Whole-Genome Sequencing to Characterize Monogenic and Polygenic Contributions in Patients Hospitalized With Early-Onset Myocardial Infarction
Open accessAbstract
Background: The relative prevalence and clinical importance of monogenic mutations related to familial hypercholesterolemia and of high polygenic score (cumulative impact of many common variants) pathways for early-onset myocardial infarction remain uncertain. Whole-genome sequencing enables simultaneous ascertainment of both monogenic mutations and polygenic score for each individual. Methods: We performed deep-coverage whole-genome sequencing of 2081 patients from 4 racial subgroups hospitalized in the United States with early-onset myocardial infarction (age ≤55 years) recruited with a 2:1 female-to-male enrollment design. We compared these genomes with those of 3761 population-based control subjects. We first identified individuals with a rare, monogenic mutation related to familial hypercholesterolemia. Second, we calculated a recently developed polygenic score of 6.6 million common DNA variants to quantify the cumulative susceptibility conferred by common variants. We defined high polygenic score as the top 5% of the control distribution because this cutoff has previously been shown to confer similar risk to that of familial hypercholesterolemia mutations. Results: The mean age of the 2081 patients presenting with early-onset myocardial infarction was 48 years, and 66% were female. A familial hypercholesterolemia mutation was present in 36 of these patients (1.7%) and was associated with a 3.8-fold (95% CI, 2.1–6.8; P <0.001) increased odds of myocardial infarction. Of the patients with early-onset myocardial infarction, 359 (17.3%) carried a high polygenic score, associated with a 3.7-fold (95% CI, 3.1–4.6; P <0.001) increased odds. Mean estimated untreated low-density lipoprotein cholesterol was 206 mg/dL in those with a familial hypercholesterolemia mutation, 132 mg/dL in those with high polygenic score, and 122 mg/dL in those in the remainder of the population. Although associated with increased risk in all racial groups, high polygenic score demonstrated the strongest association in white participants ( P for heterogeneity=0.008). Conclusions: Both familial hypercholesterolemia mutations and high polygenic score are associated with a >3-fold increased odds of early-onset myocardial infarction. However, high polygenic score has a 10-fold higher prevalence among patients presents with early-onset myocardial infarction. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT00597922.
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These developments have enabled researchers to bridge the gap between monogenic and polygenic realms, identifying common variant modifiers of monogenic phenotypes and low-frequency and rare variants influencing common multifactorial traits.
“These developments have enabled researchers to bridge the gap between the monogenic and polygenic realms, identifying common variant modifiers of monogenic phenotypes contributing to the variable expression of rare, large-effect alleles26,93, and low-frequency and rare variants that influence common multifactorial traits94,95.”
From A brief history of human disease genetics (2020) · cites this paper as [93]
Shifting from array-based genotyping to sequence-based analysis facilitates risk prediction by integrating rare large-effect alleles with polygenic scores.
“Increasingly, the shift from array-based genotyping to sequence-based analysis is facilitating risk prediction, which integrates information from rare, large-effect alleles with that from polygenic scores93.”
From A brief history of human disease genetics (2020) · cites this paper as [93]
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References 28 · Cited by 315
- Authors
- Khera, A. V., Chaffin, M., Zekavat, S. M., Collins, R. L., Roselli, C., Natarajan, P., Lichtman, J. H., D’Onofrio, G., Mattera, J., Dreyer, R., Spertus, J. A., Taylor, K. D., Psaty, B. M., Rich, S. S., Post, W., Gupta, N., Gabriel, S., Lander, E., Ida Chen, Y.-D., Talkowski, M. E., Rotter, J. I., Krumholz, H. M., & Kathiresan, S.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Yale University (US)
- Yale New Haven Hospital (US)
- University of Missouri–Kansas City (US)
- The Lundquist Institute (US)
- Kaiser Permanente (US)
- University of Virginia (US)
- Johns Hopkins University (US)
- Institute for Translational Genomics and Population Sciences, LABioMed, Torrance, CA (K.D.T., Y.-D.I.C., J.I.R.).
- Corresponding author
- Khera, Amit V.
- Chaffin, Mark
- Krumholz, Harlan M.
- Kathiresan, Sekar
- ORCID
- Khera, Amit V.
- Chaffin, Mark
- Zekavat, Seyedeh M.
- Collins, Ryan L.
- Roselli, Carolina
- Natarajan, Pradeep
- Lichtman, Judith H.
- D’Onofrio, Gail
- Mattera, Jennifer
- Dreyer, Rachel
- Spertus, John A.
- Taylor, Kent D.
- Psaty, Bruce M.
- Rich, Stephen S.
- Post, Wendy
- Gupta, Namrata
- Gabriel, Stacey
- Lander, Eric
- Talkowski, Michael E.
- Rotter, Jerome I.
- Krumholz, Harlan M.
- Kathiresan, Sekar
- Venue
- Circulation
- Published
- 2019-03-26
- Volume
- 139
- Issue
- 13
- Pages
- 1593-1602
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- PMC
- PMC6433484
- OpenAlex
- W2901728784
- Cited by
- 315
- References
- 28
- Citations by year
- 2026: 28 · 2025: 40 · 2024: 49 · 2023: 35 · 2022: 50 · 2021: 44 · 2020: 46 · 2019: 23
- FWCI
- 38.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- other-oa
- Locations
- Circulation (publishedVersion, other-oa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- American Heart Association (N01-HC-95167, N01HC95161, N01-HC-95162, N01-HC-95160, UL1-TR-001420, N01HC95169, UL1-TR-001079, N01-HC-95164, N01HC95163, UL1 TR000040, N01-HC 95168, N01HC95165, N01HC95166, 17SDG33680041, N01-HC-95159, HHSN268201500003I)
- Massachusetts General Hospital (HL007208)
- Harvard Catalyst (TR001100)
- National Institutes of Health (n01hc095162-003, UL1TR001420, n01hc095159-004, N01-HC- 95166, n01hc095160-004, N01-HC9-5168, n01hc095167-003, n01hc095165-005, N01-HC- 95159, n01hc095161-004, n01hc095166-004, UL1TR001079, HHSN268201500003I, n01hc095168-003, n01hc095163-003, KL2TR001100, n01hc095169-004, UL1TR000040, n01hc095164-003, UL1-TR-, N01‐HC‐95161, N01- HC-95163, N01- HC-95165, N01‐HC‐95160, N01 HC95164, N01-HC-95167, N01-HC95162, N01‐HC‐95169, DK-063491, HL007208, UL1TR001881)
- National Heart, Lung, and Blood Institute (DK-063491, R01HL127564, N01 HC-95159, N01-HC-95160, N01-HC095169, N01 HC-95168, N01-HC-095165, N01 HC95167, (N01-HC-95163, N01 HC095163, HL007208, N01 HC-95161, N01 HC95165, N01HC95164, HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95167, N01-HC-95168, N01-HC-95169, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-095159, N01 HC095168, N01-HC-095162, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-095161, HHSN268201500003C, HHSN26820150 0003I, R01HL081153, HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01, N01 HC095167, R01HL105756, N01-HC-095160, N01 HC095166, HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01 HC095164, UL1-TR-000040, UL1-TR-001079, UL1-TR-001420, N01‐HC‐95166, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, N01-HC-95169, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95159, N01-HC-95160, N01 HC-95162, R01HL142711, T32HL007208, N01 HC95169)
- National Human Genome Research Institute (K08HG010155, UM1HG008895, T32HG002295)
- National Institute of General Medical Sciences (T32GM007205)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK063491)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- familial hypercholesterolemia, monogenic mutations, polygenic risk score, common genetic variants, LDL cholesterol, genetic risk, racial differences
- MeSH terms
- Whole Genome Sequencing, Aged, Female, Humans, Hyperlipoproteinemia Type II, Cholesterol, LDL, Male, Middle Aged, Myocardial Infarction, Genome, Human, Genetic Predisposition to Disease, Multifactorial Inheritance
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