MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics
BackgroundMultivariate 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 test...
Ausführliche Beschreibung
Autor*in: |
Jake Lin [verfasserIn] Rubina Tabassum [verfasserIn] Samuli Ripatti [verfasserIn] Matti Pirinen [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Übergeordnetes Werk: |
In: Frontiers in Genetics - Frontiers Media S.A., 2011, 11(2020) |
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Übergeordnetes Werk: |
volume:11 ; year:2020 |
Links: |
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DOI / URN: |
10.3389/fgene.2020.00431 |
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Katalog-ID: |
DOAJ03350038X |
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520 | |a BackgroundMultivariate 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.ResultsWe 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.ConclusionMetaPhat 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. | ||
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10.3389/fgene.2020.00431 doi (DE-627)DOAJ03350038X (DE-599)DOAJdaa2f2820d7240b3ba335cff3d10ee97 DE-627 ger DE-627 rakwb eng QH426-470 Jake Lin verfasserin aut MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMultivariate 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.ResultsWe 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.ConclusionMetaPhat 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. multivariate analysis genotype phenotype correlation studies feature selection Bayesian information criteria visualilzation canonical correlation Genetics Rubina Tabassum verfasserin aut Samuli Ripatti verfasserin aut Samuli Ripatti verfasserin aut Samuli Ripatti verfasserin aut Matti Pirinen verfasserin aut Matti Pirinen verfasserin aut Matti Pirinen verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00431 kostenfrei https://doaj.org/article/daa2f2820d7240b3ba335cff3d10ee97 kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00431/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 |
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10.3389/fgene.2020.00431 doi (DE-627)DOAJ03350038X (DE-599)DOAJdaa2f2820d7240b3ba335cff3d10ee97 DE-627 ger DE-627 rakwb eng QH426-470 Jake Lin verfasserin aut MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMultivariate 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.ResultsWe 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.ConclusionMetaPhat 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. multivariate analysis genotype phenotype correlation studies feature selection Bayesian information criteria visualilzation canonical correlation Genetics Rubina Tabassum verfasserin aut Samuli Ripatti verfasserin aut Samuli Ripatti verfasserin aut Samuli Ripatti verfasserin aut Matti Pirinen verfasserin aut Matti Pirinen verfasserin aut Matti Pirinen verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00431 kostenfrei https://doaj.org/article/daa2f2820d7240b3ba335cff3d10ee97 kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00431/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 |
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10.3389/fgene.2020.00431 doi (DE-627)DOAJ03350038X (DE-599)DOAJdaa2f2820d7240b3ba335cff3d10ee97 DE-627 ger DE-627 rakwb eng QH426-470 Jake Lin verfasserin aut MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMultivariate 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.ResultsWe 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.ConclusionMetaPhat 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. multivariate analysis genotype phenotype correlation studies feature selection Bayesian information criteria visualilzation canonical correlation Genetics Rubina Tabassum verfasserin aut Samuli Ripatti verfasserin aut Samuli Ripatti verfasserin aut Samuli Ripatti verfasserin aut Matti Pirinen verfasserin aut Matti Pirinen verfasserin aut Matti Pirinen verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00431 kostenfrei https://doaj.org/article/daa2f2820d7240b3ba335cff3d10ee97 kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00431/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 |
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10.3389/fgene.2020.00431 doi (DE-627)DOAJ03350038X (DE-599)DOAJdaa2f2820d7240b3ba335cff3d10ee97 DE-627 ger DE-627 rakwb eng QH426-470 Jake Lin verfasserin aut MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMultivariate 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.ResultsWe 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.ConclusionMetaPhat 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. multivariate analysis genotype phenotype correlation studies feature selection Bayesian information criteria visualilzation canonical correlation Genetics Rubina Tabassum verfasserin aut Samuli Ripatti verfasserin aut Samuli Ripatti verfasserin aut Samuli Ripatti verfasserin aut Matti Pirinen verfasserin aut Matti Pirinen verfasserin aut Matti Pirinen verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00431 kostenfrei https://doaj.org/article/daa2f2820d7240b3ba335cff3d10ee97 kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00431/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 |
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10.3389/fgene.2020.00431 doi (DE-627)DOAJ03350038X (DE-599)DOAJdaa2f2820d7240b3ba335cff3d10ee97 DE-627 ger DE-627 rakwb eng QH426-470 Jake Lin verfasserin aut MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundMultivariate 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.ResultsWe 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.ConclusionMetaPhat 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. multivariate analysis genotype phenotype correlation studies feature selection Bayesian information criteria visualilzation canonical correlation Genetics Rubina Tabassum verfasserin aut Samuli Ripatti verfasserin aut Samuli Ripatti verfasserin aut Samuli Ripatti verfasserin aut Matti Pirinen verfasserin aut Matti Pirinen verfasserin aut Matti Pirinen verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2020) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2020 https://doi.org/10.3389/fgene.2020.00431 kostenfrei https://doaj.org/article/daa2f2820d7240b3ba335cff3d10ee97 kostenfrei https://www.frontiersin.org/article/10.3389/fgene.2020.00431/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 |
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MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics |
abstract |
BackgroundMultivariate 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.ResultsWe 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.ConclusionMetaPhat 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. |
abstractGer |
BackgroundMultivariate 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.ResultsWe 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.ConclusionMetaPhat 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. |
abstract_unstemmed |
BackgroundMultivariate 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.ResultsWe 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.ConclusionMetaPhat 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. |
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MetaPhat: Detecting and Decomposing Multivariate Associations From Univariate Genome-Wide Association Statistics |
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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.ConclusionMetaPhat 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. 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