Discovering patterns of pleiotropy in genome-wide association studies

Abstract

Motivation: Genome-wide association studies have had great success in identifying human genetic variants associated with disease, disease risk factors, and other biomedical phenotypes. Many variants are associated with multiple traits, even after correction for trait-trait correlation. Discovering subsets of variants associated with a shared subset of phenotypes could help reveal disease mechanisms, suggest new therapeutic options, and increase the power to detect additional variants with similar pattern of associations. Here we introduce two methods based on a Bayesian framework, SNP And Pleiotropic PHenotype Organization (SAPPHO), one modeling independent phenotypes (SAPPHO-I) and the other incorporating a full phenotype covariance structure (SAPPHO-C).These two methods learn patterns of pleiotropy from genotype and phenotype data, using identified associations to discover additional associations with shared patterns.Results: The SAPPHO methods, along with other recent approaches for pleiotropic association tests, were assessed using data from the Atherosclerotic Risk in Communities (ARIC) study of 8,000 individuals, whose gold-standard associations were provided by meta-analysis of 40,000 to 100,000 individuals from the CHARGE consortium. Using power to detect gold-standard associations * Corresponding author Email address: joel.bader@jhu.edu (Joel S. Bader) Preprint submitted to Nature Communications February 28, 2018peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/273540 doi: bioRxiv preprint first posted online Feb. 28, 2018; at genome-wide significance (0.05 family-wise error rate) as a metric, SAPPHO performed best. The SAPPHO methods were also uniquely able to select the most significant variants in a parsimonious model, excluding other less likely variants within a linkage disequilibrium block. For meta-analysis, the SAPPHO methods implement summary modes that use sufficient statistics rather than full phenotype and genotype data. Meta-analysis applied to CHARGE detected 16 additional associations to the gold-standard loci, as well as 124 novel loci, at 0.05 false discovery rate. Reasons for the superior performance were explored by performing simulations over a range of scenarios describing different genetic architectures. With SAPPHO we were able to learn genetic structures that were hidden using the traditional univariate tests.

HOPS: a quantitative score reveals pervasive horizontal pleiotropy in human genetic variation is driven by extreme polygenicity of human traits and diseases

“…The term "pleiotropy" refers to a single genetic variant having multiple distinct phenotypic effects. In general terms, the existence and extent of pleiotropy has far-reaching implications on our understanding of how genotypes map to phenotypes (1), of the genetic architectures of traits (2,3), of the biology underlying common diseases (4) and of the dynamics of natural selection (5). However, beyond this general idea of the importance of pleiotropy, it quickly becomes difficult to discuss in specifics, because of the difficulty in defining what counts as a direct causal effect and what counts as a separate phenotypic effect.…”

Integrating natural history collections and comparative genomics to study the genetic architecture of convergent evolution

“…Existence of pleiotropy in complex traits has been widely reported in genome-wide association studies (GWAS) [118], and this observation has been a constant challenge for evolutionary-development (evo-devo) studies [119]. Patterns of pleiotropic variants may confound linking genotypic signatures to a particular trait and systematic approaches are required to identify pleiotropic variants and their associations to infer molecular mechanisms shared by multiple traits [120]. For example, pigmentation has been a widely used natural trait to assess the importance of convergent evolution at the genetic level, with Agouti and MC1R being identified as obvious candidate genes to have a strong effect on pigmentation in vertebrates [121].…”

Integrating natural history-derived phenomics with comparative genomics to study the genetic architecture of convergent evolution

“…Existence of pleiotropy in complex traits has been widely reported in genomewide association studies (GWAS) [103], and this observation has been a constant challenge for evolutionary-development (evo-devo) studies [104]. Patterns of pleiotropic variants may confound linking genotypic signatures to a particular trait and systematic approaches are required to identify pleiotropic variants and their associations to infer molecular mechanisms shared by multiple traits [105]. For example, pigmentation has been widely used natural trait to assess the importance of convergent evolution at genetic level with Agouti and MC1R being identified as obvious candidate genes to have strong effect on pigmentation in vertebrates [106].…”