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

: Marttinen P, Pirinen M, Sarin AP, Gillberg J, Kettunen J, Surakka I, Kangas AJ, Soininen P, O'Reilly P, Kaakinen M, Kahonen M, Lehtimaki T, Ala-Korpela M, Raitakari OT, Salomaa V, Jarvelin MR, Ripatti S, Kaski S

Publisher: OXFORD UNIV PRESS

: 2014

: Bioinformatics

: BIOINFORMATICS

: 30

: 14

: 2026

: 2034

: 9

: 1367-4803

DOI: https://doi.org/10.1093/bioinformatics/btu140

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.