ◽

Vol 61 (8) ◽

pp. 1427-1437 ◽

Cited By ~ 6

Author(s):

Brian R Rice ◽

Samuel B Fernandes ◽

Alexander E Lipka

Keyword(s):

Quantitative Trait ◽

Genome Wide Association Study ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Multiple Traits ◽

Leaf Architecture ◽

Genome Wide ◽

Multivariate Gwas ◽

Genomic Regions

Abstract Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.

Download Full-text