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

Abstract

With the large volume of clinical and epidemiological data being collected, increasingly linked to extensive genotypic data, coupled with expanding high-performance computational resources, there are considerable opportunities for comprehensively exploring the networks of connections that exist between the phenome and the genome. These networks can be identified through Phenome-Wide Association Studies (PheWAS) where the association between a collection of genetic variants, or in some cases a particular clinical lab variable, and a wide and diverse range of phenotypes, diagnoses, traits, and/or outcomes are evaluated. This is a departure from the more familiar genome-wide association study (GWAS) approach, which has been used to identify single nucleotide polymorphisms (SNPs) associated with one outcome or a very limited phenotypic domain. In addition to highlighting novel connections between multiple phenotypes and elucidating more of the phenotype-genotype landscape, PheWAS can generate new hypotheses for further exploration, and can also be used to narrow the search space for research using comprehensive data collections. The complex results of PheWAS also have the potential for uncovering new mechanistic insights. We review here how the PheWAS approach has been used with data from epidemiological studies, clinical trials, and de-identified electronic health record data. We also review methodologies for the analyses underlying PheWAS, and emerging methods developed for evaluating the comprehensive results of PheWAS including genotype-phenotype networks. This review also highlights PheWAS as an important tool for identifying new biomarkers, elucidating the genetic architecture of complex traits, and uncovering pleiotropy. There are many directions and new methodologies for the future of PheWAS analyses, from the phenotypic data to the genetic data, and herein we also discuss some of these important future PheWAS developments.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

“…Incorporating other structured data, such as continuous lab values, is more challenging and may require pre‐processing. The development and use of automated algorithms for making these data useful for phenotyping are essential . Additional expert input (eg, through a consortium) can be used to create phenotype definitions, however, establishing a well‐accepted definition requires time, careful thought, and discussion.…”

“…The development and use of automated algorithms for making these data useful for phenotyping are essential. 50 Additional expert input (eg, through a consortium) can be used to create phenotype definitions, however, establishing a well-accepted definition requires time, careful thought, and discussion. The eMERGE Phenotype Knowledgebase 51 (PheKB) details existing phenotyping algorithms for individual phenotypes that incorporate additional patient information.…”

Current Scope and Challenges in Phenome-Wide Association Studies

“…The PheWAS methodology will become a run-of-the-mill approach to generate new hypotheses to study the interconnection between a wide range of disorders and associations across the genome. Although, there can be some challenges with the genome-wide PheWAS analysis such as multiple hypothesis testing and computational burden, which lead to a challenge in identifying true pleiotropic associations, biologically relevant associations, and interpreting the results in a high-throughput manner[13–16]. …”

“…The first PheWAS analysis illustrated the value of using billing codes within EHR for retrospective genomic studies. Since then, the utility of EHR data has exponentially grown for genomic studies from understanding the underlying biology of complex diseases to novel drug targets and their side effects[13–16]. The genetic component of PheWAS is not limited to SNPs; we can use structural variations (copy number variations), mitochondrial variation[17], gene regions for low-frequency and rare variants (population allele frequency < 1%)[18] as well as non-genetic measures such as clinical laboratory measures[19] and quantitative measures derived from biomarkers.…”

Phenome-Wide Association Studies: Embracing Complexity for Discovery

“…Thus far, many of the examples of the use of phenomic and genetic data described here can be applied to PheWAS. PheWAS began with exploring the association between SNPs and up to thousands of phenotypes, using high-throughput linear or logistic regression depending on the phenotypes [104,105] . These studies using SNPs for PheWAS have been pursued with 116 a range of data sets, such as data from a series of epidemiological studies across genetic ancestry from the Population Architecture Using Genomics and Epidemiology (PAGE) I Study [106] as well as with data from the NHANES conducted by the CDC accessed by the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) [107] .…”