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Genome partitioning of genetic variation for complex traits using common SNPs

Nature Genetics volume 43, pages 519–525 (2011)Cite this article

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Abstract

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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Acknowledgements

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

  1. Queensland Statistical Genetics Laboratory, Queensland Institute of Medical Research, Brisbane, Australia

    Jian Yang & Peter M Visscher

  2. Office of Population Genomics, National Human Genome Research Institute (NHGRI), Bethesda, Maryland, USA

    Teri A Manolio

  3. Massachussetts Eye and Ear Infirmary, Harvard Medical School, Boston, Massachusetts, USA

    Louis R Pasquale

  4. Human Genetics Center and Division of Epidemiology, University of Texas Health Science Center, Houston, Texas, USA

    Eric Boerwinkle

  5. Division of Cancer Epidemiology and Genetics, National Cancer Institute (NCI), Bethesda, Maryland, USA

    Neil Caporaso

  6. Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA

    Julie M Cunningham

  7. Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA

    Mariza de Andrade

  8. Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark

    Bjarke Feenstra & Mads Melbye

  9. Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Eleanor Feingold

  10. Division of Endocrinology, Metabolism and Molecular Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA

    M Geoffrey Hayes

  11. Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK

    William G Hill

  12. Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland, USA

    Maria Teresa Landi

  13. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota, USA

    Alvaro Alonso

  14. Montréal Heart Institute, Université de Montréal, Montréal, Quebec, Canada

    Guillaume Lettre

  15. Human and Statistical Genetics Program, School of Medicine, Washington University, St. Louis, Missouri, USA

    Peng Lin

  16. Center for Inherited Disease Research, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Hua Ling & Elizabeth Pugh

  17. Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA

    William Lowe

  18. Department of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA

    Rasika A Mathias

  19. Department of Nutrition, School of Public Health, Harvard University, Boston, Massachusetts, USA

    Marilyn C Cornelis

  20. Department of Biostatistics, University of Washington, Seattle, Washington, USA

    Bruce S Weir

  21. Department of Food and Agricultural Systems, University of Melbourne, Victoria, Australia

    Michael E Goddard

  22. Biosciences Research Division, Department of Primary Industries, Bundoora, Victoria, Australia

    Michael E Goddard

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  1. Jian Yang
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Contributions

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.

Corresponding author

Correspondence to Peter M Visscher.

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The authors declare no competing financial interests.

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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). https://doi.org/10.1038/ng.823

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