Phenome-wide association studies (PheWASs) for functional variants

Abstract

The genome-wide association study (GWAS) is a powerful approach for studying the genetic complexities of human disease. Unfortunately, GWASs often fail to identify clinically significant associations and describing function can be a challenge. GWAS is a phenotype-to-genotype approach. It is now possible to conduct a converse genotype-to-phenotype approach using extensive electronic medical records to define a phenome. This approach associates a single genetic variant with many phenotypes across the phenome and is called a phenome-wide association study (PheWAS). The majority of PheWASs conducted have focused on variants identified previously by GWASs. This approach has been efficient for rediscovering gene-disease associations while also identifying pleiotropic effects for some single-nucleotide polymorphisms (SNPs). However, the use of SNPs identified by GWAS in a PheWAS is limited by the inherent properties of the GWAS SNPs, including weak effect sizes and difficulty when translating discoveries to function. To address these challenges, we conducted a PheWAS on 105 presumed functional stop-gain and stop-loss variants genotyped on 4235 Marshfield Clinic patients. Associations were validated on an additional 10 640 Marshfield Clinic patients. PheWAS results indicate that a nonsense variant in ARMS2 (rs2736911) is associated with age-related macular degeneration (AMD). These results demonstrate that focusing on functional variants may be an effective approach when conducting a PheWAS.

Current Scope and Challenges in Phenome-Wide Association Studies

“…The majority of PheWAS studies have used data from de-identified Electronic Health Records [9,11,17,20–28] (EHRs) linked to genotype data, while a few have been performed in large-scale epidemiologic studies[29–32] and clinical trials[33,34]. The representation of the phenome varies in each of these types of studies.…”

Phenome-Wide Association Studies: Leveraging Comprehensive Phenotypic and Genotypic Data for Discovery

“…Using public repositories of evidence for SNP functionality can provide a way to focus on SNPs more likely to impact phenotype. One PheWAS within the Marshfield PMRP used 105 presumed functional stop-gain and stop-loss variants, and identified a nonsense variant in ARMS2 associated with age-related macular degeneration [12]. In another PheWAS from the eMERGE network, Verma et al[37], used multiple sources of information to identify 25 SNPs known or highly likely to be stop-gain inducing variants.…”

“…PheWAS began with investigation of the association between multiple SNPs and de-identified electronic health record (EHR) data [5], and has now been used successfully several times with EHR data [6–12]. Since then, PheWAS has been used with epidemiological study data and clinical trials data [13–15].…”

Fangjiomics: revealing adaptive omics pharmacological mechanisms of the myriad combination therapies to achieve personalized medicine

“…Recent findings from studies of pharmacogenomics and the newly-developed pharmacoproteomics [3] and pharmacometabolomics [4] have revealed pervasive effects of genetic polymorphisms on drug efficacy or toxicity, not only urging for personalized drug treatments according to individual patient's genetic characteristics but also adding more layers of complexity to the combination of multi-drug regimen [4] . Furthermore, the current definition of clinical phenotypes of disease (eg, hypertension, heart failure, type-2 diabetes, etc) and related drug therapy has shown serious limitations that conceal the ever-increasing details of genomic variants discovered by the genome-wide association studies (GWAs) [5] . Clearly, newer approaches are needed for Western medicine to refine the symptoms-or evidence-based definition Editorial of disease and drug responses so that the intrinsic complexity of the disease, the outcomes of drug treatment, and the impact of multiple environmental factors can be unequivocally characterized and classified.…”