Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits

Yang, Jian, Ferreira, Teresa, Morris, Andrew P., Medland, Sarah E., Madden, Pamela A. F., Heath, Andrew C., Martin, Nicholas G., Montgomery, Grant W., Weedon, Michael N., Loos, Ruth J., Frayling, Timothy M., McCarthy, Mark I., Hirschhorn, Joel N., Goddard, Michael E., Visscher, Peter M., Genetic Invest ANthropometric Trai and DIAbet Genetics Replication Meta-A (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nature Genetics, 44 4: 369-375. doi:10.1038/ng.2213


Author Yang, Jian
Ferreira, Teresa
Morris, Andrew P.
Medland, Sarah E.
Madden, Pamela A. F.
Heath, Andrew C.
Martin, Nicholas G.
Montgomery, Grant W.
Weedon, Michael N.
Loos, Ruth J.
Frayling, Timothy M.
McCarthy, Mark I.
Hirschhorn, Joel N.
Goddard, Michael E.
Visscher, Peter M.
Genetic Invest ANthropometric Trai
DIAbet Genetics Replication Meta-A
Title Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits
Journal name Nature Genetics   Check publisher's open access policy
ISSN 1061-4036
1546-1718
Publication date 2012-04-01
Sub-type Article (original research)
DOI 10.1038/ng.2213
Open Access Status DOI
Volume 44
Issue 4
Start page 369
End page 375
Total pages 7
Place of publication New York, NY, United States
Publisher Nature Publishing Group
Language eng
Abstract We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.
Keyword Genome-Wide Association
Body-Mass Index
Common Snps
Susceptibility Loci
Genetic-Variation
Human Height
Heritability
Disease
Risk
Map
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID A19169
MC_UU_12013/4
MR/J012165/1
Institutional Status UQ
Additional Notes Published online 18 March 2012.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2013 Collection
UQ Diamantina Institute Publications
 
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