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MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics
BACKGROUND: Multivariate testing tools that integrate multiple genome-wide association studies (GWAS) have become important as the number of phenotypes gathered from study cohorts and biobanks has increased. While these tools have been shown to boost statistical power considerably over univariate te...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242752/ https://www.ncbi.nlm.nih.gov/pubmed/32499813 http://dx.doi.org/10.3389/fgene.2020.00431 |
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author | Lin, Jake Tabassum, Rubina Ripatti, Samuli Pirinen, Matti |
author_facet | Lin, Jake Tabassum, Rubina Ripatti, Samuli Pirinen, Matti |
author_sort | Lin, Jake |
collection | PubMed |
description | BACKGROUND: Multivariate testing tools that integrate multiple genome-wide association studies (GWAS) have become important as the number of phenotypes gathered from study cohorts and biobanks has increased. While these tools have been shown to boost statistical power considerably over univariate tests, an important remaining challenge is to interpret which traits are driving the multivariate association and which traits are just passengers with minor contributions to the genotype-phenotypes association statistic. RESULTS: We introduce MetaPhat, a novel bioinformatics tool to conduct GWAS of multiple correlated traits using univariate GWAS results and to decompose multivariate associations into sets of central traits based on intuitive trace plots that visualize Bayesian Information Criterion (BIC) and P-value statistics of multivariate association models. We validate MetaPhat with Global Lipids Genetics Consortium GWAS results, and we apply MetaPhat to univariate GWAS results for 21 heritable and correlated polyunsaturated lipid species from 2,045 Finnish samples, detecting seven independent loci associated with a cluster of lipid species. In most cases, we are able to decompose these multivariate associations to only three to five central traits out of all 21 traits included in the analyses. We release MetaPhat as an open source tool written in Python with built-in support for multi-processing, quality control, clumping and intuitive visualizations using the R software. CONCLUSION: MetaPhat efficiently decomposes associations between multivariate phenotypes and genetic variants into smaller sets of central traits and improves the interpretation and specificity of genome-phenome associations. MetaPhat is freely available under the MIT license at: https://sourceforge.net/projects/meta-pheno-association-tracer. |
format | Online Article Text |
id | pubmed-7242752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72427522020-06-03 MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics Lin, Jake Tabassum, Rubina Ripatti, Samuli Pirinen, Matti Front Genet Genetics BACKGROUND: Multivariate testing tools that integrate multiple genome-wide association studies (GWAS) have become important as the number of phenotypes gathered from study cohorts and biobanks has increased. While these tools have been shown to boost statistical power considerably over univariate tests, an important remaining challenge is to interpret which traits are driving the multivariate association and which traits are just passengers with minor contributions to the genotype-phenotypes association statistic. RESULTS: We introduce MetaPhat, a novel bioinformatics tool to conduct GWAS of multiple correlated traits using univariate GWAS results and to decompose multivariate associations into sets of central traits based on intuitive trace plots that visualize Bayesian Information Criterion (BIC) and P-value statistics of multivariate association models. We validate MetaPhat with Global Lipids Genetics Consortium GWAS results, and we apply MetaPhat to univariate GWAS results for 21 heritable and correlated polyunsaturated lipid species from 2,045 Finnish samples, detecting seven independent loci associated with a cluster of lipid species. In most cases, we are able to decompose these multivariate associations to only three to five central traits out of all 21 traits included in the analyses. We release MetaPhat as an open source tool written in Python with built-in support for multi-processing, quality control, clumping and intuitive visualizations using the R software. CONCLUSION: MetaPhat efficiently decomposes associations between multivariate phenotypes and genetic variants into smaller sets of central traits and improves the interpretation and specificity of genome-phenome associations. MetaPhat is freely available under the MIT license at: https://sourceforge.net/projects/meta-pheno-association-tracer. Frontiers Media S.A. 2020-05-15 /pmc/articles/PMC7242752/ /pubmed/32499813 http://dx.doi.org/10.3389/fgene.2020.00431 Text en Copyright © 2020 Lin, Tabassum, Ripatti and Pirinen. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Lin, Jake Tabassum, Rubina Ripatti, Samuli Pirinen, Matti MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics |
title | MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics |
title_full | MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics |
title_fullStr | MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics |
title_full_unstemmed | MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics |
title_short | MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics |
title_sort | metaphat: detecting and decomposing multivariate associations from univariate genome-wide association statistics |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242752/ https://www.ncbi.nlm.nih.gov/pubmed/32499813 http://dx.doi.org/10.3389/fgene.2020.00431 |
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