Browsing by Author "Chen, J."

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Environmental Marketing as a Tool of Competitive Advantage: A Study Based on Organic Food in China
(2011) Wijesinghe, J.C.; Chen, J.
For the global economy to become ecologically sustainable, it is necessary to organise business and industry along ecologically sound principles and technologies. Due to the fact that, in recent years, both producers and consumers have been turning towards more environmentally friendly goods and services, environmental issues have become a major marketing focus. Therefore changes in patterns of consumption are just as important as changes to the production process. This research aims to investigate how consumer attitudes towards the production of environmentally friendly ?organic food? can change a firms? environmental behaviour to gain competitive advantages. This research is mainly based on Chinese consumers buying behaviour of organic food. Chinese consumers mostly consider ?Certification?, ?Branding?, ?Sensory appeal?, ?Social status?, and ?Value for money?. Food manufacturers may focus on these factors to gain competitive advantage over other manufacturers through environmental marketing.
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.
Medical students' and trainees' country-by-gender profiles: Hofstede's Cultural Dimensions across sixteen diverse countries
(Frontiers Media S.A, 2022) Monrouxe, L.V.; Chandratilake, M.; Chen, J.; Chhabra, S.; Zheng, L.; Costa, P.S.; Lee, Y.M.; Karnieli-Miller, O.; Nishigori, H.; Ogden, K.; Pawlikowska, T.; Riquelme, A.; Sethi, A.; Soemantri, D.; Wearn, A.; Wolvaardt, L.; Yusoff, M.S.B.; Yau, S.Y.
Purpose: The global mobility of medical student and trainee populations has drawn researchers' attention to consider internationalization in medical education. Recently, researchers have focused on cultural diversity, predominately drawing on Hofstede's cross-cultural analysis of cultural dimensions from general population data to explain their findings. However, to date no research has been specifically undertaken to examine cultural dimensions within a medical student or trainee population. This is problematic as within-country differences between gender and professional groups have been identified within these dimensions. We address this gap by drawing on the theoretical concept of national context effects: specifically Hofstede's six-dimensional perspective. In doing so we examine medical students' and trainees' country profiles across dimensions, country-by-gender clustering, and differences between our data and Hofstede's general population data. Methods: We undertook a cross-cultural online questionnaire study (eight languages) containing Hofstede's 2013 Values Survey. Our questionnaire was live between 1st March to 19th Aug 2018, and December 2018 to mitigate country holiday periods. We recruited undergraduate medical students and trainees with at least 6-months' clinical training using school-specific methods including emails, announcements, and snowballing. Results: We received 2,529 responses. Sixteen countries were retained for analyses (n = 2,307, 91%): Australia, Chile, China, Hong Kong, India, Indonesia, Ireland, Israel, Japan, Malaysia, New Zealand, Pakistan, South Africa, South Korea, Sri-Lanka, Taiwan. Power distance and masculinity are homogenous across countries. Uncertainty avoidance shows the greatest diversity. We identified four country clusters. Masculinity and uncertainty are uncorrelated with Hofstede's general population data. Conclusions: Our medical student and trainee data provides medical education researchers with more appropriate cultural dimension profiles than those from general population data. Country cluster profiles stimulate useful hypotheses for further research, especially as patterning between clusters cuts across traditional Eastern-Western divides with national culture being stronger than gendered influences. The Uncertainty dimension with its complex pattern across clusters is a particularly fruitful avenue for further investigation.
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.
Root-foraging behavior ensures the integrated growth of Vallisneria natans in heterogeneous sediments
(Environmental Science and Pollution Research, 2017) Chen, J.; Hu, X.; Cao, T.; Zhang, X.; Xi, Y.; Wen, X.; Su, H.; De Silva, W.; Zhu, T.; Ni, L.; Xie, P.
Abstract The present study was carried out to determine the efficacy of root foraging and the physiological response of Vallisnaria natans grown in heterogeneous sediments. V. natans was cultivated in two homogeneous and two heterogeneous sediments. The results suggested that V. natans grown in heterogeneous sediments presented a significantly higher root proportion in its total biomass, exhibited root foraging, and grew well, as indicated by a total biomass, ramet number, and plant height very close to those of plants grown in nutrient-rich clay sediment. Moreover, the more sensitive physiological response of the roots than the stems or the leaves to sediment nutrients suggested that root foraging occurred, and the approached values between the two heterogeneous sediments and the homogeneous clay sediment indicated that V. natans could satisfy its nutrient requirements via root foraging. The results may be useful in the recovery of macrophytes that remodel part (rather than all) of the substrate and can potentially improve habitats that are unsuitable for plant growth.
A Scoping review of clinical reasoning research with Asian healthcare professionals
(Kluwer Academic Publishers, 2021) Lee, C.Y.; Jenq, C.C.; Chandratilake, M.; Chen, J.; Chen, M.M.; Nishigori, H.; Wajid, G.; Yang, P.H.; Yusoff, M.S.B.; Monrouxe, L.
ABSTRACT: Clinical reasoning is the thought process that guides practice. Although a plethora of clinical reasoning studies in healthcare professionals exists, the majority appear to originate from Western cultures. A scoping review was undertaken to examine clinical reasoning related research across Asian cultures. PubMed, SciVerse Scopus, Web of Science and Airiti Library databases were searched. Inclusion criteria included full-text articles published in Asian countries (2007 to 2019). Search terms included clinical reasoning, thinking process, differential diagnosis, decision making, problem-based learning, critical thinking, healthcare profession, institution, medical students and nursing students. After applying exclusion criteria, n = 240 were included in the review. The number of publications increased in 2012 (from 5%, n = 13 in 2011 to 9%, n = 22) with a steady increase onwards to 12% (n = 29) in 2016. South Korea published the most articles (19%, n = 46) followed by Iran (17%, n = 41). Nurse Education Today published 11% of the articles (n = 26), followed by BMC Medical Education (5%, n = 13). Nursing and Medical students account for the largest population groups studied. Analysis of the articles resulted in seven themes: Evaluation of existing courses (30%, n = 73) being the most frequently identified theme. Only seven comparative articles showed cultural implications, but none provided direct evidence of the impact of culture on clinical reasoning. We illuminate the potential necessity of further research in clinical reasoning, specifically with a focus on how clinical reasoning is affected by national culture. A better understanding of current clinical reasoning research in Asian cultures may assist curricula developers in establishing a culturally appropriate learning environment. KEYWORDS: Asia; Clinical reasoning; Health professions; Scoping review.
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.; 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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.