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Author reply to A commentary on Pitfalls of predicting complex traits from SNPs

Nature Reviews Genetics volume 14, page 894 (2013)Cite this article

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Following our recent Opinion article (Pitfalls of predicting complex traits from SNPs. Nature Rev. Genet. 14, 507–515 (2013))1, we received correspondence by de los Campos and Sorensen (A commentary on Pitfalls of predicting complex traits from SNPs. Nature Rev. Genet. 14, 894 (2013))2. We thank them for their comments, which follows their recent work3. de los Campos and Sorensen agree that maximum prediction accuracy depends on h2M, which is defined as the variance explained by genotyped markers in the population. They claim that estimates of h2M in a finite sample (h2G-BLUP or h2G) may overestimate h2M, and that this is exacerbated for unrelated individuals. We respond by showing how and why we disagree with these claims.

h2G and h2G-BLUP are estimates of the same parameter from equivalent models4,5,6,7 and so, for the same data set, they must have the same value. Both measure the proportion of the phenotypic variance that is explained by the markers. This proportion depends on linkage disequilibrium (LD) between the single-nucleotide polymorphisms (SNPs) and causal variants (also known as quantitative trait loci (QTLs)). If the LD is imperfect, then h2M will be less than the conventional heritability (h2), which is the proportion of variance explained by all causal variants. The extent of LD depends on the relatedness of the sample of individuals used. If closely related individuals are included in the sample, there is long-range LD generated even between SNPs and QTLs on different chromosomes. Thus, inclusion of close relatives increases h2M and its estimates. Usually, the parameter we wish to estimate is the h2M among individuals who are no more closely related than randomly sampled individuals from the population8.

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Authors and Affiliations

  1. The Queensland Brain Institute, The University of Queensland, QBI Building, St Lucia, 4071, Queensland, Australia

    Naomi R. Wray, Jian Yang & Peter M. Visscher

  2. The University of Queensland Diamantina Institute, Level 7, 37 Kent Street

    Jian Yang & Peter M. Visscher

  3. Translational Research Institute, Woolloongabba, 4102, Queensland, Australia

    Jian Yang & Peter M. Visscher

  4. Biosciences Research Division, Department of Primary Industries, GPO Box 4440, Melbourne, 3001, Victoria, Australia

    Ben J. Hayes & Michael E. Goddard

  5. Dairy Futures Cooperative Research Centre, AgriBio, Centre for AgriBioscience, 5 Ring Road

    Ben J. Hayes

  6. La Trobe University, Bundoora, 3083, Victoria, Australia

    Ben J. Hayes

  7. La Trobe University, Bundoora, 3086, Victoria, Australia

    Ben J. Hayes

  8. Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, 02115, Massachusetts, USA

    Alkes L. Price

  9. the Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, 02115, Massachusetts, USA

    Alkes L. Price

  10. the Broad Institute of MIT and Harvard, Cambridge, 02142, Massachusetts, USA

    Alkes L. Price

  11. the Program in Molecular and Genetic Epidemiology, Harvard School of Public Health, 655 Huntington Avenue, Boston, 02115, Massachusetts, USA

    Alkes L. Price

  12. University of Melbourne, Melbourne, 3010, Victoria, Australia

    Michael E. Goddard

Authors
  1. Naomi R. Wray
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  2. Jian Yang
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  3. Ben J. Hayes
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  5. Michael E. Goddard
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Correspondence to Peter M. Visscher.

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The authors declare no competing financial interests.

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Wray, N., Yang, J., Hayes, B. et al. Author reply to A commentary on Pitfalls of predicting complex traits from SNPs. Nat Rev Genet 14, 894 (2013). https://doi.org/10.1038/nrg3457-c2

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