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Incorporating multiple sets of eQTL weights into gene‐by‐environment interaction analysis identifies novel susceptibility loci for pancreatic cancer
Author(s) -
Yang Tianzhong,
Tang Hongwei,
Risch Harvey A.,
Olson Sarah H.,
Peterson Gloria,
Bracci Paige M.,
Gallinger Steven,
Hung Rayjean J.,
Neale Rachel E.,
Scelo Ghislaine,
Duell Eric J.,
Kurtz Robert C.,
Khaw KayTee,
Severi Gianluca,
Sund Malin,
Wareham Nick,
Amos Christopher I.,
Li Donghui,
Wei Peng
Publication year - 2020
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.22348
Subject(s) - expression quantitative trait loci , biology , computational biology , weighting , genome wide association study , pancreatic cancer , false discovery rate , genetic association , multiple comparisons problem , type i and type ii errors , genomics , quantitative trait locus , trait , genetics , sample size determination , gene , cancer , genome , computer science , statistics , single nucleotide polymorphism , genotype , medicine , mathematics , radiology , programming language
It is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene‐by‐environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data‐adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case–Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome‐wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking‐related pancreatic cancer susceptibility genes, TRIP10 and KDM3A , were identified. The aGEw test is implemented in an R package aGE.