Genome-wide association studies have identified thousands of loci for common diseases, but, for the majority of these, the mechanisms underlying disease susceptibility remain unknown. Most associated variants are not correlated with protein-coding changes, suggesting that polymorphisms in regulatory regions probably contribute to many disease phenotypes. Here we describe the Genotype-Tissue Expression (GTEx) project, which will establish a resource database and associated tissue bank for the scientific community to study the relationship between genetic variation and gene expression in human tissues

Understanding the functional consequences of genetic variation, and how it affects complex human disease and quantitative traits, remains a critical challenge for biomedicine. We present an analysis of RNA sequencing data from 1641 samples across 43 tissues from 175 individuals, generated as part of the pilot phase of the Genotype-Tissue Expression (GTEx) project. We describe the landscape of gene expression across tissues, catalog thousands of tissue-specific and shared regulatory expression quantitative trait loci (eQTL) variants, describe complex network relationships, and identify signals from genome-wide association studies explained by eQTLs. These findings provide a systematic understanding of the cellular and biological consequences of human genetic variation and of the heterogeneity of such effects among a diverse set of human tissues.

To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.The genome sequences of humans and several model organisms have established a nearly complete list of the genes required to enact cellular, developmental, and behavioral processes in these organisms (1-4). The next major challenges are to elucidate the functions of the large fraction of genes in the genome whose functions are currently unknown and to discover how the genes interact to perform specific biological processes. DNA microarrays provide us with a first step toward the goal of uncovering gene function on a global scale. Because genes that encode proteins that participate in the same pathway or are part of the same protein complex are often coregulated, clusters of genes with related functions often exhibit expression patterns that are correlated under a large number of diverse conditions in DNA microarray experiments (5-8).However, coregulation does not necessarily imply that genes are functionally related. For example, cis-regulatory DNA motifs are predicted to occur by chance in the genome and might lead to serendipitous transcriptional regulation of nearby genes. In experiments limited to a single species, it would be difficult or even impossible to distinguish accidentally regulated genes from those that are physiologically important. However, evolutionary conservation is a powerful criterion to identify genes that are functionally important from a set of coregulated genes. Coregulation of a pair of genes over large evolutionary distances implies that the coregulation confers a selective advantage, most likely because the genes are functionally related. Because small and subtle changes in fitness can confer selective advantage during evolution, the test for related gene function using evolutionary conservation in the wild is more sensitive than scoring the phenotype resulting from strong loss-of-function mutants in the laboratory.The recent availability of large sets of DNA microarray data for humans, flies, worms, and yeast makes it possible to measure evolutionarily conserved coexpression on a genomewide scale (9-11). We developed a computational method to analyze 3182 DNA microarrays from humans, flies...

The Immunological Genome Project combines immunology and computational biology laboratories in an effort to establish a complete 'road map' of gene-expression and regulatory networks in all immune cells.