Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics

Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understandi...

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Main Authors:	Giambartolomei, Claudia, Vukcevic, Damjan, Schadt, Eric E., Franke, Lude, Hingorani, Aroon D., Wallace, Chris, Plagnol, Vincent
Format:	Online
Language:	English
Published:	Public Library of Science 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4022491/

id	pubmed-4022491
recordtype	oai_dc
spelling	pubmed-40224912014-05-21 Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics Giambartolomei, Claudia Vukcevic, Damjan Schadt, Eric E. Franke, Lude Hingorani, Aroon D. Wallace, Chris Plagnol, Vincent Research Article Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways. Public Library of Science 2014-05-15 /pmc/articles/PMC4022491/ /pubmed/24830394 http://dx.doi.org/10.1371/journal.pgen.1004383 Text en © 2014 Giambartolomei et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Giambartolomei, Claudia Vukcevic, Damjan Schadt, Eric E. Franke, Lude Hingorani, Aroon D. Wallace, Chris Plagnol, Vincent
spellingShingle	Giambartolomei, Claudia Vukcevic, Damjan Schadt, Eric E. Franke, Lude Hingorani, Aroon D. Wallace, Chris Plagnol, Vincent Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics
author_facet	Giambartolomei, Claudia Vukcevic, Damjan Schadt, Eric E. Franke, Lude Hingorani, Aroon D. Wallace, Chris Plagnol, Vincent
author_sort	Giambartolomei, Claudia
title	Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics
title_short	Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics
title_full	Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics
title_fullStr	Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics
title_full_unstemmed	Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics
title_sort	bayesian test for colocalisation between pairs of genetic association studies using summary statistics
description	Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.
publisher	Public Library of Science
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4022491/
_version_	1612089659474575360

Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics

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