This track shows genetic variants likely affecting proximal gene expression in 49 human tissues
Genotype-Tissue Expression (GTEx)
V8 data release.
The data items displayed are gene expression quantitative trait loci within 1MB
of gene transcription start sites (cis-eQTLs), significantly associated with
gene expression and in the credible set of variants for the gene at a high
Both the CAVIAR and DAP-G tracks show gene/variant pairs for 49 GTEx tissues.
Variants are linked to the genes they interact with by a line. Variants
are represented by thicker-width, single-base items. Genes are represented as
thinner-width items covering the length of the gene. The direction of the
chevrons on the line indicate whether the variant is upstream or downstream of
the gene with the chevrons always pointing from the variant to the gene. If a
variant is internal to the gene, then the variant is shown as a thicker segment
than the gene. Items in the track are colored according to their tissue, with
the color matching those in the GTEx Gene V8 Track.
Hovering over items in the track display will show the variant ID (often a
dbSNP rsID), the target gene, tissue, and posterior probablity (Causal
Posterior Probability (CPP) for CAVIAR; SNP Posterior Inclusion Probability
(PIP) for DAP-G). Clicking an item will show the details of that interaction
with link outs to view more details on the GTEx website.
Track configuration supports filtering by tissue, gene, or posterior probability.
Details on GTEx v8 analysis, including code, can be found in the
GTEx GWAS Analysis Github.
Raw data for these analyses are available from the
track at UCSC was created using the CAVIAR high-confidence set, which
represents the high causal variants that have a causal posterior probability
(CPP) of > 0.1.
The DAP-G track at
UCSC was created using the DAP-G 95% credible set, which represents varaints
with strong eQTLs signals, which are signal clusters with signal-level
posterior inclusion probability (SPIP) > 0.95.
The raw data for this track can be accessed in multiple ways. It can be explored interactively
using the Table Browser or
Data Integrator. You can also access the data
entries in JSON format through our
The data in this track are organized in bigBed file format. The underlying files
can be obtained from our downloads server:
Individual regions or the whole set of genome-wide annotations can be obtained using our tool
bigBedToBed which can be compiled from the source code or downloaded as a precompiled
binary for your system from the utilities directory linked below. For example, to extract only
annotations in a given region, you could use the following command:
-chrom=chr16 -start=34990190 -end=36727467 stdout
Thanks to GTEx investigators, analysts, and portal team for providing this data.
The GTEx Consortium atlas of genetic regulatory effects across human tissues.
Science. 2020 Sep 11;369(6509):1318-1330.
PMID: 32913098; PMC: PMC7737656
Lee Y, Luca F, Pique-Regi R, Wen X.
Bayesian Multi-SNP Genetic Association Analysis: Control of FDR and Use of
bioRxiv. 2018 May 8.
Wen X, Lee Y, Luca F, Pique-Regi R.
Efficient Integrative Multi-SNP Association Analysis via Deterministic Approximation of
Am J Hum Genet. 2016 Jun 2;98(6):1114-1129.
PMID: 27236919; PMC: PMC4908152
Ongen H, Buil A, Brown AA, Dermitzakis ET, Delaneau O.
Fast and efficient QTL mapper for thousands of molecular phenotypes.
Bioinformatics. 2016 May 15;32(10):1479-85.
PMID: 26708335; PMC: PMC4866519
Hormozdiari F, Kostem E, Kang EY, Pasaniuc B, Eskin E.
Identifying causal variants at loci with multiple signals of association.
Genetics. 2014 Oct;198(2):497-508.
PMID: 25104515; PMC: PMC4196608
The Genotype-Tissue Expression (GTEx) project.
Nat Genet. 2013 Jun;45(6):580-5.
PMID: 23715323; PMC: PMC4010069
GTEx Portal Documentation