Abstract
All of Us is a biorepository aiming to advance biomedical research by providing various types of data in diverse human populations. Here we present a demonstration project validating the program’s genomic data in 98,622 participants. We sought to replicate known genetic associations for three diseases (atrial fibrillation [AF], coronary artery disease, type 2 diabetes [T2D]) and two quantitative traits (height and low-density lipoprotein [LDL]) by conducting common and rare variant analyses. We identified one known risk locus for AF, five loci for T2D, 143 loci for height, and nine loci for LDL. In gene-based burden tests for rare loss-of-function variants, we replicated associations between TTN and AF, GIGYF1 and T2D, ADAMTS17, ACAN, NPR2 and height, APOB, LDLR, PCSK9 and LDL. Our results are consistent with previous literature, indicating that the All of Us program is a reliable resource for advancing the understanding of complex diseases in diverse human populations.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Data availability
Access to individual-level data from the All of Us research program is available to researchers whose institution has signed a data use agreement with All of Us (https://www.researchallofus.org/register/). All of Us provides a publicly available data browser (https://databrowser.researchallofus.org/) containing aggregate-level participant data for users to explore the available data, including genomic variants. Electronic health records (EHR) data, used for phenotyping, belongs to the registered tier dataset. Whole-genome sequencing data belongs to the controlled tier dataset, which requires additional training to access.
References
The “All of Us” Research Program | NEJM. Accessed November 1, 2021. https://www.nejm.org/doi/full/10.1056/NEJMsr1809937
Roselli C, Rienstra M, Ellinor PT. Genetics of atrial fibrillation in 2020. Circ Res. 2020;127:21–33. https://doi.org/10.1161/CIRCRESAHA.120.316575
McPherson R, Tybjaerg-Hansen A. Genetics of coronary artery disease. Circ Res. 2016;118:564–78. https://doi.org/10.1161/CIRCRESAHA.115.306566
Ali O. Genetics of type 2 diabetes. World J Diabetes. 2013;4:114–23. https://doi.org/10.4239/wjd.v4.i4.114
A large electronic-health-record-based genome-wide study of serum lipids | Nature Genetics. Accessed November 2, 2021. https://www.nature.com/articles/s41588-018-0064-5
Yengo L, Sidorenko J, Kemper KE, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet. 2018;27:3641–9. https://doi.org/10.1093/hmg/ddy271
Khurshid S, Choi SH, Weng LC, et al. Frequency of cardiac rhythm abnormalities in a half million adults. Circ Arrhythm Electrophysiol. 2018;11:e006273. https://doi.org/10.1161/CIRCEP.118.006273
MidSouth CDRN - Coronary Heart Disease Algorithm | PheKB. Accessed November 2, 2021. https://phekb.org/phenotype/midsouth-cdrn-coronary-heart-disease-algorithm
Type 2 Diabetes Mellitus | PheKB. Accessed November 2, 2021. https://www.phekb.org/phenotype/type-2-diabetes-mellitus
Mbatchou J, Barnard L, Backman J, et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat Genet. 2021;53:1097–103. https://doi.org/10.1038/s41588-021-00870-7
Zhou W, Nielsen JB, Fritsche LG, et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet. 2018;50:1335–41. https://doi.org/10.1038/s41588-018-0184-y
Ryan AK, Blumberg B, Rodriguez-Esteban C, et al. Pitx2 determines left-right asymmetry of internal organs in vertebrates. Nature. 1998;394:545–51. https://doi.org/10.1038/29004
Tessari A, Pietrobon M, Notte A, et al. Myocardial Pitx2 differentially regulates the left atrial identity and ventricular asymmetric remodeling programs. Circ Res. 2008;102:813–22. https://doi.org/10.1161/CIRCRESAHA.107.163188
Roselli C, Chaffin MD, Weng LC, et al. Multi-ethnic genome-wide association study for atrial fibrillation. Nat Genet. 2018;50:1225–33. https://doi.org/10.1038/s41588-018-0133-9
Bulik-Sullivan BK, Loh PR, Finucane HK, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47:291–5. https://doi.org/10.1038/ng.3211
van der Harst P, Verweij N. Identification of 64 novel genetic loci provides an expanded view on the genetic architecture of coronary artery disease. Circ Res. 2018;122:433–43. https://doi.org/10.1161/CIRCRESAHA.117.312086
Soranzo N, Sanna S, Wheeler E, et al. Common variants at 10 genomic loci influence hemoglobin A1C levels via glycemic and nonglycemic pathways. Diabetes. 2010;59:3229–39. https://doi.org/10.2337/db10-0502
Mahajan A, Go MJ, Zhang W, et al. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet. 2014;46:234–44. https://doi.org/10.1038/ng.2897
Cook JP, Morris AP. Multi-ethnic genome-wide association study identifies novel locus for type 2 diabetes susceptibility. Eur J Hum Genet. 2016;24:1175–80. https://doi.org/10.1038/ejhg.2016.17
Morris AP, Voight BF, Teslovich TM, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44:981–90. https://doi.org/10.1038/ng.2383
Yang J, Ferreira T, Morris AP, et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet. 2012;44:369–75. https://doi.org/10.1038/ng.2213
Yang J, Benyamin B, McEvoy BP, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet. 2010;42:565–9. https://doi.org/10.1038/ng.608
Waterworth DM, Ricketts SL, Song K, et al. Genetic variants influencing circulating lipid levels and risk of coronary artery disease. Arterioscler Thromb Vasc Biol. 2010;30:2264–76. https://doi.org/10.1161/ATVBAHA.109.201020
Klarin D, Damrauer SM, Cho K, et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat Genet. 2018;50:1514–23. https://doi.org/10.1038/s41588-018-0222-9
Teslovich TM, Musunuru K, Smith AV, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466:707–13. https://doi.org/10.1038/nature09270
Sandhu MS, Waterworth DM, Debenham SL, et al. LDL-cholesterol concentrations: a genome-wide association study. Lancet 2008;371:483–91. https://doi.org/10.1016/S0140-6736(08)60208-1
Sakaue S, Kanai M, Tanigawa Y, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021;53:1415–24. https://doi.org/10.1038/s41588-021-00931-x
Jurgens SJ, Choi SH, Morrill VN, et al. Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank. Nat Genet. Published online February 17, 2022:1-11. https://doi.org/10.1038/s41588-021-01011-w
McLaren W, Gil L, Hunt SE, et al. The ensembl variant effect predictor. Genome Biol. 2016;17:122 https://doi.org/10.1186/s13059-016-0974-4
Peterson RE, Kuchenbaecker K, Walters RK, et al. Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. Cell. 2019;179:589–603. https://doi.org/10.1016/j.cell.2019.08.051
Acknowledgements
We would like to thank the All of Us research program participants, as this study and the database are possible because of their contributions. All of Us established core values and responsible strategies to sustain public trust in biomedical research. We hope the partnership between the participants and the program will benefit the participants and improve the health of future generations.
Funding
The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549; 1 OT2 OD026554; 1 OT2 OD026557; 1 OT2 OD026556; 1 OT2 OD026550; 1 OT2 OD 026552; 1 OT2 OD026553; 1 OT2 OD026548; 1 OT2 OD026551; 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205; 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277; 3 OT2 OD025315; 1 OT2 OD025337; 1 OT2 OD025276. In addition, the All of Us Research Program would not be possible without the partnership of its participants. Dr. Ellinor is supported by grants from the National Institutes of Health (1RO1HL092577, 1R01HL157635, 1R01HL157635), from the American Heart Association (18SFRN34110082), and from the European Union (MAESTRIA 965286). Dr. Lubitz previously received support from NIH grants R01HL139731 and R01HL157635, and American Heart Association 18SFRN34250007 during this project. Dr. Choi was previously supported by the NHLBI BioData Catalyst Fellows program.
Author information
Authors and Affiliations
Consortia
Contributions
XW and SHC conceptualized the study and analyzed the data. SHC, SAL, and PTE supervised this work. JR helped with analysis and manuscript editing. JK helped with phenotype definitions. AR and KRM are members of the All of Us research program and provided support for this work, including manuscript review. The All of Us Research Program provided all the data used in the current study. HC, NSV, LO, and GAT provided feedback for this project. XW, SHC, and SAL wrote the manuscript. All co-authors reviewed the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing non-financial interests but the following competing financial interests: PTE receives sponsored research support from Bayer AG, IBM Research, Bristol Myers Squibb, and Pfizer; he has also served on advisory boards or consulted for Bayer AG and MyoKardia. SAL is a full-time employee of Novartis as of July 18, 2022. SAL has received sponsored research support from Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, Fitbit, Medtronic, Premier, and IBM, and has consulted for Bristol Myers Squibb, Pfizer, Blackstone Life Sciences, and Invitae.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
About this article
Cite this article
Wang, X., Ryu, J., Kim, J. et al. Common and rare variants associated with cardiometabolic traits across 98,622 whole-genome sequences in the All of Us research program. J Hum Genet 68, 565–570 (2023). https://doi.org/10.1038/s10038-023-01147-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s10038-023-01147-z
This article is cited by
-
Genomic data in the All of Us Research Program
Nature (2024)