Journal/Magazine Articles

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This collection contains original research articles, review articles and case reports published in local and international peer reviewed journals by the staff members of the Faculty of Medicine

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    A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids
    (American Society of Human Genetics., 2022) Ramdas, S.; Judd, J.; Graham, S.E.; Kanoni, S.; Wang, Y.; Surakka, I.; Wenz, B.; Clarke, S.L.; Chesi, A.; Wells, A.; 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.; 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.; Gomez, C.M.; Pitsillides, A.; Hottenga, J.J.; Wood, A.R.; Ji, Y.; Gao, Z.; Haworth, S.; Mitchell, R.E.; Chai, J.F.; Aadahl, M.; Bjerregaard, A.A.; Yao, J.; Manichaikul, A.; JaneLee, W.; Hsiung, C.A.; Warren, H.R.; Ramirez, J.; Jensen, J.B.; Kårhus, L.; Goel, A.; Lleal, M.S.; 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.; Schönherr, S.; Forer, L.; Scholz, M.; Galesloot, T.E.; Bradfield, J.P.; Ruotsalainen, S.E.; Daw, E.W.; Zmuda, J.M; Mitchell, J.S.; Fuchsberger, C.; Christensen, H.; Brody, J.A.; Le, P.; Feitosa, M.F.; Wojczynski, M.K.; Hemerich, D.; Preuss, M.; Mangino, M.; Christofidou, P.; Verweij, N.; Benjamins, J.W.; Engmann, J.; Noah, T.L.; Verma, A.; Slieker, R.C.; Lo, K.S.; Zilhao, N.R.; Kleber, M.E.; Delgado, G.E.; Huo, S.; Ikeda, D.D.; Iha, H.; Yang, J.; Liu, J.; Demirkan, A.; Leonard, H.L.; Marten,J.; Emmel, C.; Schmidt, B.; Smyth, L.J.; Cañadas-Garre, M.; Wang, C.; Nakatochi, M.; Wong, A.; Hutri-Kähönen , N.; Sim, X.; Xia, R.; Huerta-Chagoya, A.; Fernandez-Lopez, J.C.; Lyssenko, V; Nongmaithem, S.S.; Sankareswaran, A.; 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.; Morgan, A.; Meidtner, K.; Bielak, L.F.; Smith, J.A.; Hebbar, P.; Farmaki, A.E.; Hofer, E.; Lin, M.; Concas, M.P.; Vaccargiu, S.; Most, P.J.; Pitkänen, N.; Cade, B.E.; 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.; Nethander, M.; Freitag-Wolf, S.; Southam, L.; Rayner, N.W.; Wang, C.A.; Lin, S.; Wang, J.S.; Couture, C.; Lyytikäinen, L.P.; Nikus, K.; Partida, G.C.; Vestergaard, H.; Hidalgo, B.; Giannakopoulou, O.; Cai, Q.; Obura, M.O.; Setten, J.; He, K.Y.; 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.; Kleijn, D.; 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.D.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.; 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.; Peyser, P.A.; Kato, N.; Schulze, M.B.; Girotto, G.; Böger, C.A.; Jung, B.; Joshi, P.K.; Bennett, D.A.; Jager, P.L.D.; 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.; Luna, T.T.; Salinas, C.A.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.; Walker, M.; Scott, L.J.; Koistinen, H.A.; Chandak, G.R.; Mercader, J.M.; Villalpando, C.G.; Orozco, L.; Fornage, M.; Tai, E.S.; 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.; Duijn, C.M; Jin, Z.B.; Jin, Z.B.; Lu, F.; Hishigaki, H.; Lin, X.; März, W.; Gudnason, V.; Tardif, J.C.; Lettre, G.; Hart, L.M.T.; Elders, P.J.M.; Rader, D.J.; Loos, S.M.; 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.; 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.; Kanamori, D.O.M.; Dijk, K.W.; Watkins, H.; Strachan, D.P.; Grarup, N.; Sever, P.; Poulter, N.; Sheu, W.H.H.; 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.; Peters, A.; Gieger, C.; Hattersley, A.T.; Pedersen, N.L.; Magnusson, P.K.E.; Boomsma, D.I.; de Geus, E.J.C.; Cupples, L.A.; Meurs, J.B.J.; Ikram, A.; Ghanbari, M.; Larsen, P.G.; Huang, W.; Kim, Y.J.; Tabara, Y.; Wareham, N.J.; Langenberg, C.; Zeggini, E.; Tuomilehto, J.; 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.; 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.E.; Ho, Y.L.; Lynch, J.A.; Tsao, P.S.; Chang, K.M.; Cho, K.; O'Donnell, C.J.; Gaziano, J.M.; Wilson, P.; Mohlke, K.L.; Frayling, T.M.; Hirschhorn, J.N.; Kathiresan, S.; Boehnke, M.; Million Veterans Program; Global Lipids Genetics Consortium; Grant, S.; Natarajan, P.; Sun, Y.V.; Morris, A.P.; Deloukas, P.; Peloso, G.; Assimes, T.L.; Willer, C.J.; Zhu, X.; Brown, C.D.
    A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.
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    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.
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    The Trans-ancestral genomic architecture of glycemic traits
    (Nature Pub. Co., 2021) Chen, J.; Spracklen, C.N.; Marenne, G.; Varshney, A.; Corbin, L.J.; Luan, J.; Willems, S.M.; Wu, Y.; Zhang, X.; Horikoshi, M.; Boutin, T.S.; Mägi, R.; Waage, J.; Li-Gao, R.; Chan, K.H.K; Yao, J.; Anasanti, M.D.; Chu, A.Y.; Claringbould, A.; Heikkinen, J.; Hong, J.; Hottenga, J.J.; Huo, S.; Kaakinen, M.A.; Louie, T.; März, W.; Moreno-Macias, H.; Ndungu, A.; Nelson, S.C.; Nolte, I.M.; North, K.E.; Raulerson, C.K.; Ray, D.; Rohde, R.; Rybin, D.; Schurmann, C.; Sim, X.; Southam, L.; Stewart, I.D.; Wang, C.A.; Wang, Y.; Wu, P.; Zhang, W.; Ahluwalia, T.S.; Appel, E.V.R.; Bielak, L.F.; Brody, J.A.; Burtt, N.P.; Cabrera, C.P.; Cade, B.E.; Chai, J.F.; Chai, X.; Chang, L.C.; Chen, C.H.; Chen, B.H.; Chitrala, K.N.; Chiu, Y.F.; De Haan, H.G.; Delgado, G.E.; Demirkan, A.; Duan, Q.; Engmann, J.; Fatumo, S.A.; Gayán, J.; Giulianini, F.; Gong, J.H.; Gustafsson, S.; Hai, Y.; Hartwig, F.P.; He, J.; Heianza, Y.; Huang, T.; Huerta-Chagoya, A.; Hwang, M.Y.; Jensen, R.A.; Kawaguchi, T.; Kentistou, K.A.; Kim, Y.J.; Kleber, M.E.; Kooner, I.K.; Lai, S.; Lange, L.A.; Langefeld, C.D.; Lauzon, M.; Li, M.; Ligthart, S.; Liu, J.; Loh, M.; Long, J.; Lyssenko, V.; Mangino, M.; Marzi, C.; Montasser, M.E.; Nag, A.; Nakatochi, M.; Noce, D.; Noordam, R.; Pistis, G.; Preuss, M.; Raffield, L.; Rasmussen-Torvik, L.J.; Rich, S.S.; Robertson, N.R.; Rueedi, R.; Ryan, K.; Sanna, S.; Saxena, R.; Schraut, K.E.; Sennblad, B.; Setoh, K.; Smith, A.V.; Sparsø, T.; Strawbridge, R.J.; Takeuchi, F.; Tan, J.; Trompet, S.; Van den Akker, E.; Van der Most, P.J.; Verweij, N.; Vogel, M.; Wang, H.; Wang, C.; Wang, N.; Warren, H.R.; Wen, W.; Wilsgaard, T.; Wong, A.; Wood, A.R.; Xie, T.; Zafarmand, M.H.; Zhao, J.H.; Zhao, W.; Amin, N.; Arzumanyan, Z.; Astrup, A.; Bakker, S.J.L.; Baldassarre, D.; Beekman, M.; Bergman, R.N.; Bertoni, A.; Blüher, M.; Bonnycastle, L.L.; Bornstein, S.R.; Bowden, D.W.; Cai, Q.; Campbell, A.; Campbell, H.; Chang, Y.C.; de Geus, E.J.C.; Dehghan, A.; Du, S.; Eiriksdottir, G.; Farmaki, A.E.; Frånberg, M.; Fuchsberger, C.; Gao, Y.; Gjesing, A.P.; Goel, A.; Han, S.; Hartman, C.A.; Herder, C.; Hicks, A.A.; Hsieh, C.H.; Hsueh, W.A.; Ichihara, S.; Igase, M.; Ikram, M.A.; Johnson, W.C.; Jørgensen, M.E.; Joshi, P.K.; Kalyani, R.R.; Kandeel, F.R.; Katsuya, T.; Khor, C.C.; Kiess, W.; Kolcic, I.; Kuulasmaa, T.; Kuusisto, J.; Läll, K.; Lam, K.; Lawlor, D.A.; Lee, N.R.; Lemaitre, R.N.; Li, H.; Lifelines Cohort Study; Lin, S.Y.; Lindström, J.; Linneberg, A.; Liu, J.; Lorenzo, C.; Matsubara, T.; Matsuda, F.; Mingrone, G.; Mooijaart, S.; Moon, S.; Nabika, T.; Nadkarni, G.N.; Nadler, J.L.; Nelis, M.; Neville, M.J.; Norris, J.M.; Ohyagi, Y.; Peters, A.; Peyser, P.A.; Polasek, O.; Qi, Q.; Raven, D.; Reilly, D.F.; Reiner, A.; Rivideneira, F.; Roll, K.; Rudan, I.; Sabanayagam, C.; Sandow, K.; Sattar, N.; Schürmann, A.; Shi, J.; Stringham, H.M.; Taylor, K.D.; Teslovich, T.M.; Thuesen, B.; Timmers, P.R.H.J.; Tremoli, E.; Tsai, M.Y.; Uitterlinden, A.; van Dam, R.M.; van Heemst, D.; van Hylckama Vlieg, A.; van Vliet-Ostaptchouk, J.V.; Vangipurapu, J.; Vestergaard, H.; Wang, T.; Willems van Dijk, K.; Zemunik, T.; Abecasis, G.R.; Adair, L.S.; Aguilar-Salinas, C.A.; Alarcón-Riquelme, M.E.; An, P.; Aviles-Santa, L.; Becker, D.M.; Beilin, L.J.; Bergmann, S.; Bisgaard, H.; Black, C.; Boehnke, M.; Boerwinkle, E.; Böhm, B.O.; Bønnelykke, K.; Boomsma, D.I.; Bottinger, E.P.; Buchanan, T.A.; Canouil, M.; Caulfield, M.J.; Chambers, J.C.; Chasman, D.I.; Chen, Y.I.; Cheng, C.Y.; Collins, F.S.; Correa, A.; Cucca, F.; de Silva, H.J.; Dedoussis, G.; Elmståhl, S.; Evans, M.K.; Ferrannini, E.; Ferrucci, L.; Florez, J.C.; Franks, P.W.; Frayling, T.M.; Froguel, P.; Gigante, B.; Goodarzi, M.O.; Gordon-Larsen, P.; Grallert, H.; Grarup, N.; Grimsgaard, S.; Groop, L.; Gudnason, V.; Guo, X.; Hamsten, A.; Hansen, T.; Hayward, C.; Heckbert, S.R.; Horta, B.L.; Huang, W.; Ingelsson, E.; James, P.S.; Jarvelin, M.R.; Jonas, J.B.; Jukema, J.W.; Kaleebu, P.; Kaplan, R.; Kardia, S.L.R.; Kato, N.; Keinanen-Kiukaanniemi, S.M.; Kim, B.J.; Kivimaki, M.; Koistinen, H.A.; Kooner, J.S.; Körner, A.; Kovacs, P.; Kuh, D.; Kumari, M.; Kutalik, Z.; Laakso, M.; Lakka, T.A.; Launer, L.J.; Leander, K.; Li, H.; Lin, X.; Lind, L.; Lindgren, C.; Liu, S.; Loos, R.J.F.; Magnusson, P.K.E.; Mahajan, A.; Metspalu, A.; Mook-Kanamori, D.O.; Mori, T.A.; Munroe, P.B.; Njølstad, I.; O'Connell, J.R.; Oldehinkel, A.J.; Ong, K.K.; Padmanabhan, S.; Palmer, C.N.A.; Palmer, N.D.; Pedersen, O.; Pennell, C.E.; Porteous, D.J.; Pramstaller, P.P.; Province, M.A.; Psaty, B.M.; Qi, L.; Raffel, L.J.; Rauramaa, R.; Redline, S.; Ridker, P.M.; Rosendaal, F.R.; Saaristo, T.E.; Sandhu, M.; Saramies, J.; Schneiderman, N.; Schwarz, P.; Scott, L.J.; Selvin, E.; Sever, P.; Shu, X.O.; Slagboom, P.E.; Small, K.S.; Smith, B.H.; Snieder, H.; Sofer, T.; Sørensen, T.I.A.; Spector, T.D.; Stanton, A.; Steves, C.J.; Stumvoll, M.; Sun, L.; Tabara, Y.; Tai, E.S.; Timpson, N.J.; Tönjes, A.; Tuomilehto, J.; Tusie, T.; Uusitupa, M.; van der Harst, P.; van Duijn, C.; Vitart, V.; Vollenweider, P.; Vrijkotte, T.G.M.; Wagenknecht, L.E.; Walker, M.; Wang, Y.X.; Wareham, N.J.; Watanabe, R.M.; Watkins, H.; Wei, W.B.; Wickremasinghe, A.R.; Willemsen, G.; Wilson, J.F.; Wong, T.Y.; Wu, J.Y.; Xiang, A.H.; Yanek, L.R.; Yengo, L.; Yokota, M.; Zeggini, E.; Zheng, W.; Zonderman, A.B.; Rotter, J.I.; Gloyn, A.L.; McCarthy, M.I.; Dupuis, J.; Meigs, J.B.; Scott, R.A.; Prokopenko, I.; Leong, A.; Liu, C.T.; Parker, S.C.J.; Mohlke, K.L.; Langenberg, C.; Wheeler, E.; Morris, A.P.; Barroso, I.; Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC) Collaborators.
    ABSTRACT: Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.
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    Physical activity tracking among Sri Lankan adults: findings from a 7-year follow-up of the Ragama Health Study
    (SAGE Publications, 2021) Pinidiyapathirage, J.; Kasturiratne, A.; Bennie, J.A.; Pathmeswaran, A.; Biddle, S.J.H.; de Silva, H.J.; Chackrewarthy, S.; Dassanayake, A.S.; Ranawaka, U.; Kato, N.; Wickremasinghe, A.R.
    ABSTRACT: Limited data are available on physical activity tracking among adults in low- and middle-income countries. Using a longitudinal design, we assessed trends and correlates of physical activity among Sri Lankan adults. Individuals selected through age-stratified random sampling, were screened initially in 2007 (n = 2986) and reevaluated in 2014 (n = 2148). On both occasions, structured interviews and clinical measurements were completed. Approximately 40% of the participants engaged in recommended levels of physical activity both at baseline and follow-up. One-fifth reported increased physical activity at follow-up, a similar proportion reported being persistently inactive or a reduction in physical activity. In the adjusted analysis, being persistently active was associated with male sex, a lower educational level and income, being free of any chronic disease conditions, better self-rated health, and sitting time <8 hours. Our findings support public health interventions to help maintain recommended physical activity levels over time, particularly for subgroups at high-risk of physical inactivity. KEYWORDS: Sri Lanka; lower middle-income countries; non-communicable diseases; physical activity; population studies.
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    The clinical utility of accurate NAFLD ultrasound grading: Results from a community-based, prospective cohort study
    (Elsevier Science Ireland Ltd, 2021) Niriella, M.A.; Ediriweera, D.S.; Kasturiratne, A.; Gunasekara, D.; de Silva, S.T.; Dassanayake, A.S.; de Silva, A.P.; Kato, N.; Pathmeswaran, A.; Wickremasinghe, A.R.; de Silva, H.J.
    OBJECTIVES: Despite its widespread use there is no consensus on ultrasound criteria to diagnose fatty liver. METHOD: In an ongoing, cohort-study, participants were initially screened in 2007 and reassessed in 2014 by interview, anthropometric measurements, liver ultrasonography, and blood tests. We evaluated utility of increased hepatic echogenicity alone (intermediate) compared to using additional criteria which included signal attenuation and/or vascular blunting along with increase of hepatic echogenicity (moderate-severe), to diagnose fatty liver in NAFLD. We made a comparison of the two radiologically defined groups, in order to choose a classification method for NAFLD, which may better predict baseline adverse metabolic traits (MT), and adverse metabolic and cardiovascular events (CVE) after 7-year of follow-up. RESULTS: Of 2985 recruited in 2007, 940 (31.5 %) had moderate-severe NAFLD, 595 (19.9 %) intermediate NAFLD, and 957 (32.1 %) were controls (no fatty liver). 2148 (71.9 %) attended follow-up in 2014; they included 708 who had moderate-severe NAFLD, 446 intermediate NAFLD and 674 controls, at baseline (in 2007). At baseline, adverse anthropometric indices and MTs were significantly higher in both moderate-severe NAFLD and intermediate NAFLD compared to controls, except for low HDL. They were commoner in moderate-severe NAFLD than in intermediate NAFLD. After seven years, the odds of developing new-onset metabolic traits and CVEs were significantly higher compared to controls only in moderate-severe NAFLD. CONCLUSIONS: Only moderate-severe NAFLD predicted risk of incident adverse MTs and CVEs. However, both moderate-severe and intermediate NAFLD were associated with higher prevalence of adverse anthropometric and metabolic traits, thereby identifying individuals who need medical intervention even among those with milder degrees of fatty liver. We therefore recommend using increased hepatic echogenicity, and not only the more stringent criteria (which include signal attenuation and/or vascular blunting), for the diagnosis of fatty liver in individuals with NAFLD. KEYWORDS: Cardiovascular events; Fatty liver; NAFLD; Outcomes; Ultrasonography; Ultrasound criteria.
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    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.
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    Metabolic syndrome, but not non-alcoholic fatty liver disease, increases 10-year mortality: A prospective, community-cohort study
    (Wiley-Blackwell, 2020) Niriella, M.A.; Kasturiratne, A.; Beddage, T.U.; Withanage, S. A.; Goonatilleke, D.C.; Abeysinghe, C.P.; de Mel, R.T.; Balapitiya, T.L.; de Silva, S.T.; Pathmeswaran, A.; Wickremasinghe, A.R.; Kato, N.; de Silva, H.J.
    BACKGROUND:Data on outcomes of non-alcoholic fatty liver disease (NAFLD) from South Asia are lacking. We compared mortality, among those with- and without-NAFLD, after 10-years follow-up among urban, adult Sri Lankans.METHOD:Participants (aged 35-64 years), selected by age-stratified random sampling, were screened by structured-interview in 2007. Anthropometric measurements, liver ultrasonography and biochemical/serological tests were done. NAFLD was diagnosed on ultrasound criteria, safe-alcohol consumption (Asian-standards) and absence of hepatitis B/C. Subjects without NAFLD were those without any ultrasound criteria of fatty liver, safe-alcohol consumption and absence of hepatitis B/C. The cohort was re-evaluated to assess mortality in 2017. Participants or their households were contacted by telephone/post, and deaths confirmed by home-visits and death certificate review. Cox-regression was used to determine predictors of all-cause mortality (ACM) and cardiovascular mortality (CVM) in those with- and without-NAFLD.RESULTS:2724 (91.2%) of 2985 original participants were contacted (851-with NAFLD and 1072-without NAFLD). Overall there were 169 (6.2%) deaths [41-deaths among NAFLD (17-cardiovascular; 9-cancer-related; 4-liver-specific; 11-other) and 79-deaths among no-NAFLD (28-cardiovascular; 17-cancer-related; 1-liver-specific; 33-other)]. Metabolic syndrome (MetS), low-education level, higher age and male-gender independently predicted ACM. MetS, increasing age and male-gender independently predicted CVM. NAFLD did not predict either ACM or CVM. In those with NAFLD, MetS and age >55-years were independently associated with ACM, while MetS and male-gender were associated with CVM.CONCLUSION:In this community-based study, increasing age, male-gender and MetS, but not NAFLD, predicted 10-year ACM and CVM. Among those with NAFLD, only those metabolically abnormal were at a higher risk for mortality.
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    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.
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    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).
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    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