Functional variants associated with complex traits tend to fall in non-coding regions and affect regulatory mechanisms that are not yet well characterized. Furthermore, it is generally difficult to determine in which tissues and conditions they may have a functional impact. This is because the effect of a genetic variant on a molecular pathway, and ultimately on the individual's phenotype, may be modulated by """"""""environmental"""""""" factors. We denominate such variants """"""""gene-expression environment-specific quantitative trait nucleotides"""""""" GxE-QTNs. Achieving a better understanding of the mechanisms underlying GxE-QTNs is a critical step in understanding the link between genotype and complex phenotype. It is also crucial to develop computationally efficient and statistically sound methods capable to integrate tissue/condition-specific functional genomics data to predict and validate when a sequence variant is functional. We propose to develop novel experimental and computational approaches to screen, analyze and functionally characterize genetic variants for complex traits modulated by environmental exposures. To identify and characterize GxE-QTNs, we will analyze allele specific gene expression in a panel of relevant tissues (e.g. the vascular endothelium for cardiovascular diseases) under a series of controlled environmental conditions (e.g. glucocorticoids treatment, as a proxy for stress exposure). The proposed computational tools will integrate different sources of evidence including data collected by ENCODE, RoadMap Epigenome and GTEx projects to functionally annotate GWAS variants. The experimental and computational tools developed by this project have widespread applicability, for example, can be used to tackle the functional basis of complex traits in other environmental contexts (e.g. other types of stress and hormonal levels) and genetic backgrounds. Relevance to public health: Our findings will represent the first comprehensive catalog of genetic variants that interact with environmental exposure in determining human complex traits.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
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Special Emphasis Panel (ZGM1)
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Krasnewich, Donna M
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Wayne State University
Schools of Medicine
United States
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Wen, Xiaoquan; Pique-Regi, Roger; Luca, Francesca (2017) Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization. PLoS Genet 13:e1006646
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