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Phenome-Wide Association Studies: Embracing Complexity for Discovery
Author(s) -
Sarah A. Pendergrass,
Anurag Verma,
Anna Okula,
Molly A. Hall,
Dana C. Crawford,
Marylyn D. Ritchie
Publication year - 2015
Publication title -
human heredity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.423
H-Index - 62
eISSN - 1423-0062
pISSN - 0001-5652
DOI - 10.1159/000381851
Subject(s) - phenome , genetic architecture , genome wide association study , pleiotropy , biology , genetic association , exposome , computational biology , leverage (statistics) , data science , quantitative trait locus , genetics , phenotype , computer science , single nucleotide polymorphism , genotype , artificial intelligence , gene
The inherent complexity of biological systems can be leveraged for a greater understanding of the impact of genetic architecture on outcomes, traits, and pharmacological response. The genome-wide association study (GWAS) approach has well-developed methods and relatively straight-forward methodologies; however, the bigger picture of the impact of genetic architecture on phenotypic outcome still remains to be elucidated even with an ever-growing number of GWAS performed. Greater consideration of the complexity of biological processes, using more data from the phenome, exposome, and diverse -omic resources, including considering the interplay of pleiotropy and genetic interactions, may provide additional leverage for making the most of the incredible wealth of information available for study. Here, we describe how incorporating greater complexity into analyses through the use of additional phenotypic data and widespread deployment of phenome-wide association studies may provide new insights into genetic factors influencing diseases, traits, and pharmacological response.

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