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Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index

Abstract

We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that 97% and 68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all 17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for body mass index (BMI), and we find evidence that height- and BMI-associated variants have been under natural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60–70% for height and 30–40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.

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Figure 1: Estimates of heritability using sequence variants under different simulation scenarios based on the UK10K-WGS data set.
Figure 2: Fitting region-specific LD heterogeneity in the genome using a sliding window approach.
Figure 3: Proportion of variation at sequence variants captured by 1000 Genomes Project imputation in the UK10K-WGS data set.
Figure 4: Evidence for height- and BMI-associated genetic variants being under natural selection.
Figure 5: Single-variant tagging of sequence variants by 1000 Genomes Project–imputed variants.

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References

  1. Welter, D. et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 42, D1001–D1006 (2014).

    Article  CAS  Google Scholar 

  2. Manolio, T.A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    Article  CAS  Google Scholar 

  3. Yang, J. et al. Ubiquitous polygenicity of human complex traits: genome-wide analysis of 49 traits in Koreans. PLoS Genet. 9, e1003355 (2013).

    Article  CAS  Google Scholar 

  4. Lee, S.H., Wray, N.R., Goddard, M.E. & Visscher, P.M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011).

    Article  Google Scholar 

  5. Wood, A.R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46, 1173–1186 (2014).

    Article  CAS  Google Scholar 

  6. Speliotes, E.K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    Article  CAS  Google Scholar 

  7. Perry, J.R. et al. Parent-of-origin–specific allelic associations among 106 genomic loci for age at menarche. Nature 514, 92–97 (2014).

    Article  CAS  Google Scholar 

  8. Jostins, L. et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

    Article  CAS  Google Scholar 

  9. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).

  10. 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  11. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  Google Scholar 

  12. Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).

    Article  CAS  Google Scholar 

  13. UK10K Consortium. The UK10K project: rare variants in health and disease. Nature (in the press).

  14. Lee, S.H. et al. Estimation of SNP heritability from dense genotype data. Am. J. Hum. Genet. 93, 1151–1155 (2013).

    Article  CAS  Google Scholar 

  15. Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    Article  CAS  Google Scholar 

  16. Speed, D., Hemani, G., Johnson, M.R. & Balding, D.J. Improved heritability estimation from genome-wide SNPs. Am. J. Hum. Genet. 91, 1011–1021 (2012).

    Article  CAS  Google Scholar 

  17. Gusev, A. et al. Quantifying missing heritability at known GWAS loci. PLoS Genet. 9, e1003993 (2013).

    Article  Google Scholar 

  18. Visscher, P.M., Goddard, M.E., Derks, E.M. & Wray, N.R. Evidence-based psychiatric genetics, AKA the false dichotomy between common and rare variant hypotheses. Mol. Psychiatry 17, 474–485 (2012).

    Article  CAS  Google Scholar 

  19. Eyre-Walker, A. Evolution in health and medicine Sackler colloquium: genetic architecture of a complex trait and its implications for fitness and genome-wide association studies. Proc. Natl. Acad. Sci. USA 107, 1752–1756 (2010).

    Article  CAS  Google Scholar 

  20. Simons, Y.B., Turchin, M.C., Pritchard, J.K. & Sella, G. The deleterious mutation load is insensitive to recent population history. Nat. Genet. 46, 220–224 (2014).

    Article  CAS  Google Scholar 

  21. Uricchio, L.H., Witte, J.S. & Hernandez, R.D. Selection and explosive growth may hamper the performance of rare variant association tests. bioRxiv doi:10.1101/015917 (2015).

  22. Locke, A.E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    Article  CAS  Google Scholar 

  23. Lee, S.H., Yang, J., Goddard, M.E., Visscher, P.M. & Wray, N.R. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism–derived genomic relationships and restricted maximum likelihood. Bioinformatics 28, 2540–2542 (2012).

    Article  CAS  Google Scholar 

  24. Lee, S.H. et al. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat. Genet. 44, 247–250 (2012).

    Article  CAS  Google Scholar 

  25. Treangen, T.J. & Salzberg, S.L. Repetitive DNA and next-generation sequencing: computational challenges and solutions. Nat. Rev. Genet. 13, 36–46 (2012).

    Article  CAS  Google Scholar 

  26. Sims, D., Sudbery, I., Ilott, N.E., Heger, A. & Ponting, C.P. Sequencing depth and coverage: key considerations in genomic analyses. Nat. Rev. Genet. 15, 121–132 (2014).

    Article  CAS  Google Scholar 

  27. Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits (Sinauer Associates, 1998).

  28. Visscher, P.M., McEvoy, B. & Yang, J. From Galton to GWAS: quantitative genetics of human height. Genet. Res. (Camb.) 92, 371–379 (2010).

    Article  Google Scholar 

  29. Zaitlen, N. et al. Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. PLoS Genet. 9, e1003520 (2013).

    Article  CAS  Google Scholar 

  30. Hemani, G. et al. Inference of the genetic architecture underlying BMI and height with the use of 20,240 sibling pairs. Am. J. Hum. Genet. 93, 865–875 (2013).

    Article  CAS  Google Scholar 

  31. Zaitlen, N. et al. Leveraging population admixture to characterize the heritability of complex traits. Nat. Genet. 46, 1356–1362 (2014).

    Article  CAS  Google Scholar 

  32. Morrison, A.C. et al. Whole-genome sequence–based analysis of high-density lipoprotein cholesterol. Nat. Genet. 45, 899–901 (2013).

    Article  CAS  Google Scholar 

  33. Huang, L. et al. Genotype-imputation accuracy across worldwide human populations. Am. J. Hum. Genet. 84, 235–250 (2009).

    Article  CAS  Google Scholar 

  34. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G.R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    Article  CAS  Google Scholar 

  35. Pasaniuc, B. et al. Extremely low-coverage sequencing and imputation increases power for genome-wide association studies. Nat. Genet. 44, 631–635 (2012).

    Article  CAS  Google Scholar 

  36. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  Google Scholar 

  37. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  Google Scholar 

  38. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  Google Scholar 

  39. Patterson, H.D. & Thompson, R. Recovery of inter-block information when block sizes are unequal. Biometrika 58, 545–554 (1971).

    Article  Google Scholar 

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Acknowledgements

This research was supported by the Australian National Health and Medical Research Council (grants 1052684, 1078037 and 1050218), the Australian Research Council (grant 130102666), the US National Institutes of Health (R01MH100141), the Sylvia and Charles Viertel Charitable Foundation and the University of Queensland Foundation. This study makes use of data from the database of Genotypes and Phenotypes (dbGaP) available under accessions phs000090, phs000091 and phs000428 and the EGCUT, LifeLines, TwinGene and UK10K studies (see the Supplementary Note for the full set of acknowledgments for these data).

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J.Y. and P.M.V. conceived and designed the study. J.Y. performed statistical analyses and simulations. M.E.G., J.Y. and P.M.V. derived the theory. A.B., Z.Z. and G.H. performed the imputation analysis. S.H.L., M.R.R., M.C.K. and N.R.W. provided statistical support. A.A.E.V., J.R.B.P., I.M.N., J.V.v.V.-O., H.S., the LifeLines Cohort Study, T.E., L.M., R.M., A.M., A.H., P.K.E.M., N.L.P., E.I. and N.S. contributed to data collection. J.Y. wrote the manuscript with the participation of all authors.

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Correspondence to Jian Yang.

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The authors declare no competing financial interests.

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Supplementary Figures 1–19, Supplementary Tables 1–4 and Supplementary Note. (PDF 5370 kb)

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Yang, J., Bakshi, A., Zhu, Z. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat Genet 47, 1114–1120 (2015). https://doi.org/10.1038/ng.3390

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