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. 2017 Apr 7;13(4):e1006711.
doi: 10.1371/journal.pgen.1006711. eCollection 2017 Apr.

Phenome-wide heritability analysis of the UK Biobank

Tian Ge  1   2   3 , Chia-Yen Chen  2   3   4 , Benjamin M Neale  2   3   4 , Mert R Sabuncu  1   5 , Jordan W Smoller  2   3
Affiliations

Phenome-wide heritability analysis of the UK Biobank

Tian Ge et al. PLoS Genet. .

Erratum in

Abstract

Heritability estimation provides important information about the relative contribution of genetic and environmental factors to phenotypic variation, and provides an upper bound for the utility of genetic risk prediction models. Recent technological and statistical advances have enabled the estimation of additive heritability attributable to common genetic variants (SNP heritability) across a broad phenotypic spectrum. Here, we present a computationally and memory efficient heritability estimation method that can handle large sample sizes, and report the SNP heritability for 551 complex traits derived from the interim data release (152,736 subjects) of the large-scale, population-based UK Biobank, comprising both quantitative phenotypes and disease codes. We demonstrate that common genetic variation contributes to a broad array of quantitative traits and human diseases in the UK population, and identify phenotypes whose heritability is moderated by age (e.g., a majority of physical measures including height and body mass index), sex (e.g., blood pressure related traits) and socioeconomic status (education). Our study represents the first comprehensive phenome-wide heritability analysis in the UK Biobank, and underscores the importance of considering population characteristics in interpreting heritability.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The number of traits in each of the 11 phenotypic domains that make up the 551 traits analyzed in the UK Biobank: cognitive function (5 traits), early life factors (7 traits), health and medical history (60 traits), hospital in-patient main diagnosis ICD-10 codes (194 traits), life style and environment (88 traits), physical measures (50 traits), psychosocial factors (40 traits), self-reported cancer codes (9 traits), self-reported non-cancer illness codes (79 traits), sex-specific factors (14 traits), and sociodemographics (5 traits).
Fig 2
Fig 2. SNP heritability estimates and their standard errors of the traits in the UK Biobank that show significantly different SNP heritability in females and males after multiple testing correction.
The heritability estimates of rheumatoid arthritis, endocrine/diabetes and wheat products intake reported here are based on genome-wide SNPs and will be markedly reduced when the major histocompatibility complex (MHC) region (chr6:25-35Mb) is excluded from analysis, and thus need to be interpreted with caution.
Fig 3
Fig 3
(A) The age-varying SNP heritability estimates and their standard errors (shaded region) for the 12 traits whose heritability significantly decreases with age after accounting for multiple testing correction; (B) The stratified heritability estimates and standard errors (shaded region) of education (has college or university degree or not), on which the socioeconomic status (SES) measured by the Townsend deprivation index has a significant moderating effect after multiple testing correction.
See this image and copyright information in PMC

References

    1. Visscher P. M., Hill W. G., Wray N. R. (2008). Heritability in the genomics era—concepts and misconceptions. Nature Reviews Genetics, 9(4), 255–266. doi: 10.1038/nrg2322 - DOI - PubMed
    1. Polderman T. J., Benyamin B., De Leeuw C. A., Sullivan P. F., Van Bochoven A., Visscher P. M., et al. (2015). Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nature Genetics, 47(7), 702–709. doi: 10.1038/ng.3285 - DOI - PubMed
    1. Lee S. H., Wray N. R., Goddard M. E., Visscher P. M. (2011). Estimating missing heritability for disease from genome-wide association studies. The American Journal of Human Genetics, 88(3), 294–305. doi: 10.1016/j.ajhg.2011.02.002 - DOI - PMC - PubMed
    1. Yang J., Benyamin B., McEvoy B. P., Gordon S., Henders A. K., Nyholt D. R., et al. (2010). Common SNPs explain a large proportion of the heritability for human height. Nature Genetics, 42(7), 565–569. doi: 10.1038/ng.608 - DOI - PMC - PubMed
    1. Yang J., Lee S. H., Goddard M. E., Visscher P. M. (2011). GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics, 88(1), 76–82. doi: 10.1016/j.ajhg.2010.11.011 - DOI - PMC - PubMed

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