Maturation and application of phenome-wide association studies

• Published in: Trends in genetics Vol. 38; no. 4; pp. 353 - 363
• Main Authors: Liu, Shiying, Crawford, Dana C.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.04.2022
• Subjects: Algorithms; cross-phenotype associations; Genome-Wide Association Study; phenome-wide association studies; Phenotype; pleiotropy; Polymorphism, Single Nucleotide; Software; PheWAS; PRS; GWAS; phenome-wide association studies; EHRs; ICD; pleiotropy; cross-phenotype associations
• ISSN: 0168-9525; 1362-4555
• DOI: 10.1016/j.tig.2021.12.002

In the past 10 years since its introduction, phenome-wide association studies (PheWAS) have uncovered novel genotype–phenotype relationships. Along the way, PheWAS have evolved in many aspects as a study design with the expanded availability of large data repositories with genome-wide data linked to detailed phenotypic data. Advancement in methods, including algorithms, software, and publicly available integrated resources, makes it feasible to more fully realize the potential of PheWAS, overcoming the previous computational and analytical limitations. We review here the most recent improvements and notable applications of PheWAS since the second half of the decade from its inception. We also note the challenges that remain embedded along the entire PheWAS analytical pipeline that necessitate further development of tools and resources to further advance the understanding of the complex genetic architecture underlying human diseases and traits. Pleiotropy, the concept that a gene or genetic variant affects more than one phenotype or trait, is at least a century old.By contrast, phenome-wide association studies (PheWAS), an approach used to identify cross-phenotype associations, were introduced only in the past decade.Still relatively young, PheWAS has rapidly matured into a widely used study design and analytical approach that can still benefit from improvements in its pipeline as genome-wide datasets expand in their breadth and depth, challenging the computational and statistical limits of today’s PheWAS.