Computational Tools for Discovery and Interpretation of Expression Quantitative Trait Loci

Abstract

Expression quantitative trait locus (eQTL) analysis is rapidly moving from a cutting-edge concept in genomics to a mature area of investigation, with important connections to genome-wide association studies for human disease, pharmacogenomics and toxicogenomics. Despite the importance of the topic, many investigators must develop their own code or use tools not specifically suited for eQTL analysis. Convenient computational tools are becoming available, but they are not widely publicized, and investigators who are interested in discovery or eQTL, or in using them to interpret genome-wide association study results may have difficulty navigating the available resources. The purpose of this review is to help investigators find appropriate programs for eQTL analysis and interpretation.

The Next Generation of Risk Assessment Multi-Year Study—Highlights of Findings, Applications to Risk Assessment, and Future Directions

“…Each chemical exposure–health outcome pair involves combinations of these sources, and different decision contexts present distinct needs regarding the identification—and extent of characterization—of interindividual variability in the human population (see Figure 2). New approaches to examining variability in responses include a ) computational modeling, in which variability in parameter values is simulated and differences among subpopulations are explored (Diaz Ochoa et al 2013; Knudsen and DeWoskin 2011; Knudsen et al 2015; Shah and Wambaugh 2010); b ) HT in vitro data analysis of cell lines with different genetic backgrounds from the 1000 Genomes effort (Abdo et al 2015a, 2015b; Eduati et al 2015; Lock et al 2012; O’Shea et al 2011); c ) human clinical and in vivo animal studies in genetically diverse individuals to identify genetic and epigenetic determinants of susceptibility (French et al 2015; Harrill et al 2009a, 2009b; McCullough et al 2016); d ) comprehensive scanning of gene coding regions in diverse individuals to examine the relationships among environmental exposures, interindividual sequence variation in human genes, and population disease risks (Mortensen and Euling 2013; NIEHS 2015); e ) genome-wide association studies to uncover genomic loci that might contribute to risk of disease (NHGRI 2015; Wright et al 2012); and f ) association studies correlating phenotypic differences among diverse populations with expression patterns for groups of genes based on coexpression (Friend 2013; Patel et al 2012, 2013a; Weiss et al 2012). Additionally, understanding of the contribution of epigenomics to disease is the focus of much research (Ghantous et al 2015).…”

Associations of common variants in the BST2 region with HIV-1 acquisition in African American and European American people who inject drugs

“…We also queried the University of Chicago QTL browser [55], which revealed 6 SNPs in BST2 and its 50kb flanking regions as QTLs. Four of the 6 SNPs were identified by Degner et al [56] as DNase sensitivity QTLs in lymphoblast cell lines from African subjects: (1) rs73921425, shown as chr19.17377626 based on its NCBI build 36 position, was reported by Skelton et al [13] and located 2 base pairs away from rs12609479; (2) rs28413174, shown as chr19.17377620, was also reported by Skelton et al [13] but was not associated with HIV-1 acquisition in our study (meta-analysis P =0.098); (3) rs8102791, shown as chr19.17366933, was associated with HIV-1 acquisition at meta-analysis P =8.18×10 −3 ; and (4) rs8106139, shown as chr19.17367176, was associated with HIV-1 acquisition at meta-analysis P =4.72×10 −3 .…”

“…SNP regulatory potential was assessed using four bioinformatics databases: (1) RegulomeDB [52], (2) SNPinfo [53], (3) HaploReg v2 [54], and (4) University of Chicago expression quantitative trait loci (eQTL) browser [55]. Each database provided unique information on predicted regulatory states from the Encyclopedia of DNA Elements, the Roadmap Epigenome Mapping Consortium, the Genotype-Tissue Expression project, large-scale QTL studies, and elsewhere.…”

From association to prediction: statistical methods for the dissection and selection of complex traits in plants

“…Thus a GWAS for gene expression should use the standard statistical models employed in a typical GWAS (e.g. [15 ]) instead of those available in computational tools designed for an eQTL analysis, many of which do not account for population structure and relatedness [48,49].…”