Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
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
_version_ 1782324030786764800
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
work_keys_str_mv AT marttinenpekka assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT pirinenmatti assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT sarinanttipekka assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT gillbergjussi assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT kettunenjohannes assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT surakkaida assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT kangasanttij assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT soininenpasi assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT oreillypaul assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT kaakinenmarika assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT kahonenmika assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT lehtimakiterho assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT alakorpelamika assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT raitakariollit assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT salomaaveikko assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT jarvelinmarjoriitta assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT ripattisamuli assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression
AT kaskisamuel assessingmultivariategenemetabolomeassociationswithrarevariantsusingbayesianreducedrankregression