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The GTEx (Genotype-Tissue Expression) Project

Correlations between genotype and tissue-specific gene expression levels will help identify regions of the genome that influence whether and how much a gene is expressed. Top: DNA and RNA is collected and analyzed from multiple tissues. Each donor has different genetic variants. In this example they are G/G, A/G, and A/A. Middle: Gene expression levels (RNA) are measured in each tissue from each individual and correlated with that individual’s genotype. Each donor has different gene expression (RNA) levels. In this example donor 1 has the highest while donor 3 has the lowest RNA levels in brain. Donor 2 is in the middle. RNA expression levels are treated as quantitative traits or expression quantitative trait loci (eQTLs). When eQTLs are correlated with genetic variation new genetic variants associated with, and potentially causal to, gene expression are discovered. In this example the genetic variant G/G is associated with high gene expression in brain while the genetic variant A/A is associated with low gene expression in brain. The genetic variant A/G is associated with intermediate gene expression in brain. When eQTLs between healthy and unhealthy individuals are compared, novel genes contributing to disease can be discovered. Bottom: The eQTL data and the donor tissues are deposited in the GTEx Portal  and tissue repository  for use by the scientific community.

The GTEx (Genotype-Tissue Expression) Project. Correlations between genotype and tissue-specific gene expression levels is helping to identify regions of the genome that influence whether and how much a gene is expressed. GTEx is helping researchers to understand inherited susceptibility to disease by developing a resource database  and tissue bank  for current and future studies.

GTEx has collected multiple human tissues from over 900 donors – half the data is available to researchers through the GTEx Portal  – who are also densely genotyped, to assess genetic variation within their genomes. By analyzing global RNA expression within individual tissues and treating the expression levels of genes as quantitative traits, variations in gene expression that are highly correlated with genetic variation can be identified as expression quantitative trait loci, or eQTLs. The GTEx Portal, where the eQTLs and other gene expression data are shared, is a resource for the scientific community to study human gene expression and regulation and its relationship to genetic variation.

During the past decade over 2,000 genome-wide association studies (GWAS) were published examining common genetic variants in different individuals to see if any variant is associated with a trait, most commonly a disease. Despite the rapid progress achieved using GWAS (See: http://www.genome.gov/26525384 ) to identify genetic changes associated with common human diseases, such as heart disease, cancer, diabetes, asthma, and stroke, a large majority of these genetic changes lies outside of the protein-coding regions of genes and often even outside of the genes themselves, making it difficult to discern which genes are affected and by what mechanism. GTEx, by correlating genetic variation with gene expression in multiple tissues through eQTLs, is beginning to bridge the gap between genetic variation and disease by identifying tissue-specific genes potentially causal to disease. The comprehensive identification of human eQTLs will greatly help to identify genes whose expression is affected by genetic variation, and will provide a valuable basis on which to study the mechanism of that gene regulation. As new genetic variants are discovered GTEx is expected to stimulate research towards understanding the genetic basis of complex diseases.

GTEx also involves consultation and research into the ethical, legal and social issues raised by the research, support for statistical methods development, and additional analysis beyond gene expression (Enhanced GTEx projects –eGTEx). The associated tissue repository  serves as a resource for many additional kinds of analyses in the future.

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