Rare variant contribution to the heritability of coronary artery disease.

Humans Coronary Artery Disease / genetics Genetic Predisposition to Disease Linkage Disequilibrium Polymorphism, Single Nucleotide Male Female Gene Frequency Genome-Wide Association Study White People / genetics Case-Control Studies Whole Genome Sequencing Genetic Variation Middle Aged

Journal

Nature communications

ISSN: 2041-1723

Titre abrégé: Nat Commun

Pays: England

ID NLM: 101528555

Informations de publication

Date de publication:
09 Oct 2024

Historique:

received: 07 02 2024

accepted: 26 09 2024

medline: 10 10 2024

pubmed: 10 10 2024

entrez: 9 10 2024

Statut: epublish

Résumé

Whole genome sequences (WGS) enable discovery of rare variants which may contribute to missing heritability of coronary artery disease (CAD). To measure their contribution, we apply the GREML-LDMS-I approach to WGS of 4949 cases and 17,494 controls of European ancestry from the NHLBI TOPMed program. We estimate CAD heritability at 34.3% assuming a prevalence of 8.2%. Ultra-rare (minor allele frequency ≤ 0.1%) variants with low linkage disequilibrium (LD) score contribute ~50% of the heritability. We also investigate CAD heritability enrichment using a diverse set of functional annotations: i) constraint; ii) predicted protein-altering impact; iii) cis-regulatory elements from a cell-specific chromatin atlas of the human coronary; and iv) annotation principal components representing a wide range of functional processes. We observe marked enrichment of CAD heritability for most functional annotations. These results reveal the predominant role of ultra-rare variants in low LD on the heritability of CAD. Moreover, they highlight several functional processes including cell type-specific regulatory mechanisms as key drivers of CAD genetic risk.

Identifiants

DOI: 10.1038/s41467-024-52939-6 PMID: 39384761

pubmed: 39384761

doi: 10.1038/s41467-024-52939-6

pii: 10.1038/s41467-024-52939-6

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

8741

Subventions

Organisme : U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)

ID : R35-GM124836

Organisme : U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)

ID : R01-HL139865, R01-HL155915

Investigateurs

Pramod Anugu (P)

Paul Auer (P)

Lucas Barwick (L)

Diane Becker (D)

Cara Carty (C)

Peter Castaldi (P)

Mark Chaffin (M)

Yi-Cheng Chang (YC)

Seung Hoan Choi (SH)

Ren-Hua Chung (RH)

Carolyn Crandall (C)

Sean David (S)

Lisa de Las Fuentes (L)

Ranjan Deka (R)

Dawn DeMeo (D)

Paul S de Vries (PS)

Qing Duan (Q)

Charles Eaton (C)

Lynette Ekunwe (L)

Adel El Boueiz (A)

Shanshan Gao (S)

Yan Gao (Y)

Margery Gass (M)

Auyon Ghosh (A)

Daniel Grine (D)

Michael Hall (M)

Craig Hersh (C)

Brian Hobbs (B)

Chao Agnes Hsiung (CA)

Yi-Jen Hung (YJ)

Haley Huston (H)

Chii Min Hwu (CM)

Rebecca Jackson (R)

Jill Johnsen (J)

Christoph Lange (C)

Ethan Lange (E)

Meryl LeBoff (M)

Wen-Jane Lee (WJ)

Yun Li (Y)

Simin Liu (S)

Yu Liu (Y)

Susan Mathai (S)

Hao Mei (H)

Rakhi Naik (R)

Take Naseri (T)

Bonnie Neltner (B)

Heather Ochs-Balcom (H)

David T Paik (DT)

Cora Parker (C)

Marco Perez (M)

Ulrike Peters (U)

Lawrence S Phillips (LS)

Julia Powers Becker (JP)

Muagututi'a Sefulva Reupena (MS)

Carolina Roselli (C)

Pamela Russell (P)

Ester Cerdeira Sabino (EC)

Kevin Sandow (K)

Karen Schwander (K)

Frank Sciurba (F)

Brian Silver (B)

Sylvia Smoller (S)

Beverly Snively (B)

Garrett Storm (G)

Yun Ju Sung (YJ)

Hua Tang (H)

Margaret Taub (M)

Lesley Tinker (L)

David Tirschwell (D)

Hemant Tiwari (H)

Dhananjay Vaidya (D)

Tarik Walker (T)

Robert Wallace (R)

Avram Walts (A)

Lu-Chen Weng (LC)

Ivana Yang (I)

Snow Xueyan Zhao (SX)

Informations de copyright

Références

Tsao, C. W. et al. Heart disease and stroke statistics—2023 update: a report from the american heart association. Circulation 147, e93–e621 (2023).

Roth, G. A. et al. Global burden of cardiovascular diseases and risk factors, 1990–2019. J. Am. Coll. Cardiol. 76, 2982–3021 (2020).

pubmed: 33309175 pmcid: 7755038 doi: 10.1016/j.jacc.2020.11.010

Wienke, A., Holm, N. V., Skytthe, A. & Yashin, A. I. The heritability of mortality due to heart diseases: a correlated frailty model applied to danish twins. Twin Res. 4, 266–274 (2001).

pubmed: 11665307 doi: 10.1375/twin.4.4.266

Zdravkovic, S. et al. Heritability of death from coronary heart disease: a 36-year follow-up of 20 966 Swedish twins. J. Intern. Med. 252, 247–254 (2002).

pubmed: 12270005 doi: 10.1046/j.1365-2796.2002.01029.x

Chen, Z. & Schunkert, H. Genetics of coronary artery disease in the post‐GWAS era. J. Intern. Med. 290, 980–992 (2021).

pubmed: 34237186 doi: 10.1111/joim.13362

Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat. Genet. 54, 1803–1815 (2022).

pubmed: 36474045 pmcid: 9729111 doi: 10.1038/s41588-022-01233-6

Tcheandjieu, C. et al. Large-scale genome-wide association study of coronary artery disease in genetically diverse populations. Nat. Med. 28, 1679–1692 (2022).

pubmed: 35915156 pmcid: 9419655 doi: 10.1038/s41591-022-01891-3

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

pubmed: 35256806 pmcid: 9119698 doi: 10.1038/s41588-021-00997-7

Jang, S.-K. et al. Rare genetic variants explain missing heritability in smoking. Nat. Hum. Behav. 6, 1577–1586 (2022).

pubmed: 35927319 pmcid: 9985486 doi: 10.1038/s41562-022-01408-5

Wessel, J. et al. Rare non-coding variation identified by large scale whole genome sequencing reveals unexplained heritability of type 2 diabetes. Preprint at medRxiv http://medrxiv.org/lookup/doi/10.1101/2020.11.13.20221812 (2020).

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

pubmed: 33568819 pmcid: 7875770 doi: 10.1038/s41586-021-03205-y

Hanks, S. C. et al. Extent to which array genotyping and imputation with large reference panels approximate deep whole-genome sequencing. Am. J. Hum. Genet. 109, 1653–1666 (2022).

pubmed: 35981533 pmcid: 9502057 doi: 10.1016/j.ajhg.2022.07.012

Erdmann, J., Kessler, T., Munoz Venegas, L. & Schunkert, H. A decade of genome-wide association studies for coronary artery disease: the challenges ahead. Cardiovasc. Res. 114, 1241–1257 (2018).

Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

pubmed: 22955828 pmcid: 3771521 doi: 10.1126/science.1222794

Won, H.-H. et al. Disproportionate contributions of select genomic compartments and cell types to genetic risk for coronary artery disease. PLOS Genet. 11, e1005622 (2015).

pubmed: 26509271 pmcid: 4625039 doi: 10.1371/journal.pgen.1005622

Nasser, J. et al. Genome-wide enhancer maps link risk variants to disease genes. Nature 593, 238–243 (2021).

pubmed: 33828297 pmcid: 9153265 doi: 10.1038/s41586-021-03446-x

Preissl, S., Gaulton, K. J. & Ren, B. Characterizing cis-regulatory elements using single-cell epigenomics. Nat. Rev. Genet. 24, 21–43 (2023).

pubmed: 35840754 doi: 10.1038/s41576-022-00509-1

Örd, T. et al. Single-cell epigenomics and functional fine-mapping of atherosclerosis GWAS loci. Circ. Res. 129, 240–258 (2021).

pubmed: 34024118 pmcid: 8260472 doi: 10.1161/CIRCRESAHA.121.318971

Turner, A. W. et al. Single-nucleus chromatin accessibility profiling highlights regulatory mechanisms of coronary artery disease risk. Nat. Genet. 54, 804–816 (2022).

pubmed: 35590109 pmcid: 9203933 doi: 10.1038/s41588-022-01069-0

Sullivan, P. F. et al. Leveraging base-pair mammalian constraint to understand genetic variation and human disease. Science 380, eabn2937 (2023).

pubmed: 37104612 pmcid: 10259825 doi: 10.1126/science.abn2937

Evans, L. M. 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).

pubmed: 29700474 pmcid: 5934350 doi: 10.1038/s41588-018-0108-x

Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w

pubmed: 22728672 doi: 10.4161/fly.19695

Li, X. et al. Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nat. Genet. 52, 969–983 (2020).

pubmed: 32839606 pmcid: 7483769 doi: 10.1038/s41588-020-0676-4

Li, Z. et al. A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies. Nat. Methods 19, 1599–1611 (2022).

pubmed: 36303018 pmcid: 10008172 doi: 10.1038/s41592-022-01640-x

Zhou, H. et al. FAVOR: functional annotation of variants online resource and annotator for variation across the human genome. Nucleic Acids Res. 51, D1300–D1311 (2023).

pubmed: 36350676 doi: 10.1093/nar/gkac966

Chen, S. et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024).

pubmed: 38057664 doi: 10.1038/s41586-023-06045-0

Siepel, A., Pollard, K. S. & Haussler, D. New methods for detecting lineage-specific selection. In Lecture Notes in Bioinformatics 3909, 190–205 (Springer-Verlag, 2006).

Weiner, D. J. et al. Polygenic architecture of rare coding variation across 394,783 exomes. Nature 614, 492–499 (2023).

pubmed: 36755099 pmcid: 10614218 doi: 10.1038/s41586-022-05684-z

Zaitlen, N. & Kraft, P. Heritability in the genome-wide association era. Hum. Genet. 131, 1655–1664 (2012).

pubmed: 22821350 pmcid: 3432754 doi: 10.1007/s00439-012-1199-6

Ben-Eghan, C. et al. Don’t ignore genetic data from minority populations. Nature 585, 184–186 (2020).

pubmed: 32901124 doi: 10.1038/d41586-020-02547-3

Petrazzini, B. O. et al. Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease. Nat. Genet. 56, 1412–1419 (2024).

pubmed: 38862854 doi: 10.1038/s41588-024-01791-x

O’Connor, L. J. et al. Extreme polygenicity of complex traits is explained by negative selection. Am. J. Hum. Genet. 105, 456–476 (2019).

pubmed: 31402091 pmcid: 6732528 doi: 10.1016/j.ajhg.2019.07.003

Zeng, J. et al. Widespread signatures of natural selection across human complex traits and functional genomic categories. Nat. Commun. 12, 1164 (2021).

pubmed: 33608517 pmcid: 7896067 doi: 10.1038/s41467-021-21446-3

Backman, J. D. et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature 599, 628–634 (2021).

pmcid: 8596853 doi: 10.1038/s41586-021-04103-z

Singh, T. et al. Rare coding variants in ten genes confer substantial risk for schizophrenia. Nature 604, 509–516 (2022).

pubmed: 35396579 pmcid: 9805802 doi: 10.1038/s41586-022-04556-w

Akingbuwa, W. A., Hammerschlag, A. R., Bartels, M., Nivard, M. G. & Middeldorp, C. M. Ultra-rare and common genetic variant analysis converge to implicate negative selection and neuronal processes in the aetiology of schizophrenia. Mol. Psychiatry 27, 3699–3707 (2022).

pubmed: 35665764 pmcid: 9708595 doi: 10.1038/s41380-022-01621-8

Zhou, D., Zhou, Y., Xu, Y., Meng, R. & Gamazon, E. R. A phenome-wide scan reveals convergence of common and rare variant associations. Genome Med. 15, 101 (2023).

pubmed: 38017547 pmcid: 10683189 doi: 10.1186/s13073-023-01253-9

Duffy, Á. et al. Development of a human genetics-guided priority score for 19,365 genes and 399 drug indications. Nat. Genet. 56, 51–59 (2024).

pubmed: 38172303 doi: 10.1038/s41588-023-01609-2

Minikel, E. V., Painter, J. L., Dong, C. C. & Nelson, M. R. Refining the impact of genetic evidence on clinical success. Nature 629, 624–629 (2024).

pubmed: 38632401 pmcid: 11096124 doi: 10.1038/s41586-024-07316-0

Popejoy, A. B. & Fullerton, S. M. Genomics is failing on diversity. Nature 538, 161–164 (2016).

pubmed: 27734877 pmcid: 5089703 doi: 10.1038/538161a

Sirugo, G., Williams, S. M. & Tishkoff, S. A. The missing diversity in human genetic studies. Cell 177, 26–31 (2019).

pubmed: 30901543 pmcid: 7380073 doi: 10.1016/j.cell.2019.02.048

Fatumo, S. et al. A roadmap to increase diversity in genomic studies. Nat. Med. 28, 243–250 (2022).

pubmed: 35145307 pmcid: 7614889 doi: 10.1038/s41591-021-01672-4

Zaitlen, N. et al. Leveraging population admixture to characterize the heritability of complex traits. Nat. Genet. 46, 1356–1362 (2014).

pubmed: 25383972 pmcid: 4244251 doi: 10.1038/ng.3139

Luo, Y. et al. Estimating heritability and its enrichment in tissue-specific gene sets in admixed populations. Hum. Mol. Genet. 30, 1521–1534 (2021).

pubmed: 33987664 pmcid: 8330913

Chan, T. F. et al. Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics. Am. J. Hum. Genet. 110, 1853–1862 (2023).

Visscher, P. M. et al. Statistical power to detect genetic (Co)variance of complex traits using SNP data in unrelated samples. PLoS Genet. 10, e1004269 (2014).

pubmed: 24721987 pmcid: 3983037 doi: 10.1371/journal.pgen.1004269

Speed, D., Kaphle, A. & Balding, D. J. SNP‐based heritability and selection analyses: Improved models and new results. BioEssays 44, 2100170 (2022).

doi: 10.1002/bies.202100170

Conomos, M. P., Miller, M. B. & Thornton, T. A. Robust inference of population structure for ancestry prediction and correction of stratification in the presence of relatedness. Genet. Epidemiol. 39, 276–293 (2015).

pubmed: 25810074 pmcid: 4836868 doi: 10.1002/gepi.21896

Conomos, M. P., Reiner, A. P., Weir, B. S. & Thornton, T. A. Model-free estimation of recent genetic relatedness. Am. J. Hum. Genet. 98, 127–148 (2016).

pubmed: 26748516 pmcid: 4716688 doi: 10.1016/j.ajhg.2015.11.022

Privé, F., Luu, K., Blum, M. G. B., McGrath, J. J. & Vilhjálmsson, B. J. Efficient toolkit implementing best practices for principal component analysis of population genetic data. Bioinformatics 36, 4449–4457 (2020).

pubmed: 32415959 pmcid: 7750941 doi: 10.1093/bioinformatics/btaa520

Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

pubmed: 19648217 pmcid: 2752134 doi: 10.1101/gr.094052.109

Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

pubmed: 21167468 pmcid: 3014363 doi: 10.1016/j.ajhg.2010.11.011

Lee, S. H., Wray, N. R., Goddard, M. E. & Visscher, P. M. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294–305 (2011).

pubmed: 21376301 pmcid: 3059431 doi: 10.1016/j.ajhg.2011.02.002