Imperial College London
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ProfessorMarjo-RiittaJarvelin

Faculty of Medicine, School of Public Health

Chair in Lifecourse Epidemiology
 
 
 
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Contact

 

m.jarvelin

 
 
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Location

 

302School of Public HealthWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kaakinen:2017:10.1038/ejhg.2017.90,
author = {Kaakinen, M and Magi, R and Fischer, K and Heikkinen, J and Jarvelin, M-R and Morris, AP and Prokopenko, I},
doi = {10.1038/ejhg.2017.90},
journal = {European Journal of Human Genetics},
pages = {988--994},
title = {A rare-variant test for high-dimensional data},
url = {http://dx.doi.org/10.1038/ejhg.2017.90},
volume = {25},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Kaakinen,M
AU  - Magi,R
AU  - Fischer,K
AU  - Heikkinen,J
AU  - Jarvelin,M-R
AU  - Morris,AP
AU  - Prokopenko,I
DO  - 10.1038/ejhg.2017.90
EP  - 994
PY  - 2017///
SN  - 1018-4813
SP  - 988
TI  - A rare-variant test for high-dimensional data
T2  - European Journal of Human Genetics
UR  - http://dx.doi.org/10.1038/ejhg.2017.90
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000405480900014&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/52485
VL  - 25
ER  -
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