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.

Much of a cell's activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.

Genetics aims to understand the relation between genotype and phenotype. However, because complete deletion of most yeast genes ( approximately 80%) has no obvious phenotypic consequence in rich medium, it is difficult to study their functions. To uncover phenotypes for this nonessential fraction of the genome, we performed 1144 chemical genomic assays on the yeast whole-genome heterozygous and homozygous deletion collections and quantified the growth fitness of each deletion strain in the presence of chemical or environmental stress conditions. We found that 97% of gene deletions exhibited a measurable growth phenotype, suggesting that nearly all genes are essential for optimal growth in at least one condition.

Genetic variation influences gene expression, and this can be efficiently mapped to specific genomic regions and variants. We used gene expression profiling of EBV-transformed lymphoblastoid cell lines of all 270 individuals of the HapMap consortium to elucidate the detailed features of genetic variation underlying gene expression variation. We find gene expression levels to be heritable and differentiation between populations in agreement with earlier small-scale studies. A detailed association analysis of over 2.2 million common SNPs per population (5% frequency HapMap) with gene expression identified at least 1348 genes with association signals in cis and at least 180 in trans. Replication in at least one independent population was achieved for 37% of cis-signals and 15% of trans-signals, respectively. Our results strongly support an abundance of cis-regulatory variation in the human genome. Detection of trans-effects is limited but suggests that regulatory variation may be the key primary effect contributing to phenotypic variation in humans. Finally, we explore a variety of methodologies that improve the current state of analysis of gene expression variation.Understanding the molecular basis of human phenotypic variation is a key goal of human genetics, encompassing disease susceptibility, variable response to drugs and ultimately treatment and public health. Over the past decades studies have described and analyzed the genetic basis of human phenotypic variation ranging from whole organism phenotypes such as height 1, to molecular level phenotypes such as lipid levels 2,3. Previous studies have also investigated the effects of nucleotide variation in specific genes or genomic regions on complex and monogenic diseases. Recently, there has been an explosion of genome-wide studies examining the genetic basis of complex diseases by exploring the effects of genetic variation such as single nucleotide polymorphisms (SNPs) 4-7 and copy number variants (CNVs) 8-10 some of which are clearly in non-coding regions of the genome 4-7,11. Technological advances have now made genome-wide association studies a reasonable and affordable approach to the study of complex phenotypes 12.* Correspondence should be addressed to: Emmanouil T. Dermitzakis (md4@sanger.ac.uk; +44-1223-494866), Panagiotis Deloukas (panos@sanger.ac.uk; +44-1223-494909), Barbara E. Stranger (bes@sanger.ac.uk; +44-1223-834244), Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, CB10 1SA, Hinxton, Cambridge, UK. We estimated the median and variance of each of the 47,294 probe types for each population, and analyzed the distribution of variance and median values of normalized values by Gene Ontology (GO) categories 26 after summarizing them in GO-slim categories 27. Specific GO-slim categories such as "chaperone regulatory activity" showed an excess of high variance of gene expression, while genes with extracellular function showed low levels of variation. "Chaperone regulatory activity" genes and "translational regulatory activity" g...

Understanding the consequences of regulatory variation in the human genome remains a major challenge, with important implications for understanding gene regulation and interpreting the many disease-risk variants that fall outside of protein-coding regions. Here, we provide a direct window into the regulatory consequences of genetic variation by sequencing RNA from 922 genotyped individuals. We present a comprehensive description of the distribution of regulatory variation-by the specific expression phenotypes altered, the properties of affected genes, and the genomic characteristics of regulatory variants. We detect variants influencing expression of over ten thousand genes, and through the enhanced resolution offered by RNAsequencing, for the first time we identify thousands of variants associated with specific phenotypes including splicing and allelic expression. Evaluating the effects of both long-range intra-chromosomal and trans (cross-chromosomal) regulation, we observe modularity in the regulatory network, with three-dimensional chromosomal configuration playing a particular role in regulatory modules within each chromosome. We also observe a significant depletion of regulatory variants affecting central and critical genes, along with a trend of reduced effect sizes as variant frequency increases, providing evidence that purifying selection and buffering have limited the deleterious impact of regulatory variation on the cell. Further, generalizing beyond observed variants, we have analyzed the genomic properties of variants associated with expression and splicing and developed a Bayesian model to predict regulatory consequences of genetic variants, applicable to the interpretation of individual genomes and disease studies. Together, these results represent a critical step toward characterizing the complete landscape of human regulatory variation.