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Oxford University Press (OUP), Bioinformatics, 14(30), p. 2026-2034

DOI: 10.1093/bioinformatics/btu140

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Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression

Journal article published in 2014 by Pekka Marttinen, Matti Pirinen, Antti-Pekka Sarin, Jussi Gillberg, Johannes Kettunen, Ida Surakka, Antti J. Kangas, Pasi Soininen, Paul F. O’Reilly, Marika Kaakinen ORCID, M. Kahonen ORCID, Mika Kähönen, Terho Lehtimäki, Mika Ala-Korpela, Olli T. Raitakari and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

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.