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Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization

We propose a novel statistical framework for integrating the result from molecular quantitative trait loci (QTL) mapping into genome-wide genetic association analysis of complex traits, with the primary objectives of quantitatively assessing the enrichment of the molecular QTLs in complex trait-asso...

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Detalles Bibliográficos
Autores principales: Wen, Xiaoquan, Pique-Regi, Roger, Luca, Francesca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5363995/
https://www.ncbi.nlm.nih.gov/pubmed/28278150
http://dx.doi.org/10.1371/journal.pgen.1006646
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author Wen, Xiaoquan
Pique-Regi, Roger
Luca, Francesca
author_facet Wen, Xiaoquan
Pique-Regi, Roger
Luca, Francesca
author_sort Wen, Xiaoquan
collection PubMed
description We propose a novel statistical framework for integrating the result from molecular quantitative trait loci (QTL) mapping into genome-wide genetic association analysis of complex traits, with the primary objectives of quantitatively assessing the enrichment of the molecular QTLs in complex trait-associated genetic variants and the colocalizations of the two types of association signals. We introduce a natural Bayesian hierarchical model that treats the latent association status of molecular QTLs as SNP-level annotations for candidate SNPs of complex traits. We detail a computational procedure to seamlessly perform enrichment, fine-mapping and colocalization analyses, which is a distinct feature compared to the existing colocalization analysis procedures in the literature. The proposed approach is computationally efficient and requires only summary-level statistics. We evaluate and demonstrate the proposed computational approach through extensive simulation studies and analyses of blood lipid data and the whole blood eQTL data from the GTEx project. In addition, a useful utility from our proposed method enables the computation of expected colocalization signals using simple characteristics of the association data. Using this utility, we further illustrate the importance of enrichment analysis on the ability to discover colocalized signals and the potential limitations of currently available molecular QTL data. The software pipeline that implements the proposed computation procedures, enloc, is freely available at https://github.com/xqwen/integrative.
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spelling pubmed-53639952017-04-06 Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization Wen, Xiaoquan Pique-Regi, Roger Luca, Francesca PLoS Genet Research Article We propose a novel statistical framework for integrating the result from molecular quantitative trait loci (QTL) mapping into genome-wide genetic association analysis of complex traits, with the primary objectives of quantitatively assessing the enrichment of the molecular QTLs in complex trait-associated genetic variants and the colocalizations of the two types of association signals. We introduce a natural Bayesian hierarchical model that treats the latent association status of molecular QTLs as SNP-level annotations for candidate SNPs of complex traits. We detail a computational procedure to seamlessly perform enrichment, fine-mapping and colocalization analyses, which is a distinct feature compared to the existing colocalization analysis procedures in the literature. The proposed approach is computationally efficient and requires only summary-level statistics. We evaluate and demonstrate the proposed computational approach through extensive simulation studies and analyses of blood lipid data and the whole blood eQTL data from the GTEx project. In addition, a useful utility from our proposed method enables the computation of expected colocalization signals using simple characteristics of the association data. Using this utility, we further illustrate the importance of enrichment analysis on the ability to discover colocalized signals and the potential limitations of currently available molecular QTL data. The software pipeline that implements the proposed computation procedures, enloc, is freely available at https://github.com/xqwen/integrative. Public Library of Science 2017-03-09 /pmc/articles/PMC5363995/ /pubmed/28278150 http://dx.doi.org/10.1371/journal.pgen.1006646 Text en © 2017 Wen 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 (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wen, Xiaoquan
Pique-Regi, Roger
Luca, Francesca
Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization
title Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization
title_full Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization
title_fullStr Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization
title_full_unstemmed Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization
title_short Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization
title_sort integrating molecular qtl data into genome-wide genetic association analysis: probabilistic assessment of enrichment and colocalization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5363995/
https://www.ncbi.nlm.nih.gov/pubmed/28278150
http://dx.doi.org/10.1371/journal.pgen.1006646
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