Variant level heritability estimates of type 2 diabetes in African Americans.
Disparities
Genetic polymorphisms
Genomics
Heritable quantitative trait
Type 2 diabetes mellitus
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
18 Jun 2024
18 Jun 2024
Historique:
received:
13
07
2023
accepted:
12
06
2024
medline:
19
6
2024
pubmed:
19
6
2024
entrez:
18
6
2024
Statut:
epublish
Résumé
Type 2 diabetes (T2D) is caused by both genetic and environmental factors and is associated with an increased risk of cardiorenal complications and mortality. Though disproportionately affected by the condition, African Americans (AA) are largely underrepresented in genetic studies of T2D, and few estimates of heritability have been calculated in this race group. Using genome-wide association study (GWAS) data paired with phenotypic data from ~ 19,300 AA participants of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, Genetics of Hypertension Associated Treatments (GenHAT) study, and the Electronic Medical Records and Genomics (eMERGE) network, we estimated narrow-sense heritability using two methods: Linkage-Disequilibrium Adjusted Kinships (LDAK) and Genome-Wide Complex Trait Analysis (GCTA). Study-level heritability estimates adjusting for age, sex, and genetic ancestry ranged from 18% to 34% across both methods. Overall, the current study narrows the expected range for T2D heritability in this race group compared to prior estimates, while providing new insight into the genetic basis of T2D in AAs for ongoing genetic discovery efforts.
Identifiants
pubmed: 38890458
doi: 10.1038/s41598-024-64711-3
pii: 10.1038/s41598-024-64711-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
14009Informations de copyright
© 2024. The Author(s).
Références
Virani, S. S. et al. Heart disease and stroke statistics-2021 update: A report from the american heart association. Circulation 143, e254–e743. https://doi.org/10.1161/CIR.0000000000000950 (2021).
doi: 10.1161/CIR.0000000000000950
pubmed: 33501848
Prasad, R. B. & Groop, L. Genetics of type 2 diabetes-pitfalls and possibilities. Genes 6, 87–123. https://doi.org/10.3390/genes6010087 (2015).
doi: 10.3390/genes6010087
pubmed: 25774817
pmcid: 4377835
Factors, E. R. et al. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: A collaborative meta-analysis of 102 prospective studies. Lancet 375, 2215–2222. https://doi.org/10.1016/S0140-6736(10)60484-9 (2010).
doi: 10.1016/S0140-6736(10)60484-9
Mayer-Davis, E. J. et al. Incidence trends of type 1 and type 2 diabetes among youths, 2002–2012. N. Engl. J. Med. 376, 1419–1429. https://doi.org/10.1056/NEJMoa1610187 (2017).
doi: 10.1056/NEJMoa1610187
pubmed: 28402773
pmcid: 5592722
Lawrence, J. M. et al. Trends in prevalence of type 1 and type 2 diabetes in children and adolescents in the US, 2001–2017. JAMA 326, 717–727. https://doi.org/10.1001/jama.2021.11165 (2021).
doi: 10.1001/jama.2021.11165
pubmed: 34427600
pmcid: 8385600
Stankov, K., Benc, D. & Draskovic, D. Genetic and epigenetic factors in etiology of diabetes mellitus type 1. Pediatrics 132, 1112–1122. https://doi.org/10.1542/peds.2013-1652 (2013).
doi: 10.1542/peds.2013-1652
pubmed: 24190679
Lyssenko, V. et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N. Engl. J. Med. 359, 2220–2232. https://doi.org/10.1056/NEJMoa0801869 (2008).
doi: 10.1056/NEJMoa0801869
pubmed: 19020324
Galaviz, K. I., Narayan, K. M. V., Lobelo, F. & Weber, M. B. Lifestyle and the prevention of type 2 diabetes: A status report. Am. J. Lifestyle Med. 12, 4–20. https://doi.org/10.1177/1559827615619159 (2018).
doi: 10.1177/1559827615619159
pubmed: 30202378
Kaprio, J. et al. Concordance for type 1 (insulin-dependent) and type 2 (non-insulin-dependent) diabetes mellitus in a population-based cohort of twins in Finland. Diabetologia 35, 1060–1067. https://doi.org/10.1007/BF02221682 (1992).
doi: 10.1007/BF02221682
pubmed: 1473616
Newman, B. et al. Concordance for type 2 (non-insulin-dependent) diabetes mellitus in male twins. Diabetologia 30, 763–768. https://doi.org/10.1007/BF00275741 (1987).
doi: 10.1007/BF00275741
pubmed: 3428496
Poulsen, P., Kyvik, K. O., Vaag, A. & Beck-Nielsen, H. Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance–a population-based twin study. Diabetologia 42, 139–145. https://doi.org/10.1007/s001250051131 (1999).
doi: 10.1007/s001250051131
pubmed: 10064092
Medici, F., Hawa, M., Ianari, A., Pyke, D. A. & Leslie, R. D. Concordance rate for type II diabetes mellitus in monozygotic twins: Actuarial analysis. Diabetologia 42, 146–150. https://doi.org/10.1007/s001250051132 (1999).
doi: 10.1007/s001250051132
pubmed: 10064093
Chen, X. et al. Dominant genetic variation and missing heritability for human complex traits: Insights from twin versus genome-wide common SNP models. Am. J. Hum. Genet. 97, 708–714. https://doi.org/10.1016/j.ajhg.2015.10.004 (2015).
doi: 10.1016/j.ajhg.2015.10.004
pubmed: 26544805
pmcid: 4667127
Wang, Y., Vik, J. O., Omholt, S. W. & Gjuvsland, A. B. Effect of regulatory architecture on broad versus narrow sense heritability. PLoS Comput. Biol. 9, e1003053. https://doi.org/10.1371/journal.pcbi.1003053 (2013).
doi: 10.1371/journal.pcbi.1003053
pubmed: 23671414
pmcid: 3649986
Karavolias, N. G. et al. Low additive genetic variation in a trait under selection in domesticated rice. G3 (Bethesda) 10, 2435–2443. https://doi.org/10.1534/g3.120.401194 (2020).
doi: 10.1534/g3.120.401194
pubmed: 32439738
Genomes Project, C. et al. 2012 An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56-65, https://doi.org/10.1038/nature11632 (2012).
Hall, J. B. & Bush, W. S. Analysis of heritability using genome-wide data. Curr. Protoc. Hum. Genet. https://doi.org/10.1002/cphg.25 (2016).
doi: 10.1002/cphg.25
pubmed: 27727439
pmcid: 5127448
Miljkovic-Gacic, I. et al. Genetic determination of adiponectin and its relationship with body fat topography in multigenerational families of African heritage. Metabolism 56, 234–238. https://doi.org/10.1016/j.metabol.2006.09.019 (2007).
doi: 10.1016/j.metabol.2006.09.019
pubmed: 17224338
pmcid: 2895550
Freedman, B. I. et al. Genome-wide scans for heritability of fasting serum insulin and glucose concentrations in hypertensive families. Diabetologia 48, 661–668. https://doi.org/10.1007/s00125-005-1679-5 (2005).
doi: 10.1007/s00125-005-1679-5
pubmed: 15747111
Poveda, A. et al. The heritable basis of gene-environment interactions in cardiometabolic traits. Diabetologia 60, 442–452. https://doi.org/10.1007/s00125-016-4184-0 (2017).
doi: 10.1007/s00125-016-4184-0
pubmed: 28004149
Vattikuti, S., Guo, J. & Chow, C. C. Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits. PLoS Genet. 8, e1002637. https://doi.org/10.1371/journal.pgen.1002637 (2012).
doi: 10.1371/journal.pgen.1002637
pubmed: 22479213
pmcid: 3315484
Almgren, P. et al. Heritability and familiality of type 2 diabetes and related quantitative traits in the Botnia Study. Diabetologia 54, 2811–2819. https://doi.org/10.1007/s00125-011-2267-5 (2011).
doi: 10.1007/s00125-011-2267-5
pubmed: 21826484
Mansour, O., Golden, S. H. & Yeh, H. C. Disparities in mortality among adults with and without diabetes by sex and race. J. Diabetes Complicat. 34, 107496. https://doi.org/10.1016/j.jdiacomp.2019.107496 (2020).
doi: 10.1016/j.jdiacomp.2019.107496
Lanting, L. C., Joung, I. M., Mackenbach, J. P., Lamberts, S. W. & Bootsma, A. H. Ethnic differences in mortality, end-stage complications, and quality of care among diabetic patients: A review. Diabetes Care 28, 2280–2288. https://doi.org/10.2337/diacare.28.9.2280 (2005).
doi: 10.2337/diacare.28.9.2280
pubmed: 16123507
Horowitz, C. R. et al. Race, genomics and chronic disease: what patients with African ancestry have to say. J. Health Care Poor Underserved 28, 248–260. https://doi.org/10.1353/hpu.2017.0020 (2017).
doi: 10.1353/hpu.2017.0020
pubmed: 28238999
pmcid: 5577001
Srivastava, A. K., Williams, S. M. & Zhang, G. Heritability estimation approaches utilizing genome-wide data. Curr. Protoc. 3, e734. https://doi.org/10.1002/cpz1.734 (2023).
doi: 10.1002/cpz1.734
pubmed: 37068172
pmcid: 10923601
Golan, D., Lander, E. S. & Rosset, S. Measuring missing heritability: Inferring the contribution of common variants. Proc. Natl. Acad. Sci. USA 111, E5272-5281. https://doi.org/10.1073/pnas.1419064111 (2014).
doi: 10.1073/pnas.1419064111
pubmed: 25422463
pmcid: 4267399
Goodarzi, M. O. & Rotter, J. I. Genetics insights in the relationship between type 2 diabetes and coronary heart disease. Circ. Res. 126, 1526–1548. https://doi.org/10.1161/CIRCRESAHA.119.316065 (2020).
doi: 10.1161/CIRCRESAHA.119.316065
pubmed: 32437307
pmcid: 7250006
Morris, A. P. Progress in defining the genetic contribution to type 2 diabetes susceptibility. Curr. Opin. Genet. Dev. 50, 41–51. https://doi.org/10.1016/j.gde.2018.02.003 (2018).
doi: 10.1016/j.gde.2018.02.003
pubmed: 29477131
Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513. https://doi.org/10.1038/s41588-018-0241-6 (2018).
doi: 10.1038/s41588-018-0241-6
pubmed: 30297969
pmcid: 6287706
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. https://doi.org/10.1038/s41588-020-0637-y (2020).
doi: 10.1038/s41588-020-0637-y
pubmed: 32541925
pmcid: 7343592
Mahajan, A. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat. Genet. 54, 560–572. https://doi.org/10.1038/s41588-022-01058-3 (2022).
doi: 10.1038/s41588-022-01058-3
pubmed: 35551307
pmcid: 9179018
DeForest, N. & Majithia, A. R. Genetics of type 2 diabetes: Implications from large-scale studies. Curr. Diab. Rep. 22, 227–235. https://doi.org/10.1007/s11892-022-01462-3 (2022).
doi: 10.1007/s11892-022-01462-3
pubmed: 35305202
pmcid: 9072491
Ng, M. C. et al. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLoS Genet. 10, e1004517. https://doi.org/10.1371/journal.pgen.1004517 (2014).
doi: 10.1371/journal.pgen.1004517
pubmed: 25102180
pmcid: 4125087
Palmer, N. D. et al. A genome-wide association search for type 2 diabetes genes in African Americans. PLoS One 7, e29202. https://doi.org/10.1371/journal.pone.0029202 (2012).
doi: 10.1371/journal.pone.0029202
pubmed: 22238593
pmcid: 3251563
Chen, J. et al. Genome-wide association study of type 2 diabetes in Africa. Diabetologia 62, 1204–1211. https://doi.org/10.1007/s00125-019-4880-7 (2019).
doi: 10.1007/s00125-019-4880-7
pubmed: 31049640
pmcid: 6560001
Ge, T. et al. Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations. Genome Med. 14, 70. https://doi.org/10.1186/s13073-022-01074-2 (2022).
doi: 10.1186/s13073-022-01074-2
pubmed: 35765100
pmcid: 9241245
Jung, H. U. et al. Gene-environment interaction explains a part of missing heritability in human body mass index. Commun. Biol. 6, 324. https://doi.org/10.1038/s42003-023-04679-4 (2023).
doi: 10.1038/s42003-023-04679-4
pubmed: 36966243
pmcid: 10039928
Manolio, T. A. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363, 166–176. https://doi.org/10.1056/NEJMra0905980 (2010).
doi: 10.1056/NEJMra0905980
pubmed: 20647212
Howard, V. J. et al. The reasons for geographic and racial differences in stroke study: Objectives and design. Neuroepidemiology 25, 135–143. https://doi.org/10.1159/000086678 (2005).
doi: 10.1159/000086678
pubmed: 15990444
Armstrong, N. D. et al. Genetic contributors of incident stroke in 10,700 African Americans With hypertension: A meta-analysis from the genetics of hypertension associated treatments and reasons for geographic and racial differences in stroke studies. Front. Genet. 12, 781451. https://doi.org/10.3389/fgene.2021.781451 (2021).
doi: 10.3389/fgene.2021.781451
pubmed: 34992631
pmcid: 8724550
Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287. https://doi.org/10.1038/ng.3656 (2016).
doi: 10.1038/ng.3656
pubmed: 27571263
pmcid: 5157836
Major cardiovascular events in hypertensive patients randomized to doxazosin vs chlorthalidone: the antihypertensive and lipid-lowering treatment to prevent heart attack trial (ALLHAT). ALLHAT Collaborative Research Group. JAMA 283, 1967–1975 (2000).
Arnett, D. K. et al. Pharmacogenetic approaches to hypertension therapy: Design and rationale for the genetics of hypertension associated treatment (GenHAT) study. Pharmacogenomics J. 2, 309–317. https://doi.org/10.1038/sj.tpj.6500113 (2002).
doi: 10.1038/sj.tpj.6500113
pubmed: 12439737
Consortium, e. Lessons learned from the eMERGE Network: balancing genomics in discovery and practice. HGG Adv 2, 100018, https://doi.org/10.1016/j.xhgg.2020.100018 (2021).
Stanaway, I. B. et al. The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype. Genet. Epidemiol. 43, 63–81. https://doi.org/10.1002/gepi.22167 (2019).
doi: 10.1002/gepi.22167
pubmed: 30298529
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283. https://doi.org/10.1038/ng.3643 (2016).
doi: 10.1038/ng.3643
pubmed: 27548312
pmcid: 5388176
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909. https://doi.org/10.1038/ng1847 (2006).
doi: 10.1038/ng1847
pubmed: 16862161
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. https://doi.org/10.1016/j.ajhg.2010.11.011 (2011).
doi: 10.1016/j.ajhg.2010.11.011
pubmed: 21167468
pmcid: 3014363
Evans, L. M. et al. Narrow-sense heritability estimation of complex traits using identity-by-descent information. Heredity (Edinb) 121, 616–630. https://doi.org/10.1038/s41437-018-0067-0 (2018).
doi: 10.1038/s41437-018-0067-0
pubmed: 29588506
Charles, B. A., Shriner, D. & Rotimi, C. N. Accounting for linkage disequilibrium in association analysis of diverse populations. Genet. Epidemiol. 38, 265–273. https://doi.org/10.1002/gepi.21788 (2014).
doi: 10.1002/gepi.21788
pubmed: 24464495
Speed, D., Hemani, G., Johnson, M. R. & Balding, D. J. Improved heritability estimation from genome-wide SNPs. Am. J. Hum. Genet. 91, 1011–1021. https://doi.org/10.1016/j.ajhg.2012.10.010 (2012).
doi: 10.1016/j.ajhg.2012.10.010
pubmed: 23217325
pmcid: 3516604
Ren, D. et al. Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation. Genet. Sel. Evol. 54, 47. https://doi.org/10.1186/s12711-022-00737-3 (2022).
doi: 10.1186/s12711-022-00737-3
pubmed: 35761182
pmcid: 9235212
Ma, Y. et al. Excess heritability contribution of alcohol consumption variants in the “Missing Heritability” of type 2 diabetes mellitus. Int. J. Mol. Sci. https://doi.org/10.3390/ijms222212318 (2021).
doi: 10.3390/ijms222212318
pubmed: 35008888
pmcid: 8745443
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. https://doi.org/10.1016/j.ajhg.2011.02.002 (2011).
doi: 10.1016/j.ajhg.2011.02.002
pubmed: 21376301
pmcid: 3059431
Wang, L. et al. Trends in prevalence of diabetes and control of risk factors in diabetes among US adults, 1999–2018. JAMA https://doi.org/10.1001/jama.2021.9883 (2021).
doi: 10.1001/jama.2021.9883
pubmed: 34962526
pmcid: 8715349