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Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression
Motivation: A typical genome-wide association study searches for associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype. However, there is a growing interest to investigate associations between genomics data and multivariate phenotypes, for example, in gene expression...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4080737/ https://www.ncbi.nlm.nih.gov/pubmed/24665129 http://dx.doi.org/10.1093/bioinformatics/btu140 |
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author | Marttinen, Pekka Pirinen, Matti Sarin, Antti-Pekka Gillberg, Jussi Kettunen, Johannes Surakka, Ida Kangas, Antti J. Soininen, Pasi O’Reilly, Paul Kaakinen, Marika Kähönen, Mika Lehtimäki, Terho Ala-Korpela, Mika Raitakari, Olli T. Salomaa, Veikko Järvelin, Marjo-Riitta Ripatti, Samuli Kaski, Samuel |
author_facet | Marttinen, Pekka Pirinen, Matti Sarin, Antti-Pekka Gillberg, Jussi Kettunen, Johannes Surakka, Ida Kangas, Antti J. Soininen, Pasi O’Reilly, Paul Kaakinen, Marika Kähönen, Mika Lehtimäki, Terho Ala-Korpela, Mika Raitakari, Olli T. Salomaa, Veikko Järvelin, Marjo-Riitta Ripatti, Samuli Kaski, Samuel |
author_sort | Marttinen, Pekka |
collection | PubMed |
description | Motivation: A typical genome-wide association study searches for associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype. However, there is a growing interest to investigate associations between genomics data and multivariate phenotypes, for example, in gene expression or metabolomics studies. A common approach is to perform a univariate test between each genotype–phenotype pair, and then to apply a stringent significance cutoff to account for the large number of tests performed. However, this approach has limited ability to uncover dependencies involving multiple variables. Another trend in the current genetics is the investigation of the impact of rare variants on the phenotype, where the standard methods often fail owing to lack of power when the minor allele is present in only a limited number of individuals. Results: We propose a new statistical approach based on Bayesian reduced rank regression to assess the impact of multiple SNPs on a high-dimensional phenotype. Because of the method’s ability to combine information over multiple SNPs and phenotypes, it is particularly suitable for detecting associations involving rare variants. We demonstrate the potential of our method and compare it with alternatives using the Northern Finland Birth Cohort with 4702 individuals, for whom genome-wide SNP data along with lipoprotein profiles comprising 74 traits are available. We discovered two genes (XRCC4 and MTHFD2L) without previously reported associations, which replicated in a combined analysis of two additional cohorts: 2390 individuals from the Cardiovascular Risk in Young Finns study and 3659 individuals from the FINRISK study. Availability and implementation: R-code freely available for download at http://users.ics.aalto.fi/pemartti/gene_metabolome/. Contact: samuli.ripatti@helsinki.fi; samuel.kaski@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-4080737 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-40807372014-07-03 Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression Marttinen, Pekka Pirinen, Matti Sarin, Antti-Pekka Gillberg, Jussi Kettunen, Johannes Surakka, Ida Kangas, Antti J. Soininen, Pasi O’Reilly, Paul Kaakinen, Marika Kähönen, Mika Lehtimäki, Terho Ala-Korpela, Mika Raitakari, Olli T. Salomaa, Veikko Järvelin, Marjo-Riitta Ripatti, Samuli Kaski, Samuel Bioinformatics Original Papers Motivation: A typical genome-wide association study searches for associations between single nucleotide polymorphisms (SNPs) and a univariate phenotype. However, there is a growing interest to investigate associations between genomics data and multivariate phenotypes, for example, in gene expression or metabolomics studies. A common approach is to perform a univariate test between each genotype–phenotype pair, and then to apply a stringent significance cutoff to account for the large number of tests performed. However, this approach has limited ability to uncover dependencies involving multiple variables. Another trend in the current genetics is the investigation of the impact of rare variants on the phenotype, where the standard methods often fail owing to lack of power when the minor allele is present in only a limited number of individuals. Results: We propose a new statistical approach based on Bayesian reduced rank regression to assess the impact of multiple SNPs on a high-dimensional phenotype. Because of the method’s ability to combine information over multiple SNPs and phenotypes, it is particularly suitable for detecting associations involving rare variants. We demonstrate the potential of our method and compare it with alternatives using the Northern Finland Birth Cohort with 4702 individuals, for whom genome-wide SNP data along with lipoprotein profiles comprising 74 traits are available. We discovered two genes (XRCC4 and MTHFD2L) without previously reported associations, which replicated in a combined analysis of two additional cohorts: 2390 individuals from the Cardiovascular Risk in Young Finns study and 3659 individuals from the FINRISK study. Availability and implementation: R-code freely available for download at http://users.ics.aalto.fi/pemartti/gene_metabolome/. Contact: samuli.ripatti@helsinki.fi; samuel.kaski@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2014-07-15 2014-03-24 /pmc/articles/PMC4080737/ /pubmed/24665129 http://dx.doi.org/10.1093/bioinformatics/btu140 Text en © The Author 2014. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Marttinen, Pekka Pirinen, Matti Sarin, Antti-Pekka Gillberg, Jussi Kettunen, Johannes Surakka, Ida Kangas, Antti J. Soininen, Pasi O’Reilly, Paul Kaakinen, Marika Kähönen, Mika Lehtimäki, Terho Ala-Korpela, Mika Raitakari, Olli T. Salomaa, Veikko Järvelin, Marjo-Riitta Ripatti, Samuli Kaski, Samuel Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression |
title | Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression |
title_full | Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression |
title_fullStr | Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression |
title_full_unstemmed | Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression |
title_short | Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression |
title_sort | assessing multivariate gene-metabolome associations with rare variants using bayesian reduced rank regression |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4080737/ https://www.ncbi.nlm.nih.gov/pubmed/24665129 http://dx.doi.org/10.1093/bioinformatics/btu140 |
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