A fast non-parametric test of association for multiple traits

The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it reli...

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Autores principales: Garrido-Martín, Diego, Calvo, Miquel, Reverter, Ferran, Guigó, Roderic
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10571397/
https://www.ncbi.nlm.nih.gov/pubmed/37828616
http://dx.doi.org/10.1186/s13059-023-03076-8
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author Garrido-Martín, Diego
Calvo, Miquel
Reverter, Ferran
Guigó, Roderic
author_facet Garrido-Martín, Diego
Calvo, Miquel
Reverter, Ferran
Guigó, Roderic
author_sort Garrido-Martín, Diego
collection PubMed
description The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations to assess significance, which hinders the analysis of large datasets. Here, we derive the limiting null distribution of the PERMANOVA test statistic, providing a framework for the fast computation of asymptotic p values. Our asymptotic test presents controlled type I error and high power, often outperforming parametric approaches. We illustrate its applicability in the context of QTL mapping and GWAS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03076-8.
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spelling pubmed-105713972023-10-14 A fast non-parametric test of association for multiple traits Garrido-Martín, Diego Calvo, Miquel Reverter, Ferran Guigó, Roderic Genome Biol Method The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations to assess significance, which hinders the analysis of large datasets. Here, we derive the limiting null distribution of the PERMANOVA test statistic, providing a framework for the fast computation of asymptotic p values. Our asymptotic test presents controlled type I error and high power, often outperforming parametric approaches. We illustrate its applicability in the context of QTL mapping and GWAS. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03076-8. BioMed Central 2023-10-12 /pmc/articles/PMC10571397/ /pubmed/37828616 http://dx.doi.org/10.1186/s13059-023-03076-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Method
Garrido-Martín, Diego
Calvo, Miquel
Reverter, Ferran
Guigó, Roderic
A fast non-parametric test of association for multiple traits
title A fast non-parametric test of association for multiple traits
title_full A fast non-parametric test of association for multiple traits
title_fullStr A fast non-parametric test of association for multiple traits
title_full_unstemmed A fast non-parametric test of association for multiple traits
title_short A fast non-parametric test of association for multiple traits
title_sort fast non-parametric test of association for multiple traits
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10571397/
https://www.ncbi.nlm.nih.gov/pubmed/37828616
http://dx.doi.org/10.1186/s13059-023-03076-8
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