Browsing by Author "Isono, M."

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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.
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.
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.
A saturated map of common genetic variants associated with human height
(Nature Publishing Group, 2022) Vedantam, S.; Marouli, E.; Sidorenko, J.; Bartell, E.; Sakaue, S.; Graff, M.; Eliasen, A.U.; Jiang, Y.; Raghavan, S.; Miao, J.; Arias, J.D.; Graham, S.E.; Mukamel, R.E.; Spracklen, C.N.; Yin, X.; Chen, S.H.; Ferreira, T.; Highland, H.H.; Ji, Y.; Karaderi. T,; Lin, K.; Lüll, K.; Malden, D.E.; Medina-Gomez, C.; Machado, M.; Moore, A.; Rüeger, S.; Sim. X,; Vrieze, S.; Ahluwalia, T.S.; Akiyama, M.; Allison, M.A.; Alvarez, M.; Andersen, M.K.; Ani, A.; Appadurai, V.; Arbeeva, L.; Bhaskar, S.; Bielak, L.F.; Bollepalli, S.; Bonnycastle, L.L.; Bork-Jensen, J.; Bradfield, J.P.; Bradford, Y.; Braund, P.S.; Brody, J.A.; Burgdorf, K.S.; Cade, B.E.; Cai, H.; Cai, Q.; Campbell, A.; Cañadas-Garre, M.; Catamo, E.; Chai, J.F.; Chai, X.; Chang, L.C.; Chen, C.H.; Chesi, A.; Choi, S.H.; Chung, R.H.; Cocca, M.; Concas, M.P.; Couture, C.; Cuellar-Partida, G.; Danning, R.; Daw, E.W.; Degenhard, F.; Delgado, G.E.; Delitala, A.; Demirkan, A.; Deng, X.; Devineni, P.; Dietl, A.; Dimitriou, M.; Dimitrov, L.; Dorajoo, R.; Ekici, A.B.; Engmann, J.E.; Fairhurst-Hunter, Z.; Farmaki, A.E.; Faul, J.D.; Fernandez-Lopez, J.C.; Forer, L.; Francescatto, M.; Freitag-Wolf, S.; Fuchsberger, C.; Galesloot, T.E.; Gao, Y.; Gao, Z.; Geller, F.; Giannakopoulou, O.; Giulianini,F.; Gjesing, A.P.; Goel, A.; Gordon, S.D.; Gorski, M.; Grove, J.; Guo, X.; Gustafsson, S.; Haessler, J.; Hansen, T.F.; Havulinna, A.S.; Haworth, S.J.; He, J.; Heard-Costa, N.; Hebbar, P.; Hindy, G.; Ho, Y.A.; Hofer, E.; Holliday, E.; Horn, K.; Hornsby, W.E.; Hottenga, J.J.; Huang, H.; Huang, J.; Huerta-Chagoya, A.; Huffman, J.E.; Hung, Y.J.; Huo, S.; Hwang, M.Y.; Ha, H.; Ikeda, D.D.; Isono, M.; Jackson, A.U.; Jäger, S.; Jansen, I.E.; Johansson, I.; Jonas, J.B.; Jonsson, A.; Jørgensen, T.; Kalafati, I.P.; Kanai, M.; Kanoni, S.; Kårhus, L.L.; Kasturiratne, A.; Katsuya, T.; Kawaguchi, T.; Kember, R.L.; Kentistou, K.A.; Kim, H.N.; Kim, Y.J.; Kleber, M.E.; Knol, M.J.; Kurbasic, A.; Lauzon, M.; Le, P.; Lea, R.; Lee, J.Y.; Leonard, H.L.; Li, S.A.; Li, X.; Li, X.; Liang, J.; Lin, H.; Lin, S.Y.; Liu, J.; Liu, X.; Lo, K.S.; Long, J.; Lores-Motta, L.; Luan, J.; Lyssenko, V.; Lyytikäinen, L.P.; Mahajan, A.; Mamakou, V.; Mangino, M.; Manichaikul, A.; Marten, J.,; Mattheisen, M.; Mavarani, L.; McDaid, A.F.; Meidtner, K.; Melendez, T.L.; Mercader, J.M.; Milaneschi, Y.; Miller, J.E.; Millwood, I.Y.; Mishra, P.P.; Mitchell, R.E.; Møllehave, L.T.; Morgan, A.; Mucha, S.; Munz, M.; Nakatochi, M.; Nelson, C.P.; Nethander, M.; Nho, C.W.; Nielsen, A.A.; Nolte, I.M.; Nongmaithem, S.S.; Noordam, R.; Ntalla, I.; Nutile, T.; Pandit, A.; Christofidou, P.; Pärna, K.; Pauper, M.; Petersen, E.R.B.; Petersen, L.V.; Pitkänen, N.; Polašek, O.; Poveda, A.; Preuss, M.H.; Pyarajan, S.; Raffield, L.M.; Rakugi, H.; Ramirez, J.; Rasheed, A.; Raven, D.; Rayner, N.W.; Riveros, C.; Rohde, R.; Ruggiero, D.; Ruotsalainen, S.E.; Ryan, K.A.; Sabater-Lleal, M.; Saxena, R.; Scholz, M.; Sendamarai, A.; Shen, B.; Shi, J.; Shin, J.H.; Sidore, C.; Sitlani, C.M.; Slieker, R.C.; Smit, R.A.J.; Smith, A.V.; Smith, J.A.; Smyth, L.J.; Southam, L.; Steinthorsdottir, V.; Sun, L.; Takeuchi, F.; Tallapragada, D.S.P.; Taylor, K.D.; Tayo, B.O.; Tcheandjieu, C.; Terzikhan, N.; Tesolin, P.; Teumer, A.; Theusch, E.; Thompson, D.J.; Thorleifsson, G.; Timmers, P.R.H.J.; Trompet, S.; Turman, C.; Vaccargiu, S.; van der Laan, S.W.; van der Most, P.J.; van Klinken, J.B.; van Setten, J.; Verma, S.S.; Verweij, N.; Veturi, Y.; Wang, C.A.; Wang, C.; Wang, L.; Wang, Z.; Warren, H.R.; Bin Wei, W.; Wickremasinghe, A.R.; Wielscher, M.; Wiggins, K.L.; Winsvold, B.S.; Wong, A.; Wu, Y.; Wuttke, M.; Xia, R.; Xie, T.; Yamamoto, K.; Yang, J.; Yao, J.; Young, H.; Yousri, N.A.; Yu, L.; Zeng, L.; Zhang, W.; Zhang, X.; Zhao, J.H.; Zhao. W.; Zhou, W.; Zimmermann, M.E.; Zoledziewska, M.; Adair, L.S.; Adams, H.H.H.; Aguilar-Salinas, C.A.; Al-Mulla, F.; Arnett, D.K.; Arnett, D.K.; Asselbergs, F.W.; Åsvold, B.O.; Attia, J.; Banas, B.; Bandinelli, S.; Bennett D.A.; Bergler, T.; Bharadwaj, D.; Biino, G.; Bisgaard, H.; Boerwinkle, E.; Böger, C.A.; Bønnelykke, K.; Boomsma, D.I.; Børglum, A.D.; Borja, J.B.; Bouchard, C.; Bowden, D.W.; Brandslund, I.; Brumpton, B.; Buring, J.E.; Caulfield, M.J.; Chambers, J.C.; Chandak, G.R.; Chanock, S.J.; Chaturvedi, N.; Chen, Y.I.; Chen, Z.; Cheng, C.Y.; Christophersen, I.E.; Ciullo, M.; Cole, J.W.; Collins, F.S.; Cooper, R.S.; Cruz, M.; Cucca, F.; Cupples, L.A.; Cutler, M.J.; Damrauer, S.M.; Dantoft, T.M.; de Borst, G.J.; de Groot, L.C.P.G.M.; de Jager, P.L.; de Kleijn, D.P.V.; de Silva, H.J.; Dedoussis, G.V.; den Hollander, A.I.; Du, S.; Easton, D.F.; Elders, P.J.M.; Eliassen, A.H.; Ellinor, P.T.; Elmståhl, S.; Erdmann, J.; Evans, M.K.; Fatkin, D.; Feenstra, B.; Feitosa, M.F.; Ferrucci, L.; Ford, I.; Fornage, M.; Franke, A.; Franks, P.W.; Freedman, B.I.; Gasparini, P.; Gieger, C.; Girotto, G.; Goddard, M.E.; Golightly, Y.M.; Gonzalez-Villalpando. 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Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes1. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel2) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10-20% (14-24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.
Systematic fine-mapping of association with BMI and type 2 diabetes at the FTO locus by integrating results from multiple ethnic groups
(Public Library of Science, 2014) Akiyama, K.; Takeuchi, F.; Isono, M.; Chackrewarthy, S.; Nguyen, Q.N.; Wen, W.; Yamamoto, K.; Katsuya, T.; Kasturiratne, A.; Pham, S.T.; Zheng, W.; Matsushita, Y.; Kishimoto, M.; Do, L.D.; Shu, X.O.; Wickremasinghe, A.R.; Kajio, H.; Kato, N.
BACKGROUND/OBJECTIVE: The 16q12.2 locus in the first intron of FTO has been robustly associated with body mass index (BMI) and type 2 diabetes in genome-wide association studies (GWAS). To improve the resolution of fine-scale mapping at FTO, we performed a systematic approach consisting of two parts. METHODS: The first part is to partition the associated variants into linkage disequilibrium (LD) clusters, followed by conditional and haplotype analyses. The second part is to filter the list of potential causal variants through trans-ethnic comparison. RESULTS: We first examined the LD relationship between FTO SNPs showing significant association with type 2 diabetes in Japanese GWAS and between those previously reported in European GWAS. We could partition all the assayed or imputed SNPs showing significant association in the target FTO region into 7 LD clusters. Assaying 9 selected SNPs in 4 Asian-descent populations--Japanese, Vietnamese, Sri Lankan and Chinese (n≤26,109 for BMI association and n≤24,079 for type 2 diabetes association), we identified a responsible haplotype tagged by a cluster of SNPs and successfully narrowed the list of potential causal variants to 25 SNPs, which are the smallest in number among the studies conducted to date for FTO. CONCLUSIONS: Our data support that the power to resolve the causal variants from those in strong LD increases consistently when three distant populations--Europeans, Asians and Africans--are included in the follow-up study. It has to be noted that this fine-mapping approach has the advantage of applicability to the existing GWAS data set in combination with direct genotyping of selected variants.
Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation
(Nature Publishing Company, 2015) Kato, N.; Loh, M.; Takeuchi, F.; Verweij, N.; Wang, X.; Zhang, W.; Kelly, T.N.; Saleheen, D.; Lehne, B.; Leach, I.M.; Drong, A.W.; Abbott, J.; Wahl, S.; Tan, S.T.; Scott, W.R.; Campanella, G.; Chadeau-Hyam, M.; Afzal, U.; Ahluwalia, T.S.; Bonder, M.J.; Chen, P.; Dehghan, A.; Edwards, T.L.; Esko, T.; Go, M.J.; Harris, S.E.; Hartiala, J.; Kasela, S.; Kasturiratne, A.; Khor, C.C.; Kleber, M.E.; Li, H.; Mok, Z.Y.; Nakatochi, M.; Sapari, N.S.; Saxena, R.; Stewart, A.F.; Stolk, L.; Tabara, Y.; Teh, A.L.; Wu, Y.; Wu, J.Y.; Zhang, Y.; Aits, I.; Da Silva Couto Alves, A.; Das, S.; Dorajoo, R.; Hopewell, J.C.; Kim, Y.K.; Koivula, R.W.; Luan, J.; Lyytikäinen, L.P.; Nguyen, Q.N.; Pereira, M.A.; Postmus, I.; Raitakari, O.T.; Bryan, M.S.; Scott, R.A.; Sorice, R.; Tragante, V.; Traglia, M.; White, J.; Yamamoto, K.; Zhang, Y.; Adair, L.S.; Ahmed, A.; Akiyama, K.; Asif, R.; Aung, T.; Barroso, I.; Bjonnes, A.; Braun, T.R.; Cai, H.; Chang, L.C.; Chen, C.H.; Cheng, C.Y.; Chong, Y.S.; Collins, R.; Courtney, R.; Davies, G.; Delgado, G.; Do, L.D.; Doevendans, P.A.; Gansevoort, R.T.; Gao, Y.T.; Grammer, T.B.; Grarup, N.; Grewal, J.; Gu, D.; Wander, G.S.; Hartikainen, A.L.; Hazen, S.L.; He, J.; Heng, C.K.; Hixson, J.E.; Hofman, A.; Hsu, C.; Huang, W.; Husemoen, L.L.; Hwang, J.Y.; Ichihara, S.; Igase, M.; Isono, M.; Justesen, J.M.; Katsuya, T.; Kibriya, M.G.; Kim, Y.J.; Kishimoto, M.; Koh, W.P.; Kohara, K.; Kumari, M.; Kwek, K.; Lee, N.R.; Lee, J.; Liao, J.; Lieb, W.; Liewald, D.C.; Matsubara, T.; Matsushita, Y.; Meitinger, T.; Mihailov, E.; Milani, L.; Mills, R.; Mononen, N.; Müller-Nurasyid, M.; Nabika, T.; Nakashima, E.; Ng, H.K.; Nikus, K.; Nutile, T.; Ohkubo, T.; Ohnaka, K.; Parish, S.; Paternoster, L.; Peng, H.; Peters, A.; Pham, S.T.; Pinidiyapathirage, M.J.; Rahman, M.; Rakugi, H.; Rolandsson, O.; Rozario, M.A.; Ruggiero, D.; Sala, C.F.; Sarju, R.; Shimokawa, K.; Snieder, H.; Sparso, T.; Spiering, W.; Starr, J.M.; Stott, D.J.; Stram, D.O.; Sugiyama, T.; Szymczak, S.; Tang, W.H.; Tong, L.; Trompet, S.; Turjanmaa, V.; Ueshima, H.; Uitterlinden, A.G.; Umemura, S.; Vaarasmaki, M.; van Dam, R.M.; van Gilst, W.H.; van Veldhuisen, D.J.; Viikari, J.S.; Waldenberger, M.; Wang, Y.; Wang, A.; Wilson, R.; Wong, T.Y.; Xiang, Y.B.; Yamaguchi, S.; Ye, X.; Young, R.D.; Young, T.L.; Yuan, J.M.; Zhou, X.; Asselbergs, F.W.; Ciullo, M.; Clarke, R.; Deloukas, P.; Franke, A.; Franks, P.W.; Franks, S.; Friedlander, Y.; Gross, M.D.; Guo, Z.; Hansen, T.; Jarvelin, M.R.; Jorgensen, T.; Jukema, J.W.; Kähönen, M.; Kajio, H.; Kivimaki, M.; Lee, J.Y.; Lehtimäki, T.; Linneberg, A.; Miki, T.; Pedersen, O.; Samani, N.J.; Sorensen, T.I.; Takayanagi, R.; Toniolo, D.; BIOS-consortium; CARDIo GRAMplusCD; LifeLines Cohort Study; InterAct Consortium; Ahsan, H.; Allayee, H.; Chen, Y.T.; Danesh, J.; Deary, I.J.; Franco, O.H.; Franke, L.; Heijman, B.T.; Holbrook, J.D.; Isaacs, A.; Kim, B.J.; Lin, X.; Liu, J.; März, W.; Metspalu, A.; Mohlke, K.L.; Sanghera, D.K.; Shu, X.O.; van Meurs, J.B.; Vithana, E.; Wickremasinghe, A.R.; Wijmenga, C.; Wolffenbuttel, B.H.; Yokota, M.; Zheng, W.; Zhu, D.; Vineis, P.; Kyrtopoulos, S.A.; Kleinjans, J.C.; McCarthy, M.I.; Soong, R.; Gieger, C.; Scott, J.; Teo, Y.Y.; He, J.; Elliott, P.; Tai, E.S.; van der Harst, P.; Kooner, J.S.; Chambers, J.C.
We carried out a trans-ancestry genome-wide association and replication study of blood pressurephenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10(-11) to 5.0 × 10(-21)). The sentinel blood pressure SNPs are enriched for association with DNAmethylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNAmethylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10(-6)). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.