Pathway polygenic risk scores (pPRS) for the analysis of gene-environment interaction.

Authors

Gauderman WJ, Fu Y, Queme B, Kawaguchi E, Wang Y, Morrison J, Brenner H, Chan A, Gruber SB, Keku T, Li L, Moreno V, Pellatt AJ, Peters U, Samadder NJ, Schmit SL, Ulrich CM, Um C, Wu A, Lewinger JP, ...show more Drew DA, Mi H

Affiliations

• Division of Biostatistics and Health Data Science, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, United States of America.
• Division of Bioinformatics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, United States of America.
• Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
• Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.
• Center for Precision Medicine and Department of Medical Oncology, City of Hope National Medical Center, Duarte, California, United States of America.
• University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
• Department of Family Medicine, UVA Comprehensive Cancer Center, UVA School of Medicine, Charlottesville, Virginia, United States of America.
• Oncology Data Analytics Program, Catalan Institute of Oncology (ICO), L'Hospitalet de Llobregat, Barcelona, Spain.
• Intermountain Health, Salt Lake City, Utah, United States of America.
• Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, United States of America.

Abstract

A polygenic risk score (PRS) is used to quantify the combined disease risk of many genetic variants. For complex human traits there is interest in determining whether the PRS modifies, i.e. interacts with, important environmental (E) risk factors. Detection of a PRS by environment (PRS x E) interaction may provide clues to underlying biology and can be useful in developing targeted prevention strategies for modifiable risk factors. The standard PRS may include a subset of variants that interact with E but a much larger subset of variants that affect disease without regard to E. This latter subset will dilute the underlying signal in former subset, leading to reduced power to detect PRS x E interaction. We explore the use of pathway-defined PRS (pPRS) scores, using state of the art tools to annotate subsets of variants to genomic pathways. We demonstrate via simulation that testing targeted pPRS x E interaction can yield substantially greater power than testing overall PRS x E interaction. We also analyze a large study (N = 78,253) of colorectal cancer (CRC) where E = non-steroidal anti-inflammatory drugs (NSAIDs), a well-established protective exposure. While no evidence of overall PRS x NSAIDs interaction (p = 0.41) is observed, a significant pPRS x NSAIDs interaction (p = 0.0003) is identified based on SNPs within the TGF-β/ gonadotropin releasing hormone receptor (GRHR) pathway. NSAIDS is protective (OR=0.84) for those at the 5th percentile of the TGF-β/GRHR pPRS (low genetic risk, OR), but significantly more protective (OR=0.70) for those at the 95th percentile (high genetic risk). From a biological perspective, this suggests that NSAIDs may act to reduce CRC risk specifically through genes in these pathways. From a population health perspective, our result suggests that focusing on genes within these pathways may be effective at identifying those for whom NSAIDs-based CRC-prevention efforts may be most effective.