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Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits

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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Fig. 1: Comparison of heritability estimation methods. Mean \({\hat{h}}_{{\bf{SNP}}}^{{\bf{2}}}\) across 100 replicates from GRMs built from WGS SNPs in the least structured subsamples.
Fig. 2: Partitioned heritability methods to explore allelic spectra of traits. Mean \({\hat{h}}_{{\bf{SNP}}}^{{\bf{2}}}\) for four MAF bins across 100 replicates from multicomponent approaches in unrelated individuals using WGS SNPs in the least structured subsample.
Fig. 3: Influence of model assumptions using phenotypes simulated under alternative genetic architectures.Mean \({\hat{h}}_{{\bf{SNP}}}^{{\bf{2}}}\) across 100 replicates from GRMs built from imputed SNPs in the least structured subsamples across different model assumptions (bars) and different ways of simulating CVs (x axes).
Fig. 4: Influence of model assumptions using phenotypes simulated with LD-dependent genetic architecture. Mean \({\hat{h}}_{{\bf{SNP}}}^{{\bf{2}}}\) across 100 replicates from GRMs built from imputed SNPs in the least structured subsamples across different model assumptions (bars) and different ways of simulating CVs (x axes).
Fig. 5: Bias of heritability estimates under different model assumptions. Boxplots of the absolute bias of heritability estimates (\(\left|E\left({\hat{h}}_{{\rm{SNP}}}^{2}\right)-{h}^{2}\right|\)) across all simulated phenotypes.
Fig. 6: Estimated \({\hat{h}}_{{\bf{SNP}}}^{{\bf{2}}}\) using multiple methods with imputed variants for six complex traits in the UK Biobank.

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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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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.

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Correspondence to Luke M. Evans or Matthew C. Keller.

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Evans, L.M., Tahmasbi, R., Vrieze, S.I. et al. Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. Nat Genet 50, 737–745 (2018). https://doi.org/10.1038/s41588-018-0108-x

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