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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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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. |
format | Online Article Text |
id | pubmed-5363995 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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