Expression quantitative trait loci (eQTL) studies seek to identify genomic variants that influence the expression of particular genes, and thereby influence higher level biological functions. The study of eQTLs has proven to be a useful tool in the study of biological pathways that underlie disease in human and other populations. Until recently, most eQTL analyses in humans were carried out using samples from blood. However, the Genotype-Tissue Expression (GTEx) consortium and other groups have recently begun assembling large data bases that include genomic variants and expression in multiple tissues. The research supported by the grant will address a number of statistical challenges that arise from these large, multi-tissue data sets. These include doing inference across many (20-30) tissues at the same time, identifying genomic control of genes that are located far away from a variant, and accurately quantifying and estimating the statistics effect of a genomic variant on the expression of a gene.

The supported research has four specific Aims: (1) to develop bipartite extensions of statistical tools from the analysis of networks that can enhance the identification of distal (trans) eQTLs; (2) to develop new statistical methods for fast eQTL association mapping that provide reliable estimates of effect size; (3) to extend an existing multi-tissue eQTL procedure developed by the PIs into a High-Tissue modeling platform capable of handling existing data sets with 20 to 30 tissues; and (4) to develop gene-based statistical models for eQTL analysis. Development of the proposed methods will be driven by recent, large-scale eQTL studies in which the PIs have played key roles. The resulting computational tools will address current, critical shortcomings in the analysis of these new data sets, and will have broad utility for the wider eQTL analysis community.

PIs Nobel and Wright have been members of the Analysis Working Group of the Genotype Tissue Expression (GTEx) Consortium since 2010. Their labs have contributed software and statistical analyses to the ongoing activities of Consortium, including a recent cover article in Science. The research in the grant will extend the PI’s existing work, and will explore new software and analysis methods that can be applied by biomedical researchers working in genomics and other fields.

Authors/roles: Andrew Nobel and Fred Wright, co-PIs

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