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Bioinformatics. 2014 Jul 15;30(14):2026-34. doi: 10.1093/bioinformatics/btu140. Epub 2014 Mar 24.

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

Author information

1
Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Esbo, Finland, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA, USA Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Computational Medicine, Institute of Health Sciences, University of Oulu and Oulu University Hospital, Oulu, NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland, Department of Epidemiology and Biostatistics, MRC Health Protection, Agency (HPA) Centre for Environment and Health, School of Public Health, Imperial College, London, UK, Institute of Health Sciences, Biocenter Oulu, University of Oulu, Oulu, Department of Clinical Physiology, Tampere University Hospital and University of Tampere, Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere School of Medicine, Tampere, Finland, Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK, Department of Clinical Physiology and Nuclear Medicine, Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku and Turku University Hospital, Turku, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Unit of Primary Care, Oulu University Hospital, Department of Children and Young People and Families, National Institute for Health and Welfare, Oulu, Finland, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK, Hjelt Institute and Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, FinlandDepartment of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Esbo, Finland, Center for Communicable Dise
2
Department of Information and Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Esbo, Finland, Center for Communicable Disease Dynamics, Harvard School of Public Health, Boston, MA, USA Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Computational Medicine, Institute of Health Sciences, University of Oulu and Oulu University Hospital, Oulu, NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland, Department of Epidemiology and Biostatistics, MRC Health Protection, Agency (HPA) Centre for Environment and Health, School of Public Health, Imperial College, London, UK, Institute of Health Sciences, Biocenter Oulu, University of Oulu, Oulu, Department of Clinical Physiology, Tampere University Hospital and University of Tampere, Department of Clinical Chemistry, Fimlab Laboratories, University of Tampere School of Medicine, Tampere, Finland, Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK, Department of Clinical Physiology and Nuclear Medicine, Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku and Turku University Hospital, Turku, Department of Chronic Disease Prevention, National Institute for Health and Welfare, Helsinki, Unit of Primary Care, Oulu University Hospital, Department of Children and Young People and Families, National Institute for Health and Welfare, Oulu, Finland, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK, Hjelt Institute and Department of Computer Science, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.

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.

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/.

PMID:
24665129
PMCID:
PMC4080737
DOI:
10.1093/bioinformatics/btu140
[Indexed for MEDLINE]
Free PMC Article
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