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Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank

Nature Genetics volume 54, pages 240–250 (2022)Cite this article

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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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Fig. 1: Rare genetic variation for 57 cardiometabolic and other medical disorders in the UK Biobank.
Fig. 2: Rare genetic variation for 26 quantitative cardiometabolic traits in the UK Biobank.
Fig. 3: Pleiotropy of rare variants in metabolic genes.
Fig. 4: Putatively pathogenic variants in Mendelian cardiovascular disease genes in the UK Biobank.

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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).

References

  1. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Roselli, C. et al. Multi-ethnic genome-wide association study for atrial fibrillation. Nat. Genet. 50, 1225–1233 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Shah, S. et al. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat. Commun. 11, 163 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Pirruccello, J. P. et al. Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy. Nat. Commun. 11, 2254 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ntalla, I. et al. Multi-ancestry GWAS of the electrocardiographic PR interval identifies 202 loci underlying cardiac conduction. Nat. Commun. 11, 2542 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Vujkovic, M. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat. Genet. 52, 680–691 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Carrier, L. et al. Mapping of a novel gene for familial hypertrophic cardiomyopathy to chromosome 11. Nat. Genet. 4, 311–313 (1993).

    Article  CAS  PubMed  Google Scholar 

  9. Ahlberg, G. et al. Rare truncating variants in the sarcomeric protein titin associate with familial and early-onset atrial fibrillation. Nat. Commun. 9, 4316 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Keating, M. et al. Linkage of a cardiac arrhythmia, the long QT syndrome, and the Harvey ras-1 gene. Science 252, 704–706 (1991).

    Article  CAS  PubMed  Google Scholar 

  11. Gerull, B. et al. Mutations in the desmosomal protein plakophilin-2 are common in arrhythmogenic right ventricular cardiomyopathy. Nat. Genet. 36, 1162–1164 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Do, R. et al. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 518, 102–106 (2015).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  15. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Cohen, J. C., Boerwinkle, E., Mosley, T. H. Jr. & Hobbs, H. H. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. N. Engl. J. Med. 354, 1264–1272 (2006).

    Article  CAS  PubMed  Google Scholar 

  17. Lambert, G., Sjouke, B., Choque, B., Kastelein, J. J. & Hovingh, G. K. The PCSK9 decade. J. Lipid Res. 53, 2515–2524 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wang, Y. & Liu, Z. P. PCSK9 inhibitors: novel therapeutic strategies for lowering LDL cholesterol. Mini Rev. Med. Chem. 19, 165–176 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Choi, S. H. et al. Monogenic and polygenic contributions to atrial fibrillation risk: results from a national biobank. Circ. Res. 126, 200–209 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Szustakowski, J. D. et al. Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Nat. Genet. 53, 942–948 (2021).

    Article  CAS  PubMed  Google Scholar 

  21. Carey, D. J. et al. The Geisinger MyCode community health initiative: an electronic health record-linked biobank for precision medicine research. Genet. Med. 18, 906–913 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zhou, W. et al. GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer. Nat. Commun. 11, 3981 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hwangbo, Y. & Park, Y. J. Genome-wide association studies of autoimmune thyroid diseases, thyroid function, and thyroid cancer. Endocrinol. Metab. 33, 175–184 (2018).

    Article  CAS  Google Scholar 

  24. Mallawaarachchi, A. C., Furlong, T. J., Shine, J., Harris, P. C. & Cowley, M. J. Population data improves variant interpretation in autosomal dominant polycystic kidney disease. Genet. Med. 21, 1425–1434 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Chakera, A. J. et al. Recognition and management of individuals with hyperglycemia because of a heterozygous glucokinase mutation. Diabetes Care 38, 1383–1392 (2015).

    Article  CAS  PubMed  Google Scholar 

  26. Bansal, V. et al. Spectrum of mutations in monogenic diabetes genes identified from high-throughput DNA sequencing of 6888 individuals. BMC Med. 15, 213 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bonnefond, A. et al. Pathogenic variants in actionable MODY genes are associated with type 2 diabetes. Nat. Metab. 2, 1126–1134 (2020).

    Article  CAS  PubMed  Google Scholar 

  28. Corden, B. et al. Association of Titin-truncating genetic variants with life-threatening cardiac arrhythmias in patients with dilated cardiomyopathy and implanted defibrillators. JAMA Netw. Open 2, e196520 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Giovannone, B. et al. Two novel proteins that are linked to insulin-like growth factor (IGF-I) receptors by the Grb10 adapter and modulate IGF-I signaling. J. Biol. Chem. 278, 31564–31573 (2003).

    Article  CAS  PubMed  Google Scholar 

  30. Plasschaert, R. N. & Bartolomei, M. S. Tissue-specific regulation and function of Grb10 during growth and neuronal commitment. Proc. Natl Acad. Sci. USA 112, 6841–6847 (2015).

    Article  CAS  PubMed  Google Scholar 

  31. Satterstrom, F. K. et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell 180, 568–584 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Basu, S. et al. DBC1, p300, HDAC3, and Siah1 coordinately regulate ELL stability and function for expression of its target genes. Proc. Natl Acad. Sci. USA 117, 6509–6520 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Qiang, L. et al. Hepatic SirT1-dependent gain of function of stearoyl-CoA desaturase-1 conveys dysmetabolic and tumor progression functions. Cell Rep. 11, 1797–1808 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lang, W. & Frishman, W. H. Angiopoietin-like 3 protein inhibition: a new frontier in lipid-lowering treatment. Cardiol. Rev. 27, 211–217 (2019).

    Article  PubMed  Google Scholar 

  35. Dewey, F. E. et al. Inactivating variants in ANGPTL4 and risk of coronary artery disease. N. Engl. J. Med. 374, 1123–1133 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Emdin, C. A. et al. Analysis of predicted loss-of-function variants in UK Biobank identifies variants protective for disease. Nat. Commun. 9, 1613 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Laver, T. W. et al. PLIN1 haploinsufficiency is not associated with lipodystrophy. J. Clin. Endocrinol. Metab. 103, 3225–3230 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Noureldein, M. H. In silico discovery of a perilipin 1 inhibitor to be used as a new treatment for obesity. Eur. Rev. Med. Pharmacol. Sci. 18, 457–460 (2014).

    CAS  PubMed  Google Scholar 

  40. Richardson, T. G. et al. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: a multivariable Mendelian randomisation analysis. PLoS Med. 17, e1003062 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Sabatti, C. et al. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat. Genet. 41, 35–46 (2009).

    Article  CAS  PubMed  Google Scholar 

  42. Zhu, R., Ou, Z., Ruan, X. & Gong, J. Role of liver X receptors in cholesterol efflux and inflammatory signaling. Mol. Med. Rep. 5, 895–900 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Lotta, L. A. et al. Association between low-density lipoprotein cholesterol-lowering genetic variants and risk of type 2 diabetes: a meta-analysis. JAMA 316, 1383–1391 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ahmadizar, F. et al. Associations of statin use with glycaemic traits and incident type 2 diabetes. Br. J. Clin. Pharmacol. 85, 993–1002 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sattar, N. et al. Statins and risk of incident diabetes: a collaborative meta-analysis of randomised statin trials. Lancet 375, 735–742 (2010).

    Article  CAS  PubMed  Google Scholar 

  47. Klimentidis, Y. C. et al. Phenotypic and genetic characterization of lower LDL cholesterol and increased type 2 diabetes risk in the UK Biobank. Diabetes 69, 2194–2205 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kichaev, G. et al. Leveraging polygenic functional enrichment to improve GWAS power. Am. J. Hum. Genet. 104, 65–75 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Tamemoto, H. et al. Insulin resistance and growth retardation in mice lacking insulin receptor substrate-1. Nature 372, 182–186 (1994).

    Article  CAS  PubMed  Google Scholar 

  52. Tian, Z. et al. ANGPTL2 activity in cardiac pathologies accelerates heart failure by perturbing cardiac function and energy metabolism. Nat. Commun. 7, 13016 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Wang, Q. et al. Rare variant contribution to human disease in 281,104 UK Biobank exomes. Nature 597, 527–532 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhao, Y. et al. GIGYF1 loss of function is associated with clonal mosaicism and adverse metabolic health. Nat. Commun. 12, 4178 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Schiabor Barrett, K. M. et al. Positive predictive value highlights four novel candidates for actionable genetic screening from analysis of 220,000 clinicogenomic records. Genet. Med. 23, 2300–2308 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Schafer, S. et al. Titin-truncating variants affect heart function in disease cohorts and the general population. Nat. Genet. 49, 46–53 (2017).

    Article  CAS  PubMed  Google Scholar 

  57. Haggerty, C. M. et al. Genomics-first evaluation of heart disease associated with Titin-truncating variants. Circulation 140, 42–54 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Mbatchou, J. et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat. Genet. 53, 1097–1103 (2021).

    Article  CAS  PubMed  Google Scholar 

  60. Zhao, Z. et al. UK Biobank whole-exome sequence binary phenome analysis with robust region-based rare-variant test. Am. J. Hum. Genet. 106, 3–12 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Gogarten, S. M. et al. Genetic association testing using the GENESIS R/Bioconductor package. Bioinformatics 35, 5346–5348 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Weng, L. C. et al. Heritability of atrial fibrillation. Circ. Cardiovasc. Genet. 10, e001838 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Aragam, K. G. et al. Phenotypic refinement of heart failure in a national biobank facilitates genetic discovery. Circulation 139, 489–501 (2019).

    Article  Google Scholar 

  64. Pirruccello, J. P. et al. Titin truncating variants in adults without known congestive heart failure. J. Am. Coll. Cardiol. 75, 1239–1241 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Tanjore, R. R., Rangaraju, A., Kerkar, P. G., Calambur, N. & Nallari, P. MYBPC3 gene variations in hypertrophic cardiomyopathy patients in India. Can. J. Cardiol. 24, 127–130 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Richard, P. et al. Hypertrophic cardiomyopathy: distribution of disease genes, spectrum of mutations, and implications for a molecular diagnosis strategy. Circulation 107, 2227–2232 (2003).

    Article  PubMed  Google Scholar 

  67. Gerull, B. et al. Mutations of TTN, encoding the giant muscle filament titin, cause familial dilated cardiomyopathy. Nat. Genet. 30, 201–204 (2002).

    Article  CAS  PubMed  Google Scholar 

  68. Herman, D. S. et al. Truncations of titin causing dilated cardiomyopathy. N. Engl. J. Med. 366, 619–628 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Choi, S. H. et al. Association between Titin loss-of-function variants and early-onset atrial fibrillation. JAMA 320, 2354–2364 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Vionnet, N. et al. Nonsense mutation in the glucokinase gene causes early-onset non-insulin-dependent diabetes mellitus. Nature 356, 721–722 (1992).

    Article  CAS  PubMed  Google Scholar 

  71. Reeders, S. T. et al. Regional localization of the autosomal dominant polycystic kidney disease locus. Genomics 3, 150–155 (1988).

    Article  CAS  PubMed  Google Scholar 

  72. Breuning, M. H. et al. Improved early diagnosis of adult polycystic kidney disease with flanking DNA markers. Lancet 2, 1359–1361 (1987).

    Article  CAS  PubMed  Google Scholar 

  73. Reeders, S. T. et al. A highly polymorphic DNA marker linked to adult polycystic kidney disease on chromosome 16. Nature 317, 542–524 (1985).

    Article  CAS  PubMed  Google Scholar 

  74. Hopkins, P. N. et al. A novel LDLR mutation, H190Y, in a Utah kindred with familial hypercholesterolemia. J. Hum. Genet. 44, 364–367 (1999).

    Article  CAS  PubMed  Google Scholar 

  75. Callis, M. et al. Mutation analysis in familial hypercholesterolemia patients of different ancestries: identification of three novel LDLR gene mutations. Mol. Cell. Probes 12, 149–152 (1998).

    Article  CAS  PubMed  Google Scholar 

  76. Jensen, H. K., Jensen, L. G., Hansen, P. S., Faergeman, O. & Gregersen, N. An Iranian-Armenian LDLR frameshift mutation causing familial hypercholesterolemia. Clin. Genet. 49, 88–90 (1996).

    Article  CAS  PubMed  Google Scholar 

  77. Peloso, G. M. et al. Rare protein-truncating variants in APOB, lower low-density lipoprotein cholesterol, and protection against coronary heart disease. Circ. Genom. Precis. Med. 12, e002376 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Patni, N., Ahmad, Z. & Wilson, D.P. in Endotext (eds Feingold, K.R. et al.) https://www.ncbi.nlm.nih.gov/books/NBK395584/ (MDText.com, 2000).

  79. Narumi, S. et al. TSHR mutations as a cause of congenital hypothyroidism in Japan: a population-based genetic epidemiology study. J. Clin. Endocrinol. Metab. 94, 1317–1323 (2009).

    Article  CAS  PubMed  Google Scholar 

  80. Heo, S., Jang, J. H. & Yu, J. Congenital hypothyroidism due to thyroglobulin deficiency: a case report with a novel mutation in TG gene. Ann. Pediatr. Endocrinol. Metab. 24, 199–202 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Watanabe, Y. et al. A novel mutation in the TG gene (G2322S) causing congenital hypothyroidism in a Sudanese family: a case report. BMC Med. Genet. 19, 69 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Shah, M. H., Bhat, V., Shetty, J. S. & Kumar, A. Whole exome sequencing identifies a novel splice-site mutation in ADAMTS17 in an Indian family with Weill–Marchesani syndrome. Mol. Vis. 20, 790–796 (2014).

    PubMed  PubMed Central  Google Scholar 

  83. Khan, A. O., Aldahmesh, M. A., Al-Ghadeer, H., Mohamed, J. Y. & Alkuraya, F. S. Familial spherophakia with short stature caused by a novel homozygous ADAMTS17 mutation. Ophthalmic Genet. 33, 235–239 (2012).

    Article  CAS  PubMed  Google Scholar 

  84. Crippa, M. et al. A balanced reciprocal translocation t(10;15)(q22.3;q26.1) interrupting ACAN gene in a family with proportionate short stature. J. Endocrinol. Invest. 41, 929–936 (2018).

    Article  CAS  PubMed  Google Scholar 

  85. Hwang, I. T. et al. Role of NPR2 mutation in idiopathic short stature: identification of two novel mutations. Mol. Genet. Genomic Med. 8, e1146 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Wang, S. R. et al. Heterozygous mutations in natriuretic peptide receptor-B (NPR2) gene as a cause of short stature. Hum. Mutat. 36, 474–481 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Olney, R. C. et al. Heterozygous mutations in natriuretic peptide receptor-B (NPR2) are associated with short stature. J. Clin. Endocrinol. Metab. 91, 1229–1232 (2006).

    Article  CAS  PubMed  Google Scholar 

  88. Klammt, J., Kiess, W. & Pfäffle, R. IGF1R mutations as cause of SGA. Best Pract. Res. Clin. Endocrinol. Metab. 25, 191–206 (2011).

    Article  CAS  PubMed  Google Scholar 

  89. Steinkellner, H. et al. Identification and molecular characterisation of a homozygous missense mutation in the ADAMTS10 gene in a patient with Weill–Marchesani syndrome. Eur. J. Hum. Genet. 23, 1186–1191 (2015).

    Article  PubMed  Google Scholar 

  90. Morales, J. et al. Homozygous mutations in ADAMTS10 and ADAMTS17 cause lenticular myopia, ectopia lentis, glaucoma, spherophakia, and short stature. Am. J. Hum. Genet. 85, 558–568 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Gloyn, A. L. Glucokinase (GCK) mutations in hyper- and hypoglycemia: maturity-onset diabetes of the young, permanent neonatal diabetes, and hyperinsulinemia of infancy. Hum. Mutat. 22, 353–362 (2003).

    Article  CAS  PubMed  Google Scholar 

  92. Reddy, M. V. et al. Exome sequencing identifies 2 rare variants for low high-density lipoprotein cholesterol in an extended family. Circ. Cardiovasc. Genet. 5, 538–546 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Musunuru, K. et al. Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia. N. Engl. J. Med. 363, 2220–2227 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Cohen, J. C. et al. Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science 305, 869–872 (2004).

    Article  CAS  PubMed  Google Scholar 

  95. Guerra, R., Wang, J., Grundy, S. M. & Cohen, J. C. A hepatic lipase (LIPC) allele associated with high plasma concentrations of high density lipoprotein cholesterol. Proc. Natl Acad. Sci. USA 94, 4532–4537 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Whitfield, A. J., Barrett, P. H., van Bockxmeer, F. M. & Burnett, J. R. Lipid disorders and mutations in the APOB gene. Clin. Chem. 50, 1725–1732 (2004).

    Article  CAS  PubMed  Google Scholar 

  97. Inazu, A. et al. Increased high-density lipoprotein levels caused by a common cholesteryl-ester transfer protein gene mutation. N. Engl. J. Med. 323, 1234–1238 (1990).

    Article  CAS  PubMed  Google Scholar 

  98. Myant, N. B. Familial defective apolipoprotein B-100: a review, including some comparisons with familial hypercholesterolaemia. Atherosclerosis 104, 1–18 (1993).

    Article  CAS  PubMed  Google Scholar 

  99. Hobbs, H. H., Brown, M. S. & Goldstein, J. L. Molecular genetics of the LDL receptor gene in familial hypercholesterolemia. Hum. Mutat. 1, 445–466 (1992).

    Article  CAS  PubMed  Google Scholar 

  100. Dron, J. S. & Hegele, R. A. Genetics of hypertriglyceridemia. Front. Endocrinol. 11, 455 (2020).

    Article  Google Scholar 

  101. Crosby, J. et al. Loss-of-function mutations in APOC3, triglycerides, and coronary disease. N. Engl. J. Med. 371, 22–31 (2014).

    Article  PubMed  Google Scholar 

  102. Berg, K. Lp(a) lipoprotein: an overview. Chem. Phys. Lipids 67–68, 9–16 (1994).

    Article  PubMed  Google Scholar 

  103. Sudlow, C. et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Liu, X., Wu, C., Li, C. & Boerwinkle, E. dbNSFP v3.0: a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs. Hum. Mutat. 37, 235–241 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  106. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Heinze, G. A comparative investigation of methods for logistic regression with separated or nearly separated data. Stat. Med. 25, 4216–4226 (2006).

    Article  PubMed  Google Scholar 

  108. Pirruccello, J. P. et al. Deep learning enables genetic analysis of the human thoracic aorta. Nat. Genet. https://doi.org/10.1038/s41588-021-00962-4 (2021).

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

    Article  PubMed  PubMed Central  Google Scholar 

  110. 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).

    Article  PubMed  Google Scholar 

  111. Type 2 Diabetes Knowledge Portal (T2D Knowledge Portal, accessed December 2020 and June 2021); http://www.type2diabetesgenetics.org

  112. Roberts, A. M. et al. Integrated allelic, transcriptional, and phenomic dissection of the cardiac effects of titin truncations in health and disease. Sci. Transl. Med. 7, 270ra6 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Priori, S. G. & Chen, S. R. Inherited dysfunction of sarcoplasmic reticulum Ca2+ handling and arrhythmogenesis. Circ. Res. 108, 871–883 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Sharifi, M., Futema, M., Nair, D. & Humphries, S. E. Genetic architecture of familial hypercholesterolaemia. Curr. Cardiol. Rep. 19, 44 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Walsh, R. et al. Reassessment of Mendelian gene pathogenicity using 7,855 cardiomyopathy cases and 60,706 reference samples. Genet. Med. 19, 192–203 (2017).

    Article  PubMed  Google Scholar 

  116. Ton, V. K., Mukherjee, M. & Judge, D. P. Transthyretin cardiac amyloidosis: pathogenesis, treatments, and emerging role in heart failure with preserved ejection fraction. Clin. Med. Insights Cardiol. 8, 39–44 (2014).

    PubMed  Google Scholar 

  117. Ellard, S. et al. Permanent neonatal diabetes caused by dominant, recessive, or compound heterozygous SUR1 mutations with opposite functional effects. Am. J. Hum. Genet. 81, 375–382 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Ashcroft, F. M. ATP-sensitive potassium channelopathies: focus on insulin secretion. J. Clin. Invest. 115, 2047–2058 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

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.

Author information

Author notes

  1. These authors contributed equally: Sean J. Jurgens, Seung Hoan Choi, Valerie N. Morrill.

  2. These authors jointly supervised this work: Steven A. Lubitz, Patrick T. Ellinor.

Authors and Affiliations

  1. Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Sean J. Jurgens, Seung Hoan Choi, Valerie N. Morrill, Mark Chaffin, James P. Pirruccello, Jennifer L. Halford, Lu-Chen Weng, Victor Nauffal, Carolina Roselli, Amelia W. Hall, Krishna G. Aragam, Steven A. Lubitz & Patrick T. Ellinor

  2. Department of Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands

    Sean J. Jurgens

  3. Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA

    James P. Pirruccello, Lu-Chen Weng, Victor Nauffal, Amelia W. Hall, Krishna G. Aragam, Steven A. Lubitz & Patrick T. Ellinor

  4. Autism & Developmental Medicine Institute, Geisinger, Danville, PA, USA

    Matthew T. Oetjens

  5. Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA

    Braxton Lagerman, David P. vanMaanen & Christopher M. Haggerty

  6. NHLBI and Boston University’s Framingham Heart Study, Framingham, MA, USA

    Kathryn L. Lunetta

  7. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

    Kathryn L. Lunetta

  8. Heart Institute, Geisinger, Danville, PA, USA

    Christopher M. Haggerty

  9. Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA

    Steven A. Lubitz & Patrick T. Ellinor

  10. Regeneron Genetics Center, Tarrytown, NY, USA

    Goncalo Abecasis, Xiaodong Bai, Suganthi Balasubramanian, Aris Baras, Christina Beechert, Boris Boutkov, Michael Cantor, Giovanni Coppola, Tanima De, Andrew Deubler, Aris Economides, Gisu Eom, Manuel A. R. Ferreira, Caitlin Forsythe, Erin D. Fuller, Zhenhua Gu, Lukas Habegger, Alicia Hawes, Marcus B. Jones, Katia Karalis, Shareef Khalid, Olga Krasheninina, Rouel Lanche, Michael Lattari, Dadong Li, Alexander Lopez, Luca A. Lotta, Kia Manoochehri, Adam J. Mansfield, Evan K. Maxwell, Jason Mighty, Lyndon J. Mitnaul, Mona Nafde, Jonas Nielsen, Sean O’Keeffe, Max Orelus, John D. Overton, Maria Sotiropoulos Padilla, Razvan Panea, Tommy Polanco, Manasi Pradhan, Ayesha Rasool, Jeffrey G. Reid, William Salerno, Thomas D. Schleicher, Alan Shuldiner, Katherine Siminovitch, Jeffrey C. Staples, Ricardo H. Ulloa, Niek Verweij, Louis Widom & Sarah E. Wolf

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  1. Sean J. Jurgens
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Consortia

Regeneron Genetics Center

  • Goncalo Abecasis
  • , Xiaodong Bai
  • , Suganthi Balasubramanian
  • , Aris Baras
  • , Christina Beechert
  • , Boris Boutkov
  • , Michael Cantor
  • , Giovanni Coppola
  • , Tanima De
  • , Andrew Deubler
  • , Aris Economides
  • , Gisu Eom
  • , Manuel A. R. Ferreira
  • , Caitlin Forsythe
  • , Erin D. Fuller
  • , Zhenhua Gu
  • , Lukas Habegger
  • , Alicia Hawes
  • , Marcus B. Jones
  • , Katia Karalis
  • , Shareef Khalid
  • , Olga Krasheninina
  • , Rouel Lanche
  • , Michael Lattari
  • , Dadong Li
  • , Alexander Lopez
  • , Luca A. Lotta
  • , Kia Manoochehri
  • , Adam J. Mansfield
  • , Evan K. Maxwell
  • , Jason Mighty
  • , Lyndon J. Mitnaul
  • , Mona Nafde
  • , Jonas Nielsen
  • , Sean O’Keeffe
  • , Max Orelus
  • , John D. Overton
  • , Maria Sotiropoulos Padilla
  • , Razvan Panea
  • , Tommy Polanco
  • , Manasi Pradhan
  • , Ayesha Rasool
  • , Jeffrey G. Reid
  • , William Salerno
  • , Thomas D. Schleicher
  • , Alan Shuldiner
  • , Katherine Siminovitch
  • , Jeffrey C. Staples
  • , Ricardo H. Ulloa
  • , Niek Verweij
  • , Louis Widom
  •  & Sarah E. Wolf

Contributions

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.

Corresponding author

Correspondence to Patrick T. Ellinor.

Ethics declarations

Competing interests

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.

Peer review

Peer review information

Nature Genetics thanks Anna Kottgen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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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.

Supplementary information

Supplementary Information

Supplementary Note, Figs. 1–14 and references.

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Supplementary Tables 1–17.

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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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