In recent years, studies that associate genetic variation with gene expression ("eQTL studies") have become a major tool for identifying regulatory genetic variation. However, the difficulty of securing primary tissue samples means that up to now these eQTL studies have been conducted in a limited range of cell and tissue types. Most notably the largest studies have been conducted in EBV-transformed lymphoblastoid cell lines, and it is unclear to what extent eQTLs identified in these cell lines wil be relevant to human disease mapping. The GTEx Project will provide data to remedy this situation, collecting RNA-seq and genotype data on 30 tissues in hundreds of individuals. However, current analytic tools are limited in their ability to fully exploit the richness of these data. In particular, available methods fall short in their ability to jointly analyze data on all tssues to maximize power, while simultaneously allowing for differences among eQTLs present in each tissue. Here we propose to develop novel statistical methods to help address these issues. We will apply these methods to identify eQTLs in the GTEx project data, integrate the GTEx data with other relevant data such as those available from the ENCODE project, and disseminate the results on the internet in a convenient form. We will also provide researchers with convenient tools to cross-reference results of the GTEx project with results of genome-wide association studies. The overall goal of the project is to build and apply an infrastructure for improved eQTL analyses, helping to maximize the utility and accessibility of GTEx data to the broad community of scientists who would like to use these data.
This project will generate and apply statistical tools for analyzing large-scale studies that aim to understand the impact of genetic variation on transcriptomes, and to better understand the different regulatory mechanisms underlying different tissue types, a fundamental issue in biology. Understanding these mechanisms, identifying the regulatory genetic variants, and correlating them with human disease, has the potential help understand the biology of disease, eventually leading to new treatment strategies.
|Castel, Stephane E; Mohammadi, Pejman; Chung, Wendy K et al. (2016) Rare variant phasing and haplotypic expression from RNA sequencing with phASER. Nat Commun 7:12817|
|Long, Quan; Argmann, Carmen; Houten, Sander M et al. (2016) Inter-tissue coexpression network analysis reveals DPP4 as an important gene in heart to blood communication. Genome Med 8:15|
|Benitez-Buelga, Carlos; VaclovÃ¡, Tereza; Ferreira, Sofia et al. (2016) Molecular insights into the OGG1 gene, a cancer risk modifier in BRCA1 and BRCA2 mutations carriers. Oncotarget 7:25815-25|
|Hartmann, Katherine; Seweryn, MichaÅ‚; Handleman, Samuel K et al. (2016) Non-linear interactions between candidate genes of myocardial infarction revealed in mRNA expression profiles. BMC Genomics 17:738|
|Hou, Liping; Bergen, Sarah E; Akula, Nirmala et al. (2016) Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum Mol Genet 25:3383-3394|
|Stacey, Simon N; Kehr, Birte; Gudmundsson, Julius et al. (2016) Insertion of an SVA-E retrotransposon into the CASP8 gene is associated with protection against prostate cancer. Hum Mol Genet 25:1008-18|
|Li, Yang I; van de Geijn, Bryce; Raj, Anil et al. (2016) RNA splicing is a primary link between genetic variation and disease. Science 352:600-4|
|Lan, Xun; Pritchard, Jonathan K (2016) Coregulation of tandem duplicate genes slows evolution of subfunctionalization in mammals. Science 352:1009-13|
|Brinkmeyer-Langford, Candice L; Guan, Jinting; Ji, Guoli et al. (2016) Aging Shapes the Population-Mean and -Dispersion of Gene Expression in Human Brains. Front Aging Neurosci 8:183|
|Gordon, Erin D; Palandra, Joe; Wesolowska-Andersen, Agata et al. (2016) IL1RL1 asthma risk variants regulate airway type 2 inflammation. JCI Insight 1:e87871|
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