Phenome-Wide Association Study (PheWAS) for Detection of Pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network

Abstract

Using a phenome-wide association study (PheWAS) approach, we comprehensively tested genetic variants for association with phenotypes available for 70,061 study participants in the Population Architecture using Genomics and Epidemiology (PAGE) network. Our aim was to better characterize the genetic architecture of complex traits and identify novel pleiotropic relationships. This PheWAS drew on five population-based studies representing four major racial/ethnic groups (European Americans (EA), African Americans (AA), Hispanics/Mexican-Americans, and Asian/Pacific Islanders) in PAGE, each site with measurements for multiple traits, associated laboratory measures, and intermediate biomarkers. A total of 83 single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) were genotyped across two or more PAGE study sites. Comprehensive tests of association, stratified by race/ethnicity, were performed, encompassing 4,706 phenotypes mapped to 105 phenotype-classes, and association results were compared across study sites. A total of 111 PheWAS results had significant associations for two or more PAGE study sites with consistent direction of effect with a significance threshold of p<0.01 for the same racial/ethnic group, SNP, and phenotype-class. Among results identified for SNPs previously associated with phenotypes such as lipid traits, type 2 diabetes, and body mass index, 52 replicated previously published genotype–phenotype associations, 26 represented phenotypes closely related to previously known genotype–phenotype associations, and 33 represented potentially novel genotype–phenotype associations with pleiotropic effects. The majority of the potentially novel results were for single PheWAS phenotype-classes, for example, for CDKN2A/B rs1333049 (previously associated with type 2 diabetes in EA) a PheWAS association was identified for hemoglobin levels in AA. Of note, however, GALNT2 rs2144300 (previously associated with high-density lipoprotein cholesterol levels in EA) had multiple potentially novel PheWAS associations, with hypertension related phenotypes in AA and with serum calcium levels and coronary artery disease phenotypes in EA. PheWAS identifies associations for hypothesis generation and exploration of the genetic architecture of complex traits.

The ubiquity of pleiotropy in human disease

“…Pleiotropy, defined here as one gene affecting more than one phenotype, has become increasingly important in interpreting both genotype-phenotype maps and underlying factors that affect comorbidity, and has been integrated into several methods for genome wide association studies (GWAS) (Chung et al 2014;Hill and Zhang 2012;Lee et al 2012;Li et al 2014a, b;Pendergrass et al 2013;Rzhetsky et al 2007;Sivakumaran et al 2011;Solovieff et al 2013;Stearns 2010;Wagner et al 2008;Wagner and Zhang 2011;Yang et al 2015). Studies of pleiotropy can impact our understanding of underlying disease processes, and implicitly, the prevention and treatment of disease as well as predicting adverse reactions to drugs.…”

“…Despite the recognized importance of pleiotropy in developing a comprehensive understanding of gene-disease relationships (Chung et al 2014;Hill and Zhang 2012;Lee et al 2012;Li et al 2014a, b;Pendergrass et al 2013;Rzhetsky Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00439-017-1854-z) contains supplementary material, which is available to authorized users. et al 2007;Sivakumaran et al 2011;Solovieff et al 2013;Stearns 2010;Wagner et al 2008;Wagner and Zhang 2011;Yang et al 2015), little has been done to investigate the relationship between pleiotropy and risk variation as measured by effect sizes.…”

Evidence for Extensive Pleiotropy Among Pharmacogenes

“…Early studies of the National Human Genome Research Institute GWAS Catalog revealed extensive pleiotropy among the common variants associated with complex traits. Many such examples have since been replicated and annotated in traditional epidemiologic and contemporary clinical collections [21][22][23][24][25][26].…”

Pathway and network-based strategies to translate genetic discoveries into effective therapies

“…Another major source of phenotyping data is the electronic health record (44,45). The breadth of these data presents both challenges and opportunities (46).…”