Abstract
Cardiometabolic diseases are the leading cause of death worldwide. Despite a known genetic component, our understanding of these diseases remains incomplete. Here, we analyzed the contribution of rare variants to 57 diseases and 26 cardiometabolic traits, using data from 200,337 UK Biobank participants with whole-exome sequencing. We identified 57 gene-based associations, with broad replication of novel signals in Geisinger MyCode. There was a striking risk associated with mutations in known Mendelian disease genes, including MYBPC3, LDLR, GCK, PKD1 and TTN. Many genes showed independent convergence of rare and common variant evidence, including an association between GIGYF1 and type 2 diabetes. We identified several large effect associations for height and 18 unique genes associated with blood lipid or glucose levels. Finally, we found that between 1.0% and 2.4% of participants carried rare potentially pathogenic variants for cardiometabolic disorders. These findings may facilitate studies aimed at therapeutics and screening of these common disorders.
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Data availability
Summary results for the main analyses have been made available through the Cardiovascular Disease Knowledge Portal (https://cvd.hugeamp.org/downloads.html; direct download using https://personal.broadinstitute.org/ryank/Ellinor_ukbb_200k_exome.zip). Access to individual-level UK Biobank data, both phenotypic and genetic, is available to bona fide researchers through application on the UK Biobank website (https://www.ukbiobank.ac.uk). The exome sequencing data can be found in the UK Biobank showcase portal https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=170. Additional information about registration for access to the data is available at http://www.ukbiobank.ac.uk/register-apply/. Use of UK Biobank data was performed under application number 17488. Summary statistics from previous GWAS which were used in this study are publicly available through the Type 2 Diabetes Knowledge Portal (https://t2d.hugeamp.org); MAGMA results referenced in this manuscript were downloaded on 7 December 2020, while index single variant results were downloaded on 7 June 2021. Other datasets used in this manuscript include: the dbNSFP database v.4.1a (https://sites.google.com/site/jpopgen/dbNSFP); gnomAD exomes v.2.1 (https://gnomad.broadinstitute.org/downloads); the ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/) downloaded in November 2020; the Invitae Arrhythmia and Cardiomyopathy panel (https://www.invitae.com/en/physician/tests/02101/) and the Invitae Hypercholesterolemia panel (https://www.invitae.com/en/physician/tests/02401/) accessed on 10 November 2020; the Invitae Monogenic Diabetes panel (https://www.invitae.com/pt/physician/tests/55001/) accessed in January 2021; the Online Mendelian Inheritance in Man (OMIM) database (omim.org) accessed on 10 November 2020; Ensembl release 95 (https://gnomad.broadinstitute.org/downloads); and the GTEx dataset v.8 (https://gtexportal.org/home/).
Code availability
The code used for gene-based analyses is an adaptation of the R package GENESIS v.2.18 (https://rdrr.io/bioc/GENESIS/man/GENESIS-package.html) and has been made available through the following GitHub repository: https://github.com/seanjosephjurgens/UKBB_200KWES_CVD. Quality control of individual-level data was performed using Hail v.0.2 (https://hail.is), PLINK v.2.0.a (https://www.cog-genomics.org/plink/2.0/) and KING v.2.2.5 (https://www.kingrelatedness.com/Download.shtml). Variant annotation was performed using VEP v.95 (https://github.com/Ensembl/ensembl-vep) with the LOFTEE plug-in (https://github.com/konradjk/loftee). All analyses that were run in R were run in R v.4.0 (https://www.r-project.org).
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Acknowledgements
We gratefully thank all UK Biobank and MyCode participants, as this study would not have been possible without their contributions. This work was supported by funding from the Fondation Leducq (14CVD01), by grants from the National Institutes of Health (1RO1HL092577, K24HL105780) and by a grant from the American Heart Association (18SFRN34110082) to P.T.E. This work was further supported by a grant from the National Institutes of Health (1R01HL139731) and by a grant from the American Heart Association (18SFRN34250007) to S.A.L. This work was also supported by an American Heart Association Strategically Focused Research Networks postdoctoral fellowship (18SFRN34110082) to L.-C.W. and A.W.H. This work was also supported by a National Institutes of Health (NIH) grant 1R01HL139731 to L.-C.W. This work was supported by the John S. LaDue Memorial Fellowship for Cardiovascular Research, a Sarnoff Scholar award from the Sarnoff Cardiovascular Research Foundation and by a National Institutes of Health grant (K08HL159346) to J.P.P. This work was further supported by a grant from the National Institutes of Health (1K08HL153937) and a grant from the American Heart Association (862032) to K.G.A. This work was supported by a National Institutes of Health grant (T32HL007604) to V.N. This work was also supported by student scholarships from the Dutch Heart Foundation (Nederlandse Hartstichting) and the Amsterdams Universiteitsfonds to S.J.J. This work was supported by the BioData Ecosystem fellowship to S.H.C.
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S.J.J., S.H.C., S.A.L. and P.T.E. conceived and designed the study. S.J.J., S.H.C., V.N.M., M.C., J.P.P and J.L.H. performed data curation and data processing, for data other than the MyCode dataset. S.J.J., S.H.C. and V.N.M. performed statistical analyses, for data other than the MyCode dataset. M.T.O., B.L., D.P.v.M. and C.M.H. performed data curation, data processing and statistical analyses in the MyCode dataset. S.J.J., S.H.C. and M.C. performed data visualization. K.G.A., K.L.L., S.A.L. and P.T.E. supervised the overall study. S.J.J., S.H.C. and P.T.E. drafted the manuscript. L.-C.W., V.N., C.R. and A.W.H. contributed critically to the analysis plan and revisions of the manuscript. All authors critically revised and approved the manuscript. Contributions by consortium members from the Regeneron Genetics Center are provided in the Supplementary Note.
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P.T.E. has received sponsored research support from Bayer AG and IBM Research. P.T.E. has also served on advisory boards or consulted for Bayer AG, MyoKardia 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. L.-C.W. is supported by a grant from IBM to the Broad Institute. The remaining authors have no relevent competing interests to declare.
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Extended data
Extended Data Fig. 1 Meta-analysis results for GIGYF1 rare variants and type 2 diabetes across three cohorts.
Data are presented in a forest plot, with study-specific odds ratios (OR) with 95% confidence intervals (95% CI), and a meta-analysis OR shown with a diamond where the edges of the diamond show the meta-analysis 95% CI. Meta-analysis results are obtained from an inverse-variance weighted fixed-effects meta-analysis approach. Study-specific and meta-analysis P values are two-sided and unadjusted for multiple testing. To evaluate heterogeneity between studies, an I2 index for heterogeneity and a P value from Cochran’s Q test are provided, which show limited evidence of heterogeneity.
Extended Data Fig. 2 Carrier frequencies of putatively pathogenic variants in monogenic diabetes genes.
The top of the graph is a bar chart showing carrier frequencies for loss-of-function (LOF) variants and pathogenic/likely pathogenic (P/LP) variants for genes in which variants are known to cause dominant type 2 diabetes or maturity-onset diabetes of the young (MODY). For ABCC8 and KCNJ11, analyses were restricted to previously reported P/LP variants only. The bottom of the graph is a pruned heatmap showing associations between such variants with diabetes and chronic kidney disease, where blue indicates lower risk of disease and red indicates increased risk of the disease. P values were computed using saddle point approximation and were obtained from logistic mixed-effects models, adjusting for sex, age, sequencing batch, associated principal components (PCs), a sparse kinship matrix. P values shown are two-sided and unadjusted for multiple testing. Odds ratios (OR) were obtained from Firth’s regression models adjusting for sex, age, sequencing batch and associated PCs among unrelated samples. For clarity, associations with P > 0.05 and 0.7 < OR < 1.43 have been made white. Only GCK (45 carriers) and HNF1A (29 carriers) showed robust associations with diabetes. Of note, PDX1 carriers are driven by a single likely pathogenic missense variant, p.Cys18Arg (n = 112 carriers). Our results therefore indicate that this allele specifically does not represent a highly penetrant pathogenic variant, but do not necessarily translate to the 13 carriers of LOF variants.
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Supplementary Note, Figs. 1–14 and references.
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Jurgens, S.J., Choi, S.H., Morrill, V.N. et al. Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank. Nat Genet 54, 240–250 (2022). https://doi.org/10.1038/s41588-021-01011-w
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DOI: https://doi.org/10.1038/s41588-021-01011-w
