Abstract
Multiple methods have been developed to estimate narrow-sense heritability, h2, using single nucleotide polymorphisms (SNPs) in unrelated individuals. However, a comprehensive evaluation of these methods has not yet been performed, leading to confusion and discrepancy in the literature. We present the most thorough and realistic comparison of these methods to date. We used thousands of real whole-genome sequences to simulate phenotypes under varying genetic architectures and confounding variables, and we used array, imputed, or whole genome sequence SNPs to obtain ‘SNP-heritability’ estimates. We show that SNP-heritability can be highly sensitive to assumptions about the frequencies, effect sizes, and levels of linkage disequilibrium of underlying causal variants, but that methods that bin SNPs according to minor allele frequency and linkage disequilibrium are less sensitive to these assumptions across a wide range of genetic architectures and possible confounding factors. These findings provide guidance for best practices and proper interpretation of published estimates.
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Acknowledgements
We thank D. Speed (University College London) for providing LDAK5. We thank the Keller and Vrieze lab groups, the Institute for Behavioral Genetics, N. Wray, A. Price, and S. Caron for helpful comments. This work was supported by NIH grant R01MH100141 (to M.C.K.), NHMRC grants 1078037 (to P.M.V.) and 1113400 (to P.M.V. and J.Y.), Sylvia & Charles Viertel Charitable Foundation Senior Medical Research Fellowship (to J.Y.), and NIH grants R01DA037904 and R01HG008983 (to S.V.). This work used the Janus supercomputer, which is supported by the National Science Foundation (award number CNS-0821794), the University of Colorado Boulder, the University of Colorado Denver, and the National Center for Atmospheric Research. The Janus supercomputer is operated by the University of Colorado Boulder. We thank the participants of the individual Haplotype Reference Consortium cohorts. This research has been conducted using the UK Biobank Resource.
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Author notes
A list of members and affiliations appears in the Supplementary Note.
Affiliations
Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA
- Luke M. Evans
- , Rasool Tahmasbi
- , Douglas W. Bjelland
- , Teresa R. de Candia
- & Matthew C. Keller
Department of Psychology, University of Minnesota, Minneapolis, MN, USA
- Scott I. Vrieze
Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Gonçalo R. Abecasis
- & Sayantan Das
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Steven Gazal
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Steven Gazal
- & Benjamin M. Neale
Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, VIC, Australia
- Michael E. Goddard
Agriculture Victoria, Bundoora, VIC, Australia
- Michael E. Goddard
Institute for Molecular Bioscience and the Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Jian Yang
- & Peter M. Visscher
Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, USA
- Matthew C. Keller
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Consortia
Haplotype Reference Consortium
Contributions
L.M.E. and M.C.K. conceived and designed the study. L.M.E. performed the statistical analyses and simulations. R.T., S.I.V., S.G., G.R.A., S.D., D.W.B., T.R.d.C., M.E.G., B.M.N., J.Y., and P.M.V. provided statistical support. The Haplotype Reference Consortium, G.R.A., and S.D. contributed to data collection and management. L.M.E. and M.C.K. wrote the manuscript with participation of all authors.
Competing interests
The authors declare no competing interests.
Corresponding authors
Correspondence to Luke M. Evans or Matthew C. Keller.
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