Acknowledgement
This work was supported by intramural grants from the National Institute of Health, Republic of Korea (2019-NI-097-02). Genotype data were provided by the Collaborative Genome Program for Fostering New Post-Genome Industry (3000-3031b).
References
- Claussnitzer M, Cho JH, Collins R, Cox NJ, Dermitzakis ET, Hurles ME, et al. A brief history of human disease genetics. Nature 2020;577:179-189. https://doi.org/10.1038/s41586-019-1879-7
- Flannick J, Florez JC. Type 2 diabetes: genetic data sharing to advance complex disease research. Nat Rev Genet 2016;17:535-549. https://doi.org/10.1038/nrg.2016.56
- Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS discovery: biology, function, and translation. Am J Hum Genet 2017;101:5-22. https://doi.org/10.1016/j.ajhg.2017.06.005
- Buniello A, MacArthur JA, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 2019;47:D1005-D1012. https://doi.org/10.1093/nar/gky1120
- Sirugo G, Williams SM, Tishkoff SA. The missing diversity in human genetic studies. Cell 2019;177:26-31. https://doi.org/10.1016/j.cell.2019.02.048
- Martin AR, Kanai M, Kamatani Y, Okada Y, Neale BM, Daly MJ. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 2019;51:584-591. https://doi.org/10.1038/s41588-019-0379-x
- Mahajan A, Taliun D, Thurner M, Robertson NR, Torres JM, Rayner NW, et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat Genet 2018;50:1505-1513. https://doi.org/10.1038/s41588-018-0241-6
- Spracklen CN, Horikoshi M, Kim YJ, Lin K, Bragg F, Moon S, et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature 2020;582:240-245. https://doi.org/10.1038/s41586-020-2263-3
- Peprah E, Xu H, Tekola-Ayele F, Royal CD. Genome-wide association studies in Africans and African Americans: expanding the framework of the genomics of human traits and disease. Public Health Genomics 2015;18:40-51. https://doi.org/10.1159/000367962
- Gan W, Walters RG, Holmes MV, Bragg F, Millwood IY, Banasik K, et al. Evaluation of type 2 diabetes genetic risk variants in Chinese adults: findings from 93,000 individuals from the China Kadoorie Biobank. Diabetologia 2016;59:1446-1457. https://doi.org/10.1007/s00125-016-3920-9
- Almawi WY, Nemr R, Keleshian SH, Echtay A, Saldanha FL, AlDoseri FA, et al. A replication study of 19 GWAS-validated type 2 diabetes at-risk variants in the Lebanese population. Diabetes Res Clin Pract 2013;102:117-122. https://doi.org/10.1016/j.diabres.2013.09.001
- Choi JY, Jang HM, Han S, Hwang MY, Kim BJ, Kim YJ. Recapitulation of previously reported associations for type 2 diabetes and metabolic traits in the 126K East Asians. Genomics Inform 2019;17:e48. https://doi.org/10.5808/GI.2019.17.4.e48
- Almgren P, Lehtovirta M, Isomaa B, Sarelin L, Taskinen MR, Lyssenko V, et al. Heritability and familiality of type 2 diabetes and related quantitative traits in the Botnia Study. Diabetologia 2011;54:2811-2819. https://doi.org/10.1007/s00125-011-2267-5
- Hou K, Burch KS, Majumdar A, Shi H, Mancuso N, Wu Y, et al. Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture. Nat Genet 2019;51:1244-1251. https://doi.org/10.1038/s41588-019-0465-0
- Kim Y, Han BG; KoGES group. Cohort profile: The Korean Genome and Epidemiology Study (KoGES) Consortium. Int J Epidemiol 2017;46:e20. https://doi.org/10.1093/ije/dyv316
- Johnson R, McNutt P, MacMahon S, Robson R. Use of the Friedewald formula to estimate LDL-cholesterol in patients with chronic renal failure on dialysis. Clin Chem 1997;43:2183-2184. https://doi.org/10.1093/clinchem/43.11.2183
- Moon S, Kim YJ, Han S, Hwang MY, Shin DM, Park MY, et al. The Korea Biobank Array: design and identification of coding variants associated with blood biochemical traits. Sci Rep 2019;9:1382. https://doi.org/10.1038/s41598-018-37832-9
- Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM. Robust relationship inference in genome-wide association studies. Bioinformatics 2010;26:2867-2873. https://doi.org/10.1093/bioinformatics/btq559
- Abraham G, Qiu Y, Inouye M. FlashPCA2: principal component analysis of Biobank-scale genotype datasets. Bioinformatics 2017;33:2776-2778. https://doi.org/10.1093/bioinformatics/btx299
- Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res 2002;12:996-1006. https://doi.org/10.1101/gr.229102. Article published online before print in May 2002
- 1000 Genomes Project Consortium; Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature 2015;526:68-74. https://doi.org/10.1038/nature15393
- Loh PR, Palamara PF, Price AL. Fast and accurate long-range phasing in a UK Biobank cohort. Nat Genet 2016;48:811-816. https://doi.org/10.1038/ng.3571
- Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 2018;562:203-209. https://doi.org/10.1038/s41586-018-0579-z
- Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J; Schizophrenia Working Group of the Psychiatric Genomics Consortium, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 2015;47:291-295. https://doi.org/10.1038/ng.3211
- Prive F, Arbel J, Vilhjalmsson BJ. LDpred2: better, faster, stronger. Bioinformatics 2020;36:5424-5431.
- Morris AP. Fine mapping of type 2 diabetes susceptibility loci. Curr Diab Rep 2014;14:549. https://doi.org/10.1007/s11892-014-0549-2
- McCarthy MI. The importance of global studies of the genetics of type 2 diabetes. Diabetes Metab J 2011;35:91-100. https://doi.org/10.4093/dmj.2011.35.2.91