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Genome partitioning of genetic variation for complex traits using common SNPs


We estimate and partition genetic variation for height, body mass index (BMI), von Willebrand factor and QT interval (QTi) using 586,898 SNPs genotyped on 11,586 unrelated individuals. We estimate that 45%, 17%, 25% and 21% of the variance in height, BMI, von Willebrand factor and QTi, respectively, can be explained by all autosomal SNPs and a further 0.5–1% can be explained by X chromosome SNPs. We show that the variance explained by each chromosome is proportional to its length, and that SNPs in or near genes explain more variation than SNPs between genes. We propose a new approach to estimate variation due to cryptic relatedness and population stratification. Our results provide further evidence that a substantial proportion of heritability is captured by common SNPs, that height, BMI and QTi are highly polygenic traits, and that the additive variation explained by a part of the genome is approximately proportional to the total length of DNA contained within genes therein.

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Figure 1: Variance explained by chromosomes.
Figure 2: Estimates of the variance explained by genic and intergenic regions on each chromosome for height by the joint analysis using 11,586 unrelated individuals in the combined dataset.
Figure 3: Variance due to cryptic relatedness and population stratification.
Figure 4: The sum of variance explained by the GWAS associated SNPs on each chromosome in the GIANT meta-analysis of height23 against the estimate of variance explained by each chromosome for height by the joint analysis using the combined data of 11,586 unrelated individuals in the present study.


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Funding support for the Gene, Environment Association Studies (GENEVA) project has been provided through the US National Institutes of Health Genes, Environment and Health Initiative. For the ARIC project, support was from U01 HG 004402 (PI: E.A. Boerwinkle). For the NHS and HPFS support is from U01 HG 004399 and U01 HG 004728 (PIs: F.B. Hu and L.R. Pasquale). The genotyping for the ARIC, NHS and HPFS studies was performed at the Broad Institute of MIT and Harvard with funding support from U01 HG04424 (PI: S. Gabriel). The GENEVA Coordinating Center receives support from U01 HG 004446 (PI: B.S. Weir). Assistance with GENEVA data cleaning was provided by the National Center for Biotechnology Information. D. Crosslin and C. Laurie of the GENEVA project assisted in making the data available for analysis. A Physician Scientist Award from Research to Prevent Blindness in New York City also supports L.R.P. M.C.C. is a recipient of a Canadian Institutes of Health Research Fellowship. We acknowledge funding from the Australian National Health and Medical Research Council (NHMRC grants 389892 and 613672) and the Australian Research Council (ARC grants DP0770096 and DP1093900). We thank D. Posthuma for discussions and the referees for constructive comments.

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Authors and Affiliations



P.M.V., M.E.G., B.S.W. and T.A.M. designed the study. J.Y. performed all statistical analyses. J.Y. and P.M.V. wrote the first draft of the paper. L.R.P., E.B., N.C., J.M.C., M.d.A., B.F., E.F., M.G.H., W.G.H., M.T.L., A.A., G.L., P.L., H.L., W.L., R.A.M., M.M., E.P. and M.C.C. contributed by providing genotype and phenotype data, by giving advice on analyses and interpretation of results and/or by giving advice on the contents of the paper.

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Correspondence to Peter M Visscher.

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Yang, J., Manolio, T., Pasquale, L. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet 43, 519–525 (2011).

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