Browsing by Author "Takeuchi, F."

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Association of genetic variants with non-alcoholic fatty liver disease in an urban Sri Lankan community
(Wiley-Blackwell, 2015) Kasturiratne, A.; Akiyama, K.; Niriella, M.A.; Takeuchi, F.; Isono, M.; Dassanayake, A.S.; de Silva, A.P.; Wickremasinghe, A.R.; Kato, N.; de Silva, H.J.
The Burden of diabetes mellitus and impaired fasting glucose in an urban population of Sri Lanka
(Wiley-Blackwell, 2013) Pinidiyapathirage, M.J.; Kasturiratne, A.; Ranawaka, U.K.; Gunasekara, D.; Wijekoon, N.; Medagoda, K.; Perera, S.; Takeuchi, F.; Kato, N.; Warnakulasuriya, T.; Wickremasinghe, A.R.
AIMS: To describe the burden of diabetes mellitus and impaired fasting glucose in middle-aged residents (35-64 years) in an urban area of Sri Lanka. METHODS: A cross-sectional survey was conducted in the Ragama Medical Officer of Health area, from which 2986 participants (1349 men and 1637 women) were randomly selected from the electoral registry between January and December 2007. The participants underwent a physical examination and had their height, weight, waist and hip circumferences and blood pressure measured by trained personnel. Fasting blood samples were taken for measurement of glucose, HbA(1c) and lipids. The prevalence of diabetes (fasting plasma glucose > 7 mmol/l) and impaired fasting glycaemia (fasting plasma glucose 5.6-6.9 mmol/l) and major predictors of diabetes in Sri Lanka were estimated from the population-based data. RESULTS: Age-adjusted prevalence of diabetes mellitus in this urban population was 20.3% in men and 19.8% in women. Through the present screening, 263 patients with diabetes and 1262 with impaired fasting glucose levels were identified. The prevalence of newly detected diabetes was 35.7% of all patients with diabetes. Among patients with diabetes, only 23.8% were optimally controlled. In the regression models, high BMI, high waist circumference, high blood pressure and hypercholesterolaemia increased the fasting plasma glucose concentration, independent of age, sex and a family history of diabetes. CONCLUSIONS: Our data demonstrate the heavy burden of diabetes in this urban population. Short- and long-term control strategies are required, not only for optimal therapy among those affected, but also for nationwide primary prevention of diabetes
The burden of diabetes mellitus in an urban population of Sri Lanka
(Sri Lanka Medical Association, 2011) Pinidiyapathirage, M.J.; Kasturiratne, A.; Williams, S.; Wijekoon, N.; Pathmeswaran, A.; Ranawaka, U.K.; Warnakulasuriya, T.; Takeuchi, F.; Kato, N.; Wickremasinghe, A.R.
INTRODUCTION AND OBJECTIVES: To describe the burden of diabetes in middle and old aged residents (35-64 years) in an urban area of Sri Lanka. METHODS: A cross-sectional survey was conducted in the Ragama Medical Officer of Health area, in which 2986 participants (1349 men and 1637 women) were randomly selected from the electoral registry between January and December 2007. The participants underwent a physical examination and had their height, weight, waist and hip circumferences and 51ood pressure measured by trained personnel. Blood samples were taken after a 14 hour fast for measurement of glucose, HbAlc and lipids. The prevalence of diabetes (fasting plasma glucose [FPG] >7mmol/L) and impaired fasting glycaemia [IFG] (FPG=5.6-6.9mmol/L) and major predictors of diabetes in Sri Lanka were estimated from the population based data. RESULTS: In the whole sample two thirds of the participants were diagnosed as either people with diabetes (20%) or IFG (45%). Among the diabetics only 23.8% were optimally controlled. Through the present screening, 235 diabetics (7.9%) and 1257 (42.1%) participants with impaired fasting glucose levels were newly identified. Old age (OR=5.1, 55-64 years vs. 35-44 years), male sex (OR=3.1), family history (OR- 2.7), central obesity (OR-1.8), and reduced physical activity (OR=1.3) were significantly associated with increased risk of diabetes. CONCLUSIONS: Our data demonstrate the heavy burden of diabetes in the general population. Short and long term control strategies are required not only for optimal-therapy among those affected but also for nationwide primary prevention of pre-diabetes.
Characterising private and shared signatures of positive selection in 37 Asian populations
(Nature Publishing Group, 2017) Liu, X.; Lu, D.; Saw, W.Y.; Wangkumhang, P.; Ngamphiw, C.; Fucharoen, S.; Lert-Itthiporn, W.; Chin-Inmanu, K.; Chau, T.N.; Anders, K.; Kasturiratne, A.; de Silva, H.J.; Katsuya, T.; Kimura, R.; Nabika, T.; Ohkubo, T.; Tabara, Y.; Takeuchi, F.; Yamamoto, K.; Yokota, M.; Mamatyusupu, D.; Yang, W.; Chung, Y.J.; Jin, L.; Hoh, B.P.; Wickremasinghe, A.R.; Ong, R.H.; Khor, C.C.; Dunstan, S.J.; Simmons, C.; Tongsima, S.; Suriyaphol, P.; Kato, N.; Xu, S.; Teo, Y.Y.
The Asian Diversity Project (ADP) assembled 37 cosmopolitan and ethnic minority populations in Asia that have been densely genotyped across over half a million markers to study patterns of genetic diversity and positive natural selection. We performed population structure analyses of the ADP populations and divided these populations into four major groups based on their genographic information. By applying a highly sensitive algorithm haploPS to locate genomic signatures of positive selection, 140 distinct genomic regions exhibiting evidence of positive selection in at least one population were identified. We examined the extent of signal sharing for regions that were selected in multiple populations and observed that populations clustered in a similar fashion to that of how the ancestry clades were phylogenetically defined. In particular, populations predominantly located in South Asia underwent considerably different adaptation as compared with populations from the other geographical regions. Signatures of positive selection present in multiple geographical regions were predicted to be older and have emerged prior to the separation of the populations in the different regions. In contrast, selection signals present in a single population group tended to be of lower frequencies and thus can be attributed to recent evolutionary events
Common variants at the GCK, GCKR, G6PC2-ABCB11 andMTNR1B loci are associated with fasting glucose in two Asian populations
(Springer-Verlag, 2010) Takeuchi, F.; Katsuya, T.; Chackrewarthy, S.; Yamamoto, K.; Fujioka, A.; Serizawa, M.; Fujisawa, T.; Nakashima, E.; Ohnaka, K.; Ikegami, H.; Sugiyama, T.; Nabika, T.; Kasturiratne, A.; Yamaguchi, S.; Kono, S.; Takayanagi, R.; Yamori, Y.; Kobayashi, S.; Ogihara, T.; de Silva, A.; Wickremasinghe, R.; Kato, N.
A Comparison between revised NCEP ATP III and IDF definitions in diagnosing metabolic syndrome in an urban Sri Lankan population: the Ragama Health Study
(Hindawi Publishing Corporation, 2013) Chackrewarthy, S.; Gunasekara, D.; Pathmeswaran, A.; Wijekoon, C.N.; Ranawaka, U.K.; Kato, N.; Takeuchi, F.; Wickremasinghe, A.R.
Background. The prevalence of metabolic syndrome (MetS) within individual cohorts varies with the definition used. The aim of this study was to compare the prevalence of MetS between IDF and revised NCEP ATP III criteria in an urban Sri Lankan population and to investigate the characteristics of discrepant cases. Methods. 2985 individuals, aged 35-65 years, were recruited to the study. Anthropometric and blood pressure measurements and laboratory investigations were carried out following standard protocols. Results. Age and sex-adjusted prevalences of MetS were 46.1% and 38.9% by revised NCEP and IDF definitions, respectively. IDF criteria failed to identify 21% of men and 7% of women identified by the revised NCEP criteria. The discrepant group had more adverse metabolic profiles despite having a lower waist circumference than those diagnosed by both criteria. Conclusion. MetS is common in this urban Sri Lankan cohort regardless of the definition used. The revised NCEP definition was more appropriate in identifying the metabolically abnormal but nonobese individuals, especially among the males predisposed to type 2 diabetes or cardiovascular disease. Further research is needed to determine the suitability of the currently accepted Asian-specific cut-offs for waist circumference in Sri Lankan adults. Copyright © 2013 S. Chackrewarthy et al
Comparison of urban diabetics with optimal and suboptimal control
(BMJ Publishing Group, 2011) Pinidiyapathirage, J.; Warnakulasuriya, T.; Kasturiratne, A.; Ranawaka, U.; Gunasekara, D.; Wijekoon, N.; Medagoda, K.; Perera, S.; Takeuchi, F.; Kato, N.; Wickremasinghe, A.R.
INTRODUCTION: The prevalence of Diabetes Mellitus in Sri Lanka is increasing. We describe the characteristics of patients with optimal and suboptimal control of diabetes among known diabetics in a 35–64-year-old urban population resident in the Ragama Medical Officer of Health (Ragama MOH) area of Sri Lanka. METHODS: A cross sectional study was conducted among 2986 randomly selected 35–64 year olds in the Ragama MOH area from January to September 2007. A detailed history was taken and participants were subjected to a physical examination and assay of fasting blood glucose and HbA1C. A HBA1C <6.5 was taken as evidence of optimal control. RESULTS: There were 474 persons (194 males and 280 females) who gave a past history of diabetes. 9 males and 9 females were not on any treatment. 27 persons (9 males and 18 females) were on insulin. Of the 474 diabetics, 113 (48 males and 65 females) had a HbA1c <6.5. The average fasting blood glucose of diabetics with optimal control was 120+21 mg/dl. The mean fasting blood glucose level of the 361 subjects with sub optimal control was 190+70 mg/dl. Optimal glycaemic control was not associated with alcohol intake, smoking, obesity, central obesity and low physical activity levels. CONCLUSIONS: Most known diabetics had access to treatment but only approximately 25% were optimally treated. The need to optimally manage these patients is highlighted.
Comparison of urban diabetics with optimal and suboptimal control
(British Medical Association, London, 2011) Pinidiyapathirage, M.; Warnakulasuriya, T.; Kasturiratne, A.; Ranawaka, U.; Gunasekera, D.; Wijekoon, N.; Medagoda, K.; Perera, S.; Takeuchi, F.; Kato, N.; Wickremasinghe, A.R.
Introduction The prevalence of Diabetes Mellitus in Sri Lanka is increasing. We describe the characteristics of patients with optimal and suboptimal control of diabetes among known diabetics in a 35–64-year-old urban population resident in the Ragama Medical Officer of Health (Ragama MOH) area of Sri Lanka. Methods A cross sectional study was conducted among 2986 randomly selected 35–64 year olds in the Ragama MOH area from January to September 2007. A detailed history was taken and participants were subjected to a physical examination and assay of fasting blood glucose and HbA1C. A HBA1C <6.5 was taken as evidence of optimal control. Results There were 474 persons (194 males and 280 females) who gave a past history of diabetes. 9 males and 9 females were not on any treatment. 27 persons (9 males and 18 females) were on insulin. Of the 474 diabetics, 113 (48 males and 65 females) had a HbA1c <6.5. The average fasting blood glucose of diabetics with optimal control was 120+21 mg/dl. The mean fasting blood glucose level of the 361 subjects with sub optimal control was 190+70 mg/dl. Optimal glycaemic control was not associated with alcohol intake, smoking, obesity, central obesity and low physical activity levels. Conclusions Most known diabetics had access to treatment but only approximately 25% were optimally treated. The need to optimally manage these patients is highlighted.
Gene-educational attainment interactions in a multi-ancestry genome-wide meta-analysis identify novel blood pressure loci
(Stockton Press., 2021) de Las Fuentes, L.; Sung, Y. J.; Noordam, R.; Winkler, T.; Feitosa, M.F.; Schwander, K.; Bentley, A.R.; Brown, M.R.; Guo, X.; Manning, A.; Chasman, D.I.; Aschard, H.; Bartz, T. M.; Bielak, L.F.; Campbell, A.; Cheng, C.Y.; Dorajoo, R.; Hartwig, F. P.; Horimoto, A.R.V.R.; Li, C.; Li-Gao, R.; Liu, Y.; Marten, J.; Musani, S.K.; Ntalla, I.; Rankinen, T.; Richard, M.; Sim, X.; Smith, A.V.; Tajuddin, S.M.; Tayo, B.O.; Vojinovic, D.; Warren, H.R.; Xuan, D.; Alver, M.; Boissel, M.; Chai, J.F.; Chen, X.; Christensen, K.; Divers, J.; Evangelou, E.; Gao, C.; Girotto, G.; Harris, S.E.; He, M.; Hsu, F.C.; Kühnel, B.; Laguzzi, F.; Li, X.; Lyytikäinen, L. P.; Nolte, I. M.; Poveda, A.; Rauramaa, R.; Riaz, M.; Rueedi, R.; Shu, X.O.; Snieder, H.; Sofer, T.; Takeuchi, F.; Verweij, N.; Ware, E.B.; Weiss, S.; Yanek, L.R.; Amin, N.; Arking, D.E.; Arnett, D.K.; Bergmann, S.; Boerwinkle, E.; Brody, J.A.; Broeckel, U.; Brumat, M.; Burke, G.; Cabrera, C.P.; Canouil, M.; Chee, M.L.; Chen, Y. I.; Cocca, M.; Connell, J.; de Silva, H.J.; de Vries, P. S.; Eiriksdottir, G.; Faul, J.D.; Fisher, V.; Forrester, T.; Fox, E.F.; Friedlander, Y.; Gao, H.; Gigante, B.; Giulianini, F.; Gu, C.C.; Gu, D.; Harris, T. B.; He, J.; Heikkinen, S.; Heng, C. K.; Hunt, S.; Ikram, M. A.; Irvin, M.R.; Kähönen, M.; Kavousi, M.; Khor, C.C.; Kilpeläinen, T.O.; Koh, W.P.; Komulainen, P.; Kraja, A.T.; Krieger, J.E.; Langefeld, C. D.; Li, Y.; Liang, J.; Liewald, D.C.M.; Liu, C.T.; Liu, J.; Lohman, K.K.; Mägi, R.; McKenzie, C.A.; Meitinger, T.; Metspalu, A.; Milaneschi, Y.; Milani, L.; Mook-Kanamori, D.O.; Nalls, M.A.; Nelson, C.P.; Norris, J. M.; O'Connell, J.; Ogunniyi, A.; Padmanabhan, S.; Palmer, N.D.; Pedersen, N. L.; Perls, T.; Peters, A.; Petersmann, A.; Peyser, P. A.; Polasek, O.; Porteous, D. J.; Raffel, L. J.; Rice, T. K.; Rotter, J.I.; Rudan, I.; Rueda-Ochoa, O.L.; Sabanayagam, C.; Salako, B. L.; Schreiner, P.J.; Shikany, J.M.; Sidney, S.S.; Sims, M.; Sitlani, C.M.; Smith, J. A.; Starr, J. M.; Strauch, K.; Swertz, M. A.; Teumer, A.; Tham, Y. C.; Uitterlinden, A.G.; Vaidya, D.; van der Ende, M.Y.; Waldenberger, M.; Wang, L.; Wang, Y. X.; Wei, W.B.; Weir, D.R.; Wen, W.; Yao, J.; Yu, B.; Yu, C.; Yuan, J. M.; Zhao, W.; Zonderman, A.B.; Becker, D.M.; Bowden, D.W.; Deary, I. J.; Dörr, M.; Esko, T.; Freedman, B. I.; Froguel, P.; Gasparini, P.; Gieger, C.; Jonas, J.B.; Kammerer, C.M.; Kato, N.; Lakka, T. A.; Leander, K.; Lehtimäki, T.; Lifelines Cohort Study; Magnusson, P. K. E.; Marques-Vidal, P.; Penninx, B. W. J. H.; Samani, N. J.; van der Harst, P.; Wagenknecht, L. E.; Wu, T.; Zheng, W.; Zhu, X.; Bouchard, C.; Cooper, R. S.; Correa, A.; Evans, M. K.; Gudnason, V.; Hayward, C.; Horta, B. L.; Kelly, T. N.; Kritchevsky, S. B.; Levy, D.; Palmas, W. R.; Pereira, A. C.; Province, M. M.; Psaty, B. M.; Ridker, P. M.; Rotimi, C. N.; Tai, E. S.; van Dam, R. M.; van Duijn, C. M.; Wong, T. Y.; Rice, K.; Gauderman, W. J.; Morrison, A. C.; North, K. E.; Kardia, S. L. R.; Caulfield, M. J.; Elliott, P.; Munroe, P. B.; Franks, P. W.; Rao, D. C.; Fornage, M.
ABSTRACT:Educational attainment is widely used as a surrogate for socioeconomic status (SES). Low SES is a risk factor for hypertension and high blood pressure (BP). To identify novel BP loci, we performed multi-ancestry meta-analyses accounting for gene-educational attainment interactions using two variables, "Some College" (yes/no) and "Graduated College" (yes/no). Interactions were evaluated using both a 1 degree of freedom (DF) interaction term and a 2DF joint test of genetic and interaction effects. Analyses were performed for systolic BP, diastolic BP, mean arterial pressure, and pulse pressure. We pursued genome-wide interrogation in Stage 1 studies (N = 117 438) and follow-up on promising variants in Stage 2 studies (N = 293 787) in five ancestry groups. Through combined meta-analyses of Stages 1 and 2, we identified 84 known and 18 novel BP loci at genome-wide significance level (P < 5 × 10-8). Two novel loci were identified based on the 1DF test of interaction with educational attainment, while the remaining 16 loci were identified through the 2DF joint test of genetic and interaction effects. Ten novel loci were identified in individuals of African ancestry. Several novel loci show strong biological plausibility since they involve physiologic systems implicated in BP regulation. They include genes involved in the central nervous system-adrenal signaling axis (ZDHHC17, CADPS, PIK3C2G), vascular structure and function (GNB3, CDON), and renal function (HAS2 and HAS2-AS1, SLIT3). Collectively, these findings suggest a role of educational attainment or SES in further dissection of the genetic architecture of BP.
Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci
(Frontiers Research Foundation, 2023) de Las, F.L.; Schwande, K.L.; Brown, M.R.; Bentley, A.R.; Winkler, T.W.; Sung, Y.J.; Munroe, P.B.; Miller, C.L.; Aschard, H.; Aslibekyan, S.; Bartz, T.M.; Bielak, L.F.; Chai, J.F.; Cheng, C.Y.; Dorajoo, R.; Feitosa, M.F.; Guo, X.; Hartwig, F.P.; Horimoto, A.; Kolčić, I.; Lim, E.; Liu, Y.; Manning, A.K.; Marten, J.; Musani, S.K.; Noordam, R.; Padmanabhan, S.; Rankinen, T.; Richard, M.A.; Ridker, P.M.; Smith, A.V.; Vojinovic, D.; Zonderman, A.B.; Alver, M.; Boissel, M.; Christensen, K.; Freedman, B.I.; Gao, C.; Giulianini, F.; Harris, S.E.; He, M.; Hsu, F.C.; Kühnel, B.; Laguzzi, F.; Li, X.; Lyytikäinen, L.P.; Nolte, I.M.; Poveda, A.; Rauramaa, R.; Riaz, M.; Robino, A.; Sofer, T.; Takeuchi, F.; Tayo, B.O.; van der, M.P.J.; Verweij, N.; Ware, E.B.; Weiss, S.; Wen, W.; Yanek, L.R.; Zhan, Y.; Amin, N.; Arking, D.E.; Ballantyne, C.; Boerwinkle, E.; Brody, J.A.; Broeckel, U.; Campbell, A.; Canouil, M.; Chai, X.; Chen, Y.I.; Chen, X.; Chitrala, K.N.; Concas, M.P.; de Faire, U.; de Mutsert, R.; de Silva, H.J.; de Vries, P.S.; Do, A.; Faul, J.D.; Fisher, V.; Floyd, J.S.; Forrester, T.; Friedlander, Y.; Girotto, G.; Gu, C.C.; Hallmans, G.; Heikkinen, S.; Heng, C.K.; Homuth, G.; Hunt, S.; Ikram, M.A.; Jacobs, D.R.J.R.; Kavousi, M.; Khor, C.C.; Kilpeläinen, T.O.; Koh, W.P.; Komulainen, P.; Langefeld, C.D.; Liang, J.; Liu, K.; Liu, J.; Lohman, K.; Mägi, R.; Manichaikul, A.W.; McKenzie, C.A.; Meitinger, T.; Milaneschi, Y.; Nauck, M.; Nelson, C.P.; O'Connell, J.R.; Palmer, N.D.; Pereira, A.C.; Perls, T.; Peters, A.; Polašek, O.; Raitakari, O.T.; Rice, K.; Rice, T.K.; Rich, S.S.; Sabanayagam, C.; Schreiner, P.J.; Shu, X.; Sidney, S.; Sims, M.; Smith, J.A.; Starr, J.M.; Strauch, K.; Tai, E.S.; Taylor, K.D.; Tsai, M.Y.; Uitterlinden, A.G.; Heemst, D.V.; Waldenberger, M.; Wang, Y.; Wei, W.; Wilson, G.; Xuan, D.; Yao, J.; Yu, C.; Yuan, J.; Zhao, W.; Becker, D.M.; Bonnefond, A.; Bowden, D.W.; Cooper, R.S.; Deary, I.J.; Divers, J.; Esko, T.; Franks, P.W.; Froguel, P.; Gieger, C.; Jonas, J.B.; Kato, N.; Lakka, T.A.; Leander, K.; Lehtimäki, T.; Magnusson, P.K.E.; North, K.E.; Ntalla, I.; Penninx, B.; Samani, N.J.; Snieder, H.; Spedicati, B.; Harst, P.V.D.; Völzke, H.; Wagenknecht, L.E.; Weir, D.R.; Wojczynski, M.K.; Wu, T.; Zheng, W.; Zhu, X.; Bouchard, C.; Chasman, D.I.; Evans, M.K.; Fox, E.R.; Gudnason, V.; Hayward, C.; Horta, B.L.; Kardia, S.L.R.; Krieger, J.E.; Mook-Kanamori, D.O.; Peyser, P.A.; Province, M.M.; Psaty, B.M.; Rudan, I.; Sim, X.; Smith, B.H.; Dam, R.M.V.; Duijn, C.M.V.; Wong, T.Y.; Arnett, D.K.; Rao, D.C.; Gauderman, J.; Liu, C.; Morrison, A.C.; Rotter, J.I.; Fornage, M.
INTRODUCTION: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. METHODS: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: "Some College" (yes/no, for any education beyond high school) and "Graduated College" (yes/no, for completing a 4-year college degree). Genome-wide significant (p < 5 × 10-8) and suggestive (p < 1 × 10-6) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). RESULTS: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. DISCUSSION: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.
Genetic drivers of heterogeneity in type 2 diabetes pathophysiology
(Nature Publishing Group, 2024) Suzuki, K.; Hatzikotoulas, K.; Southam, L.; Taylor, H.J.; Yin, X.; Lorenz, K.M.; Mandla, R.; Huerta-Chagoya, A.; Melloni, G.E.M.; Kanoni, S.; Rayner, N.W.; Bocher, O.; Arruda, A.L.; Sonehara, K.; Namba, S.; Namba, S.S.K.; Preuss, M.H.; Petty, L.E.; Schroeder, P.; Vanderwerff, B.; Kals, M.; Bragg. F.; Lin, K.; Guo, X.; Zhang, W.; Yao, J.; Kim, Y.J.; Graff, M.; Takeuchi, F.; Nano, J.; Lamri, A.; Nakatochi, M.; Moon, S.; Scott, R.A.; Cook, J.P.; Lee, J.J.; Pan, I.; Taliun, D.; Parra, E.J.; Chai. J.F.; Bielak, L.F.; Tabara, Y.; Hai, Y.; Thorleifsson, G.; Grarup, N.; Sofer, T.; Wuttke, M.; Sarnowski, C.; Gieger, C.; Nousome, D.; Trompet, S.; Kwak, S.H.; Long, J.; Sun, M.; Tong, L.; Chen, W.M.; Nongmaithem, S.S.; Noordam, R.; Lim, V.J.Y.; Tam, C.H.T.; Joo, Y.Y.; Chen, C.H.; Raffield, L.M.; Prins, B.P.; Nicolas, A.; Yanek, L.R.; Chen, G.; Brody, J.A.; Kabagambe, E.; An, P.; Xiang, A.H.; Choi, H.S.; Cade, B.E.; Tan, J.; Broadaway, K.A.; Williamson, A.; Kamali, Z.; Cui, J.; Thangam, M.; Adair, L.S.; Adeyemo, A.; Aguilar-Salinas, C.A.; Ahluwalia, T.S.; Anand, S.S.; Bertoni, A.; Bork-Jensen, J.; Brandslund, I.; Buchanan, T.A.; Burant, C.F.; Butterworth, A.S.; Canouil, M.; Chan, J.C.N.; Chang, L.C.; Chee, M.L.; Chen, J.; Chen, S.H.; Chen, Y.T.; Chen, Z.; Chuang, L.M.; Cushman, M.; Danesh, J.; Das, S.K.; de Silva, H.J.; Dedoussis, G.; Dimitrov, L.; Doumatey, A.P.; Du, S.; Duan, Q.; Eckardt, K.U.; Emery, L.S.; Evans, D.S.; Evans, M.K.; Fischer, K.; Floyd, J.S.; Ford, I.; Franco, O.H.; Frayling, T.M.; Freedman, B.I.; Genter, P.; Gerstein, H.C.; Giedraitis, V.; González-Villalpando, C.; González-Villalpando, M.E.; Gordon-Larsen, P.; Gross, M.; Guare, L.A.; Hackinger, S.; Hakaste, L.; Han, S.; Hattersley, A.T.; Herder, C.; Horikoshi, M.; Howard, A.; Hsueh, W.; Huang, M.; Huang, W.; Hung, Y.; Hwang, M.Y.; Hwu, C.; Ichihara, S.; Ikram, M.A.; Ingelsson, M.; Islam, M.T.; Isono, M.; Jang, H.; Jasmine, F.; Jiang, G.; Jonas, J.B.; Jørgensen, T.; Kamanu, F.K.; Kandeel, F.R.; Kasturiratne, A.; Katsuya, T.; Kaur, V.; Kawaguchi,T.; Keaton, J.M.; Kho, A.N.; Khor, C.; Kibriya, M.G.; Kim, D.; Kronenberg, F.; Kuusisto , J.; Läll, K.; Lange, L.A.; Lee, K.M.; Lee, M.; Lee, N.R.; Leong, A.; Li, L.; Li, Y.; Li-Gao, R.; Ligthart, S.; Lindgren, C.M.; Linneberg, A.; Liu, C.; Liu, J.; Locke, A.E.; Louie, T.; Luan, J.; Luk, A.O.; Luo, X.; Lv, J.; Lynch, J.A.; Lyssenko, V.; Maeda, S.; Mamakou, V.; Mansuri, S.R.; Matsuda, K.; Meitinger, T.; Melander, O.; Metspalu, A.; Mo, H.; Morris, A.D.; Moura, F.A.; Nadler, J.L.; Nalls, M.A.; Nayak, U.; Ntalla, I.; Okada, Y.; Orozco, L.; Patel, S.R.; Patil, S.; Pei, P.; Pereira, M.A.; Peters, A.; Pirie, F.J.; Polikowsky, H.G.; Porneala, B.; Prasad, G.; Rasmussen-Torvik, L.J.; Reiner, A.P.; Roden, M.; Rohde, R.; Roll, K.; Sabanayagam, C.; Sandow, K.; Sankareswaran , A.; Sattar,N.; Schönherr, S.; Shahriar, M.; Shen , B.; Shi, J.; Shin, D.M.; Shojima, N.; Smith, J.A.; So, W.Y.; Stančáková, A.; Steinthorsdottir, V.; Stilp, A.M.; Strauch, K.; Taylor, K.D.; Thorand, B.; Thorsteinsdottir, U.; Tomlinson, B.; Tran, T.C.; Tsai, F.; Tuomilehto, J.; Tusie-Luna, T.; Udler , M.S.; Valladares-Salgado, A.; Dam, R.M.V.; Klinken, J.B.V.; Varma, R.; Wacher-Rodarte, N.; Wheeler,E.; Wickremasinghe, A.R.; Dijk, K.W.V.; Witte, D.R.; Yajnik, C.S.; Yamamoto, K.; Yamamoto, K.; Yoon, K.; Yu, C.; Yuan, J.; Yusuf, S.; Zawistowski, M.; Zhang, L.; Zheng, W.; Raffel, L.J.; Igase, M.; Ipp, E.; Redline, S.; Cho, Y.S.; Lind, L.; Province, M.A.; Fornage, .M.; Hanis, C.L.; Ingelsson, E.; Zonderman, A.B.; Psaty, B.M.; Wang, Y.; Rotimi, C.N.; Becker,D.M.; Matsuda,F.; Liu, Y.; Yokota,M.; Kardia, S.L.R.; Peyser, P.A.; Pankow, J.S.; Engert, J.C.; Bonnefond, A.; Froguel, P.; Wilson, J.G.; Sheu, W.H.H.; Wu, J.; Hayes, M.G.; Ma, R.C.W.; Wong, T.; Mook-Kanamori, D.O.; Tuomi, T.; Chandak, G.R.; Collins, F.S.; Bharadwaj, D.; Paré, G.; Sale, M.M.; Ahsan, H.; Motala, A.A.; Shu , X.; Park, K.; Jukema, J.W.; Cruz, M.; Chen, Y.I.; Rich, S.S.; McKean-Cowdin, R.; Grallert, H.; Cheng, C.; Ghanbari,M.; Tai , E.; Dupuis, J.; Kato, N.; Laakso, M.; Köttgen, A.; Koh, W.; Bowden, D.W.; Palmer, C.N.A.; Kooner, J.S.; Kooperberg, C.; Liu, S.; North, K.E.; Saleheen, D.; Hansen, T.; Pedersen, O.; Wareham, N.J.; Lee, J.; Kim, B.; Millwood , I.Y.; Walters, R.G.; Stefansson, K.; Ahlqvist, E.; Goodarzi, M.O.; Mohlke, K.L.; Langenberg, C.; Haiman, C.A.; Loos, R.J.F.; Florez, J.C.; Rader, D.J.; Ritchie, M.D.; Zöllner, S.; Mägi, R.; Marston, N.A.; Ruff, C.T.; Heel , D.A.V.; Finer, S.; Denny, J.C.; Yamauchi, T.; Kadowaki, T.; Chambers, J.C.; Ng, M.C.Y.; Sim, X.; Below, J.E.; Tsao, P.S.; Chang, K.; McCarthy, M.I.; Meigs, J.B.; Mahajan, A.; Spracklen, C.N.; Mercader, J.M.; Boehnke, M.; Rotter, J.I.; Vujkovic, M.; Voight, B.F.; Morris, A.P.; Zeggini, E.
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.
Genetic variants of NAFLD in an urban Sri Lankan community
(Wiley Blackwell Scientific Publications, 2013) Niriella, M.A.; Kasturiratne, A.; Akiyama, K.; Takeuchi, F.; Isono, M.; Dassanayake, A.S.; de Silva, A.P.; Wickremasinghe, A.R.; Kato, N.; de Silva, H.J.
OBJECTIVE: Recently, genome-wide association studies (GWAS) have successfully identified loci associated with susceptibility to non-alcoholic fatty liver disease (NAFLD) in populations of European descent. No large-scale genetic studies have been performed thus far in South Asian populations. Therefore, as part of a community-based cohort study in an urban adult population of Sri Lankans, we investigated associations of genetic variants with NAFLD, diagnosed on established ultrasound criteria, and its related phenotypes. METHODS: We selected 10 single nucleotide polymorphisms (SNPs), all previously reported to be associated with NAFLD in populations of European and/or South Asian ancestry, for a case-control replication study. They included loci derived from GWAS [PNPLA3 (rs738409), LYPLAL1 (rs12137855), GCKR (rs780094), PPP1R3B (rs4240624) and NCAN (rs2228603)] plus those from candidate gene studies [APOC3 (rs2854117 and rs2854116), ADIPOR2 (rs767870) and STAT3 (rs6503695 and rs9891119)]. Genotype data of 2988 participants were used for the analysis. RESULTS: A significant NAFLD association was observed for PNPLA3 (rs738409) [OR = 1.25, 95% CI 1.08–1.44, P = 0.003)]; rs738409 was also associated with a trend towards lower serum triglycerides APOC3 variants were significantly (P = 7.3–7.5 × 10–8) associated with higher triglycerides, but not with NAFLD (OR = 0.86). Apart from SNP–lipid associations previously reported at the GCKR, PPP1R3B and NCAN loci, there were no other prominent associations. CONCLUSION: Our data confirm that the PNPLA3 gene variant is significantly associated with NAFLD in the general Sri Lankan population but could not replicate previously reported disease associations at other loci, reinforcing the importance of further large-scale study on genetic variants in diverse populations to better understand the pathophysiology of NAFLD.
Genome-wide association study in individuals of South Asian ancestry identifies six new type-2 diabetes susceptibility loci
(Nature Publishing Company, 2011) Kooner, J.S.; Saleheen, D.; Sim, X.; Sehmi, J.; Zhang, W.; Frossard, P.; Been, L.F.; Chia, K.S.; Dimas, A.S.; Hassanali, N.; Jafar, T.; Jowett, J.B.; Li, X.; Radha, V.; Rees, S.D.; Takeuchi, F.; Young, R.; Aung, T.; Basit, A.; Chidambaram, M.; Das, D.; Grundberg, E.; Hedman, A.K.; Hydrie, Z.I.; Islam, M.; Khor, C.C.; Kowlessur, S.; Kristensen, M.M.; Liju, S.; Lim, W.Y.; Matthews, D.R.; Liu, J.; Morris, A.P.; Nica, A.C.; Pinidiyapathirage, M.J.; Prokopenko, I.; Rasheed, A.; Samuel, M.; Shah, N.; Shera, A.S.; Small, K.S.; Suo, C.; Wickremasinghe, A.R.; Wong, T.Y.; Yang, M.; Zhang, F.
We carried out a genome-wide association study of type-2 diabetes (T2D) in individuals of South Asian ancestry. Our discovery set included 5,561 individuals with T2D (cases) and 14,458 controls drawn from studies in London, Pakistan and Singapore. We identified 20 independent SNPs associated with T2D at P < 10(-4) for testing in a replication sample of 13,170 cases and 25,398 controls, also all of South Asian ancestry. In the combined analysis, we identified common genetic variants at six loci (GRB14, ST6GAL1, VPS26A, HMG20A, AP3S2 and HNF4A) newly associated with T2D (P = 4.1 × 10(-8) to P = 1.9 × 10(-11)). SNPs at GRB14 were also associated with insulin sensitivity (P = 5.0 × 10(-4)), and SNPs at ST6GAL1 and HNF4A were also associated with pancreatic beta-cell function (P = 0.02 and P = 0.001, respectively). Our findings provide additional insight into mechanisms underlying T2D and show the potential for new discovery from genetic association studies in South Asians, a population with increased susceptibility to T2D.
Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture oftype 2 diabetes susceptibility
(Nature Publishing Company, 2014) Mahajan, A.; Go, M.J.; Zhang, W.; Below, J.E.; Gaulton, K.J.; Ferreira, T.; Horikoshi, M.; Johnson, A.D.; Ng, M.C.; Prokopenko, I.; Saleheen, D.; Wang, X.; Zeggini, E.; Abecasis, G.R.; Adair, L.S.; Almgren, P.; Atalay, M.; Aung, T.; Baldassarre, D.; Balkau, B.; Bao, Y.; Barnett, A.H.; Barroso, I.; Basit, A.; Been, L.F.; Beilby, J.; Bell, G.I.; Benediktsson, R.; Bergman, R.N.; Boehm, B.O.; Boerwinkle, E.; Bonnycastle, L.L.; Burtt, N.; Cai, Q.; Campbell, H.; Carey, J.; Cauchi, S.; Caulfield, M.; Chan, J.C.; Chang, L.C.; Chang, T.J.; Chang, Y.C.; Charpentier, G.; Chen, C.H.; Chen, H.; Chen, Y.T.; Chia, K.S.; Chidambaram, M.; Chines, P.S.; Cho, N.H.; Cho, Y.M.; Chuang, L.M.; Collins, F.S.; Cornelis, M.C.; Couper, D.J.; Crenshaw, A.T.; van Dam, R.M.; Danesh, J.; Das, D.; de Faire, U.; Dedoussis, G.; Deloukas, P.; Dimas, A.S.; Dina, C.; Doney, A.S.; Donnelly, P.J.; Dorkhan, M.; van Duijn, C.; Dupuis, J.; Edkins, S.; Elliott, P.; Emilsson, V.; Erbel, R.; Eriksson, J.G.; Escobedo, J.; Esko, T.; Eury, E.; Florez, J.C.; Fontanillas, P.; Forouhi, N.G.; Forsen, T.; Fox, C.; Fraser, R.M.; Frayling, T.M.; Froguel, P.; Frossard, P.; Gao, Y.; Gertow, K.; Gieger, C.; Gigante, B.; Grallert, H.; Grant, G.B.; Grrop, L.C.; Groves, C.J.; Grundberg, E.; Guiducci, C.; Hamsten, A.; Han, B.G.; Hara, K.; Hassanali, N.; Hattersley, A.T.; Hayward, C.; Hedman, A.K.; Herder, C.; Hofman, A.; Holmen, O.L.; Hovingh, K.; Hreidarsson, A.B.; Hu, C.; Hu, F.B.; Hui, J.; Humphries, S.E.; Hunt, S.E.; Hunter, D.J.; Hveem, K.; Hydrie, Z.I.; Ikegami, H.; Illig, T.; Ingelsson, E.; Islam, M.; Isomaa, B.; Jackson, A.U.; Jafar, T.; James, A.; Jia, W.; Jöckel, K.H.; Jonsson, A.; Jowett, J.B.; Kadowaki, T.; Kang, H.M.; Kanoni, S.; Kao, W.H.; Kathiresan, S.; Kato, N.; Katulanda, P.; Keinanen-Kiukaanniemi, K.M.; Kelly, A.M.; Khan, H.; Khaw, K.T.; Khor, C.C.; Kim, H.L.; Kim, S.; Kim, Y.J.; Kinnunen, L.; Klopp, N.; Kong, A.; Korpi-Hyövälti, E.; Kowlessur, S.; Kraft, P.; Kravic, J.; Kristensen, M.M.; Krithika, S.; Kumar, A.; Kumate, J.; Kuusisto, J.; Kwak, S.H.; Laakso, M.; Lagou, V.; Lakka, T.A.; Langenberg, C.; Langford, C.; Lawrence, R.; Leander, K.; Lee, J.M.; Lee, N.R.; Li, M.; Li, X.; Li, Y.; Liang, J.; Liju, S.; Lim, W.Y.; Lind, L.; Lindgren, C.M.; Lindholm, E.; Liu, C.T.; Liu, J.J.; Lobbens, S.; Long, J.; Loos, R.J.; Lu, W.; Luan, J.; Lyssenko, V.; Ma, R.C.; Maeda, S.; Mägi, R.; Männisto, S.; Matthews, D.R.; Meigs, J.B.; Melander, O.; Metspalu, A.; Meyer, J.; Mirza, G.; Mihailov, E.; Moebus, S.; Mohan, V.; Mohlke, K.L.; Morris, A.D.; Mühleisen, T.W.; Müller-Nurasyid, M.; Musk, B.; Nakamura, J.; Nakashima, E.; Navarro, P.; Ng, P.K.; Nica, A.C.; Nilsson, P.M.; Njolstad, I.; Nöthen, M.M.; Ohnaka, K.; Ong, T.H.; Owen, K.R.; Palmer, C.N.; Pankow, J.S.; Park, K.S.; Parkin, M.; Pechlivanis, S.; Pedersen, N.L.; Peltonen, L.; Perry, J.R.; Peters, A.; Pinidiyapathirage, J.M.; Platou, C.G.; Potter, S.; Price, J.F.; Qi, L.; Radha, V.; Rallidis, L.; Rasheed, A.; Rathman, W.; Rauramaa, R.; Raychaudhuri, S.; Rayner, N.W.; Rees, S.D.; Rehnberg, E.; Ripatti, S.; Robertson, N.; Roden, M.; Rossin, E.J.; Rudan, I.; Rybin, D.; Saaristo, T.E.; Salomaa, V.; Saltevo, J.; Samuel, M.; Sanghera, D.K.; Saramies, J.; Scott, J.; Scott, L.J.; Scott, R.A.; Segrè, A.V.; Sehmi, J.; Sennblad, B.; Shah, N.; Shah, S.; Shera, A.S.; Shu, X.O.; Shuldiner, A.R.; Sigurdsson, G.; Sijbrands, E.; Silveira, A.; Sim, X.; Sivapalaratnam, S.; Small, K.S.; So, W.Y.; Stancáková, A.; Stefansson, K.; Steinbach, G.; Steinthorsdottir, V.; Stirrups, K.; Strawbridge, R.J.; Stringham, H.M.; Sun, Q.; Suo, C.; Syvänen, A.C.; Takayanagi, R.; Takeuchi, F.; Tay, W.T.; Teslovich, T.M.; Thorand, B.; Thorleifsson, G.; Thorsteinsdottir, U.; Tikkanen, E.; Trakalo, J.; Tremoli, E.; Trip, M.D.; Tsai, F.J.; Tuomi, T.; Tuomilehto, J.; Uitterlinden, A.G.; Valladares-Salgado, A.; Vedantam, S.; Veglia, F.; Voight, B.F.; Wang, C.; Wareham, N.J.; Wennauer, R.; Wickremasinghe, A.R.; Wilsgaard, T.; Wilson, J.F.; Wiltshire, S.; Winckler, W.; Wong, T.Y.; Wood, A.R.; Wu, J.Y.; Wu, Y.; Yamamoto, K.; Yamauchi, T.; Yang, M.; Yengo, L.; Yokota, M.; Young, R.; Zabaneh, D.; Zhang, F.; Zhang, R.; Zheng., W.; Zimmet, P.Z.; Altshuler, D.; Bowden, D.W.; Cho, Y.S.; Cox, N.J.; Cruz, M.; Hanis, C.L.; Kooner, J.; Lee, J.Y.; Seielstad, M.; Teo, Y.Y.; Boehnke, M.; Parra, E.J.; Chambers, J.C.; Tai, E.S.; McCarthy, M.I.; Morris, A.P.
To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.
Identification of genetic effects underlying type 2 diabetes in South Asian and European populations
(Nature Publishing Group UK, 2022) Loh, M.; Zhang, W.; Ng, H.K.; Schmid, K.; Lamri, A.; Tong, L.; Ahmad, M.; Lee, J.J.; Ng, M.C.Y.; Petty, L.E.; Spracklen, C.N.; Takeuchi, F.; Islam, M.T.; Jasmine, F.; Kasturiratne, A.; Kibriya, M.; Mohlke, K.L.; Paré, G.; Prasad, G.; Shahriar, M.; Chee, M.L.; de Silva, H.J.; Engert, J.C.; Gerstein, H.C.; Mani, K.R.; Sabanayagam, C.; Vujkovic, M.; Wickremasinghe, A.R.; Wong, T.Y.; Yajnik, C.S.; Yusuf, S.; Ahsan, H.; Bharadwaj, D.; Anand, S.S.; Below, J.E.; Boehnke, M.; Bowden, D.W.; Chandak, G.R.; Cheng, C.Y.; Kato, N.; Mahajan, A.; Sim, X.; McCarthy, M.I.; Morris, A.P.; Kooner, J.S.; Saleheen, D.; Chambers, J.C.
South Asians are at high risk of developing type 2 diabetes (T2D). We carried out a genome-wide association meta-analysis with South Asian T2D cases (n = 16,677) and controls (n = 33,856), followed by combined analyses with Europeans (neff = 231,420). We identify 21 novel genetic loci for significant association with T2D (P = 4.7 × 10-8 to 5.2 × 10-12), to the best of our knowledge at the point of analysis. The loci are enriched for regulatory features, including DNA methylation and gene expression in relevant tissues, and highlight CHMP4B, PDHB, LRIG1 and other genes linked to adiposity and glucose metabolism. A polygenic risk score based on South Asian-derived summary statistics shows ~4-fold higher risk for T2D between the top and bottom quartile. Our results provide further insights into the genetic mechanisms underlying T2D, and highlight the opportunities for discovery from joint analysis of data from across ancestral populations.
Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis
(BioMed Central Ltd, 2022) Kanoni, S.; Graham, S.E.; Wang, Y.; Surakka, I.; Ramdas, S.; Zhu, X.; Clarke, S.L.; Bhatti, K.F.; Vedantam, S.; Winkler, T.W.; Locke, A.E.; Marouli, E.; Zajac, G.J.M.; Wu, K.H.; Ntalla, I.; Hui, Q.; Klarin, D.; Hilliard, A.T.; Wang, Z.; Xue, C.; Thorleifsson, G.; Helgadottir, A.; Gudbjartsson, D.F.; Holm, H.; Olafsson, I.; Hwang, M.Y.; Han, S.; Akiyama, M.; Sakaue, S.; Terao, C.; Kanai, M.; Zhou, W.; Brumpton, B.M.; Rasheed, H.; Havulinna, A.S.; Veturi, Y.; Pacheco, J.A.; Rosenthal, E.A.; Lingren, T.; Feng, Q.; Kullo, I.J.; Narita, A.; Takayama, J.; Martin, H.C.; Hunt, K.A.; Trivedi, B.; Haessler, J.; Giulianini, F.; Bradford, Y.; Miller, J.E.; Campbell, A.; Lin, K.; Lin, K.; Millwood, I.Y.; Rasheed, A.; Hindy, G.; Faul, J.D.; Zhao, W.; Weir, D.R.; Turman, C.; Huang, H.; Graff, M.; Choudhury, A.; Sengupta, D.; Mahajan, A.; Brown, M.R.; Zhang, W.; Yu, K.; Schmidt, E.M.; Pandit, A.; Gustafsson, S.; Yin, X.; Luan, J.; Zhao, J.H.; Matsuda, F.; Jang, H.M.; Yoon, K.; Medina-Gomez, C.; Pitsillides, A.; Hottenga, J.J.; Wood, A.R.; Ji, Y.; Gao, Z.; Haworth, S.; Yousri, N.A.; Mitchell, R.E.; Chai, J.F.; Aadahl, M.; Bjerregaard, A.A.; Yao, J.; Manichaikul, A.; Hwu, C.M.; Hung, Y.J.; Warren, H.R.; Ramirez, J.; Bork-Jensen, J.; Kårhus, L.L.; Goel, A.; Sabater-Lleal, M.; Noordam, R.; Mauro, P.; Matteo, F.; McDaid, A.F.; Marques-Vidal, P.; Wielscher, M.; Trompet, S.; Sattar, N.; Møllehave, L.T.; Munz, M.; Zeng, L.; Huang, J.; Yang, B.; Poveda, A.; Kurbasic, A.; Lamina, C.; Forer, L.; Scholz, M.; Galesloot, T.E.; Bradfield, J.P.; Ruotsalainen, S.E.; Daw, E.; Zmuda, J.M.; Mitchell, J.S.; Fuchsberger, C.; Christensen, H.; Brody, J.A.; Vazquez-Moreno, M.; Feitosa, M.F.; Wojczynski, M.K.; Wang, Z.; Preuss, M.H.; Mangino, M.; Christofidou, P.; Verweij, N.; Benjamins, J.W.; Engmann, J.; Tsao, N.L.; Verma, A.; Slieker, R.C.; Lo, K.S.; Zilhao, N.R.; Le, P.; Kleber, M.E.; Delgado, G.E.; Huo, S.; Ikeda, D.D.; Iha, H.; Yang, J.; Liu, J.; Demirkan, A.; Leonard, H.L.; Marten, J.; Frank, M.; Schmidt, B.; Smyth, L.J.; Cañadas-Garre, M.; Wang, C.; Nakatochi, M.; Wong, A.; Hutri-Kähönen, N.; Lyssenko, V.; Fernandez-Lopez, J.C.; Huerta-Chagoya, A.; Xia, R.; Sim, X.; Nongmaithem, S.S.; Bayyana, S.; Stringham, H.M.; Irvin, M.R.; Oldmeadow, C.; Kim, H.N.; Ryu, S.; Timmers, P,R,H,J,; Arbeeva, L.; Dorajoo, R.; Lange, L.A.; Prasad, G.; Lorés-Motta, L.; Pauper, M.; Long, J.; Li, X.; Theusch, E.; Takeuchi, F.; Spracklen, C.N.; Loukola, A.; Bollepalli, S.; Warner, S.C.; Wang, Y.X.; Wei, W.B.; Nutile, T.; Ruggiero, D.; Sung, Y.J.; Chen, S.; Liu, F.; Yang, J.; Kentistou, K.A.; Banas, B.; Nardone, G.G.; Meidtner, K.; Bielak, L.F.; Smith, J.A.; Hebbar, P.; Farmaki, A.E.; Hofer, E.; Lin, M.; Concas, M.P.; Vaccargiu, S.; van der Most, P.J.; Pitkänen, N.; Cade, B.E.; van der Laan, S.W.; Chitrala, K.N.; Weiss, S.; Bentley, A.R.; Doumatey, A.P.; Adeyemo, A.A.; Lee, J.Y.; Petersen, E.R.B.; Nielsen, A.A.; Choi, H.S.; Nethander, M.; Freitag-Wolf, S.; Southam, L.; Rayner, N.W.; Wang, C.A.; Lin, S.Y.; Wang, J.S.; Couture, C.; Lyytikäinen, L.P.; Nikus, K.; Cuellar-Partida, G.; Vestergaard, H.; Hidalgo, B.; Giannakopoulou, O.; Cai, Q.; Obura, M.O.; van Setten, J.; Li, X.; Liang, J.; Tang, H.; Terzikhan, N.; Shin, J.H.; Jackson, R.D.; Reiner, A.P.; Martin, L.W.; Chen, Z.; Li, L.; Kawaguchi, T.; Thiery, J.; Bis, J.C.; Launer, L.J.; Li, H.; Nalls, M.A.; Raitakari, O.T.; Ichihara, S.; Wild, S.H.; Nelson, C.P.; Campbell, H.; Jäger, S.; Nabika, T.; Al-Mulla, F.; Niinikoski, H.; Braund, P.S.; Kolcic, I.; Kovacs, P.; Giardoglou, T.; Katsuya, T.; de Kleijn, D.; de Borst, G.J.; Kim, E.K.; Adams, H.H.H.; Ikram, M.A.; Zhu, X.; Asselbergs, F.W.; Kraaijeveld, A.O.; Beulens, J.W.J.; Shu, X.O.; Rallidis, L.S.; Pedersen, O.; Hansen, T.; Mitchell, P.; Hewitt, A.W.; Kähönen, M.; Pérusse, L.; Bouchard, C.; Tönjes, A.; Chen, Y.I.; Pennell, C.E.; Mori, T.A.; Lieb, W.; Franke, A.; Ohlsson, C.; Mellström, D.; Cho, Y.S.; Lee, H.; Yuan, J.M.; Koh, W.P.; Rhee, S.Y.; Woo, J.T.; Heid, I.M.; Stark, K.J.; Zimmermann, M.E.; Völzke, H.; Homuth, G.; Evans, M.K.; Zonderman, A.B.; Polasek, O.; Pasterkamp, G.; Hoefer, I.E.; Redline, S.; Pahkala, K.; Oldehinkel, A.J.; Snieder, H.; Biino, G.; Schmidt, R.; Schmidt, H.; Bandinelli, S.; Dedoussis, G.; Thanaraj, T.A.; Kardia, S.L.R.; Peyser, P.A.; Kato, N.; Schulze, M.B.; Girotto, G.; Böger, C.A.; Jung, B.; Joshi, P.K.; Bennett, D.A.; de Jager, P.L.; Lu, X.; Mamakou, V.; Brown, M.; Caulfield, M.J.; Munroe, P.B.; Guo, X.; Ciullo, M.; Jonas, J.B.; Samani, N.J.; Kaprio, J.; Pajukanta, P.; Tusié-Luna, T.; Aguilar-Salinas, C.A.; Adair, L.S.; Bechayda, S.A.; de Silva, H.J.; Wickremasinghe, A.R.; Krauss, R.M.; Wu, J.Y.; Zheng, W.; Hollander, A.I.; Bharadwaj, D.; Correa, A.; Wilson, J.G.; Lind, L.; Heng, C.K.; Nelson, A.E.; Golightly, Y.M.; Wilson, J.F.; Penninx, B.; Kim, H.L.; Attia, J.; Scott, R.J.; Rao, D.C.; Arnett, D.K.; Hunt, S.C.; Walker, M.; Koistinen, H.A.; Chandak, G.R.; Mercader, J.M.; Costanzo, M.C.; Jang, D.; Burtt, N.P.; Villalpando, C.G.; Orozco, L.; Fornage, M.; Tai, E.; van Dam, R.M.; Lehtimäki, T.; Chaturvedi, N.; Yokota, M.; Liu, J.; Reilly, D.F.; McKnight, A.J.; Kee, F.; Jöckel, K.H.; McCarthy, M.I.; Palmer, C.N.A.; Vitart, V.; Hayward, C.; Simonsick, E.; van Duijn, C.M.; Jin, Z.B.; Qu, J.; Hishigaki, H.; Lin, X.; März, W.; Gudnason, V.; Tardif, J.C.; Lettre, G.; Hart, L.M.; Elders, P.J.M.; Damrauer, S.M.; Kumari, M.; Kivimaki, M.; van der Harst, P.; Spector, T.D.; Loos, R.J.F.; Province, M.A.; Parra, E.J.; Cruz, M.; Psaty, B.M.; Brandslund, I.; Pramstaller, P.P.; Rotimi, C.N.; Christensen, K.; Ripatti, S.; Widén, E.; Hakonarson, H.; Grant, S.F.A.; Kiemeney, L.A.L.M.; de Graaf, J.; Loeffler, M.; Kronenberg, F.; Gu, D.; Erdmann, J.; Schunkert, H.; Franks, P.W.; Linneberg, A.; Jukema, J.W.; Khera, A.V.; Männikkö, M.; Jarvelin, M.R.; Kutalik, Z.; Francesco, C.; Mook-Kanamori, D.O.; van Dijk, K.W.; Watkins, H.; Strachan, D.P.; Grarup, N.; Sever, P.; Poulter, N.; Chuang, L.M.; Rotter, J.I.; Dantoft, T.M.; Karpe, F.; Neville, M.J.; Timpson, N.J.; Cheng, C.Y.; Wong, T.Y.; Khor, C.C.; Li, H.; Sabanayagam, C.; Sabanayagam, C.; Peters, A.; Gieger, C.; Hattersley, A.T.; Pedersen, N.L.; Magnusson, P.K.E.; Boomsma, D.I.; Willemsen, A.H.M.; Cupples, L.; van Meurs, J.B.J.; Ghanbari, M.; Gordon-Larsen, P.; Huang, W.; Kim, Y.J.; Tabara, Y.; Wareham, N.J.; Langenberg, C.; Zeggini, E.; Kuusisto, J.; Laakso, M.; Ingelsson, E.; Abecasis, G.; Chambers, J.C.; Kooner, J.S.; de Vries, P.S.; Morrison, A.C.; Hazelhurst, S.; Ramsay, M.; North, K.E.; Daviglus, M.; Kraft, P.; Martin, N.G.; Whitfield, J.B.; Abbas, S.; Saleheen, D.; Walters, R.G.; Holmes, M.V.; Black, C.; Smith, B.H.; Baras, A.; Justice, A.E.; Buring, J.E.; Ridker, P.M.; Chasman, D.I.; Kooperberg, C.; Tamiya, G.; Yamamoto, M.; van Heel, D.A.; Trembath, R.C.; Wei, W.Q.; Jarvik, G.P.; Namjou, B.; Hayes, M.G.; Ritchie, M.D.; Jousilahti, P.; Salomaa, V.; Hveem, K.; Åsvold, B.O.; Kubo, M.; Kamatani, Y.; Okada, Y.; Murakami, Y.; Kim, B.J.; Thorsteinsdottir, U.; Stefansson, K.; Zhang, J.; Chen, Y.; Ho, Y.L.; Lynch, J.A.; Rader, D.J.; Tsao, P.S.; Chang, K.M.; Cho, K.; O'Donnell, C.J.; Gaziano, J.M.; Wilson P.W.F.; Frayling, T.M.; Hirschhorn, J.N.; Kathiresan, S.; Mohlke, K.L.; Sun, Y.V.; Morris, A.P.; Boehnke, M.; Brown, C.D.; Natarajan, P.; Deloukas, P.; Willer, C.J.; Assimes, T.L.; Peloso, G.M.
BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
Incidence and risk factors for non-alcoholic fatty liver disease: A 7-year follow-up study among urban, adult Sri Lankans
(Blackwell Munksgaard, 2017) Niriella, M.A.; Pathmeswaran, A.; de Silva, S.T.; Kasturiratne, A.; Perera, R.; Subasinghe, C.E.; Kodisinghe, K.; Piyaratna, C.; Rishikesawan, V.; Dassanayake, A.S.; de Silva, A.P.; Wickremasinghe, R.; Takeuchi, F.; Kato, N.; de Silva, H.J.
BACKGROUND: This study investigated incidence and risk factors for NAFLD among an adult cohort with 7-year follow-up. METHODS: The study population (age-stratified random sampling, Ragama MOH area) was screened initially in 2007 (aged 35-64 years) and re-evaluated in 2014 (aged 42-71 years). On both occasions assessed by structured interview, anthropometric measurements, liver ultrasound, biochemical and serological tests. NAFLD was diagnosed on ultrasound criteria, safe alcohol consumption and absence of hepatitis B/C markers. Non-NAFLD controls did not have any ultrasound criteria for NAFLD. An updated case-control genetic association study for 10 selected genetic variants and NAFLD was also performed. RESULTS: Out of 2985 of the original cohort, 2148 (72.0%) attended follow-up (1238 [57.6%] women; mean-age 59.2 [SD-7.6] years) in 2014, when 1320 (61.5%) were deemed NAFLD subjects. Out of 778 who initially did not have NAFLD and were not heavy drinkers throughout follow-up, 338 (43.4%) (221 [65.4%] women, mean-age 57.8 [SD-8.0] years) had developed NAFLD after 7-years (annual incidence-6.2%). Central obesity (OR=3.82 [95%-CI 2.09-6.99]), waist increase >5% (OR=2.46 [95%-CI 1.20-5.05]) overweight (OR=3.26 [95%-CI 1.90-5.60]), weight gain 5%-10% (OR=5.70 [95%-CI 2.61-12.47]), weight gain >10% (OR=16.94 [95%-CI 6.88-41.73]), raised plasma triglycerides (OR=1.96 [95%-CI 1.16-3.29]) and diabetes (OR=2.14 [95%-CI 1.13-4.06]), independently predicted the development of incident NAFLD in multivariate analysis. The updated genetic association study (1362-cases, 392-controls) showed replicated association (P=.045, 1-tailed) with NAFLD at a candidate locus: PNPLA3 (rs738409). CONCLUSIONS: In this community cohort study, the annual incidence of NAFLD was 6.2%. Incident NAFLD was associated with general and central obesity, raised triglycerides and diabetes, and showed a tendency of association with PNPLA3 gene polymorphisms.
A large-scale multi-ancestry genome-wide study accounting for smoking behavior identifies multiple significant loci for blood pressure
(University of Chicago Press, 2018) Sung, Y.J.; Winkler, T.W.; de Las Fuentes, L.; Bentley, A.R.; Brown, M.R.; Kraja, A.T.; Schwander, K.; Ntalla, I.; Guo, X.; Franceschini, N.; Lu, Y.; Cheng, C.Y.; Sim, X.; Vojinovic, D.; Marten, J.; Musani, S.K.; Li, C.; Feitosa, M.F.; Kilpelainen, T.O.; Richard, M.A.; Noordam, R.; Aslibekyan, S.; Aschard, H.; Bartz, T.M.; Dorajoo, R.; Liu, Y.; Manning, A.K.; Rankinen, T.; Smith, A.V.; Tajuddin, S.M.; Tayo, B.O.; Warren, H.R.; Zhao, W.; Zhou, Y.; Matoba, N.; Sofer, T.; Alver, M.; Amini, M.; Boissel, M.; Chai, J.F.; Chen, X.; Divers, J.; Gandin, I.; Gao, C.; Giulianini, F.; Goel, A.; Harris, S.E.; Hatwig, F.P.; Horimoto, A.R.V.R.; Hsu, F.C.; Jackson, A.U.; Kahonen, M.; Kasturiratne, A.; Kuhnel, B.; Leander, K.; Lee, W.J.; Lin, K.H.; an Luan, J.; McKenzie, C.A.; Meian, H.; Nelson, C.P.; Rauramaa, R.; Schupf, N.; Scott, R.A.; Sheu, W.H.H.; Stancakova, A.; Takeuchi, F.; van der Most, P.J.; Varga, T.V.; Wang, H.; Wang, Y.; Ware, E.B.; Weiss, S.; Wen, W.; Yanek, L.R.; Zhang, W.; Zhao, J.H.; Afag, S.; Alfred, T.; Amin, N.; Arking, D.; Aung, T.; Barr, R.G.; Bielak, L.F.; Boerwincle, E.; Bottinger, E.P.; Braund, P.S.; Brody, J.A.; Broeckel, U.; Cabrera, C.P.; Cade, B.; Caizheng, Y.; Campbell, A.; Canouil, M.; Chakravarti, A.; CHARGE Neurology Working Group; Chauhan, G.; Christensen, K.; Cocca, M.; COGENT-Kidney Consortium; Collins, F.S.; Connel, J.M.; de Mutsert, R.; de Silva, H.J.; Debette, S.; Dorr, M.; Duan, Q.; Eaton, C.B.; Ehret, G.; Evangelou, E.; FAul, J.D.; Fisher, V.A.; Forouhi, N.G.; Franco, O.H.; Friedlander, Y.; Gao, H.; GIANT Consortium; Gigante, B.; Graff, M.; Gu, C.C.; Gu, D.; Gupta, P.; Hagenaars, S.P.; Harris, T.B.; He, J.; Heikkinen, S.; Heng, C.K.; Hirata, M.; Hofman., A.; Howard, B.V.; Hunt, S.; Irvin, M.R.; Jia, Y.; Joehanes, R.; Justice, A.E.; Katsuya, T.; Kaufman, J,; Kerrison, N.D.; Khor, C.C.; Koh, W.P.; Koistinen, H.A.; Komulainen, P.; Kooperberg, C.; Krieger, J.E.; Kubo, M.; Kuusisto, J.; Lanefeld, C.D.; Langenberg, C.; Launer, L.J.; Lehne, B.; Lewis, C.E.; Li, Y.; Lifelines Cohort Study; Lim, S.H.; Lin, S.; Liu, C.T.; Liu, J.; Liu, J.; Liu, K.; Liu, Y.; Loh, M.; Lohmann, K.K.; Long, J.; Louie, T.; Magi, R.; Mahajan, A.; Meitinger, T.; Metspalu, A.; Milani, L.; Momozawa, Y.; Morris, A.P.; Mosley, T.H.Jr.; Munson, P.; Murray, A.D.; Nalls, M.A.; Nasri, U.; Norris, J.M.; North, K.; Ogunniyi, A.; Padmanabhan, S.; Palmas, W.R.; Palmer, N.D.; Pankow, J.S.; Pedersen, N.L.; Peters, A.; Peyser, P.A.; Polasek, O.; Raitakari, O.T.; Renstrom, F.; Rice, T.K.; Ridker, P.M.; Robino, A.; Robinson, J.G.; Rose, L.M.; Rudan, I.; Salako, B.L.; Sandow, K.; Schmidt, C.O.; Schreiner, P.J.; Scott, W.R.; Seshadri, S.; Sever, P.; Sitlani, C.M.; Smith, J.A.; Snieder, H.; Starr, J.M.; Strauch, K.; Tang, H.; Taylor, K.D.; Teo, Y.Y.; Tham, Y.C.; Uitterlineden, A.G.; Waldenberger, M.; Wang, L.; Wang, Y.X.; Wei, W.B.; Williams, C.; Wilson, G.; Wojczynski, M.K.; Yao, J.; Yuan, J.M.; Zonderman, A.B.; Becker, D.M.; Boehnke, M.; Bowden, D.W.; Chambers, J.C.; Chen, Y.I.; de Faire, U.; Deary, I.J.; Esco, T.; Farrall, M.; Forrester, T.; Franks, P.W.; Freedman, B.I.; Froguel, P.; Gasparini, P.; Gieger, C.; Horta, B.L.; Hung, Y.J.; Jonas, J.B.; Kato, N.; Kooner, J.S.; Laakso, M.; Lehtimaki, T.; Liang, K.W.; Magnusson, P.K.E.; Newman, A.B.; Oldehinkel, A.J.; Pereira, A.C.; Redline, S.; Rettig, R.; Samani, N.J.; Scott, J.; Shu, X.O.; van der Harst, P.; Wagenknecht, L.E.; Wareham, N.J.; Watkins, H.; Weir, D.R.; Wickremasinghe, A.R.; Wu, T.; Zheng, W.; Kamatani, Y.; Laurie, C.C.; Bouchard, C.; Cooper, R.S.; Evans, M.K.; Gudnason, V.; Kardia, S.L.R.; Kritchevsky, S.B.; Levy, D.; O'Connell, J.R.; Psaty, B.M.; van Dam, R.M.; Sims, M.; Arnett, D.K.; Mook-Kanamori, D.O.; Kelly, T.N.; Fox, E.R.; Hayward, C.; Fornage, M.; Rotimi, C.N.; Province, M.A.; van Dujin, C.M.; Tai, E.S.; Wong, T.Y.; Loos, R.J.F.; Reiner, A.P.; Rotter, J.I.; Zhu, X.; Bierut, L.J.; Gauderman, W.J.; Caulfield, M.J.; Elliott, P.; Rice, K.; Munroe, P.B.; Morrison, A.C.; Cupples, L.A.; Rao., D.C.; Chasman, D.I.
Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10-8) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 × 10-8). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2).
Lean non-alcoholic fatty liver disease (lean NAFLD): characteristics, metabolic outcomes and risk factors from a 7-year prospective, community cohort study from Sri Lanka
(Springer, 2019) Niriella, M.A.; Kasturiratne, A.; Pathmeswaran, A.; de Silva, S.T.; Perera, K. R.; Subasinghe, S.K.C.E.; Kodisinghe, S.K.; Piyaratna, T.A.C.L.; Vithiya, K.; Dassanayake, A.S.; de Silva, A.P.; Wickremasinghe, A.R.; Takeuchi, F.; Kato, N.; de Silva, H.J.
INTRODUCTION: While patients with non-alcoholic fatty liver disease (NAFLD) are mostly overweight or obese, some are lean. METHODS: In a community-based follow-up study (baseline and follow-up surveys performed in 2007 and 2014), we investigated and compared the clinical characteristics, body composition, metabolic associations and outcomes, and other risk factors among individuals with lean (BMI < 23 kg/m2) NAFLD, non-lean (BMI ≥ 23 kg/m2) NAFLD and those without NAFLD. To investigate associations of selected genetic variants, we performed a case-control study between lean NAFLD cases and lean non-NAFLD controls.RESULTS: Of the 2985 participants in 2007, 120 (4.0%) had lean NAFLD and 816 (27.3%) had non-lean NAFLD. 1206 (40.4%) had no evidence of NAFLD (non-NAFLD). Compared to non-lean NAFLD, lean NAFLD was commoner among males (p < 0.001), and had a lower prevalence of hypertension (p < 0.001) and central obesity (WC < 90 cm for males, < 80 cm for females) (p < 0.001) without prominent differences in the prevalence of other metabolic comorbidities at baseline survey. Of 2142 individuals deemed as either NAFLD or non-NAFLD in 2007, 704 NAFLD individuals [84 lean NAFLD, 620 non-lean NAFLD] and 834 individuals with non-NAFLD in 2007 presented for follow-up in 2014. There was no difference in the occurrence of incident metabolic comorbidities between lean NAFLD and non-lean NAFLD. Of 294 individuals who were non-NAFLD in 2007 and lean in both 2007 and 2014, 84 (28.6%) had developed lean NAFLD, giving an annual incidence of 4.1%. Logistic regression identified the presence of diabetes at baseline, increase in weight from baseline to follow-up and a higher educational level as independent risk factors for the development of incident lean NAFLD. NAFLD association of PNPLA3 rs738409 was more pronounced among lean individuals (one-tailed p < 0.05) compared to the whole cohort sample. CONCLUSION: Although lean NAFLD constitutes a small proportion of NAFLD, the risk of developing incident metabolic comorbidities is similar to that of non-lean NAFLD. A PNPLA3 variant showed association with lean NAFLD in the studied population. Therefore, lean NAFLD also warrants careful evaluation and follow-up.
Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation
(Nature Publishing Company, New York, 2022) Mahajan, A.; Spracklen, C.N.; Zhang, W.; Ng, M.C.Y.; Petty, L.E.; Kitajima, H.; Yu, G.Z.; Rüeger, S.; Speidel, L.; Kim, Y.J.; Horikoshi, M.; Mercader, J.M .; Taliun, D.; Moon, S.; Kwak, S.H.; Robertson, N.R.; Rayner, N.W.; Loh, M.; Kim, B.; Chiou, J.; Miguel-Escalada, I.; Parolo, P.D.B.; Lin, K.; Bragg, F.; Preuss, M.H.; Takeuchi, F.; Nano, J.; Guo, X.; Lamri, A.; Nakatoch, M.; Scott, R.A.; Lee, J.J.; Huerta-Chagoya, A.; Graff, M.; Chai, J.F.; Parra, E. J.; Yao, J.; Bielak, L.F.; Tabara, Y.; Hai, Y.; Steinthorsdottir, V.; Cook, J.P.; Kals, M.; Grarup, N.; Schmidt, E.M.; Pan, I.; Sofer, T.; Wuttke, M.; Sarnowski, C.; Gieger, C.; Nousome, D.; Trompet, S.; Long, J.; Sun, M.; Tong, L.; Chen, W.M.; Ahmad, M.; Noordam, R.; Lim, V.J.Y.; Tam, C.H.T.; Joo, Y.Y.; Chen, C.H.; Raffield, L.M.; Lecoeur, C.; Prins, B.P.; Nicolas, A.; Yanek, L.R.; Chen, G.; Jensen, R.A.; Tajuddin, S.; Kabagambe, E.K.; An, P.; Xiang, A.H.; Choi, H.S.; Cade, B.E.; Tan, J.; Flanagan, J.; Abaitua, F.; Adair, L.S.; Adeyemo, A.; Aguilar-Salinas, C.A.; Akiyama, M.; Anand, S.S.; Bertoni, A.; Bian, Z.; Bork-Jensen, J.; Brandslund, I.; Brody, J.A.; Brummett, C.M.; Buchanan, T.A.; Canouil, M.; Chan, J.C.N.; Chang, L.C.; Chee, M.L.; Chen, J.; Chen, S.H.; Chen, Y.T.; Chen, Z.; Chuang, L.M.; Cushman, M.; Das, S.K.; de Silva, H.J.; Dedoussis, G.; Dimitrov, L.; Doumatey, A.P.; Du, S.; Duan, Q.; Eckardt, K.U.; Emery, L.S.; Evans, D.S.; Evans, M.K.; Fischer, K.; Floyd, J.S.; Ford, I.; Fornage, M.; Franco, O.H.; Frayling, T.M.; Freedman, B.I.; Fuchsberger, C.; Genter, P.; Gerstein, H.C.; Giedraitis, V.; Villalpando, C.G.; Villalpando, M.E.G.; Goodarzi, M.O.; Larsen, P.G.; Gorkin, D.; Gross, M.; Guo, Y.; Hackinger, S.; Han, S.; Hattersley, A.T.; Herder, C.; Howard, A.G.; Hsueh, W.; Huang, M.; Huang, W.; Hung, Y.; Hwang, M.Y.; Hwu, C.; Ichihara, S.; Ikram, M.A.; Ingelsson, M.; Islam, M.T.; Isono, M.; Jang, H.M.; Jasmine, F.; Jiang, G.; Jonas, J.B.; Jørgensen, M.E.; Jørgensen, T.; Kamatani, Y.; Kandeel, F.R.; Kasturiratne, A.; Katsuya, T.; Kaur, V.; Kawaguchi, T.; Keaton, J.M.; Kho, A.N.; Khor, C.C.; Kibriya, M.G.; Kim, D.H.; Kohara, K.; Kriebel, J.; Kronenberg, F.; Kuusisto, J.; Läll, K.; Lange, L.A.; Lee, M.; Lee, N.R.; Leong, A.; Li, L.; Li, Y.; Li-Gao, R.; Ligthart, S.; Lindgren, C.M.; Linneberg, A.; Liu, C.; Liu, J.; Locke, A.E.; Louie, T.; Luan, J.; Luk, A.O.; Luo, X.; Lv, J.; Lyssenko, V.; Mamakou, V.; Mani, K.R.; Meitinger, T.; Metspalu, A.; Morris, A.D.; Nadkarni, G.N.; Nadler, J.L.; Nalls, M.A.; Nayak, U.; Nongmaithem, S.S.; Ntalla, I.; Okada, Y.; Orozco, L.; Patel, S.R.; Pereira, M.A.; Peters, A.; Pirie, F.J.; Porneala, B.; Prasad, G.; Preissl, S.; Rasmussen-Torvik, L.J.; Reiner, A.P.; Roden, M.; Rohde, R.; Roll, K.; Sabanayagam, C.; Sander, M.; Sandow, K.; Sattar, N.; Schönherr, S.; Schurmann, C.; Shahriar, M.; Shi, J.; Shin, D.M.; Shriner, D.; Smith, J.A.; So, W.Y.; Stančáková, A.; Stilp, A.M.; Strauch, K.; Suzuki, K.; Takahashi, A.; Taylor, K.D.; Thorand, B.; Thorleifsson, G.; Thorsteinsdottir, U.; Tomlinson, B.; Torres, J.M.; Tsai, F.; Tuomilehto, J.; Tusie-Luna, T.; Udler, M.S.; Salgado, A.V.; Dam, R.M.; Klinken, J.B.; Varma, R.; Vujkovic, M.; Wacher-Rodarte, N.; Wheeler, E.; Whitsel, E.A.; Wickremasinghe, A.R.; Dijk, K.W.; Witte, D.R.; Yajnik, C.S; Yamamoto, K.; Yamauchi, T.; Yengo, L.; Yoon, K.; Yu, C.; Yuan, J.M.; Yusuf, S.; Zhang, L.; Zheng, W.; FinnGen; eMERGE Consortium; Leslie J Raffel; Igase, M.; Ipp, E.; Redline, S.; Cho, Y.S.; Lind, L.; Province, M.A.; Hanis, C.L.; Peyser, P.A.; Ingelsson, E.; Zonderman, A.B.; Psaty, B.M.; Wang, Y.; Rotimi, C.N.; Becker, D.M.; Matsuda, F.; Liu, Y.; Zeggini, E.; Yokota, M.; Rich, S.S.; Kooperberg, C.; Pankow, J.S.; Engert, J.C.; Chen, Y.I.; Froguel, P.; Wilson, J.G.; Sheu, W.H.H.; Kardia, S.L.R.; Wu, J.Y.; Hayes, M.G.; Ma, R.C.W.; Wong, T.Y.; Groop, L.; Mook-Kanamori, D.O.; Chandak, G.R.; Collins, F.S.; Bharadwaj, D.; Paré, G.; Sale, M.M.; Ahsan, H.; Motala, A.A.; Shu, X.O.; Park, K.S.; Jukema, J.W.; Cruz, M.; Cowdin, R.M.; Grallert, H.; Cheng, C.Y.; Bottinger, E.P.; Dehghan, A.; Tai, E.S.; Dupuis, J.; Kato, N.; Laakso, M.; Köttgen, A.; Koh, W.P.; Palmer, C.N.A.; Liu, S.; Abecasis, G.; Kooner, J.S.; Loos, R.J.F.; North, K.E.; Haiman, C.A.; Florez, J.C.; Saleheen, D.; Hansen, T.; Pedersen, O.; Mägi, R.; Langenberg, C.; Wareham, N.J.; Maeda, S.; Kadowaki, T.; Lee, J.; Millwood, I.Y.; Walters, R.G.; Stefansson, K.; Myers, S.R.; Ferrer, J.; Gaulton, K.J.; Meigs, J.B.; Mohlke, K.L.; Gloyn, A.L.; Bowden, D.W.; Below, J.E.; Chambers, J.C.; Sim, X.; Boehnke, M.; Rotter, J.I.; McCarthy, M.I.; Morris, A.P.
We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10-9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.