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Discovering missing heritability in whole-genome sequencing data

Nature Genetics volume 54, pages 224–226 (2022)Cite this article

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The gap between heritability estimates from twin studies and those from genotyping array data has puzzled researchers for over a decade. New research suggests that much of the ‘missing’ heritability is due to rare variants that can only be captured by whole-genome sequencing (WGS) data.

Heritability is the fraction of phenotypic variation caused by genetic variation, a fundamental measure of how important genetic factors, relative to other factors, are in determining phenotype variation. The heritability of a phenotype places an upper bound on how accurately the phenotype can be predicted from genetic data and shapes how the phenotype will respond to selection. For decades, canonical heritability estimates came from twin studies. Such studies typically estimate heritability by comparing phenotypic correlations of monozygotic (identical) and dizygotic (non-identical) twins. Twin studies showed that many human traits and diseases have substantial heritability1, but were not able to pinpoint specific variants underlying the heritability. The development of genotyping arrays, which measure a subset of single-nucleotide polymorphisms (SNPs) in the genome, and their use in genome-wide association studies (GWAS) led to the discovery of many genotype–phenotype associations. However, after several years, the variants discovered by GWAS explained only a small fraction of the heritability estimated by twin studies, with the gap labeled ‘missing heritability’2. In 2010, Yang et al.3 adapted methods from animal breeding to measure the heritability explained by all the genetic variation captured by a genotyping array, as opposed to only the heritability explained by the most strongly associated SNPs. This method showed that some of the heritability was ‘missing’ because the effects of most common SNPs were too small to be detected with available sample sizes. The methodology was termed genomic-relatedness restricted maximum likelihood (GREML) and was extended to variants imputed from reference samples with WGS data4. This better, although still imperfectly, captured heritability from low-frequency variants—but a lot of heritability remained ‘missing’. In the current issue, Wainschtein et al.5 describe a further development of this methodology, GREML-WGS, that estimates heritability using the near-complete genotype information provided by WGS data.

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Fig. 1: How mutations give rise to common and rare variants.

References

  1. Polderman, T. J. C. et al. Nat. Genet. 47, 702–709 (2015).

    Article  CAS  Google Scholar 

  2. Manolio, T. A. et al. Nature 461, 747–753 (2009).

    Article  CAS  Google Scholar 

  3. Yang, J. et al. Nat. Genet. 42, 565–9 (2010).

    Article  CAS  Google Scholar 

  4. Yang, J. et al. Nat. Genet. 47, 1114–1120 (2015).

    Article  CAS  Google Scholar 

  5. Wainschtein, P. et al. Nat. Genet. https://doi.org/10.1038/s41588-021-00997-7 (2022).

    Article  PubMed  Google Scholar 

  6. Taliun, D. et al. Nature 590, 290–299 (2021).

    Article  CAS  Google Scholar 

  7. Uricchio, L. H. Hum. Genet. 139, 5–21 (2020).

    Article  Google Scholar 

  8. Das, S. et al. Nat. Genet. 48, 1284–1287 (2016).

    Article  CAS  Google Scholar 

  9. Evans, L. M. et al. Nat. Genet. 50, 737–745 (2018).

    Article  CAS  Google Scholar 

  10. Berg, J. J. et al. eLife 8, e39725 (2019).

    Article  Google Scholar 

  11. Zaidi, A. A. & Mathieson, I. eLife 9, e61548 (2020).

    Article  CAS  Google Scholar 

  12. Silventoinen, K. et al. Twin Res. 6, 399–408 (2003).

    Article  Google Scholar 

  13. Elks, C. E. et al. Front. Endocrinol. 3, 1–16 (2012).

    Article  Google Scholar 

  14. Young, A. I. et al. Nat. Genet. 50, 1304–1310 (2018).

    Article  CAS  Google Scholar 

  15. Halldorsson, A. B. V. et al. Preprint at bioRxiv https://doi.org/10.1101/2021.11.16.468246 (2021).

  16. Border, R. et al. Preprint at bioRxiv https://doi.org/10.1101/2021.03.18.436091 (2021).

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  1. Human Genetics Department, UCLA David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Alexander I. Young

  2. UCLA Anderson School of Management, Los Angeles, CA, USA

    Alexander I. Young

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Correspondence to Alexander I. Young.

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Young, A.I. Discovering missing heritability in whole-genome sequencing data. Nat Genet 54, 224–226 (2022). https://doi.org/10.1038/s41588-022-01012-3

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