Article | Published:

Relatedness disequilibrium regression estimates heritability without environmental bias

Abstract

Heritability measures the proportion of trait variation that is due to genetic inheritance. Measurement of heritability is important in the nature-versus-nurture debate. However, existing estimates of heritability may be biased by environmental effects. Here, we introduce relatedness disequilibrium regression (RDR), a novel method for estimating heritability. RDR avoids most sources of environmental bias by exploiting variation in relatedness due to random Mendelian segregation. We used a sample of 54,888 Icelanders who had both parents genotyped to estimate the heritability of 14 traits, including height (55.4%, s.e. 4.4%) and educational attainment (17.0%, s.e. 9.4%). Our results suggest that some other estimates of heritability may be inflated by environmental effects.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    Sesardic, N. Making Sense of Heritability (Cambridge Univ. Press, Cambridge, 2005).

  2. 2.

    Polderman, T. J. C. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).

  3. 3.

    Boomsma, D., Busjahn, A. & Peltonen, L. Classical twin studies and beyond. Nat. Rev. Genet. 3, 872–82 (2002).

  4. 4.

    Yang, J., Zeng, J., Goddard, M. E., Wray, N. R. & Visscher, P. M. Concepts, estimation and interpretation of SNP-based heritability. Nat. Genet. 49, 1304–1310 (2017).

  5. 5.

    Visscher, P. M. et al. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2, e41 (2006).

  6. 6.

    Speed, D. et al. Reevaluation of SNP heritability in complex human traits. Nat. Genet. 49, 986–992 (2017).

  7. 7.

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

  8. 8.

    Hill, W. D. et al. Genomic analysis of family data reveals additional genetic effects on intelligence and personality. https://doi.org/10.1038/s41380-017-0005-1 (2018).

  9. 9.

    Muñoz, M. et al. Evaluating the contribution of genetics and familial shared environment to common disease using the UK Biobank. Nat. Genet. 48, 980 (2016).

  10. 10.

    Heckerman, D. et al. Linear mixed model for heritability estimation that explicitly addresses environmental variation. Proc. Natl. Acad. Sci. USA 113, 7377–7382 (2016).

  11. 11.

    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).

  12. 12.

    Baud, A. et al. Genetic variation in the social environment contributes to health and disease. PLoS Genet. 13, e1006498 (2017).

  13. 13.

    Bijma, P. Estimating indirect genetic effects: precision of estimates and optimum designs. Genetics 186, 1013–1028 (2010).

  14. 14.

    Kong, A. et al. The nature of nurture: effects of parental genotypes. Science 359, 424–428 (2018).

  15. 15.

    Spielman, R. S., McGinnis, R. E. & Ewens, W. J. Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am. J. Hum. Genet. 52, 506–16 (1993).

  16. 16.

    Ewens, W. J. & Spielman, R. S. The transmission/disequilibrium test: history, subdivision, and admixture. Am. J. Hum. Genet. 57, 455–464 (1995).

  17. 17.

    Thomson, G. Mapping disease genes: family-based association studies. Am. J. Hum. Genet. 57, 487–498 (1995).

  18. 18.

    Carey, G. Sibling imitation and contrast effects. Behav. Genet. 16, 319–341 (1986).

  19. 19.

    Pedersen, N. L., Lichtenstein, P. & Svedberg, P. The Swedish Twin Registry in the Third Millennium. Twin Res. 5, 427–432 (2002).

  20. 20.

    Bycroft, C. et al. Genome-wide genetic data on ~500,000 UK Biobank participants. https://doi.org/10.1101/166298 (2017).

  21. 21.

    Krapohl, E. & Plomin, R. Genetic link between family socioeconomic status and children’s educational achievement estimated from genome-wide SNPs. Mol. Psychiatry 21, 437–443 (2016).

  22. 22.

    Lee, J. et al. Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment. https://doi.org/10.1038/s41588-018-0147-3 (2018).

  23. 23.

    Branigan, A. R., Mccallum, K. J. & Freese, J. Variation in the heritability of educational attainment: an international meta-analysis. Soc. Forces 92, 109–140 (2013).

  24. 24.

    Gudbjartsson, D. F. et al. Large-scale whole-genome sequencing of the Icelandic population. Nat. Genet. 47, 435–444 (2015).

  25. 25.

    Kong, A. et al. Detection of sharing by descent, long-range phasing and haplotype imputation. Nat. Genet. 40, 1068–75 (2008).

  26. 26.

    Kong, A. et al. Parental origin of sequence variants associated with complex diseases. Nature 462, 868–874 (2009).

  27. 27.

    Tuvblad, C., Grann, M. & Lichtenstein, P. Heritability for adolescent antisocial behavior differs with socioeconomic status: gene-environment interaction. J. Child Psychol. Psychiatry 47, 734–743 (2006).

  28. 28.

    Stoolmiller, M. Implications of the restricted range of family environments for estimates of heritability and nonshared environment in behavior: genetic adoption studies. Psychol. Bull. 125, 392 (1999).

  29. 29.

    Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. https://doi.org/10.1038/nature17671 (2016).

  30. 30.

    Gudbjartsson, D. F. et al. Many sequence variants affecting diversity of adult human height. Nat. Genet. 40, 609–15 (2008).

  31. 31.

    Thorleifsson, G. et al. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat. Genet. 41, 18–24 (2009).

  32. 32.

    Elks, C. E. et al. Thirty new loci for age at menarche identified by a meta-analysis of genome-wide association studies. Nat. Genet. 42, 1077–1085 (2010).

  33. 33.

    Young, A. I. & Durbin, R. Estimation of epistatic variance components and heritability in founder populations and crosses. Genetics 198, 1405–1416 (2014).

  34. 34.

    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).

  35. 35.

    Tamar, S. Confidence intervals for heritability via Haseman-Elston regression. Stat. Appl. Genet. Mol. Biol. 16, 259 (2017).

  36. 36.

    Carlsson, S., Ahlbom, A., Lichtenstein, P. & Andersson, T. Shared genetic influence of BMI, physical activity and type 2 diabetes: a twin study. Diabetologia 56, 1031–1035 (2013).

  37. 37.

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

  38. 38.

    Baker, J. H., Thornton, L. M., Bulik, C. M., Kendler, K. S. & Lichtenstein, P. Shared genetic effects between age at menarche and disordered eating. J. Adolesc. Heal. 51, 491–496 (2012).

  39. 39.

    Rahman, I. et al. Genetic dominance influences blood biomarker levels in a sample of 12,000 Swedish elderly twins. Twin Res. Hum. Genet. 12, 286–294 (2009).

  40. 40.

    Arpegård, J. et al. Comparison of heritability of Cystatin C- and creatinine-based estimates of kidney function and their relation to heritability of cardiovascular disease. J. Am. Heart Assoc. 4, e001467 (2015).

Download references

Acknowledgements

A.I.Y. was supported by a Wellcome Trust Doctoral Studentship (099670/Z/12/Z) for part of this project. A.I.Y. and A.K. were supported by the Li Ka Shing Foundation for part of this project.

Author information

A.I.Y. conceived and designed the study, performed statistical analyses, contributed analysis tools, developed theoretical results, and wrote the paper. M.L.F. performed statistical analyses and contributed analysis tools. D.F.G. contributed analysis tools, processed raw genotype/sequencing data, and collected and processed phenotype data. G.T. contributed analysis tools, and collected and processed phenotype data. G.B. collected and processed phenotype data. P.S. collected and processed phenotype data. G.M. processed raw genotype/sequence data. U.T. supervised generation of genotype/sequence data and phenotype data. K.S. jointly supervised research and wrote the paper. A.K. conceived and designed the study, jointly supervised research, and wrote the paper.

Correspondence to Alexander I. Young or Augustine Kong.

Ethics declarations

Competing interests

The following authors affiliated with deCODE Genetics are or were employed by the company, which is owned by Amgen, Inc.: A.I.Y., M.L.F., D.F.G., G.T., G.B., P.S., U.T., K.S., and A.K.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 Determination of offspring relatedness.

The diagram shows how the identity-by-descent sharing states of two individuals i and j are determined by the identity-by-descent sharing states of their parents and the segregation events in the parents during meiosis. The identity-by-descent sharing states of i and j are represented by the four chromosomes in the centre, with black bands indicating regions shared identical-by-descent. The four chromosomes represent the four possible pairs of homologous chromosomes (maternal-maternal, paternal-maternal, maternal-paternal, and paternal-paternal): the identity-by-descent sharing between the chromosome inherited from i’s father, Pi, and j ’s mother, Mj, etc. The identity-by-descent sharing states of the four possible pairs of parents, one from each individual, are shown in the corners (Pi and Pj, Pj and Mi, Pj and Mi, and Mj and Mi). The segregation event in i’s father is represented by I(Pi), the segregation event in j ’s mother represented by I(Mj), etc. Note that for simplicity we ignore recombination in this diagram. See the Relatedness Disequilibrium Lemma in the Supplementary Note for a mathematical description of this process and its consequences.

Figure Supplementary 2 RDR variance component estimates.

Estimated variance components of the RDR covariance model for 14 quantitative traits in Iceland (Supplementary Table 4), expressed as a % of phenotypic variance, shown with intervals +/- 1.96 standard errors around the estimate. Trait abbreviations: BMI, body mass index; AFCW, age at first child in women; AFCM, age at first child in men; education (years), educational attainment (years); HDL, high density lipoprotein; MCH, mean cell haemoglobin; MCHC, mean cell heamoglobin concentration; MCV, mean cell volume.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1 and 2, Supplementary Tables 1–9 and Supplementary Note

Reporting Summary

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Further reading

Fig. 1: Relatedness disequilibrium.
Fig. 2: Comparison of heritability estimates from different methods.
Supplementary Figure 1: Determination of offspring relatedness.
Figure Supplementary 2: RDR variance component estimates.