The eQTL browser was updated on 22 April 2010.

eQTL Browser Documentation

eQTL Browser Documentation

Rationale

Several recent large-scale studies (1-6) have identified thousands of putative expression quantitative trait loci (eQTL) throughout the human genome. These loci may represent novel functional sites with DNA-level variation relevant to many human phenotypes and health and therefore are likely to be of interest to a large research community. We have created a web browser to both summarize the results of these studies and to allow for quick and easy comparisons across studies. Please cite the original studies where appropriate. We hope that you find our browser useful and we welcome comments or suggestions for improvements.

Browser Usage

Search Function

The browser is meant to be searchable based on genome coordinates (build 36), gene name, and SNP identifier. To view all putative eQTL on chromosome 1 between coordinates 100,000 and 200,000 type chr1:100000..200000 in the search box. To see if SNP rs11171739 represents an eQTL in any of the included studies, simply type rs11171739 into the search box. To search by gene, we recommend that you surround your search with the wild-card character * in order to avoid being taken directly to the Entrez gene model. For example, to see if gene RPS26 has any eQTL, we recommend that you type *RPS26* rather than just RPS26. Using the wild-card character should bring up all matches to RPS26 including both the Entrez gene match and any SNPs labeled as an eQTL for RPS26.

Data Downloads

The results shown on the individual tracks may be downloaded using the "Download GFF File" function in the Reports & Analysis tab. When downloading the GFF files in a given browser window please take note that all the tracks that are displayed in that window will be downloaded. We recommend turning off certain tracks, for example, the Entrez gene track, when downloading eQTL results.

Individual Studies

Below you will find summaries describing the data included in our browser. For detailed descriptions of the data, we refer you to the individual studies cited in this document

Schadt et. al (2008)

Schadt et al. (1) identified eQTL specifically in the human liver, a tissue that is relevant to several common human diseases. The data came from 427 human liver samples. Whole genome genotyping was performed on both the Affymetrix 500K chip and the Illumina 650 K chip. Expression levels were assayed by cDNA hybridization on a custom Agilent array. Statistical analyses were done in several steps. A cis candidate region was defined as within 1 MB from either the transcription start site (TSS) or the transcription end site (TES) for each gene. The genotypes at all SNPs within the cis candidate region were tested for non-independence against the transcription levels of the nearby gene. Here, the non-parametric Kruskal-Wallace test was used allowing an empirical false discovery rate of 0.1. An initial pass for trans eQTL tested all transcript expression levels against all SNPs again using the Kruskal-Wallace test with an FDR of 0.1. Finally, the authors tested the subset of SNPs (~3000) that were identified as cis eQTL against all transcript expression levels using the same test and FDR. We include all of these putative eQTL in our browser. For each individual putative eQTL, the Schadt et al. track plots the base-10 negative logarithm of the reported P-value of the Kruskal-Wallace test against genome location. The "All putative eQTL SNPs" track includes all of these Schadt et al. SNPs. The border of Schadt et al. SNPs are colored green. Cis eQTL are hollow triangles while trans eQTL are filled. The "All putative eQTL SNPs" links to details for that SNP.

Myers et al. (2007)

Myers et al. (2) identified eQTL in the human cortex from the control brain. Genotyping was done on the Affymetrix 500K array. Expression levels were assayed with the Illumina HumanRefSeq-8 expression bead chip. A cis candidate region was defined as within 1 MB of the TSS or within 1MB of the TES and within the transcript. The authors use simulations to evaluate global significance. For those putative eQTL with empirically corrected P-value > 0.05 (not corrected for multiple transcript testing), they report a Wald P-value. In this browser, we plot the base-10 negative logarithm of the reported Wald P-value against genome location. We also include each of these SNPs in the “ All putative eQTL SNPs” track bordered in blue. Cis eQTL are hollow triangles while trans eQTL are filled. The "All putative eQTL SNPs" links to details for that SNP.

Stranger et al. (2007)

Stranger et al. (3) identified eQTL in Epstein-Barr virus–transformed lymphoblastoid cell lines in the sample of 210 unrelated HapMap individuals. Genotype data were selected for HapMap phase II SNPs with MAF > 5% from each of the four populations, which resulted in ~ 2.2 million SNPs per population. Expression levels were assayed using the Illumina whole-genome (WG-6 version 1) arrays (Illumina Sentrix Human-6 Expression BeadChip version 1), using a final set of 14,456 expression probes in 13,643 distinct genes. We report nominal linear regression p-values for SNPs with significant cis-associations (using a permutation based significance threshold of 0.001) in at least one population (from Stranger et al Supplementary Table 1). Results from the pooled populations were very similar to the -log10(P) track from Veyrieras et al (2008) and are therefore not shown here.

Veyrieras et al. (2008)

Veyrieras et al. (4) re-analyzed the gene-expression data from Stranger et al. (3) using the Epstein-Barr virus–transformed lymphoblastoid cell lines from 210 unrelated HapMap individuals. Genotypes were obtained from all HapMap phase II SNPs (~3.3 million SNPs) and gene-expression data were processed to a final set of 11,446 gene-expression measurements (with 1 probe per gene). Using a novel Bayesian hierarchical model a high-resolution map was created identifying new variants that affect gene-expression in cis. These results are shown in two separate tracks, denoting the linear-regression results (p-values), and the results from the Bayesian hierarchical model (posterior probability) in the pooled sample of 210 individuals.

Pickrell et al. (2010)

Let the authors explain it.

Montgomery et al. (2010)

Let the authors explain it

Zeller et al. (2010)

Let the authors explain it

Citations

(1)

Eric E. Schadt, Cliona Molony, Eugene Chudin, Ke Hao, Xia Yang, Pek Y. Lum, Andrew Kasarskis, Bin Zhang, Susanna Wang, Christine Suver, Jun Zhu, Joshua Millstein, Solveig Sieberts, John Lamb, Debraj GuhaThakurta, Jonathan Derry, John D. Storey, Iliana Avila-Campillo, Mark J. Kruger, Jason M. Johnson, Carol A. Rohl, Atila van Nas, Margarete Mehrabian, Thomas A. Drake, Aldons J. Lusis, Ryan C. Smith, F. Peter Guengerich, Stephen C. Strom, Erin Schuetz, Thomas H. Rushmore, Roger Ulrich. (2008) "Mapping the genetic architecture of gene expression in human liver". Plos Biology. 6 (5) p. e107

(2)

Amanda J Myers, J Raphael Gibbs, Jennifer A Webster, Kristen Rohrer, Alice Zhao, Lauren Marlowe, Mona Kaleem, Doris Leung, Leslie Bryden, Priti Nath, Victoria L Zismann, Keta Joshipura, Matthew J Huentelman, Diane Hu-Lince, Keith D Coon, David W Craig, John V Pearson, Peter Holmans, Christopher B Heward, Eric M Reiman, Dietrich Stephan, John Hardy. (2007) "A survey of genetic human cortical gene expression". Nature Genetics. 39 (12) p. 1494

(3)

Barbara E Stranger, Alexandra C Nica, Matthew S Forrest, Antigone Dimas, Christine P Bird, Claude Beazley, Catherine E Ingle, Mark Dunning, Paul Flicek, Daphne Koller, Stephen Montgomery, Simon Tavaré, Panos Deloukas, Emmanouil T Dermitzakis. (2007) "Population genomics of human gene expression". Nature Genetics. 39(10) p. 1217

(4)

Jean-Baptiste Veyrieras, Sridhar Kudaravalli, Su Yeon Kim, Emmanouil T. Dermitzakis, Yoav Gilad, Matthew Stephens, Jonathan K. Pritchard. (2008) "High-resolution mapping of expression-QTLs yields insight into human gene regulation". Plos Genetics. 4(10) p. e1000214

(5)

Joseph K. Pickrell, John C. Marioni, Athma A. Pai, Jacob F. Degner, Barbara E. Engelhardt, Everlyne Nkadori, Jean-Baptiste Veyrieras, Matthew Stephens, Yoav Gilad, Jonathan K. Pritchard "Understanding mechanisms underlying human gene expression variation with RNA sequencing". Nature 464 p. 768-772

(6)

Stephen B. Montgomery, Micha Sammeth, Maria Gutierrez-Arcelus, Radoslaw P. Lach, Catherine Ingle, James Nisbett, Roderic Guigo, Emmanouil T. Dermitzakis (2010) "Transcriptome genetics using second generation sequencing in a Caucasian population". Nature. 464 p. 773-777

(7)

Zeller T, Wild P, Szymczak S, Rotival M, Schillert A, et al. (2010) Genetics and Beyond – The Transcriptome of Human Monocytes and Disease Susceptibility. PLoS ONE 5(5): e10693. doi:10.1371/journal.pone.0010693