University of Oulu

Kaakinen, M., Mägi, R., Fischer, K., Heikkinen, J., Järvelin, M., Morris, A., Prokopenko, I. (2017) A rare-variant test for high-dimensional data. European Journal of Human Genetics, 25 (8), 988-994. doi:10.1038/ejhg.2017.90

A rare-variant test for high-dimensional data

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Author: Kaakinen, Marika1; Mägi, Reedik2; Fischer, Krista2;
Organizations: 1Department of Genomics of Common Disease, Imperial College London, London, UK
2Estonian Genome Center, University of Tartu, Tartu, Estonia
3Neuroepidemiology and Ageing (NEA) Research Unit, Imperial College London, London, UK
4Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
5Center for Life Course Health Research, University of Oulu, Oulu, Finland
6Unit of Primary Care, Oulu University Hospital, Oulu, Finland
7Biocenter Oulu, University of Oulu, Oulu, Finland
8Department of Biostatistics, University of Liverpool, Liverpool, UK
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
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Language: English
Published: Springer Nature, 2017
Publish Date: 2017-08-25


Genome-wide association studies have facilitated the discovery of thousands of loci for hundreds of phenotypes. However, the issue of missing heritability remains unsolved for most complex traits. Locus discovery could be enhanced with both improved power through multi-phenotype analysis (MPA) and use of a wider allele frequency range, including rare variants (RVs). MPA methods for single-variant association have been proposed, but given their low power for RVs, more efficient approaches are required. We propose multi-phenotype analysis of rare variants (MARV), a burden test-based method for RVs extended to the joint analysis of multiple phenotypes through a powerful reverse regression technique. Specifically, MARV models the proportion of RVs at which minor alleles are carried by individuals within a genomic region as a linear combination of multiple phenotypes, which can be both binary and continuous, and the method accommodates directly the genotyped and imputed data. The full model, including all phenotypes, is tested for association for discovery, and a more thorough dissection of the phenotype combinations for any set of RVs is also enabled. We show, via simulations, that the type I error rate is well controlled under various correlations between two continuous phenotypes, and that the method outperforms a univariate burden test in all considered scenarios. Application of MARV to 4876 individuals from the Northern Finland Birth Cohort 1966 for triglycerides, high- and low-density lipoprotein cholesterols highlights known loci with stronger signals of association than those observed in univariate RV analyses and suggests novel RV effects for these lipid traits.

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Series: European journal of human genetics
ISSN: 1018-4813
ISSN-E: 1476-5438
ISSN-L: 1018-4813
Volume: 25
Pages: 988 - 994
DOI: 10.1038/ejhg.2017.90
Type of Publication: A1 Journal article – refereed
Field of Science: 3111 Biomedicine
Funding: This work used the computing resources of the UK MEDical BIOinformatics partnership - aggregation, integration, visualisation and analysis of large, complex data (UK MED-BIO) which is supported by the Medical Research Council (grant number MR/L01632X/1), the Imperial College High Performance Computing Service, URL: ict/self-service/research-support/hpc/. This project was supported by the European Commission under the Marie Curie Intra-European Fellowship (project MARVEL (PIEF-GA-2013-626461)). IP was in part funded by the Elsie Widdowson Fellowship, the Wellcome Trust Seed Award in Science (WT205915) and the European Union’s Horizon 2020 research and innovation programme (DYNAhealth, project number 633595). APM is a Wellcome Trust Senior Fellow in Basic Biomedical Science (grant number WT098017). Northern Finland Birth Cohort (NFBC1966) thank the late Professor Paula Rantakallio (launch of NFBC1966), the participants in the 31 years study and the NFBC project center. NFBC1966 received financial support from University of Oulu Grant no. 65354, Oulu University Hospital Grant no. 2/97, 8/97, Ministry of Health and Social Affairs Grant no. 23/251/97, 160/97, 190/97, National Institute for Health and Welfare, Helsinki Grant no. 54121, Regional Institute of Occupational Health, Oulu, Finland Grant no. 50621, 54231
EU Grant Number: (633595) DYNAHEALTH - Understanding the dynamic determinants of glucose homeostasis and social capability to promote Healthy and active aging
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