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Concepts, estimation and interpretation of SNP-based heritability

Abstract

Narrow-sense heritability (h2) is an important genetic parameter that quantifies the proportion of phenotypic variance in a trait attributable to the additive genetic variation generated by all causal variants. Estimation of h2 previously relied on closely related individuals, but recent developments allow estimation of the variance explained by all SNPs used in a genome-wide association study (GWAS) in conventionally unrelated individuals, that is, the SNP-based heritability (). In this Perspective, we discuss recently developed methods to estimate for a complex trait (and genetic correlation between traits) using individual-level or summary GWAS data. We discuss issues that could influence the accuracy of , definitions, assumptions and interpretations of the models, and pitfalls of misusing the methods and misinterpreting the models and results.

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Figure 1: Interpretation of estimated genetic variance depends on ascertainment of the sample.
Figure 2: Relationship between SNP-based heritability on the liability scale (h2SNP(l)) and SNP-based heritability estimated from case–control samples.
Figure 3: Estimation of genetic variance depends on ascertainment of SNPs and genetic architecture.
Figure 4: Multiple-component GREML or HE regression for sets of SNPs stratified by MAF.

References

  1. 1

    Maher, B. Personal genomes: the case of the missing heritability. Nature 456, 18–21 (2008).

    CAS  Article  Google Scholar 

  2. 2

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

    CAS  Article  Google Scholar 

  3. 3

    Xiao, R. & Boehnke, M. Quantifying and correcting for the winner's curse in genetic association studies. Genet. Epidemiol. 33, 453–462 (2009).

    Article  Google Scholar 

  4. 4

    Visscher, P.M. Sizing up human height variation. Nat. Genet. 40, 489–490 (2008).

    CAS  Article  Google Scholar 

  5. 5

    Fisher, R.A. The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinb. 52, 399–433 (1918).

    Article  Google Scholar 

  6. 6

    Silventoinen, K. et al. Heritability of adult body height: a comparative study of twin cohorts in eight countries. Twin Res. 6, 399–408 (2003).

    Article  Google Scholar 

  7. 7

    Macgregor, S., Cornes, B.K., Martin, N.G. & Visscher, P.M. Bias, precision and heritability of self-reported and clinically measured height in Australian twins. Hum. Genet. 120, 571–580 (2006).

    Article  Google Scholar 

  8. 8

    Goldstein, D.B. Common genetic variation and human traits. N. Engl. J. Med. 360, 1696–1698 (2009).

    CAS  Article  Google Scholar 

  9. 9

    Eichler, E.E. et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446–450 (2010).

    CAS  Article  Google Scholar 

  10. 10

    Schork, N.J., Murray, S.S., Frazer, K.A. & Topol, E.J. Common vs. rare allele hypotheses for complex diseases. Curr. Opin. Genet. Dev. 19, 212–219 (2009).

    CAS  Article  Google Scholar 

  11. 11

    Gibson, G. Rare and common variants: twenty arguments. Nat. Rev. Genet. 13, 135–145 (2012).

    CAS  Article  Google Scholar 

  12. 12

    Visscher, P.M., Brown, M.A., McCarthy, M.I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

    CAS  Article  Google Scholar 

  13. 13

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

    CAS  Article  Google Scholar 

  14. 14

    Yang, J. 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).

    CAS  Article  Google Scholar 

  15. 15

    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).

    CAS  Article  Google Scholar 

  16. 16

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

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

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

  18. 18

    Okada, Y. et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506, 376–381 (2014).

    CAS  Article  Google Scholar 

  19. 19

    Liu, J.Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat. Genet. 47, 979–986 (2015).

    CAS  Article  Google Scholar 

  20. 20

    Liu, C. et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat. Genet. 48, 1162–1170 (2016).

    CAS  Article  Google Scholar 

  21. 21

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

    CAS  Article  Google Scholar 

  22. 22

    Loh, P.R. et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat. Genet. 47, 1385–1392 (2015).

    CAS  Article  Google Scholar 

  23. 23

    Benjamin, D.J. et al. The genetic architecture of economic and political preferences. Proc. Natl. Acad. Sci. USA 109, 8026–8031 (2012).

    CAS  Article  Google Scholar 

  24. 24

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    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).

    PubMed  PubMed Central  Google Scholar 

  26. 26

    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).

    CAS  Article  Google Scholar 

  27. 27

    Lee, S.H. et al. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984–994 (2013).

    CAS  Article  Google Scholar 

  28. 28

    Wray, N.R. Allele frequencies and the r2 measure of linkage disequilibrium: impact on design and interpretation of association studies. Twin Res. Hum. Genet. 8, 87–94 (2005).

    Article  Google Scholar 

  29. 29

    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).

    CAS  Article  Google Scholar 

  30. 30

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

    PubMed  PubMed Central  Google Scholar 

  31. 31

    Yang, J., Lee, S.H., Wray, N.R., Goddard, M.E. & Visscher, P.M. GCTA-GREML accounts for linkage disequilibrium when estimating genetic variance from genome-wide SNPs. Proc. Natl. Acad. Sci. USA 113, E4579–E4580 (2016).

    CAS  Article  Google Scholar 

  32. 32

    Krishna Kumar, S., Feldman, M.W., Rehkopf, D.H. & Tuljapurkar, S. Limitations of GCTA as a solution to the missing heritability problem. Proc. Natl. Acad. Sci. USA 113, E61–E70 (2016).

    Article  Google Scholar 

  33. 33

    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).

    CAS  Article  Google Scholar 

  34. 34

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

    CAS  Article  Google Scholar 

  35. 35

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

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Evans, L. et al. Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. bioRxiv https://dx.doi.org/10.1101/115527 (2017).

  37. 37

    Visscher, P.M. et al. Statistical power to detect genetic (co)variance of complex traits using SNP data in unrelated samples. PLoS Genet. 10, e1004269 (2014).

    Article  Google Scholar 

  38. 38

    Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).

    CAS  Article  Google Scholar 

  39. 39

    Purcell, S.M. et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185–190 (2014).

    CAS  Article  Google Scholar 

  40. 40

    Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).

    CAS  Article  Google Scholar 

  41. 41

    Speed, D., Cai, N., Johnson, M.R., Nejentsev, S. & Balding, D.J. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986–992 (2017).

    CAS  Article  Google Scholar 

  42. 42

    Gazal, S. et al. Linkage disequilibrium dependent architecture of human complex traits reveals action of negative selection. bioRxiv https://dx.doi.org/10.1101/082024 (2016).

  43. 43

    Zeng, J. et al. Widespread signatures of negative selection in the genetic architecture of human complex traits. bioRxiv https://dx.doi.org/10.1101/145755 (2017).

  44. 44

    Haseman, J.K. & Elston, R.C. The investigation of linkage between a quantitative trait and a marker locus. Behav. Genet. 2, 3–19 (1972).

    CAS  Article  Google Scholar 

  45. 45

    Golan, D., Lander, E.S. & Rosset, S. Measuring missing heritability: inferring the contribution of common variants. Proc. Natl. Acad. Sci. USA 111, E5272–E5281 (2014).

    CAS  Article  Google Scholar 

  46. 46

    Zhu, Z. et al. Dominance genetic variation contributes little to the missing heritability for human complex traits. Am. J. Hum. Genet. 96, 377–385 (2015).

    CAS  Article  Google Scholar 

  47. 47

    Hill, W.G., Goddard, M.E. & Visscher, P.M. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 4, e1000008 (2008).

    Article  Google Scholar 

  48. 48

    Rönnegård, L., Pong-Wong, R. & Carlborg, O. Defining the assumptions underlying modeling of epistatic QTL using variance component methods. J. Hered. 99, 421–425 (2008).

    Article  Google Scholar 

  49. 49

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

    Google Scholar 

  50. 50

    Pasaniuc, B. & Price, A.L. Dissecting the genetics of complex traits using summary association statistics. Nat. Rev. Genet. 18, 117–127 (2017).

    CAS  Article  Google Scholar 

  51. 51

    Palla, L. & Dudbridge, F. A fast method that uses polygenic scores to estimate the variance explained by genome-wide marker panels and the proportion of variants affecting a trait. Am. J. Hum. Genet. 97, 250–259 (2015).

    CAS  Article  Google Scholar 

  52. 52

    Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013).

    CAS  Article  Google Scholar 

  53. 53

    Shi, H., Kichaev, G. & Pasaniuc, B. Contrasting the genetic architecture of 30 complex traits from summary association data. Am. J. Hum. Genet. 99, 139–153 (2016).

    CAS  Article  Google Scholar 

  54. 54

    Bulik-Sullivan, B.K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

    CAS  Article  Google Scholar 

  55. 55

    Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    CAS  Article  Google Scholar 

  56. 56

    Finucane, H.K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  Article  Google Scholar 

  57. 57

    Yang, J. et al. Genome-wide genetic homogeneity between sexes and populations for human height and body mass index. Hum. Mol. Genet. 24, 7445–7449 (2015).

    CAS  Article  Google Scholar 

  58. 58

    Lynch, M. & Ritland, K. Estimation of pairwise relatedness with molecular markers. Genetics 152, 1753–1766 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Hayes, B.J., Visscher, P.M. & Goddard, M.E. Increased accuracy of artificial selection by using the realized relationship matrix. Genet. Res. (Camb.) 91, 47–60 (2009).

    CAS  Article  Google Scholar 

  60. 60

    Browning, B.L. & Browning, S.R. A fast, powerful method for detecting identity by descent. Am. J. Hum. Genet. 88, 173–182 (2011).

    CAS  Article  Google Scholar 

  61. 61

    Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  Google Scholar 

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Acknowledgements

We thank A. Price for his constructive and helpful comments on an earlier version of the manuscript. This research was supported by the Australian National Health and Medical Research Council (1078901, 1078037 and 1113400), the Australian Research Council (DP160101343), the US National Institutes of Health (GM099568 and MH100141-01), and the Sylvia & Charles Viertel Charitable Foundation (Senior Medical Research Fellowship). This research has been conducted using data from dbGaP (accessions phs000090.v3.p1 and phs000091.v2.p1), the UK10K Project and the UK Biobank Resource (application number 12514). A full list of acknowledgments to these data sets can be found in the Supplementary Note.

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Correspondence to Jian Yang or Peter M Visscher.

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Yang, J., Zeng, J., Goddard, M. et al. Concepts, estimation and interpretation of SNP-based heritability. Nat Genet 49, 1304–1310 (2017). https://doi.org/10.1038/ng.3941

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