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Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data


Analyses of data from genome-wide association studies on unrelated individuals have shown that, for human traits and diseases, approximately one-third to two-thirds of heritability is captured by common SNPs. However, it is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular whether the causal variants are rare, or whether it is overestimated due to bias in inference from pedigree data. Here we estimated heritability for height and body mass index (BMI) from whole-genome sequence data on 25,465 unrelated individuals of European ancestry. The estimated heritability was 0.68 (standard error 0.10) for height and 0.30 (standard error 0.10) for body mass index. Low minor allele frequency variants in low linkage disequilibrium (LD) with neighboring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection. Our results imply that rare variants, in particular those in regions of low linkage disequilibrium, are a major source of the still missing heritability of complex traits and disease.

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Fig. 1: GREML-LDMS estimates with 8 bins (2 LD bins for each of the 4 MAF bins) correcting for 20 PCs (calculated from LD-pruned HM3 SNPs) after imputing SNPs from Illumina InfiniumCore24, GSA 24 and Affymetrix Axiom arrays using HRC reference panels for n = 25,465 samples.
Fig. 2: GREML-LDMS of height and BMI for n = 25,465 samples using 3 or 4 LD groups for each MAF bin, correcting for 48, 160 or 320 PCs computed from WGS variants.
Fig. 3: Variance explained per variant (the estimate of genetic variance divided by the number of variants in each bin) from GREML-LDMS with the low-LD and low-MAF (<0.1) variants partitioned into two distinct categories according to the SnpEff putative effect of the variant (protein-altering or non-protein-altering), correcting for 48 PCs from WGS variants for n = 25,465 samples.

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Data availability

The individual-level genotype and phenotype TOPMed data used in this study are available through dbGaP. The dbGaP accession numbers for all TOPMed studies referenced in this paper are listed in Supplementary Table 1. The genotypic data are under restricted access. This research was conducted under TOPMed proposal ID 3235. Individual-level genotype and phenotype data for the UKB are available through formal application ( The UK10K data are accessible at The 1000 Genomes genotype data are available at

Code availability

The code used for the main analysis and figures is available at GRM computation, LD score calculations, PC projections and GREML analyses were performed using GCTA 1.92.4 ( WGS analyses followed the steps described at Plink 1.9 ( and 2.0 were used in the present study ( R 3.4.1 ( and Tidyverse packages ( were used to generate figures and additional analyses. KING 2.2.6 was used for IBD calculations ( All the parameters used for analyses are described in Methods.


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P.M.V. was supported by the Australian Research Council (grant nos. DP160102400 and FL180100072), the Australian National Health and Medical Research Council (grant nos. 1113400 and 1078037) and the US National Institutes of Health (NIH; grant no. R01MH100141). J.Y. was supported by the Australian Research Council (grant no. FT180100186), the Sylvia & Charles Viertel Charitable Foundation and the Westlake Education Foundation. L.Y. was supported by the Australian Research Council (grant no. DE200100425). The present study makes use of data from the TOPMed program, the UKB and the UK10K projects. WGS for the TOPMed program was supported by the NHLBI. A full list of acknowledgements is provided in the Supplementary Information.

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Authors and Affiliations




P.M.V. and J.Y. conceived the study. P.W. performed the analyses, contributed to methods and interpretations of results, and wrote the first draft of the manuscript and supplementary materials. P.M.V., J.Y. and L.Y. provided supervision and contributed to analyses, and writing and revising the manuscript. M.E.G. contributed to supervision and analysis methods. D.J. and Z.Z. contributed to the analyses. C.A.L, R.D.H., S.T.M, C.C.L, K.E.N., L.A.L. and B.S.W. provided suggestions on the analyses and details of the phenotype data. L.A.C., A.H.S., B. MK., B.M.S., B.D.M., B.M.P., C.K., C.-T. L., C.M.A., D.R., D.I.C., D.D., D.M.L.-J., D.K.A., E.A.R., E.B., J.I.R., J.R.O., L.R.Y., M.A., M.A.A., M.-L.N.M., M.K.C., M.F., N.C., N.L.S., P.T.E., R.S.V., R.A.M., R.J.F.L., S.S.R., S.A.L., S.R.H., S.R., X.G. and Y.-D.I.C. provided phenotypic and/or WGS data through the TOPMed Consortium. All authors reviewed the manuscript, suggested revisions as needed and approved the final version. A full list of members and affiliations of the NHLBI TOPMed Consortium is available at

Corresponding authors

Correspondence to Pierrick Wainschtein, Jian Yang or Peter M. Visscher.

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Competing interests

P.T.E. is supported by a grant from Bayer AG to the Broad Institute focused on the genetics and therapeutics of cardiovascular diseases. He has also served on advisory boards or consulted for Bayer AG, Quest Diagnostics and Novartis. S.A.L. receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim, Fitbit and IBM, and has consulted for Bristol Myers Squibb/Pfizer, Bayer AG and Blackstone Life Sciences. The other authors declare no competing interests.

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Supplementary Notes 1–3, Acknowledgements, Tables 1–8 and Figs. 1–43.

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Wainschtein, P., Jain, D., Zheng, Z. et al. Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nat Genet 54, 263–273 (2022).

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