RT Journal Article
SR Electronic
T1 Interactions between genetic variation and cellular environment in skeletal muscle gene expression
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 105429
DO 10.1101/105429
A1 D. Leland Taylor
A1 David A. Knowles
A1 Laura J. Scott
A1 Andrea H. Ramirez
A1 Franceso Paolo Casale
A1 Brooke N. Wolford
A1 Li Guan
A1 Arushi Varshney
A1 Ricardo Oliveira Albanus
A1 Stephen C.J. Parker
A1 Narisu Narisu
A1 Peter S. Chines
A1 Michael R. Erdos
A1 Ryan P. Welch
A1 Leena Kinnunen
A1 Jouko Saramies
A1 Jouko Sundvall
A1 Timo A. Lakka
A1 Markku Laakso
A1 Jaakko Tuomilehto
A1 Heikki A. Koistinen
A1 Oliver Stegle
A1 Michael Boehnke
A1 Ewan Birney
A1 Francis S. Collins
YR 2017
UL http://biorxiv.org/content/early/2017/02/03/105429.abstract
AB 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.