Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses

Abstract

Exome association studies to date have generally been underpowered to systematically evaluate the phenotypic impact of very rare coding variants. We leveraged extensive haplotype sharing between 49,960 exome-sequenced UK Biobank participants and the remainder of the cohort (total n ≈ 500,000) to impute exome-wide variants with accuracy R2 > 0.5 down to minor allele frequency (MAF) ~0.00005. Association and fine-mapping analyses of 54 quantitative traits identified 1,189 significant associations (P < 5 × 10−8) involving 675 distinct rare protein-altering variants (MAF < 0.01) that passed stringent filters for likely causality. Across all traits, 49% of associations (578/1,189) occurred in genes with two or more hits; follow-up analyses of these genes identified allelic series containing up to 45 distinct ‘likely-causal’ variants. Our results demonstrate the utility of within-cohort imputation in population-scale genome-wide association studies, provide a catalog of likely-causal, large-effect coding variant associations and foreshadow the insights that will be revealed as genetic biobank studies continue to grow.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Whole-exome imputation, association and fine mapping identify rare coding variants likely to causally associate with 54 quantitative traits.
Fig. 2: Association analyses of the subsequent n = 200,643 UKB exome release demonstrate robustness of likely-causal variant–trait associations ascertained using genotypes imputed from n = 49,960 exomes.
Fig. 3: Likely-causal coding variants are rare and enriched for deleteriousness.
Fig. 4: Many genes contain long allelic series of rare coding variants with consistent effect directions.

Data availability

Access to the UKB Resource is available by application (http://www.ukbiobank.ac.uk/). Exome-wide summary association statistics for the 54 quantitative traits we analyzed are available at https://data.broadinstitute.org/lohlab/UKB_exomeWAS/ and data files containing allelic series for all gene–trait associations with multiple likely-causal variants are also available at this website.

Code availability

The following publicly available software packages were used to perform analyses: Eagle2 (v.2.3.5), https://data.broadinstitute.org/alkesgroup/Eagle/; Minimac4 (v.1.0.1), https://genome.sph.umich.edu/wiki/Minimac4; BOLT–LMM (v.2.3.4), https://data.broadinstitute.org/alkesgroup/BOLT-LMM/; FINEMAP (v.1.3.1), http://www.christianbenner.com/; plink (v.1.9 and v.2.0), https://www.cog-genomics.org/plink2/ and tsinfer (v.0.1.4), https://tsinfer.readthedocs.io/en/latest/. Information from the following databases were also used: VEP (v.95 on GRCh37 with GENCODE 19), https://www.ensembl.org/vep; CADD (v.1.5), https://cadd.gs.washington.edu/download; SpliceAI (v.1.2.1) https://github.com/Illumina/SpliceAI; NHGRI–EBI GWAS Catalog (v.1.0), https://www.ebi.ac.uk/gwas/home; TOPMed (v.r2, 97,256 TOPMed samples), https://imputation.biodatacatalyst.nhlbi.nih.gov/#!pages/about; Protein Data Bank, https://www.rcsb.org/; SWISS-MODEL, https://swissmodel.expasy.org/ and PANTHER (v.15.0), http://www.pantherdb.org/. Scripts used to perform the downstream analyses described above are available at https://data.broadinstitute.org/lohlab/UKB_exomeWAS/ (https://doi.org/10.5281/zenodo.4771214).

References

  1. 1.

    International Multiple Sclerosis Genetics Consortium. Low-frequency and rare-coding variation contributes to multiple sclerosis risk. Cell 175, 1679–1687.e7 (2018).

  2. 2.

    Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Liu, D. J. et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat. Genet. 49, 1758–1766 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Liu, C. et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat. Genet. 48, 1162–1170 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Fu, W. et al. Analysis of 6,515 exomes reveals a recent origin of most human protein-coding variants. Nature 493, 216–220 (2013).

    CAS  Article  Google Scholar 

  6. 6.

    Tennessen, J. A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Dewey, F. E. et al. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354, aaf6814 (2016).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  8. 8.

    Van Hout, C. V. et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature 586, 749–756 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. 9.

    Cirulli, E. T. et al. Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts. Nat. Commun. 11, 542 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10.

    Flannick, J. et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 570, 71–76 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    DeBoever, C. et al. Medical relevance of protein-truncating variants across 337,205 individuals in the UK Biobank study. Nat. Commun. 9, 1612 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  14. 14.

    Emdin, C. A. et al. Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease. Nat. Commun. 9, 1–8 (2018).

    CAS  Article  Google Scholar 

  15. 15.

    Loh, P.-R., Palamara, P. F. & Price, A. L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Loh, P.-R. et al. Reference-based phasing using the haplotype reference consortium panel. Nat. Genet. 48, 1443–1448 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17.

    Nait Saada, J. et al. Identity-by-descent detection across 487,409 British samples reveals fine scale population structure and ultra-rare variant associations. Nat. Commun. 11, 6130 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Loh, P.-R., Genovese, G. & McCarroll, S. A. Monogenic and polygenic inheritance become instruments for clonal selection. Nature 584, 136–141 (2020).

  19. 19.

    Browning, B. L. & Browning, S. R. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98, 116–126 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  20. 20.

    McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).

  21. 21.

    Huang, J. et al. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel. Nat. Commun. 6, 8111 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  22. 22.

    Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Loh, P.-R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  27. 27.

    Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176, 535–548.e24 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  28. 28.

    Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinforma. Oxf. Engl. 32, 1493–1501 (2016).

    CAS  Article  Google Scholar 

  29. 29.

    Schaid, D. J., Chen, W. & Larson, N. B. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19, 491–504 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Szustakowski, J. D. et al. Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Preprint at medRxiv https://doi.org/10.1101/2020.11.02.20222232 (2020).

  31. 31.

    Wang, Q. et al. Surveying the contribution of rare variants to the genetic architecture of human disease through exome sequencing of 177,882 UK Biobank participants. Preprint at bioRxiv https://doi.org/10.1101/2020.12.13.422582 (2020).

  32. 32.

    Vuckovic, D. et al. The polygenic and monogenic basis of blood traits and diseases. Cell 182, 1214–1231.e11 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Haworth, S. et al. Apparent latent structure within the UK Biobank sample has implications for epidemiological analysis. Nat. Commun. 10, 333 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  34. 34.

    Mathieson, I. & McVean, G. Differential confounding of rare and common variants in spatially structured populations. Nat. Genet. 44, 243–246 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Yasoda, A. et al. Natriuretic peptide regulation of endochondral ossification: Evidence for possible roles of the C-type natriuretic peptide/guanylyl cyclase-B pathway. J. Biol. Chem. 273, 11695–11700 (1998).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  36. 36.

    Gandotra, S. et al. Perilipin deficiency and autosomal dominant partial lipodystrophy. N. Engl. J. Med. 364, 740–748 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Aslan, J. E. & McCarty, O. J. T. Rho GTPases in platelet function. J. Thromb. Haemost. 11, 35–46 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Zhao, A. Z., Huan, J.-N., Gupta, S., Pal, R. & Sahu, A. A phosphatidylinositol 3-kinase–phosphodiesterase 3B–cyclic AMP pathway in hypothalamic action of leptin on feeding. Nat. Neurosci. 5, 727–728 (2002).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Landrum, M. J. et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46, D1062–D1067 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  41. 41.

    Ahituv, N. et al. Medical sequencing at the extremes of human body mass. Am. J. Hum. Genet. 80, 779–791 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    The Gene Ontology Consortium. The Gene Ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 47, D330–D338 (2019).

  43. 43.

    Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  44. 44.

    Sinnott-Armstrong, N. et al. Genetics of 38 blood and urine biomarkers in the UK Biobank. Nat. Genet. 53, 185–194 (2021).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  45. 45.

    Povysil, G. et al. Rare-variant collapsing analyses for complex traits: guidelines and applications. Nat. Rev. Genet. 20, 747–759 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  46. 46.

    Wu, M. C. et al. Rare-variant association testing for sequencing data with the sequence kernel association test. Am. J. Hum. Genet. 89, 82–93 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Zhou, W. et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat. Genet. 50, 1335–1341 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 7 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  49. 49.

    Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–S3 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  51. 51.

    Locke, A. E. et al. Exome sequencing of Finnish isolates enhances rare-variant association power. Nature 572, 323–328 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Cunningham, D. et al. Structural and biophysical studies of PCSK9 and its mutants linked to familial hypercholesterolemia. Nat. Struct. Mol. Biol. 14, 413–419 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  53. 53.

    Biterova, E., Esmaeeli, M., Alanen, H. I., Saaranen, M. & Ruddock, L. W. Structures of Angptl3 and Angptl4, modulators of triglyceride levels and coronary artery disease. Sci. Rep. 8, 6752 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  54. 54.

    LeCour, L. et al. The structural basis for Cdc42-induced dimerization of IQGAPs. Structure 24, 1499–1508 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Berman, H. M. et al. The protein data bank. Nucleic Acids Res. 28, 235–242 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Bienert, S. et al. The SWISS-MODEL repository—new features and functionality. Nucleic Acids Res. 45, D313–D319 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank A. Gusev, M. Hujoel, P. Palamara, A. Price and S. Sunyaev for helpful discussions. This research was conducted using the UKB Resource under application no. 10438. A.R.B. was supported by US NIH grant T32 HG229516 and fellowship F31 HL154537. M.A.S. was supported by the MIT John W. Jarve (1978) Seed Fund for Science Innovation and US NIH Fellowship F31 MH124393. R.E.M. was supported by US NIH grant K25 HL150334 and NSF grant DMS-1939015. P.-R.L. was supported by US NIH grant DP2 ES030554, a Burroughs Wellcome Fund Career Award at the Scientific Interfaces, the Next Generation Fund at the Broad Institute of MIT and Harvard, and a Sloan Research Fellowship. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Computational analyses were performed on the O2 High Performance Compute Cluster, supported by the Research Computing Group, at Harvard Medical School (http://rc.hms.harvard.edu).

Author information

Affiliations

Authors

Contributions

A.R.B. and P.-R.L. performed statistical analyses and wrote the manuscript. M.A.S. and R.E.M. provided substantial input on all analyses and on the manuscript.

Corresponding authors

Correspondence to Alison R. Barton or Po-Ru Loh.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks S. Petrovski and S. Carmi for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Notes 1–5 and Figs. 1–11

Reporting Summary

Peer Review Information

Supplementary Tables

Supplementary Tables 1–15

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Barton, A.R., Sherman, M.A., Mukamel, R.E. et al. Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses. Nat Genet 53, 1260–1269 (2021). https://doi.org/10.1038/s41588-021-00892-1

Download citation

Search

Quick links