Evidence map›Paper›PMID 27866706›Full record

ArticleAmerican journal of human genetics2016

Colocalization of GWAS and eQTL Signals Detects Target Genes.

Farhad Hormozdiari, Martijn van de Bunt, Ayellet V Segrè, Xiao Li, Jong Wha J Joo, Michael Bilow, Jae Hoon Sul, Sriram Sankararaman, Bogdan Pasaniuc, Eleazar Eskin

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of human genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 501 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
501citing papers in PubMed, 7 pooled it
47.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

501 citing papers in PubMed, 7 syntheses or guidelines pooled it, 762 citations in OpenAlex.

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  7. Predicted Proteome Association Studies of Breast, Prostate, Ovarian, and Endometrial Cancers Implicate Plasma Protein Regulation in Cancer Susceptibility.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2023
    Pooled it
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  15. Proteogenomics in human populations.Nature reviews. Genetics · 2026
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441 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Farhad HormozdiariDepartment of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Martijn van de BuntOxford Centre for Diabetes, Endocrinology, & Metabolism, University of Oxford, Oxford OX3 7LJ, UK; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK.
Ayellet V SegrèCancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Xiao LiCancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Jong Wha J JooDepartment of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Michael BilowDepartment of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Jae Hoon SulDepartment of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095, USA; Semel Center for Informatics and Personalized Genomics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Sriram SankararamanDepartment of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Bogdan PasaniucDepartment of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Eleazar EskinDepartment of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: eeskin@cs.ucla.edu.
University of California, Los Angeles · USBroad Institute · USCentre for Human Genetics · GB

Funding

ULTRASTRUCTURE OF THE INTIMA IN EARLY LESION FORMATIONP01HL030568 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FOGELMAN, ALAN M · 1985 to 2019
$55.2M
Systems genomics of metabolic syndrome traitsP01HL028481 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LUSIS, ALDONS JAKE · 1985 to 2019
$50.2M
Substance Abuse & Behavioral Disinhibition: Integrating Genes & EnvironmentU01DA024417 · NIDA · UNIVERSITY OF MINNESOTA · PI IACONO, WILLIAM G. · 2007 to 2010
$10.8M
Training U54EB020403 · NIBIB · UNIVERSITY OF SOUTHERN CALIFORNIA · PI THOMPSON, PAUL M · 2014 to 2018
$10.1M
Integrative Center for Neurogenetics and Neurogenomics - OverallP30NS062691 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI COPPOLA, GIOVANNI · 2009 to 2018
$7.4M
INTEGRATIVE GENETIC APPROACHES TO GENE-AIR POLLUTION INTERACTIONS IN ASTHMAR01ES021801 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI AKBARI, OMID, ALLAYEE, HOOMAN · 2012 to 2016
$3.9M
An integrated molecular approach to understand variation in iron metabolismR01GM083198 · NIGMS · UNIVERSITY OF FLORIDA · PI VULPE, CHRISTOPHER D · 2012 to 2015
$2.7M
Genetic Regulation of Gene Expression and its Impact on Phenotypes - SupplementR01MH101782 · NIMH · STANFORD UNIVERSITY · PI SABATTI, CHIARA · 2013 to 2016
$1.3M
Correcting for Population Structure in Gene-by-Environment Interaction StudiesR01ES022282 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ESKIN, ELEAZAR · 2013 to 2015
$1.2M
Discovering the Genetic Basis of HypertensionK25HL080079 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ESKIN, ELEAZAR · 2006 to 2010
$685k
Statistical methods to infer structure and impact of ancient admixtureR00GM111744 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI SANKARARAMAN, SRIRAM · 2016 to 2017
$498k
NHLBI NIH HHS HHSN268201000029CNHLBI NIH HHS K25 HL080079NHLBI NIH HHS P01 HL028481NHLBI NIH HHS P01 HL030568NIBIB NIH HHS U54 EB020403NIDA NIH HHS U01 DA024417NIEHS NIH HHS R01 ES021801NIEHS NIH HHS R01 ES022282NIGMS NIH HHS R00 GM111744NIGMS NIH HHS R01 GM083198NIMH NIH HHS R01 MH101782NINDS NIH HHS P30 NS062691
6 · The paper itself

Abstract

The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci.

Indexed as

Models, GeneticModels, StatisticalDatasets as TopicGene Expression RegulationGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeGlucoseHumansInsulinLinkage DisequilibriumOrgan SpecificityProbabilityQuantitative Trait LociSample SizeGlucoseInsulin

Identifiers

PMID27866706
PMCPMC5142122
OpenAlexW2555614465

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.