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Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.
Συγγραφείς: Suzuki, K, Hatzikotoulas, K, Southam, L, Taylor, HJ, Yin, X, Lorenz, KM, Mandla, R, Huerta-Chagoya, A, Melloni, GEM, Kanoni, S, Rayner, NW, Bocher, O, Arruda, AL, Sonehara, K, Namba, S, Lee, SSK, Preuss, MH, Petty, LE, Schroeder, P, Vanderwerff, B, Kals, M, Bragg, F, Lin, K, Guo, X, Zhang, W, Yao, J, Kim, YJ, Graff, M, Takeuchi, F, Nano, J, Lamri, A, Nakatochi, M, Moon, S, Scott, RA, Cook, JP, Lee, J-J, Pan, I, Taliun, D, Parra, EJ, Chai, J-F, Bielak, LF, 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, SS, Noordam, R, Lim, VJY, Tam, CHT, Joo, YY, Chen, C-H, Raffield, LM, Prins, BP, Nicolas, A, Yanek, LR, Chen, G, Brody, JA, Kabagambe, E, An, P, Xiang, AH, Choi, HS, Cade, BE, Tan, J, Broadaway, KA, Williamson, A, Kamali, Z, Cui, J, Thangam, M, Adair, LS, Adeyemo, A, Aguilar-Salinas, CA, Ahluwalia, TS, Anand, SS, Bertoni, A, Bork-Jensen, J, Brandslund, I, Buchanan, TA, Burant, CF, Butterworth, AS, Canouil, M, Chan, JCN, Chang, L-C, Chee, M-L, Chen, J, Chen, S-H, Chen, Y-T, Chen, Z, Chuang, L-M, Cushman, M, Danesh, J, Das, SK, de Silva, HJ, Dedoussis, G, Dimitrov, L, Doumatey, AP, Du, S, Duan, Q, Eckardt, K-U, Emery, LS, Evans, DS, Evans, MK, Fischer, K, Floyd, JS, Ford, I, Franco, OH, Frayling, TM, Freedman, BI, Genter, P, Gerstein, HC, Giedraitis, V, González-Villalpando, C, González-Villalpando, ME, Gordon-Larsen, P, Gross, M, Guare, LA, Hackinger, S, Hakaste, L, Han, S, Hattersley, AT, Herder, C, Horikoshi, M, Howard, A-G, Hsueh, W, Huang, M, Huang, W, Hung, Y-J, Hwang, MY, Hwu, C-M, Ichihara, S, Ikram, MA, Ingelsson, M, Islam, MT, Isono, M, Jang, H-M, Jasmine, F, Jiang, G, Jonas, JB, Jørgensen, T, Kamanu, FK, Kandeel, FR, Kasturiratne, A, Katsuya, T, Kaur, V, Kawaguchi, T, Keaton, JM, Kho, AN, Khor, C-C, Kibriya, MG, Kim, D-H, Kronenberg, F, Kuusisto, J, Läll, K, Lange, LA, Lee, KM, Lee, M-S, Lee, NR, Leong, A, Li, L, Li, Y, Li-Gao, R, Ligthart, S, Lindgren, CM, Linneberg, A, Liu, C-T, Liu, J, Locke, AE, Louie, T, Luan, J, Luk, AO, Luo, X, Lv, J, Lynch, JA, Lyssenko, V, Maeda, S, Mamakou, V, Mansuri, SR, Matsuda, K, Meitinger, T, Melander, O, Metspalu, A, Mo, H, Morris, AD, Moura, FA, Nadler, JL, Nalls, MA, Nayak, U, Ntalla, I, Okada, Y, Orozco, L, Patel, SR, Patil, S, Pei, P, Pereira, MA, Peters, A, Pirie, FJ, Polikowsky, HG, Porneala, B, Prasad, G, Rasmussen-Torvik, LJ, Reiner, AP, Roden, M, Rohde, R, Roll, K, Sabanayagam, C, Sandow, K, Sankareswaran, A, Sattar, N, Schönherr, S, Shahriar, M, Shen, B, Shi, J, Shin, DM, Shojima, N, Smith, JA, So, WY, Stančáková, A, Steinthorsdottir, V, Stilp, AM, Strauch, K, Taylor, KD, Thorand, B, Thorsteinsdottir, U, Tomlinson, B, Tran, TC, Tsai, F-J, Tuomilehto, J, Tusie-Luna, T, Udler, MS, Valladares-Salgado, A, van Dam, RM, van Klinken, JB, Varma, R, Wacher-Rodarte, N, Wheeler, E, Wickremasinghe, AR, van Dijk, KW, Witte, DR, Yajnik, CS, Yamamoto, K, Yoon, K, Yu, C, Yuan, J-M, Yusuf, S, Zawistowski, M, Zhang, L, Zheng, W, VA Million Veteran Program, Raffel, LJ, Igase, M, Ipp, E, Redline, S, Cho, YS, Lind, L, Province, MA, Fornage, M, Hanis, CL, Ingelsson, E, Zonderman, AB, Psaty, BM, Wang, Y-X, Rotimi, CN, Becker, DM, Matsuda, F, Liu, Y, Yokota, M, Kardia, SLR, Peyser, PA, Pankow, JS, Engert, JC, Bonnefond, A, Froguel, P, Wilson, JG, Sheu, WHH, Wu, J-Y, Hayes, MG, Ma, RCW, Wong, T-Y, Mook-Kanamori, DO, Tuomi, T, Chandak, GR, Collins, FS, Bharadwaj, D, Paré, G, Sale, MM, Ahsan, H, Motala, AA, Shu, X-O, Park, K-S, Jukema, JW, Cruz, M, Chen, Y-DI, Rich, SS, McKean-Cowdin, R, Grallert, H, Cheng, C-Y, Ghanbari, M, Tai, E-S, Dupuis, J, Kato, N, Laakso, M, Köttgen, A, Koh, W-P, Bowden, DW, Palmer, CNA, Kooner, JS, Kooperberg, C, Liu, S, North, KE, Saleheen, D, Hansen, T, Pedersen, O, Wareham, NJ, Lee, J, Kim, B-J, Millwood, IY, Walters, RG, Stefansson, K, Ahlqvist, E, Goodarzi, MO, Mohlke, KL, Langenberg, C, Haiman, CA, Loos, RJF, Florez, JC, Rader, DJ, Ritchie, MD, Zöllner, S, Mägi, R, Marston, NA, Ruff, CT, van Heel, DA, Finer, S, Denny, JC, Yamauchi, T, Kadowaki, T, Chambers, JC, Ng, MCY, Sim, X, Below, JE, Tsao, PS, Chang, K-M, McCarthy, MI, Meigs, JB, Mahajan, A, Spracklen, CN, Mercader, JM, Boehnke, M, Rotter, JI, Vujkovic, M, Voight, BF, Morris, AP, Zeggini, E
Στοιχεία εκδότη: Nature Research
Έτος έκδοσης: 2024
Συλλογή: Queen Mary University of London: Queen Mary Research Online (QMRO)
Περιγραφή: 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.
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
Relation: Nature; https://qmro.qmul.ac.uk/xmlui/handle/123456789/95027
DOI: 10.1038/s41586-024-07019-6
Διαθεσιμότητα: https://qmro.qmul.ac.uk/xmlui/handle/123456789/95027
https://doi.org/10.1038/s41586-024-07019-6
Rights: This item is distributed under the terms of the Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ; © 2024, the Author(s). Published by Nature Research
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  Data: Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.
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– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: Nature Research
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2024
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: Queen Mary University of London: Queen Mary Research Online (QMRO)
– Name: Abstract
  Label: Description
  Group: Ab
  Data: 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 &lt; 5 &#215; 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.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article in journal/newspaper
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: Nature; https://qmro.qmul.ac.uk/xmlui/handle/123456789/95027
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1038/s41586-024-07019-6
– Name: URL
  Label: Availability
  Group: URL
  Data: https://qmro.qmul.ac.uk/xmlui/handle/123456789/95027&lt;br /&gt;https://doi.org/10.1038/s41586-024-07019-6
– Name: Copyright
  Label: Rights
  Group: Cpyrght
  Data: This item is distributed under the terms of the Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ; &#169; 2024, the Author(s). Published by Nature Research
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.2347ECCD
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        Value: 10.1038/s41586-024-07019-6
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      – TitleFull: Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.
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              M: 01
              Type: published
              Y: 2024
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