TY - JOUR T1 - Interactions between genetic variation and cellular environment in skeletal muscle gene expression JF - bioRxiv DO - 10.1101/105429 SP - 105429 AU - D. Leland Taylor AU - David A. Knowles AU - Laura J. Scott AU - Andrea H. Ramirez AU - Franceso Paolo Casale AU - Brooke N. Wolford AU - Li Guan AU - Arushi Varshney AU - Ricardo Oliveira Albanus AU - Stephen C.J. Parker AU - Narisu Narisu AU - Peter S. Chines AU - Michael R. Erdos AU - Ryan P. Welch AU - Leena Kinnunen AU - Jouko Saramies AU - Jouko Sundvall AU - Timo A. Lakka AU - Markku Laakso AU - Jaakko Tuomilehto AU - Heikki A. Koistinen AU - Oliver Stegle AU - Michael Boehnke AU - Ewan Birney AU - Francis S. Collins Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/02/03/105429.abstract N2 - From whole organisms to individual cells, responses to environmental conditions are influenced by genetic makeup, where the effect of genetic variation on a trait depends on the environmental context. RNA-sequencing quantifies gene expression as a molecular trait, and is capable of capturing both genetic and environmental effects. In this study, we explore opportunities of using allele-specific expression (ASE) to discover cis acting genotype-environment interactions (GxE) - genetic effects on gene expression that depend on an environmental condition. Treating 17 common, clinical traits as approximations of the cellular environment of 267 skeletal muscle biopsies, we identify 10 candidate interaction quantitative trait loci (iQTLs) across 6 traits (12 unique gene-environment trait pairs; 10% FDR per trait) including sex, systolic blood pressure, and low-density lipoprotein cholesterol. Although using ASE is in principle a promising approach to detect GxE effects, replication of such signals can be challenging as validation requires harmonization of environmental traits across cohorts and a sufficient sampling of heterozygotes for a transcribed SNP. Comprehensive discovery and replication will require large human transcriptome datasets, or the integration of multiple transcribed SNPs, coupled with standardized clinical phenotyping. ER -