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An integrated approach to identify environmental modulators of genetic risk factors for complex traits

ABSTRACT: Complex traits and diseases can be influenced by both genetics and environment. However, given the large number of environmental stimuli and power challenges for gene-by-environment testing, it remains a critical challenge to identify and prioritize specific disease-relevant environmental exposures. We propose a novel framework for leveraging signals from transcriptional responses to environmental perturbations to identify disease-relevant perturbations that can modulate genetic risk for complex traits and inform the functions of genetic variants associated with complex traits. We perturbed human skeletal muscle, fat, and liver relevant cell lines with 21 perturbations affecting insulin resistance, glucose homeostasis, and metabolic regulation in humans and identified thousands of environmentally responsive genes. By combining these data with GWAS from 31 distinct polygenic traits, we show that the heritability of multiple traits is enriched in regions surrounding genes responsive to specific perturbations and, further, that environmentally responsive genes are enriched for associations with specific diseases and phenotypes from the GWAS catalog. Overall, we demonstrate the advantages of large-scale characterization of transcriptional changes in diversely stimulated and pathologically relevant cells to identify disease-relevant perturbations.

ORGANISM(S): Homo sapiens

PROVIDER: GSE179347 | GEO | 2021/07/09

REPOSITORIES: GEO

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Project description:Cells must adjust their gene expression in order to compete in a constantly changing environment. Two alternative strategies could in principle ensure optimal coordination of gene expression with physiological requirement. First, the internal physiological state itself could feedback to regulated gene expression. Second, the expected physiological state could be inferred from the external environment, using evolutionary-tuned signaling pathways. Coordination of ribosomal biogenesis with the requirement for protein synthesis appears to be particularly important, since cells devote a large fraction of their biosynthetic capacity for ribosomal biogenesis. To define the relative importance of internal vs. external sensing to the regulation of ribosomal biogenesis gene expression, we subjected S. cerevisiae cells to conditions which decoupled the actual vs. environmentally-expected growth rate. Gene expression followed the environmental signal according to the expected, but not the actual, growth rate. Simultaneous monitoring of gene expression and growth rate in chemostat-grown cultures further confirmed that ribosome biogenesis genes responded rapidly to changes in the environments but were oblivious to longer-term changes in growth rate. Our results suggest that the capacity to anticipate and prepare for environmental changes presented a major selection force during yeast evolution. Keywords: Saccharomyces_Cerevisiae, Stress response, ADH1 deletion, time courses, chemostat Experiment 1: ADH1 deletion cells, which grow faster on glycerol rather then glucose medium, were grown in both glucose and glycerol medium. The cells expression on glycerol is compared to their expression glucose (5 microarrays). Experiment 2: Time course of ADH1 deletion cells upon growth on glycerol and on glucose (7 microarrays). Experiment 3: Response of steady state grown cells to environmental perturbations. Cells were grown in glucose or histidine limited chemostats and reached steady state. The cells were then subjected to perturbations such as DTT, heat shock, NaCl, Clotrimazole, H2O2, and addition of limiting factors (histidine and glucose, respectively). Overall we have examined 10 timecourses with 83 microarrays.

Project description:Most loci identified in genome wide association studies (GWAS) of complex traits reside in non-coding DNA and may contribute to phenotype via changes in gene regulation. The discovery of expression quantitative trait loci (?eQTLs?) can thus be used to more precisely identify modest but real disease associations and provide insights into their underlying molecular mechanisms. This is particularly true for analyses of expression in non-transformed cells from tissues relevant to the complex traits of interest. We have conducted two independent studies to identify genetic, including both SNPs and copy-number variants, and environmental determinants of human liver gene expression variation. We analyzed two sets of primary livers (primary dataset: n=220; replication dataset: n=60) using Agilent and Illumina expression arrays and Illumina SNP genotyping (550K). At least 30% of genetic and non-genetic factors that meet genome-wide significance (p <1 x10-9) in one study fail to replicate in the second study, suggesting that artifacts, like unknown SNPs that affect RNA-probe hybridization or hidden confounding variables, often result in statistically significant but biologically irrelevant correlations. These data confirm the value of independent replications to enrich for truly predictive eQTLs, and given our study design we are able to identify hundreds of reproducible correlations. We show that such information can be used to provide insights into disease-relevant phenotypes, with specific examples including eQTLs related to lipid levels (e.g. LDL cholesterol), immune system function (e.g. HLA), and drug response (e.g. warfarin). Furthermore, in the interest of both fine-mapping and mechanistic annotation, we hypothesized that promoters and 3?UTRs are enriched for causal eQTL variants. Therefore, we re-sequenced the promoter and 3?UTR regions of 25 genes with eQTLs, cloned each discovered haplotype, and quantified their impact on transcription using a luciferase-based assay. These data reveal multiple examples of robust, haplotype-specific in vitro functional differences that correlate directly with in vivo expression levels. This suggests that many eQTLs can be rapidly fine-mapped to one or a few single-nucleotide variants and mechanistically characterized using such assays. Integration of functional assays with eQTL discovery, and eQTLs with complex trait associations, is a powerful means to exploit GWAS data and improve their biological interpretability. RNA expression levels were quantified on Illumina gene expression microarrays for 60 normal human livers. Expression quantitative trait loci were identified by genome wide association mapping.

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An integrated approach to identify environmental modulators of genetic risk factors for complex traits

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