MetaPhat : Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics

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Lin , J , Tabassum , R , Ripatti , S & Pirinen , M 2020 , ' MetaPhat : Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics ' , Frontiers in Genetics , vol. 11 , 431 . https://doi.org/10.3389/fgene.2020.00431

Title: MetaPhat : Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics
Author: Lin, Jake; Tabassum, Rubina; Ripatti, Samuli; Pirinen, Matti
Contributor: University of Helsinki, Complex Disease Genetics
University of Helsinki, Samuli Olli Ripatti / Principal Investigator
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Centre of Excellence in Complex Disease Genetics
Date: 2020-05-15
Language: eng
Number of pages: 10
Belongs to series: Frontiers in Genetics
ISSN: 1664-8021
URI: http://hdl.handle.net/10138/318603
Abstract: 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) andP-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:.
Subject: multivariate analysis
genotype phenotype correlation studies
feature selection
Bayesian information criteria
visualilzation
canonical correlation
multivariate GWAS
pheno- and genotypes
LOCI
1184 Genetics, developmental biology, physiology
111 Mathematics
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