High-throughput allele-specific expression across 250 environmental conditions.

Gene-by-environment (GxE) interactions determine common disease risk factors and biomedically relevant complex traits. However, quantifying how the environment modulates genetic effects on human quantitative phenotypes presents unique challenges. Environmental covariates are complex and difficult to measure and control at the organismal level, as found in GWAS and epidemiological studies. An alternative approach focuses on the cellular environment using in vitro treatments as a proxy for the organismal environment. These cellular environments simplify the organism-level environmental exposures to provide a tractable influence on subcellular phenotypes, such as gene expression. Expression quantitative trait loci (eQTL) mapping studies identified GxE interactions in response to drug treatment and pathogen exposure. However, eQTL mapping approaches are infeasible for large-scale analysis of multiple cellular environments. Recently, allele-specific expression (ASE) analysis emerged as a powerful tool to identify GxE interactions in gene expression patterns by exploiting naturally occurring environmental exposures. Here we characterized genetic effects on the transcriptional response to 50 treatments in five cell types. We discovered 1455 genes with ASE (FDR < 10%) and 215 genes with GxE interactions. We demonstrated a major role for GxE interactions in complex traits. Genes with a transcriptional response to environmental perturbations showed sevenfold higher odds of being found in GWAS. Additionally, 105 genes that indicated GxE interactions (49%) were identified by GWAS as associated with complex traits. Examples include GIPR-caffeine interaction and obesity and include LAMP3-selenium interaction and Parkinson disease. Our results demonstrate that comprehensive catalogs of GxE interactions are indispensable to thoroughly annotate genes and bridge epidemiological and genome-wide association studies.

QuASAR: quantitative allele-specific analysis of reads.

MOTIVATION: Expression quantitative trait loci (eQTL) studies have discovered thousands of genetic variants that regulate gene expression, enabling a better understanding of the functional role of non-coding sequences. However, eQTL studies are costly, requiring large sample sizes and genome-wide genotyping of each sample. In contrast, analysis of allele-specific expression (ASE) is becoming a popular approach to detect the effect of genetic variation on gene expression, even within a single individual. This is typically achieved by counting the number of RNA-seq reads matching each allele at heterozygous sites and testing the null hypothesis of a 1:1 allelic ratio. In principle, when genotype information is not readily available, it could be inferred from the RNA-seq reads directly. However, there are currently no existing methods that jointly infer genotypes and conduct ASE inference, while considering uncertainty in the genotype calls. RESULTS: We present QuASAR, quantitative allele-specific analysis of reads, a novel statistical learning method for jointly detecting heterozygous genotypes and inferring ASE. The proposed ASE inference step takes into consideration the uncertainty in the genotype calls, while including parameters that model base-call errors in sequencing and allelic over-dispersion. We validated our method with experimental data for which high-quality genotypes are available. Results for an additional dataset with multiple replicates at different sequencing depths demonstrate that QuASAR is a powerful tool for ASE analysis when genotypes are not available. AVAILABILITY AND IMPLEMENTATION: http://github.com/piquelab/QuASAR. CONTACT: fluca@wayne.edu or rpique@wayne.edu SUPPLEMENTARY INFORMATION: Supplementary Material is available at Bioinformatics online.

Interpreting Coronary Artery Disease Risk Through Gene-Environment Interactions in Gene Regulation.

GWAS and eQTL studies identified thousands of genetic variants associated with complex traits and gene expression. Despite the important role of environmental exposures in complex traits, only a limited number of environmental factors were measured in these studies. Measuring molecular phenotypes in tightly controlled cellular environments provides a more tractable setting to study gene-environment interactions in the absence of other confounding variables. We performed RNA-seq and ATAC-seq in endothelial cells exposed to retinoic acid, dexamethasone, caffeine, and selenium to model genetic and environmental effects on gene regulation in the vascular endothelium-a common site of pathology in cardiovascular disease. We found that genes near regions of differentially accessible chromatin were more likely to be differentially expressed [OR = (3.41, 6.52), [Formula: see text]]. Furthermore, we confirmed that environment-specific changes in transcription factor binding are a key mechanism for cellular response to environmental stimuli. Single nucleotide polymorphisms (SNPs) in these transcription response factor footprints for dexamethasone, caffeine, and retinoic acid were enriched in GTEx eQTLs from artery tissues, indicating that these environmental conditions are latently present in GTEx samples. Additionally, SNPs in footprints for response factors in caffeine are enriched in colocalized eQTLs for coronary artery disease (CAD), suggesting a role for caffeine in CAD risk. By combining GWAS, eQTLs, and response genes, we annotated environmental components that can increase or decrease disease risk through changes in gene expression in 43 genes. Interestingly, each treatment may amplify or buffer genetic risk for CAD, depending on the particular SNP or gene considered.