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A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma

Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans ass...

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Autores principales: Ruffieux, Hélène, Carayol, Jérôme, Popescu, Radu, Harper, Mary-Ellen, Dent, Robert, Saris, Wim H. M., Astrup, Arne, Hager, Jörg, Davison, Anthony C., Valsesia, Armand
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295243/
https://www.ncbi.nlm.nih.gov/pubmed/32492067
http://dx.doi.org/10.1371/journal.pcbi.1007882
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author Ruffieux, Hélène
Carayol, Jérôme
Popescu, Radu
Harper, Mary-Ellen
Dent, Robert
Saris, Wim H. M.
Astrup, Arne
Hager, Jörg
Davison, Anthony C.
Valsesia, Armand
author_facet Ruffieux, Hélène
Carayol, Jérôme
Popescu, Radu
Harper, Mary-Ellen
Dent, Robert
Saris, Wim H. M.
Astrup, Arne
Hager, Jörg
Davison, Anthony C.
Valsesia, Armand
author_sort Ruffieux, Hélène
collection PubMed
description Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses. Diet, Obesity and Genes (DiOGenes) trial registration number: NCT00390637.
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spelling pubmed-72952432020-06-19 A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma Ruffieux, Hélène Carayol, Jérôme Popescu, Radu Harper, Mary-Ellen Dent, Robert Saris, Wim H. M. Astrup, Arne Hager, Jörg Davison, Anthony C. Valsesia, Armand PLoS Comput Biol Research Article Molecular quantitative trait locus (QTL) analyses are increasingly popular to explore the genetic architecture of complex traits, but existing studies do not leverage shared regulatory patterns and suffer from a large multiplicity burden, which hampers the detection of weak signals such as trans associations. Here, we present a fully multivariate proteomic QTL (pQTL) analysis performed with our recently proposed Bayesian method LOCUS on data from two clinical cohorts, with plasma protein levels quantified by mass-spectrometry and aptamer-based assays. Our two-stage study identifies 136 pQTL associations in the first cohort, of which >80% replicate in the second independent cohort and have significant enrichment with functional genomic elements and disease risk loci. Moreover, 78% of the pQTLs whose protein abundance was quantified by both proteomic techniques are confirmed across assays. Our thorough comparisons with standard univariate QTL mapping on (1) these data and (2) synthetic data emulating the real data show how LOCUS borrows strength across correlated protein levels and markers on a genome-wide scale to effectively increase statistical power. Notably, 15% of the pQTLs uncovered by LOCUS would be missed by the univariate approach, including several trans and pleiotropic hits with successful independent validation. Finally, the analysis of extensive clinical data from the two cohorts indicates that the genetically-driven proteins identified by LOCUS are enriched in associations with low-grade inflammation, insulin resistance and dyslipidemia and might therefore act as endophenotypes for metabolic diseases. While considerations on the clinical role of the pQTLs are beyond the scope of our work, these findings generate useful hypotheses to be explored in future research; all results are accessible online from our searchable database. Thanks to its efficient variational Bayes implementation, LOCUS can analyze jointly thousands of traits and millions of markers. Its applicability goes beyond pQTL studies, opening new perspectives for large-scale genome-wide association and QTL analyses. Diet, Obesity and Genes (DiOGenes) trial registration number: NCT00390637. Public Library of Science 2020-06-03 /pmc/articles/PMC7295243/ /pubmed/32492067 http://dx.doi.org/10.1371/journal.pcbi.1007882 Text en © 2020 Ruffieux 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
Ruffieux, Hélène
Carayol, Jérôme
Popescu, Radu
Harper, Mary-Ellen
Dent, Robert
Saris, Wim H. M.
Astrup, Arne
Hager, Jörg
Davison, Anthony C.
Valsesia, Armand
A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma
title A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma
title_full A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma
title_fullStr A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma
title_full_unstemmed A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma
title_short A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma
title_sort fully joint bayesian quantitative trait locus mapping of human protein abundance in plasma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295243/
https://www.ncbi.nlm.nih.gov/pubmed/32492067
http://dx.doi.org/10.1371/journal.pcbi.1007882
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