Author: "Fairhurst-Hunter Z" - Searchworks@Jio Institute Digital Library Search Results

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21 results on '"Fairhurst-Hunter Z"'

1. BMI and Well-being in people of East Asian and European Ancestry: a Mendelian randomisation study

Author: O’Loughlin, J, Casanova, F, Hughes, A, Fairhurst-Hunter, Z, Li, L, Chen, Z, Bowden, J, Watkins, E, Freathy, RM, Howe, LD, Walters, RG, Tyrell, J, and Group, China Kadoorie Biobank Collaborative
Published: 2023

2. A saturated map of common genetic variants associated with human height

Author: Yengo, L., 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, Shiou-Shiou, 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., Chang, Y.C., Chen, Chen, 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.S., Gorski, M., Grove, J, Lores-Motta, Laura, Pauper, M., Hollander, A.I. den, Hoyng, C.B., Kiemeney, L.A.L.M., Visscher, P.M., Hirschhorn, J.N., Yengo, L., 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, Shiou-Shiou, 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., Chang, Y.C., Chen, Chen, 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.S., Gorski, M., Grove, J, Lores-Motta, Laura, Pauper, M., Hollander, A.I. den, Hoyng, C.B., Kiemeney, L.A.L.M., Visscher, P.M., and Hirschhorn, J.N.
Abstract: Item does not contain fulltext, 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 sizes(1). 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 panel(2)) 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.
Published: 2022

3. Stroke genetics informs drug discovery and risk prediction across ancestries

Author: Mishra, A, Malik, R, Hachiya, T, Jurgenson, T, Namba, S, Posner, DC, Kamanu, FK, Koido, M, Le Grand, Q, Shi, M, He, Y, Georgakis, MK, Caro, I, Krebs, K, Liaw, Y-C, Vaura, FC, Lin, K, Winsvold, BS, Srinivasasainagendra, V, Parodi, L, Bae, H-J, Chauhan, G, Chong, MR, Tomppo, L, Akinyemi, R, Roshchupkin, GV, Habib, N, Jee, YH, Thomassen, JQ, Abedi, V, Carcel-Marquez, J, Nygaard, M, Leonard, HL, Yang, C, Yonova-Doing, E, Knol, MJ, Lewis, AJ, Judy, RL, Ago, T, Amouyel, P, Armstrong, ND, Bakker, MK, Bartz, TM, Bennett, DA, Bis, JC, Bordes, C, Borte, S, Cain, A, Ridker, PM, Cho, K, Chen, Z, Cruchaga, C, Cole, JW, de Jager, PL, de Cid, R, Endres, M, Ferreira, LE, Geerlings, MI, Gasca, NC, Gudnason, V, Hata, J, He, J, Heath, AK, Ho, Y-L, Havulinna, AS, Hopewell, JC, Hyacinth, HI, Inouye, M, Jacob, MA, Jeon, CE, Jern, C, Kamouchi, M, Keene, KL, Kitazono, T, Kittner, SJ, Konuma, T, Kumar, A, Lacaze, P, Launer, LJ, Lee, K-J, Lepik, K, Li, J, Li, L, Manichaikul, A, Markus, HS, Marston, NA, Meitinger, T, Mitchell, BD, Montellano, FA, Morisaki, T, Mosley, TH, Nalls, MA, Nordestgaard, BG, O'Donnell, MJ, Okada, Y, Onland-Moret, NC, Ovbiagele, B, Peters, A, Psaty, BM, Rich, SS, Rosand, J, Sabatine, MS, Sacco, RL, Saleheen, D, Sandset, EC, Salomaa, V, Sargurupremraj, M, Sasaki, M, Satizabal, CL, Schmidt, CO, Shimizu, A, Smith, NL, Sloane, KL, Sutoh, Y, Sun, YV, Tanno, K, Tiedt, S, Tatlisumak, T, Torres-Aguila, NP, Tiwari, HK, Tregouet, D-A, Trompet, S, Tuladhar, AM, Tybjaerg-Hansen, A, van Vugt, M, Vibo, R, Verma, SS, Wiggins, KL, Wennberg, P, Woo, D, Wilson, PWF, Xu, H, Yang, Q, Yoon, K, Lee, J-M, Cheng, Y-C, Meschia, JF, Chen, WM, Sale, MM, Zonderman, AB, Evans, MK, Wilson, JG, Correa, A, Traylor, M, Lewis, CM, Carty, CL, Reiner, A, Haessler, J, Langefeld, CD, Gottesman, RF, Yaffe, K, Liu, YM, Kooperberg, C, Lange, LA, Furie, KL, Arnett, DK, Benavente, OR, Grewal, RP, Peddareddygari, LR, Hveem, K, Lindstrom, S, Wang, L, Smith, EN, Gordon, W, van Hylckama Vlieg, A, de Andrade, M, Brody, JA, Pattee, JW, Brumpton, BM, Suchon, P, Chen, M-H, Frazer, KA, Turman, C, Germain, M, MacDonald, J, Braekkan, SK, Armasu, SM, Pankratz, N, Jackson, RD, Nielsen, JB, Giulianini, F, Puurunen, MK, Ibrahim, M, Heckbert, SR, Bammler, TK, McCauley, BM, Taylor, KD, Pankow, JS, Reiner, AP, Gabrielsen, ME, Deleuze, J-F, O'Donnell, CJ, Kim, J, McKnight, B, Kraft, P, Hansen, J-B, Rosendaal, FR, Heit, JA, Tang, W, Morange, P-E, Johnson, AD, Kabrhel, C, van Dijk, EJ, Koudstaal, PJ, Luijckx, G-J, Nederkoorn, PJ, van Oostenbrugge, RJ, Visser, MC, Wermer, MJH, Kappelle, LJ, Esko, T, Metspalu, A, Magi, R, Nelis, M, Irvin, MR, de Leeuw, F-E, Levi, CR, Maguire, J, Jimenez-Conde, J, Sharma, P, Sudlow, CLM, Rannikmae, K, Schmidt, R, Slowik, A, Pera, J, Thijs, VNS, Lindgren, AG, Ilinca, A, Melander, O, Engstrom, G, Rexrode, KM, Rothwell, PM, Stanne, TM, Johnson, JA, Danesh, J, Butterworth, AS, Heitsch, L, Boncoraglio, GB, Kubo, M, Pezzini, A, Rolfs, A, Giese, A-K, Weir, D, Ross, OA, Lemmons, R, Soderholm, M, Cushman, M, Jood, K, McDonough, CW, Bell, S, Linkohr, B, Lee, T-H, Putaala, J, Anderson, CD, Lopez, OL, Jian, X, Schminke, U, Cullell, N, Delgado, P, Ibanez, L, Krupinski, J, Lioutas, V, Matsuda, K, Montaner, J, Muino, E, Roquer, J, Sarnowski, C, Sattar, N, Sibolt, G, Teumer, A, Rutten-Jacobs, L, Kanai, M, Gretarsdottir, S, Rost, NS, Yusuf, S, Almgren, P, Ay, H, Bevan, S, Brown, RD, Carrera, C, Buring, JE, Chen, W-M, Cotlarciuc, I, de Bakker, PIW, DeStefano, AL, den Hoed, M, Duan, Q, Engelter, ST, Falcone, GJ, Gustafsson, S, Hassan, A, Holliday, EG, Howard, G, Hsu, F-C, Ingelsson, E, Harris, TB, Kissela, BM, Kleindorfer, DO, Langenberg, C, Lemmens, R, Leys, D, Lin, W-Y, Lorentzen, E, Magnusson, PK, McArdle, PF, Pulit, SL, Rice, K, Sakaue, S, Sapkota, BR, Tanislav, C, Thorleifsson, G, Thorsteinsdottir, U, Tzourio, C, van Duijn, CM, Walters, M, Wareham, NJ, Amin, N, Aparicio, HJ, Attia, J, Beiser, AS, Berr, C, Bustamante, M, Caso, V, Choi, SH, Chowhan, A, Dartigues, J-F, Delavaran, H, Dorr, M, Ford, I, Gurpreet, WS, Hamsten, A, Hozawa, A, Ingelsson, M, Iwasaki, M, Kaffashian, S, Kalra, L, Kjartansson, O, Kloss, M, Labovitz, DL, Laurie, CC, Lind, L, Lindgren, CM, Makoto, H, Minegishi, N, Morris, AP, Muller-Nurasyid, M, Norrving, B, Ogishima, S, Parati, EA, Pedersen, NL, Perola, M, Jousilahti, P, Pileggi, S, Rabionet, R, Riba-Llena, I, Ribases, M, Romero, JR, Rudd, AG, Sarin, A-P, Sarju, R, Satoh, M, Sawada, N, Sigurdsson, A, Smith, A, Stine, OC, Stott, DJ, Strauch, K, Takai, T, Tanaka, H, Touze, E, Tsugane, S, Uitterlinden, AG, Valdimarsson, EM, van der Lee, SJ, Wakai, K, Williams, SR, Wolfe, CDA, Wong, Q, Yamaji, T, Sanghera, DK, Stefansson, K, Martinez-Majander, N, Sobue, K, Soriano-Tarraga, C, Volzke, H, Akpa, O, Sarfo, FS, Akpalu, A, Obiako, R, Wahab, K, Osaigbovo, G, Owolabi, L, Komolafe, M, Jenkins, C, Arulogun, O, Ogbole, G, Adeoye, AM, Akinyemi, J, Agunloye, A, Fakunle, AG, Uvere, E, Olalere, A, Adebajo, OJ, Chen, J, Clarke, R, Collins, R, Guo, Y, Wang, C, Lv, J, Peto, R, Chen, Y, Fairhurst-Hunter, Z, Hill, M, Pozarickij, A, Schmidt, D, Stevens, B, Turnbull, I, Yu, C, Nagai, A, Murakami, Y, Shiroma, EJ, Sigurdsson, S, Ghanbari, M, Boerwinkle, E, Fongang, B, Wang, R, Ikram, MK, Volker, U, de Laat, KF, van Norden, AGW, de Kort, PL, Vermeer, SE, Brouwers, PJAM, Gons, RAR, den Heijer, T, van Dijk, GW, van Rooij, FGW, Aamodt, AH, Skogholt, AH, Willer, CJ, Heuch, I, Hagen, K, Fritsche, LG, Pedersen, LM, Ellekjaer, H, Zhou, W, Martinsen, AE, Kristoffersen, ES, Thomas, LF, Kleinschnitz, C, Frantz, S, Ungethum, K, Gallego-Fabrega, C, Lledos, M, Llucia-Carol, L, Sobrino, T, Campos, F, Castillo, J, Freijo, M, Arenillas, JF, Obach, V, Alvarez-Sabin, J, Molina, CA, Ribo, M, Munoz-Narbona, L, Lopez-Cancio, E, Millan, M, Diaz-Navarro, R, Vives-Bauza, C, Serrano-Heras, G, Segura, T, Dhar, R, Delgado-Mederos, R, Prats-Sanchez, L, Camps-Renom, P, Blay, N, Sumoy, L, Marti-Fabregas, J, Schnohr, P, Jensen, GB, Benn, M, Afzal, S, Kamstrup, PR, van Setten, J, van der Laan, SW, Vonk, JMJ, Kim, B-J, Curtze, S, Tiainen, M, Kinnunen, J, Menon, V, Sung, YJ, Saillour-Glenisson, F, Gravel, S, Millwood, IY, Gieger, C, Ninomiya, T, Grabe, HJ, Jukema, JW, Rissanen, IL, Strbian, D, Kim, YJ, Chen, P-H, Mayerhofer, E, Howson, JMM, Adams, H, Wassertheil-Smoller, S, Christensen, K, Ikram, MA, Rundek, T, Worrall, BB, Lathrop, GM, Riaz, M, Simonsick, EM, Korv, J, Franca, PHC, Zand, R, Prasad, K, Frikke-Schmidt, R, Liman, T, Haeusler, KG, Ruigrok, YM, Heuschmann, PU, Longstreth, WT, Jung, KJ, Bastarache, L, Pare, G, Damrauer, SM, Chasman, DI, Rotter, JI, Zwart, J-A, Niiranen, TJ, Fornage, M, Liaw, Y-P, Seshadri, S, Fernandez-Cadenas, I, Walters, RG, Ruff, CT, Owolabi, MO, Huffman, JE, Milani, L, Kamatani, Y, Dichgans, M, Debette, S, Mishra, A, Malik, R, Hachiya, T, Jurgenson, T, Namba, S, Posner, DC, Kamanu, FK, Koido, M, Le Grand, Q, Shi, M, He, Y, Georgakis, MK, Caro, I, Krebs, K, Liaw, Y-C, Vaura, FC, Lin, K, Winsvold, BS, Srinivasasainagendra, V, Parodi, L, Bae, H-J, Chauhan, G, Chong, MR, Tomppo, L, Akinyemi, R, Roshchupkin, GV, Habib, N, Jee, YH, Thomassen, JQ, Abedi, V, Carcel-Marquez, J, Nygaard, M, Leonard, HL, Yang, C, Yonova-Doing, E, Knol, MJ, Lewis, AJ, Judy, RL, Ago, T, Amouyel, P, Armstrong, ND, Bakker, MK, Bartz, TM, Bennett, DA, Bis, JC, Bordes, C, Borte, S, Cain, A, Ridker, PM, Cho, K, Chen, Z, Cruchaga, C, Cole, JW, de Jager, PL, de Cid, R, Endres, M, Ferreira, LE, Geerlings, MI, Gasca, NC, Gudnason, V, Hata, J, He, J, Heath, AK, Ho, Y-L, Havulinna, AS, Hopewell, JC, Hyacinth, HI, Inouye, M, Jacob, MA, Jeon, CE, Jern, C, Kamouchi, M, Keene, KL, Kitazono, T, Kittner, SJ, Konuma, T, Kumar, A, Lacaze, P, Launer, LJ, Lee, K-J, Lepik, K, Li, J, Li, L, Manichaikul, A, Markus, HS, Marston, NA, Meitinger, T, Mitchell, BD, Montellano, FA, Morisaki, T, Mosley, TH, Nalls, MA, Nordestgaard, BG, O'Donnell, MJ, Okada, Y, Onland-Moret, NC, Ovbiagele, B, Peters, A, Psaty, BM, Rich, SS, Rosand, J, Sabatine, MS, Sacco, RL, Saleheen, D, Sandset, EC, Salomaa, V, Sargurupremraj, M, Sasaki, M, Satizabal, CL, Schmidt, CO, Shimizu, A, Smith, NL, Sloane, KL, Sutoh, Y, Sun, YV, Tanno, K, Tiedt, S, Tatlisumak, T, Torres-Aguila, NP, Tiwari, HK, Tregouet, D-A, Trompet, S, Tuladhar, AM, Tybjaerg-Hansen, A, van Vugt, M, Vibo, R, Verma, SS, Wiggins, KL, Wennberg, P, Woo, D, Wilson, PWF, Xu, H, Yang, Q, Yoon, K, Lee, J-M, Cheng, Y-C, Meschia, JF, Chen, WM, Sale, MM, Zonderman, AB, Evans, MK, Wilson, JG, Correa, A, Traylor, M, Lewis, CM, Carty, CL, Reiner, A, Haessler, J, Langefeld, CD, Gottesman, RF, Yaffe, K, Liu, YM, Kooperberg, C, Lange, LA, Furie, KL, Arnett, DK, Benavente, OR, Grewal, RP, Peddareddygari, LR, Hveem, K, Lindstrom, S, Wang, L, Smith, EN, Gordon, W, van Hylckama Vlieg, A, de Andrade, M, Brody, JA, Pattee, JW, Brumpton, BM, Suchon, P, Chen, M-H, Frazer, KA, Turman, C, Germain, M, MacDonald, J, Braekkan, SK, Armasu, SM, Pankratz, N, Jackson, RD, Nielsen, JB, Giulianini, F, Puurunen, MK, Ibrahim, M, Heckbert, SR, Bammler, TK, McCauley, BM, Taylor, KD, Pankow, JS, Reiner, AP, Gabrielsen, ME, Deleuze, J-F, O'Donnell, CJ, Kim, J, McKnight, B, Kraft, P, Hansen, J-B, Rosendaal, FR, Heit, JA, Tang, W, Morange, P-E, Johnson, AD, Kabrhel, C, van Dijk, EJ, Koudstaal, PJ, Luijckx, G-J, Nederkoorn, PJ, van Oostenbrugge, RJ, Visser, MC, Wermer, MJH, Kappelle, LJ, Esko, T, Metspalu, A, Magi, R, Nelis, M, Irvin, MR, de Leeuw, F-E, Levi, CR, Maguire, J, Jimenez-Conde, J, Sharma, P, Sudlow, CLM, Rannikmae, K, Schmidt, R, Slowik, A, Pera, J, Thijs, VNS, Lindgren, AG, Ilinca, A, Melander, O, Engstrom, G, Rexrode, KM, Rothwell, PM, Stanne, TM, Johnson, JA, Danesh, J, Butterworth, AS, Heitsch, L, Boncoraglio, GB, Kubo, M, Pezzini, A, Rolfs, A, Giese, A-K, Weir, D, Ross, OA, Lemmons, R, Soderholm, M, Cushman, M, Jood, K, McDonough, CW, Bell, S, Linkohr, B, Lee, T-H, Putaala, J, Anderson, CD, Lopez, OL, Jian, X, Schminke, U, Cullell, N, Delgado, P, Ibanez, L, Krupinski, J, Lioutas, V, Matsuda, K, Montaner, J, Muino, E, Roquer, J, Sarnowski, C, Sattar, N, Sibolt, G, Teumer, A, Rutten-Jacobs, L, Kanai, M, Gretarsdottir, S, Rost, NS, Yusuf, S, Almgren, P, Ay, H, Bevan, S, Brown, RD, Carrera, C, Buring, JE, Chen, W-M, Cotlarciuc, I, de Bakker, PIW, DeStefano, AL, den Hoed, M, Duan, Q, Engelter, ST, Falcone, GJ, Gustafsson, S, Hassan, A, Holliday, EG, Howard, G, Hsu, F-C, Ingelsson, E, Harris, TB, Kissela, BM, Kleindorfer, DO, Langenberg, C, Lemmens, R, Leys, D, Lin, W-Y, Lorentzen, E, Magnusson, PK, McArdle, PF, Pulit, SL, Rice, K, Sakaue, S, Sapkota, BR, Tanislav, C, Thorleifsson, G, Thorsteinsdottir, U, Tzourio, C, van Duijn, CM, Walters, M, Wareham, NJ, Amin, N, Aparicio, HJ, Attia, J, Beiser, AS, Berr, C, Bustamante, M, Caso, V, Choi, SH, Chowhan, A, Dartigues, J-F, Delavaran, H, Dorr, M, Ford, I, Gurpreet, WS, Hamsten, A, Hozawa, A, Ingelsson, M, Iwasaki, M, Kaffashian, S, Kalra, L, Kjartansson, O, Kloss, M, Labovitz, DL, Laurie, CC, Lind, L, Lindgren, CM, Makoto, H, Minegishi, N, Morris, AP, Muller-Nurasyid, M, Norrving, B, Ogishima, S, Parati, EA, Pedersen, NL, Perola, M, Jousilahti, P, Pileggi, S, Rabionet, R, Riba-Llena, I, Ribases, M, Romero, JR, Rudd, AG, Sarin, A-P, Sarju, R, Satoh, M, Sawada, N, Sigurdsson, A, Smith, A, Stine, OC, Stott, DJ, Strauch, K, Takai, T, Tanaka, H, Touze, E, Tsugane, S, Uitterlinden, AG, Valdimarsson, EM, van der Lee, SJ, Wakai, K, Williams, SR, Wolfe, CDA, Wong, Q, Yamaji, T, Sanghera, DK, Stefansson, K, Martinez-Majander, N, Sobue, K, Soriano-Tarraga, C, Volzke, H, Akpa, O, Sarfo, FS, Akpalu, A, Obiako, R, Wahab, K, Osaigbovo, G, Owolabi, L, Komolafe, M, Jenkins, C, Arulogun, O, Ogbole, G, Adeoye, AM, Akinyemi, J, Agunloye, A, Fakunle, AG, Uvere, E, Olalere, A, Adebajo, OJ, Chen, J, Clarke, R, Collins, R, Guo, Y, Wang, C, Lv, J, Peto, R, Chen, Y, Fairhurst-Hunter, Z, Hill, M, Pozarickij, A, Schmidt, D, Stevens, B, Turnbull, I, Yu, C, Nagai, A, Murakami, Y, Shiroma, EJ, Sigurdsson, S, Ghanbari, M, Boerwinkle, E, Fongang, B, Wang, R, Ikram, MK, Volker, U, de Laat, KF, van Norden, AGW, de Kort, PL, Vermeer, SE, Brouwers, PJAM, Gons, RAR, den Heijer, T, van Dijk, GW, van Rooij, FGW, Aamodt, AH, Skogholt, AH, Willer, CJ, Heuch, I, Hagen, K, Fritsche, LG, Pedersen, LM, Ellekjaer, H, Zhou, W, Martinsen, AE, Kristoffersen, ES, Thomas, LF, Kleinschnitz, C, Frantz, S, Ungethum, K, Gallego-Fabrega, C, Lledos, M, Llucia-Carol, L, Sobrino, T, Campos, F, Castillo, J, Freijo, M, Arenillas, JF, Obach, V, Alvarez-Sabin, J, Molina, CA, Ribo, M, Munoz-Narbona, L, Lopez-Cancio, E, Millan, M, Diaz-Navarro, R, Vives-Bauza, C, Serrano-Heras, G, Segura, T, Dhar, R, Delgado-Mederos, R, Prats-Sanchez, L, Camps-Renom, P, Blay, N, Sumoy, L, Marti-Fabregas, J, Schnohr, P, Jensen, GB, Benn, M, Afzal, S, Kamstrup, PR, van Setten, J, van der Laan, SW, Vonk, JMJ, Kim, B-J, Curtze, S, Tiainen, M, Kinnunen, J, Menon, V, Sung, YJ, Saillour-Glenisson, F, Gravel, S, Millwood, IY, Gieger, C, Ninomiya, T, Grabe, HJ, Jukema, JW, Rissanen, IL, Strbian, D, Kim, YJ, Chen, P-H, Mayerhofer, E, Howson, JMM, Adams, H, Wassertheil-Smoller, S, Christensen, K, Ikram, MA, Rundek, T, Worrall, BB, Lathrop, GM, Riaz, M, Simonsick, EM, Korv, J, Franca, PHC, Zand, R, Prasad, K, Frikke-Schmidt, R, Liman, T, Haeusler, KG, Ruigrok, YM, Heuschmann, PU, Longstreth, WT, Jung, KJ, Bastarache, L, Pare, G, Damrauer, SM, Chasman, DI, Rotter, JI, Zwart, J-A, Niiranen, TJ, Fornage, M, Liaw, Y-P, Seshadri, S, Fernandez-Cadenas, I, Walters, RG, Ruff, CT, Owolabi, MO, Huffman, JE, Milani, L, Kamatani, Y, Dichgans, M, and Debette, S
Abstract: Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.
Published: 2022

4. A saturated map of common genetic variants associated with human height

Author: Yengo, L, Vedantam, S, Marouli, E, Sidorenko, J, Bartell, E, Sakaue, S, Graff, M, Eliasen, AU, Jiang, Y, Raghavan, S, Miao, J, Arias, JD, Graham, SE, Mukamel, RE, Spracklen, CN, Yin, X, Chen, S-H, Ferreira, T, Highland, HH, Ji, Y, Karaderi, T, Lin, K, Lull, K, Malden, DE, Medina-Gomez, C, Machado, M, Moore, A, Rueger, S, Sim, X, Vrieze, S, Ahluwalia, TS, Akiyama, M, Allison, MA, Alvarez, M, Andersen, MK, Ani, A, Appadurai, V, Arbeeva, L, Bhaskar, S, Bielak, LF, Bollepalli, S, Bonnycastle, LL, Bork-Jensen, J, Bradfield, JP, Bradford, Y, Braund, PS, Brody, JA, Burgdorf, KS, Cade, BE, Cai, H, Cai, Q, Campbell, A, Canadas-Garre, M, Catamo, E, Chai, J-F, Chai, X, Chang, L-C, Chang, Y-C, Chen, C-H, Chesi, A, Choi, SH, Chung, R-H, Cocca, M, Concas, MP, Couture, C, Cuellar-Partida, G, Danning, R, Daw, EW, Degenhard, F, Delgado, GE, Delitala, A, Demirkan, A, Deng, X, Devineni, P, Dietl, A, Dimitriou, M, Dimitrov, L, Dorajoo, R, Ekici, AB, Engmann, JE, Fairhurst-Hunter, Z, Farmaki, A-E, Faul, JD, Fernandez-Lopez, J-C, Forer, L, Francescatto, M, Freitag-Wolf, S, Fuchsberger, C, Galesloot, TE, Gao, Y, Gao, Z, Geller, F, Giannakopoulou, O, Giulianini, F, Gjesing, AP, Goel, A, Gordon, SD, Gorski, M, Grove, J, Guo, X, Gustafsson, S, Haessler, J, Hansen, TF, Havulinna, AS, Haworth, SJ, He, J, Heard-Costa, N, Hebbar, P, Hindy, G, Ho, Y-LA, Hofer, E, Holliday, E, Horn, K, Hornsby, WE, Hottenga, J-J, Huang, H, Huang, J, Huerta-Chagoya, A, Huffman, JE, Hung, Y-J, Huo, S, Hwang, MY, Iha, H, Ikeda, DD, Isono, M, Jackson, AU, Jager, S, Jansen, IE, Johansson, I, Jonas, JB, Jonsson, A, Jorgensen, T, Kalafati, I-P, Kanai, M, Kanoni, S, Karhus, LL, Kasturiratne, A, Katsuya, T, Kawaguchi, T, Kember, RL, Kentistou, KA, Kim, H-N, Kim, YJ, Kleber, ME, Knol, MJ, Kurbasic, A, Lauzon, M, Le, P, Lea, R, Lee, J-Y, Leonard, HL, Li, SA, Li, X, Liang, J, Lin, H, Lin, S-Y, Liu, J, Liu, X, Lo, KS, Long, J, Lores-Motta, L, Luan, J, Lyssenko, V, Lyytikainen, L-P, Mahajan, A, Mamakou, V, Mangino, M, Manichaikul, A, Marten, J, Mattheisen, M, Mavarani, L, McDaid, AF, Meidtner, K, Melendez, TL, Mercader, JM, Milaneschi, Y, Miller, JE, Millwood, IY, Mishra, PP, Mitchell, RE, Mollehave, LT, Morgan, A, Mucha, S, Munz, M, Nakatochi, M, Nelson, CP, Nethander, M, Nho, CW, Nielsen, AA, Nolte, IM, Nongmaithem, SS, Noordam, R, Ntalla, I, Nutile, T, Pandit, A, Christofidou, P, Parna, K, Pauper, M, Petersen, ERB, Petersen, L, Pitkanen, N, Polasek, O, Poveda, A, Preuss, MH, Pyarajan, S, Raffield, LM, Rakugi, H, Ramirez, J, Rasheed, A, Raven, D, Rayner, NW, Riveros, C, Rohde, R, Ruggiero, D, Ruotsalainen, SE, Ryan, KA, Sabater-Lleal, M, Saxena, R, Scholz, M, Sendamarai, A, Shen, B, Shi, J, Shin, JH, Sidore, C, Sitlani, CM, Slieker, RKC, Smit, RAJ, Smith, A, Smith, JA, Smyth, LJ, Southam, LE, Steinthorsdottir, V, Sun, L, Takeuchi, F, Tallapragada, D, Taylor, KD, Tayo, BO, Tcheandjieu, C, Terzikhan, N, Tesolin, P, Teumer, A, Theusch, E, Thompson, DJ, Thorleifsson, G, Timmers, PRHJ, Trompet, S, Turman, C, Vaccargiu, S, van der Laan, SW, van der Most, PJ, van Klinken, JB, van Setten, J, Verma, SS, Verweij, N, Veturi, Y, Wang, CA, Wang, C, Wang, L, Wang, Z, Warren, HR, Wei, WB, Wickremasinghe, AR, Wielscher, M, Wiggins, KL, Winsvold, BS, Wong, A, Wu, Y, Wuttke, M, Xia, R, Xie, T, Yamamoto, K, Yang, J, Yao, J, Young, H, Yousri, NA, Yu, L, Zeng, L, Zhang, W, Zhang, X, Zhao, J-H, Zhao, W, Zhou, W, Zimmermann, ME, Zoledziewska, M, Adair, LS, Adams, HHH, Aguilar-Salinas, CA, Al-Mulla, F, Arnett, DK, Asselbergs, FW, Asvold, BO, Attia, J, Banas, B, Bandinelli, S, Bennett, DA, Bergler, T, Bharadwaj, D, Biino, G, Bisgaard, H, Boerwinkle, E, Boger, CA, Bonnelykke, K, Boomsma, D, Borglum, AD, Borja, JB, Bouchard, C, Bowden, DW, Brandslund, I, Brumpton, B, Buring, JE, Caulfield, MJ, Chambers, JC, Chandak, GR, Chanock, SJ, Chaturvedi, N, Chen, Y-DI, Chen, Z, Cheng, C-Y, Christophersen, IE, Ciullo, M, Cole, JW, Collins, FS, Cooper, RS, Cruz, M, Cucca, F, Cupples, LA, Cutler, MJ, Damrauer, SM, Dantoft, TM, de Borst, GJ, de Groot, LCPGM, De Jager, PL, de Kleijn, DP, de Silva, HJ, Dedoussis, G, den Hollander, A, Du, S, Easton, DF, Elders, PJM, Eliassen, AH, Ellinor, PT, Elmstahl, S, Erdmann, J, Evans, MK, Fatkin, D, Feenstra, B, Feitosa, MF, Ferrucci, L, Ford, I, Fornage, M, Franke, A, Franks, PW, Freedman, B, Gasparini, P, Gieger, C, Girotto, G, Goddard, ME, Golightly, YM, Gonzalez-Villalpando, C, Gordon-Larsen, P, Grallert, H, Grant, SFA, Grarup, N, Griffiths, L, Gudnason, V, Haiman, C, Hakonarson, H, Hansen, T, Hartman, CA, Hattersley, AT, Hayward, C, Heckbert, SR, Heng, C-K, Hengstenberg, C, Hewitt, AW, Hishigaki, H, Hoyng, CB, Huang, PL, Huang, W, Hunt, SC, Hveem, K, Hypponen, E, Iacono, WG, Ichihara, S, Ikram, MA, Isasi, CR, Jackson, RD, Jarvelin, M-R, Jin, Z-B, Jockel, K-H, Joshi, PK, Jousilahti, P, Jukema, JW, Kahonen, M, Kamatani, Y, Kang, KD, Kaprio, J, Kardia, SLR, Karpe, F, Kato, N, Kee, F, Kessler, T, Khera, A, Khor, CC, Kiemeney, LALM, Kim, B-J, Kim, EK, Kim, H-L, Kirchhof, P, Kivimaki, M, Koh, W-P, Koistinen, HA, Kolovou, GD, Kooner, JS, Kooperberg, C, Kottgen, A, Kovacs, P, Kraaijeveld, A, Kraft, P, Krauss, RM, Kumari, M, Kutalik, Z, Laakso, M, Lange, LA, Langenberg, C, Launer, LJ, Le Marchand, L, Lee, H, Lee, NR, Lehtimaki, T, Li, H, Li, L, Lieb, W, Lin, X, Lind, L, Linneberg, A, Liu, C-T, Loeffler, M, London, B, Lubitz, SA, Lye, SJ, Mackey, DA, Magi, R, Magnusson, PKE, Marcus, GM, Vidal, PM, Martin, NG, Marz, W, Matsuda, F, McGarrah, RW, McGue, M, McKnight, AJ, Medland, SE, Mellstrom, D, Metspalu, A, Mitchell, BD, Mitchell, P, Mook-Kanamori, DO, Morris, AD, Mucci, LA, Munroe, PB, Nalls, MA, Nazarian, S, Nelson, AE, Neville, MJ, Newton-Cheh, C, Nielsen, CS, Nothen, MM, Ohlsson, C, Oldehinkel, AJ, Orozco, L, Pahkala, K, Pajukanta, P, Palmer, CNA, Parra, EJ, Pattaro, C, Pedersen, O, Pennell, CE, Penninx, BWJH, Perusse, L, Peters, A, Peyser, PA, Porteous, DJ, Posthuma, D, Power, C, Pramstaller, PP, Province, MA, Qi, Q, Qu, J, Rader, DJ, Raitakari, OT, Ralhan, S, Rallidis, LS, Rao, DC, Redline, S, Reilly, DF, Reiner, AP, Rhee, SY, Ridker, PM, Rienstra, M, Ripatti, S, Ritchie, MD, Roden, DM, Rosendaal, FR, Rotter, J, Rudan, I, Rutters, F, Sabanayagam, C, Saleheen, D, Salomaa, V, Samani, NJ, Sanghera, DK, Sattar, N, Schmidt, B, Schmidt, H, Schmidt, R, Schulze, MB, Schunkert, H, Scott, LJ, Scott, RJ, Sever, P, Shiroma, EJ, Shoemaker, MB, Shu, X-O, Simonsick, EM, Sims, M, Singh, JR, Singleton, AB, Sinner, MF, Smith, JG, Snieder, H, Spector, TD, Stampfer, MJ, Stark, KJ, Strachan, DP, t' Hart, LM, Tabara, Y, Tang, H, Tardif, J-C, Thanaraj, TA, Timpson, NJ, Tonjes, A, Tremblay, A, Tuomi, T, Tuomilehto, J, Tusie-Luna, M-T, Uitterlinden, AG, van Dam, RM, van der Harst, P, Van der Velde, N, van Duijn, CM, van Schoor, NM, Vitart, V, Volker, U, Vollenweider, P, Volzke, H, Wacher-Rodarte, NH, Walker, M, Wang, YX, Wareham, NJ, Watanabe, RM, Watkins, H, Weir, DR, Werge, TM, Widen, E, Wilkens, LR, Willemsen, G, Willett, WC, Wilson, JF, Wong, T-Y, Woo, J-T, Wright, AF, Wu, J-Y, Xu, H, Yajnik, CS, Yokota, M, Yuan, J-M, Zeggini, E, Zemel, BS, Zheng, W, Zhu, X, Zmuda, JM, Zonderman, AB, Zwart, J-A, Chasman, D, Cho, YS, Heid, IM, McCarthy, M, Ng, MCY, O'Donnell, CJ, Rivadeneira, F, Thorsteinsdottir, U, Sun, Y, Tai, ES, Boehnke, M, Deloukas, P, Justice, AE, Lindgren, CM, Loos, RJF, Mohlke, KL, North, KE, Stefansson, K, Walters, RG, Winkler, TW, Young, KL, Loh, P-R, Esko, T, Assimes, TL, Auton, A, Abecasis, GR, Willer, CJ, Locke, AE, Berndt, S, Lettre, G, Frayling, TM, Okada, Y, Wood, AR, Visscher, PM, Hirschhorn, JN, Yengo, L, Vedantam, S, Marouli, E, Sidorenko, J, Bartell, E, Sakaue, S, Graff, M, Eliasen, AU, Jiang, Y, Raghavan, S, Miao, J, Arias, JD, Graham, SE, Mukamel, RE, Spracklen, CN, Yin, X, Chen, S-H, Ferreira, T, Highland, HH, Ji, Y, Karaderi, T, Lin, K, Lull, K, Malden, DE, Medina-Gomez, C, Machado, M, Moore, A, Rueger, S, Sim, X, Vrieze, S, Ahluwalia, TS, Akiyama, M, Allison, MA, Alvarez, M, Andersen, MK, Ani, A, Appadurai, V, Arbeeva, L, Bhaskar, S, Bielak, LF, Bollepalli, S, Bonnycastle, LL, Bork-Jensen, J, Bradfield, JP, Bradford, Y, Braund, PS, Brody, JA, Burgdorf, KS, Cade, BE, Cai, H, Cai, Q, Campbell, A, Canadas-Garre, M, Catamo, E, Chai, J-F, Chai, X, Chang, L-C, Chang, Y-C, Chen, C-H, Chesi, A, Choi, SH, Chung, R-H, Cocca, M, Concas, MP, Couture, C, Cuellar-Partida, G, Danning, R, Daw, EW, Degenhard, F, Delgado, GE, Delitala, A, Demirkan, A, Deng, X, Devineni, P, Dietl, A, Dimitriou, M, Dimitrov, L, Dorajoo, R, Ekici, AB, Engmann, JE, Fairhurst-Hunter, Z, Farmaki, A-E, Faul, JD, Fernandez-Lopez, J-C, Forer, L, Francescatto, M, Freitag-Wolf, S, Fuchsberger, C, Galesloot, TE, Gao, Y, Gao, Z, Geller, F, Giannakopoulou, O, Giulianini, F, Gjesing, AP, Goel, A, Gordon, SD, Gorski, M, Grove, J, Guo, X, Gustafsson, S, Haessler, J, Hansen, TF, Havulinna, AS, Haworth, SJ, He, J, Heard-Costa, N, Hebbar, P, Hindy, G, Ho, Y-LA, Hofer, E, Holliday, E, Horn, K, Hornsby, WE, Hottenga, J-J, Huang, H, Huang, J, Huerta-Chagoya, A, Huffman, JE, Hung, Y-J, Huo, S, Hwang, MY, Iha, H, Ikeda, DD, Isono, M, Jackson, AU, Jager, S, Jansen, IE, Johansson, I, Jonas, JB, Jonsson, A, Jorgensen, T, Kalafati, I-P, Kanai, M, Kanoni, S, Karhus, LL, Kasturiratne, A, Katsuya, T, Kawaguchi, T, Kember, RL, Kentistou, KA, Kim, H-N, Kim, YJ, Kleber, ME, Knol, MJ, Kurbasic, A, Lauzon, M, Le, P, Lea, R, Lee, J-Y, Leonard, HL, Li, SA, Li, X, Liang, J, Lin, H, Lin, S-Y, Liu, J, Liu, X, Lo, KS, Long, J, Lores-Motta, L, Luan, J, Lyssenko, V, Lyytikainen, L-P, Mahajan, A, Mamakou, V, Mangino, M, Manichaikul, A, Marten, J, Mattheisen, M, Mavarani, L, McDaid, AF, Meidtner, K, Melendez, TL, Mercader, JM, Milaneschi, Y, Miller, JE, Millwood, IY, Mishra, PP, Mitchell, RE, Mollehave, LT, Morgan, A, Mucha, S, Munz, M, Nakatochi, M, Nelson, CP, Nethander, M, Nho, CW, Nielsen, AA, Nolte, IM, Nongmaithem, SS, Noordam, R, Ntalla, I, Nutile, T, Pandit, A, Christofidou, P, Parna, K, Pauper, M, Petersen, ERB, Petersen, L, Pitkanen, N, Polasek, O, Poveda, A, Preuss, MH, Pyarajan, S, Raffield, LM, Rakugi, H, Ramirez, J, Rasheed, A, Raven, D, Rayner, NW, Riveros, C, Rohde, R, Ruggiero, D, Ruotsalainen, SE, Ryan, KA, Sabater-Lleal, M, Saxena, R, Scholz, M, Sendamarai, A, Shen, B, Shi, J, Shin, JH, Sidore, C, Sitlani, CM, Slieker, RKC, Smit, RAJ, Smith, A, Smith, JA, Smyth, LJ, Southam, LE, Steinthorsdottir, V, Sun, L, Takeuchi, F, Tallapragada, D, Taylor, KD, Tayo, BO, Tcheandjieu, C, Terzikhan, N, Tesolin, P, Teumer, A, Theusch, E, Thompson, DJ, Thorleifsson, G, Timmers, PRHJ, Trompet, S, Turman, C, Vaccargiu, S, van der Laan, SW, van der Most, PJ, van Klinken, JB, van Setten, J, Verma, SS, Verweij, N, Veturi, Y, Wang, CA, Wang, C, Wang, L, Wang, Z, Warren, HR, Wei, WB, Wickremasinghe, AR, Wielscher, M, Wiggins, KL, Winsvold, BS, Wong, A, Wu, Y, Wuttke, M, Xia, R, Xie, T, Yamamoto, K, Yang, J, Yao, J, Young, H, Yousri, NA, Yu, L, Zeng, L, Zhang, W, Zhang, X, Zhao, J-H, Zhao, W, Zhou, W, Zimmermann, ME, Zoledziewska, M, Adair, LS, Adams, HHH, Aguilar-Salinas, CA, Al-Mulla, F, Arnett, DK, Asselbergs, FW, Asvold, BO, Attia, J, Banas, B, Bandinelli, S, Bennett, DA, Bergler, T, Bharadwaj, D, Biino, G, Bisgaard, H, Boerwinkle, E, Boger, CA, Bonnelykke, K, Boomsma, D, Borglum, AD, Borja, JB, Bouchard, C, Bowden, DW, Brandslund, I, Brumpton, B, Buring, JE, Caulfield, MJ, Chambers, JC, Chandak, GR, Chanock, SJ, Chaturvedi, N, Chen, Y-DI, Chen, Z, Cheng, C-Y, Christophersen, IE, Ciullo, M, Cole, JW, Collins, FS, Cooper, RS, Cruz, M, Cucca, F, Cupples, LA, Cutler, MJ, Damrauer, SM, Dantoft, TM, de Borst, GJ, de Groot, LCPGM, De Jager, PL, de Kleijn, DP, de Silva, HJ, Dedoussis, G, den Hollander, A, Du, S, Easton, DF, Elders, PJM, Eliassen, AH, Ellinor, PT, Elmstahl, S, Erdmann, J, Evans, MK, Fatkin, D, Feenstra, B, Feitosa, MF, Ferrucci, L, Ford, I, Fornage, M, Franke, A, Franks, PW, Freedman, B, Gasparini, P, Gieger, C, Girotto, G, Goddard, ME, Golightly, YM, Gonzalez-Villalpando, C, Gordon-Larsen, P, Grallert, H, Grant, SFA, Grarup, N, Griffiths, L, Gudnason, V, Haiman, C, Hakonarson, H, Hansen, T, Hartman, CA, Hattersley, AT, Hayward, C, Heckbert, SR, Heng, C-K, Hengstenberg, C, Hewitt, AW, Hishigaki, H, Hoyng, CB, Huang, PL, Huang, W, Hunt, SC, Hveem, K, Hypponen, E, Iacono, WG, Ichihara, S, Ikram, MA, Isasi, CR, Jackson, RD, Jarvelin, M-R, Jin, Z-B, Jockel, K-H, Joshi, PK, Jousilahti, P, Jukema, JW, Kahonen, M, Kamatani, Y, Kang, KD, Kaprio, J, Kardia, SLR, Karpe, F, Kato, N, Kee, F, Kessler, T, Khera, A, Khor, CC, Kiemeney, LALM, Kim, B-J, Kim, EK, Kim, H-L, Kirchhof, P, Kivimaki, M, Koh, W-P, Koistinen, HA, Kolovou, GD, Kooner, JS, Kooperberg, C, Kottgen, A, Kovacs, P, Kraaijeveld, A, Kraft, P, Krauss, RM, Kumari, M, Kutalik, Z, Laakso, M, Lange, LA, Langenberg, C, Launer, LJ, Le Marchand, L, Lee, H, Lee, NR, Lehtimaki, T, Li, H, Li, L, Lieb, W, Lin, X, Lind, L, Linneberg, A, Liu, C-T, Loeffler, M, London, B, Lubitz, SA, Lye, SJ, Mackey, DA, Magi, R, Magnusson, PKE, Marcus, GM, Vidal, PM, Martin, NG, Marz, W, Matsuda, F, McGarrah, RW, McGue, M, McKnight, AJ, Medland, SE, Mellstrom, D, Metspalu, A, Mitchell, BD, Mitchell, P, Mook-Kanamori, DO, Morris, AD, Mucci, LA, Munroe, PB, Nalls, MA, Nazarian, S, Nelson, AE, Neville, MJ, Newton-Cheh, C, Nielsen, CS, Nothen, MM, Ohlsson, C, Oldehinkel, AJ, Orozco, L, Pahkala, K, Pajukanta, P, Palmer, CNA, Parra, EJ, Pattaro, C, Pedersen, O, Pennell, CE, Penninx, BWJH, Perusse, L, Peters, A, Peyser, PA, Porteous, DJ, Posthuma, D, Power, C, Pramstaller, PP, Province, MA, Qi, Q, Qu, J, Rader, DJ, Raitakari, OT, Ralhan, S, Rallidis, LS, Rao, DC, Redline, S, Reilly, DF, Reiner, AP, Rhee, SY, Ridker, PM, Rienstra, M, Ripatti, S, Ritchie, MD, Roden, DM, Rosendaal, FR, Rotter, J, Rudan, I, Rutters, F, Sabanayagam, C, Saleheen, D, Salomaa, V, Samani, NJ, Sanghera, DK, Sattar, N, Schmidt, B, Schmidt, H, Schmidt, R, Schulze, MB, Schunkert, H, Scott, LJ, Scott, RJ, Sever, P, Shiroma, EJ, Shoemaker, MB, Shu, X-O, Simonsick, EM, Sims, M, Singh, JR, Singleton, AB, Sinner, MF, Smith, JG, Snieder, H, Spector, TD, Stampfer, MJ, Stark, KJ, Strachan, DP, t' Hart, LM, Tabara, Y, Tang, H, Tardif, J-C, Thanaraj, TA, Timpson, NJ, Tonjes, A, Tremblay, A, Tuomi, T, Tuomilehto, J, Tusie-Luna, M-T, Uitterlinden, AG, van Dam, RM, van der Harst, P, Van der Velde, N, van Duijn, CM, van Schoor, NM, Vitart, V, Volker, U, Vollenweider, P, Volzke, H, Wacher-Rodarte, NH, Walker, M, Wang, YX, Wareham, NJ, Watanabe, RM, Watkins, H, Weir, DR, Werge, TM, Widen, E, Wilkens, LR, Willemsen, G, Willett, WC, Wilson, JF, Wong, T-Y, Woo, J-T, Wright, AF, Wu, J-Y, Xu, H, Yajnik, CS, Yokota, M, Yuan, J-M, Zeggini, E, Zemel, BS, Zheng, W, Zhu, X, Zmuda, JM, Zonderman, AB, Zwart, J-A, Chasman, D, Cho, YS, Heid, IM, McCarthy, M, Ng, MCY, O'Donnell, CJ, Rivadeneira, F, Thorsteinsdottir, U, Sun, Y, Tai, ES, Boehnke, M, Deloukas, P, Justice, AE, Lindgren, CM, Loos, RJF, Mohlke, KL, North, KE, Stefansson, K, Walters, RG, Winkler, TW, Young, KL, Loh, P-R, Esko, T, Assimes, TL, Auton, A, Abecasis, GR, Willer, CJ, Locke, AE, Berndt, S, Lettre, G, Frayling, TM, Okada, Y, Wood, AR, Visscher, PM, and Hirschhorn, JN
Abstract: 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.
Published: 2022

5. Adiposity, metabolomic biomarkers and risk of nonalcoholic fatty liver disease: a case-cohort study

Author: Pang, Y, Kartsonaki, C, Lv, J, Millwood, IY, Fairhurst-Hunter, Z, Turnbull, I, Bragg, F, Hill, MR, Yu, C, Guo, Y, Chen, Y, Yang, L, Clarke, R, Walters, RG, Wu, M, Chen, J, Li, L, Chen, Z, and Holmes, MV
Subjects: Adult, Cohort Studies, Nutrition and Dietetics, Non-alcoholic Fatty Liver Disease, Medicine (miscellaneous), Humans, Obesity, Prospective Studies, Biomarkers, Adiposity
Abstract: Background Globally, the burden of obesity and associated nonalcoholic fatty liver disease (NAFLD) are rising, but little is known about the role that circulating metabolomic biomarkers play in mediating their association. Objectives We aimed to examine the observational and genetic associations of adiposity with metabolomic biomarkers and the observational associations of metabolomic biomarkers with incident NAFLD. Methods A case-subcohort study within the prospective China Kadoorie Biobank included 176 NAFLD cases and 180 subcohort individuals and measured 1208 metabolites in stored baseline plasma using a Metabolon assay. In the subcohort the observational and genetic associations of BMI with biomarkers were assessed using linear regression, with adjustment for multiple testing. Cox regression was used to estimate adjusted HRs for NAFLD associated with biomarkers. Results In observational analyses, BMI (kg/m2; mean: 23.9 in the subcohort) was associated with 199 metabolites at a 5% false discovery rate. The effects of genetically elevated BMI with specific metabolites were directionally consistent with the observational associations. Overall, 35 metabolites were associated with NAFLD risk, of which 15 were also associated with BMI, including glutamate (HR per 1-SD higher metabolite: 1.95; 95% CI: 1.48, 2.56), cysteine-glutathione disulfide (0.44; 0.31, 0.62), diaclyglycerol (C32:1) (1.71; 1.24, 2.35), behenoyl dihydrosphingomyelin (C40:0) (1.92; 1.42, 2.59), butyrylcarnitine (C4) (1.91; 1.38, 2.35), 2-hydroxybehenate (1.81; 1.34, 2.45), and 4-cholesten-3-one (1.79; 1.27, 2.54). The discriminatory performance of known risk factors was increased when 28 metabolites were also considered simultaneously in the model (weighted C-statistic: 0.84 to 0.90; P < 0.001). Conclusions Among relatively lean Chinese adults, a range of metabolomic biomarkers are associated with NAFLD risk and these biomarkers may lie on the pathway between adiposity and NAFLD.
Published: 2022

6. Investigation into the Health Effects of Reduced Chymase Function Using Predicted Loss-of-Function Mutations in CMA1

Author: Fairhurst-Hunter, Z, Walters, RG, Zink, A, Lin, K, Guo, Y, Yu, C, Lv, J, Li, L, Freitag, DF, Chen, Z, and Millwood, IY
Subjects: Heart Failure, Chymases, Mutation, Genetics, Pharmaceutical Science, Molecular Medicine, Humans, Renal Insufficiency, Chronic, Cardiology and Cardiovascular Medicine, Fibrosis, Genetics (clinical)
Abstract: Tissue remodelling and fibrosis which occur in response to injury play a central role in the development of many diseases. Chymase is a key enzyme believed to mediate these pathological processes. As such, chymase inhibitors have been under active development for the treatment of a number of conditions. To investigate the impact of reduced chymase function, we constructed a genetic score from two pLoF mutations in the gene encoding chymase and tested its association with diseases and biomarkers. Our study found no association between the genetically-predicted reduced chymase function score and heart failure, chronic kidney disease or other predefined conditions. We additionally found no association of the score with any physical measurements or biomarkers. Our results provide no evidence in support of chymase inhibition as a novel therapeutic strategy for the treatment or prevention of heart failure, chronic kidney disease or major cardiovascular events, as previously proposed.
Published: 2022

7. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study

Author: Schmidt, AF, Swerdlow, DI, Holmes, MV, Patel, RS, Fairhurst-Hunter, Z, Lyall, DM, Hartwig, FP, Horta, BL, Hypponen, E, Power, C, Moldovan, M, van Iperen, E, Hovingh, GK, Demuth, I, Norman, K, Steinhagen-Thiessen, E, Demuth, J, Bertram, L, Liu, T, Coassin, S, Willeit, J, Kiechl, S, Willeit, K, Mason, D, Wright, J, Morris, R, Wanamethee, G, Whincup, P, Ben-Shlomo, Y, McLachlan, S, Price, JF, Kivimaki, M, Welch, C, Sanchez-Galvez, A, Marques-Vidal, P, Nicolaides, A, Panayiotou, AG, Onland-Moret, NC, van der Schouw, YT, Matullo, G, Fiorito, G, Guarrera, S, Sacerdote, C, Wareham, NJ, Langenberg, C, Scott, R, Luan, JA, Bobak, M, Malyutina, SA, Pajak, A, Kubinova, R, Tamosiunas, A, Pikhart, H, Husemoen, LLN, Grarup, N, Pedersen, O, Hansen, T, Linneberg, A, Simonsen, KS, Cooper, J, Humphries, SE, Brilliant, M, Kitchner, T, Hakonarson, H, Carrell, DS, McCarty, CA, Kirchner, HL, Larson, EB, Crosslin, DR, de Andrade, M, Roden, DM, Denny, JC, Carty, C, Hancock, S, Attia, J, Holliday, E, Donnell, MO, Yusuf, S, Chong, M, Pare, G, van der Harst, P, Said, MA, Eppinga, RN, Verweij, N, Snieder, H, Christen, T, Mook-Kanamori, DO, Gustafsson, S, Lind, L, Ingelsson, E, Pazoki, Raha, Franco Duran, OH, Hofman, Bert, Uitterlinden, André, Dehghan, Abbas, Teumer, A, Baumeister, S, Dorr, M, Lerch, MM, Volker, U, Volzke, H, Ward, J, Pell, JP, Smith, Derek, Meade, T, Zee, AH, Baranova, EV, Young, R, Ford, I, Campbell, A (Archie), Padmanabhan, S, Bots, ML, Grobbee, DE, Froguel, P, Thuillier, D, Balkau, B, Bonnefond, A, Cariou, B, Smart, M, Bao, Y, Kumari, M, Mahajan, A, Ridker, PM, Chasman, DI, Reiner, AP, Lange, LA, Ritchie, MD, Asselbergs, FW, Casas, JP, Keating, BJ, Preiss, D, Hingorani, AD, Sattar, N, Schmidt, AF, Swerdlow, DI, Holmes, MV, Patel, RS, Fairhurst-Hunter, Z, Lyall, DM, Hartwig, FP, Horta, BL, Hypponen, E, Power, C, Moldovan, M, van Iperen, E, Hovingh, GK, Demuth, I, Norman, K, Steinhagen-Thiessen, E, Demuth, J, Bertram, L, Liu, T, Coassin, S, Willeit, J, Kiechl, S, Willeit, K, Mason, D, Wright, J, Morris, R, Wanamethee, G, Whincup, P, Ben-Shlomo, Y, McLachlan, S, Price, JF, Kivimaki, M, Welch, C, Sanchez-Galvez, A, Marques-Vidal, P, Nicolaides, A, Panayiotou, AG, Onland-Moret, NC, van der Schouw, YT, Matullo, G, Fiorito, G, Guarrera, S, Sacerdote, C, Wareham, NJ, Langenberg, C, Scott, R, Luan, JA, Bobak, M, Malyutina, SA, Pajak, A, Kubinova, R, Tamosiunas, A, Pikhart, H, Husemoen, LLN, Grarup, N, Pedersen, O, Hansen, T, Linneberg, A, Simonsen, KS, Cooper, J, Humphries, SE, Brilliant, M, Kitchner, T, Hakonarson, H, Carrell, DS, McCarty, CA, Kirchner, HL, Larson, EB, Crosslin, DR, de Andrade, M, Roden, DM, Denny, JC, Carty, C, Hancock, S, Attia, J, Holliday, E, Donnell, MO, Yusuf, S, Chong, M, Pare, G, van der Harst, P, Said, MA, Eppinga, RN, Verweij, N, Snieder, H, Christen, T, Mook-Kanamori, DO, Gustafsson, S, Lind, L, Ingelsson, E, Pazoki, Raha, Franco Duran, OH, Hofman, Bert, Uitterlinden, André, Dehghan, Abbas, Teumer, A, Baumeister, S, Dorr, M, Lerch, MM, Volker, U, Volzke, H, Ward, J, Pell, JP, Smith, Derek, Meade, T, Zee, AH, Baranova, EV, Young, R, Ford, I, Campbell, A (Archie), Padmanabhan, S, Bots, ML, Grobbee, DE, Froguel, P, Thuillier, D, Balkau, B, Bonnefond, A, Cariou, B, Smart, M, Bao, Y, Kumari, M, Mahajan, A, Ridker, PM, Chasman, DI, Reiner, AP, Lange, LA, Ritchie, MD, Asselbergs, FW, Casas, JP, Keating, BJ, Preiss, D, Hingorani, AD, and Sattar, N
Published: 2017

8. PCSK9 genetic variants and risk of type 2 diabetes: a Mendelian randomisation study

Author: Preiss, D, Holmes, M, Fairhurst-Hunter, Z, and Mahajan, A
Published: 2016

9. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study

Author: Schmidt, A.F., Swerdlow, D.I., Holmes, M.V., Patel, R.S., Fairhurst-Hunter, Z., Lyall, D.M., Hartwig, F.P., Horta, B.L., Hypponen, E., Power, C., Moldovan, M., Iperen, E. van, Hovingh, G.K., Demuth, I., Norman, K., Steinhagen-Thiessen, E., Demuth, J., Bertram, L., Liu, T., Coassin, S., Willeit, J., Kiechl, S., Willeit, K., Mason, D., Wright, J., Morris, R., Wanamethee, G., Whincup, P., Ben-Shlomo, Y., McLachlan, S., Price, J.F., Kivimaki, M., Welch, C., Sanchez-Galvez, A., Marques-Vidal, P., Nicolaides, A., Panayiotou, A.G., Onland-Moret, N.C., Schouw, Y.T. van der, Matullo, G., Fiorito, G., Guarrera, S., Sacerdote, C., Wareham, N.J., Langenberg, C., Scott, R., Luan, J.A., Bobak, M., Malyutina, S.A., Pajak, A., Kubinova, R., Tamosiunas, A., Pikhart, H., Husemoen, L.L.N., Grarup, N., Pedersen, O., Hansen, T., Linneberg, A., Simonsen, K.S., Cooper, J., Humphries, S.E., Brilliant, M., Kitchner, T., Hakonarson, H., Carrell, D.S., McCarty, C.A., Kirchner, H.L., Larson, E.B., Crosslin, D.R., Andrade, M. de, Roden, D.M., Denny, J.C., Carty, C., Hancock, S., Attia, J., Holliday, E., Donnell, M.O., Yusuf, S., Chong, M., Pare, G., Harst, P. van der, Said, M.A., Eppinga, R.N., Verweij, N., Snieder, H., Christen, T., Mook-Kanamori, D.O., Gustafsson, S., Lind, L., Ingelsson, E., Pazoki, R., Franco, O., Hofman, A., Uitterlinden, A., Dehghan, A., Teumer, A., Baumeister, S., Dorr, M., Lerch, M.M., Volker, U., Volzke, H., Ward, J., Pell, J.P., Smith, D.J., Meade, T., Maitland-van der Zee, A.H., Baranova, E.V., Young, R., Ford, I., Campbell, A., Padmanabhan, S., Bots, M.L., Grobbee, D.E., Froguel, P., Thuillier, D., Balkau, B., Bonnefond, A., Cariou, B., Smart, M., Bao, Y., Kumari, M., Mahajan, A., Ridker, P.M., Chasman, D.I., Reiner, A.P., Lange, L.A., Ritchie, M.D., Asselbergs, F.W., Casas, J.P., Keating, B.J., Preiss, D., Hingorani, A.D., Sattar, N., LifeLines Cohort Study Grp, UCLEB Consortium, Centre for Paediatric Epidemiology and Biostatistics, University College of London [London] (UCL), MRC Centre for Epidemiology of Child Health, UCL Institute of Child Health, Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Dept. of Gastroenterology, Hepatology and Endocrinology, Neuroepidemiology of Ageing Research Unit, Imperial College London, Institut des Sciences Moléculaires (ISM), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure de Chimie et de Physique de Bordeaux (ENSCPB)-Université Sciences et Technologies - Bordeaux 1-Université Montesquieu - Bordeaux 4-Institut de Chimie du CNRS (INC), Division of Community Health Sciences, St George's University of London, Department of Social Medicine, University of Bristol [Bristol], Finnish Institute of Occupational Health of Helsinki, Department of Epidemiology and Public Health, Institute of Social and Preventive Medicine, Lausanne university hospital, Computer Science Department, University of Cyprus, Cyprus Institute of Neurology and Genetics, University Medical Center [Utrecht], Department of Genetics, Biology and Biochemistry, University of Turin, Institute for Scientific Interchange Foundation, Center for Cancer Prevention, CPO-Piemonte, Unità di epidemiologia dei tumori, Università degli studi di Torino (UNITO)-HuGeF Foundation, Medical Research Council Epidemiology Unit, University of Cambridge [UK] (CAM), Serono Genetics Institute S.A.[Evry], Serono Genetics Institute, Institute of Internal and Preventive Medicine Sibe rian Branch, Russian Academy of Medical Sciences, Institute of Internal Medicine, Novosibirsk State Medical University, Centre for Environmental Health, National Institute of Public Health [Prague], Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), University of Copenhagen = Københavns Universitet (KU), Research Centre for Prevention and Health (RCPH), Department of Public Health [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)-Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)-Capital Region of Denmark, Rigshospitalet [Copenhagen], Copenhagen University Hospital, BHF Laboratories, Rayne building, Department of Medicine, 5 University Street, The Center for Applied Genomics, Children’s Hospital of Philadelphia (CHOP ), Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania [Philadelphia]-University of Pennsylvania [Philadelphia]-Children’s Hospital of Philadelphia (CHOP ), Population Health Research Institute, Institut de Physique et Chimie des Matériaux de Strasbourg (IPCMS), Université de Strasbourg (UNISTRA)-Matériaux et nanosciences d'Alsace (FMNGE), Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, University of Groningen [Groningen], Augusta University - Medical College of Georgia, University System of Georgia (USG)-University System of Georgia (USG), Limnology, Ecology, Uppsala Universitet [Uppsala], Metacohorts Consortium, Erasmus University Medical Center [Rotterdam] (Erasmus MC), King‘s College London, Interfaculty Institute for Genetics and Functional Genomics, Universität Greifswald - University of Greifswald, Institute for Community Medicine, Department of Oncology and Metabolism [Sheffield, UK], The University of Sheffield [Sheffield, U.K.], European Associated Laboratory [Sheffield, UK] (Sarcoma Research Unit), Robertson Centre for Biostatistics, University of Glasgow, Faculty of Medicine, University of Glasgow, Julius Center for Health Sciences and Primary Care, Génétique des maladies multifactorielles (GMM), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), INSERM UMRS 1178, Institut de recherche en biothérapie (IRB), Université Montpellier 1 (UM1)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), unité de recherche de l'institut du thorax UMR1087 UMR6291 (ITX), Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Department of Physics, Indian Institute of Technology Kanpur (IIT Kanpur), Department of Pathological Biochemistry, Royal Infirmary, Wareham, Nicholas [0000-0003-1422-2993], Langenberg, Claudia [0000-0002-5017-7344], Luan, Jian'an [0000-0003-3137-6337], and Apollo - University of Cambridge Repository
Subjects: Blood Glucose, Cohort Studies, Diabetes Mellitus, Type 2, Case-Control Studies, [SDV]Life Sciences [q-bio], Genetic Variation, Humans, Genetic Predisposition to Disease, Cholesterol, LDL, Mendelian Randomization Analysis, Proprotein Convertase 9, Randomized Controlled Trials as Topic
Abstract: BACKGROUND:Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductions in both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modest hyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way offsets their substantial benefits. We sought to investigate the associations of LDL cholesterol-lowering PCSK9 variants with type 2 diabetes and related biomarkers to gauge the likely effects of PCSK9 inhibitors on diabetes risk.METHODS:In this mendelian randomisation study, we used data from cohort studies, randomised controlled trials, case control studies, and genetic consortia to estimate associations of PCSK9 genetic variants with LDL cholesterol, fasting blood glucose, HbA1c, fasting insulin, bodyweight, waist-to-hip ratio, BMI, and risk of type 2 diabetes, using a standardised analysis plan, meta-analyses, and weighted gene-centric scores.FINDINGS:Data were available for more than 550 000 individuals and 51 623 cases of type 2 diabetes. Combined analyses of four independent PCSK9 variants (rs11583680, rs11591147, rs2479409, and rs11206510) scaled to 1 mmol/L lower LDL cholesterol showed associations with increased fasting glucose (0·09 mmol/L, 95% CI 0·02 to 0·15), bodyweight (1·03 kg, 0·24 to 1·82), waist-to-hip ratio (0·006, 0·003 to 0·010), and an odds ratio for type diabetes of 1·29 (1·11 to 1·50). Based on the collected data, we did not identify associations with HbA1c (0·03%, -0·01 to 0·08), fasting insulin (0·00%, -0·06 to 0·07), and BMI (0·11 kg/m2, -0·09 to 0·30).INTERPRETATION:PCSK9 variants associated with lower LDL cholesterol were also associated with circulating higher fasting glucose concentration, bodyweight, and waist-to-hip ratio, and an increased risk of type 2 diabetes. In trials of PCSK9 inhibitor drugs, investigators should carefully assess these safety outcomes and quantify the risks and benefits of PCSK9 inhibitor treatment, as was previously done for statins.FUNDING:British Heart Foundation, and University College London Hospitals NHS Foundation Trust (UCLH) National Institute for Health Research (NIHR) Biomedical Research Centre.
Published: 2016

10. PCSK9 genetic variants and risk of type 2 diabetes: A mendelian randomisation study

Author: Schmidt, A.F. (Amand F.), Swerdlow, D.I. (Daniel), Holmes, M.V. (Michael), Patel, R.S. (Riyaz), Fairhurst-Hunter, Z. (Zammy), Lyall, D.M. (Donald M.), Hartwig, F.P. (Fernando Pires), Horta, B.L. (Bernardo Lessa), Hypponen, E. (Elina), Power, C. (Christopher), Moldovan, M. (Max), Iperen, E.P.A. (Erik) van, Hovingh, G.K. (Kees), Demuth, I. (Ilja), Norman, K. (Kristina), Steinhagen-Thiessen, E. (Elisabeth), Demuth, J. (Juri), Bertram, L. (Lars), Liu, T. (Tian), Coassin, S. (Stefan), Willeit, J. (Johann), Kiechl, S. (Stefan), Willeit, K. (Karin), Mason, D. (Dan), Wright, J. (Juliet), Morris, R.W. (Richard), Wanamethee, G. (Goya), Whincup, P.H. (Peter), Ben-Shlomo, Y., McLachlan, S. (Stela), Price, J.F. (Jackie F.), Kivimaki, M. (Mika), Welch, C. (Catherine), Sanchez-Galvez, A. (Adelaida), Marques-Vidal, P. (Pedro), Nicolaides, A.N. (Andrew), Panayiotou, A.G. (Andrie), Onland-Moret, N.C. (N Charlotte), Schouw, Y.T. (Yvonne) van der, Matullo, G., Fiorito, G. (Giovanni), Guarrera, S. (Simonetta), Sacerdote, C. (Carlotta), Wareham, N.J. (Nick), Langenberg, C. (Claudia), Scott, R. (Robert), Luan, J. (Jian'an), Bobak, M. (Martin), Malyutina, S., Pajak, A. (Andrzej), Kubinova, R., Tamosiunas, A. (Abdonas), Pikhart, H. (Hynek), Husemoen, L.L.N. (Lise Lotte), Grarup, N. (Niels), Pedersen, O. (Oluf), Hansen, T. (T.), Linneberg, A. (Allan), Simonsen, K.S. (Kenneth Starup), Cooper, J. (Jim), Humphries, S.E. (Steve), Brilliant, M.H. (Murray H.), Kitchner, T.E. (Terrie E.), Hakonarson, H. (Hakon), Carrell, D.S. (David), McCarty, C.A. (Catherine A.), Kirchner, H.L. (H Lester), Larson, E.B. (Eric B.), Crosslin, D.R. (David), de Andrade, M. (Mariza), Roden, D.M. (Dan M.), Denny, J.C. (Joshua C.), Carty, C. (Cara), Hancock, S. (Stephen), Attia, J. (John), Holliday, E.G. (Elizabeth), Donnell, M.O.'. (Martin O'), Yusuf, S. (Salim), Chong, M. (Michael), Pare, G. (Guillame), Harst, P. (Pim) van der, Said, M.A. (M Abdullah), Eppinga, R.N. (Ruben N.), Verweij, N. (Niek), Snieder, H. (Harold), Christen, T. (Tim), Mook-Kanamori, D.O. (Dennis), Gustafsson, S. (Stefan), Kao, W.H.L. (Wen), Ingelsson, E. (Erik), Pazoki, R. (Raha), Franco, O.H. (Oscar), Hofman, A. (Albert), Uitterlinden, A.G. (André), Dehghan, A. (Abbas), Teumer, A. (Alexander), Baumeister, S.E. (Sebastian), Dörr, M. (Marcus), Lerch, M.M. (Markus M.), Völker, U. (Uwe), Völzke, H. (Henry), Ward, J. (Joey), Pell, J.P. (Jill P.), Smith, D.J. (Daniel J.), Meade, T. (Tom), Maitland-van der Zee, A-H. (Anke-Hilse), Baranova, E.V. (Ekaterina V.), Young, R. (Robin), Ford, I. (Ian), Campbell, A. (Archie), Padmanabhan, S. (Sandosh), Bots, M.L. (Michiel), Grobbee, D.E. (Diederick E.), Froguel, P. (Philippe), Thuillier, D. (Dorothee), Balkau, B. (Beverley), Bonnefond, A. (Amélie), Cariou, B. (Bertrand), Smart, M. (Melissa), Bao, Y. (Yanchun), Kumari, M. (Meena), Mahajan, A. (Anubha), Ridker, P.M. (Paul), Chasman, D.I. (Daniel I.), Reiner, A. (Alexander), Lange, L.A. (Leslie), Ritchie, M.D. (Marylyn D.), Asselbergs, F.W. (Folkert), Casas, J.P. (Juan), Keating, J. (John), Preiss, D. (David), Hingorani, A. (Aroon), Sattar, N. (Naveed), Schmidt, A.F. (Amand F.), Swerdlow, D.I. (Daniel), Holmes, M.V. (Michael), Patel, R.S. (Riyaz), Fairhurst-Hunter, Z. (Zammy), Lyall, D.M. (Donald M.), Hartwig, F.P. (Fernando Pires), Horta, B.L. (Bernardo Lessa), Hypponen, E. (Elina), Power, C. (Christopher), Moldovan, M. (Max), Iperen, E.P.A. (Erik) van, Hovingh, G.K. (Kees), Demuth, I. (Ilja), Norman, K. (Kristina), Steinhagen-Thiessen, E. (Elisabeth), Demuth, J. (Juri), Bertram, L. (Lars), Liu, T. (Tian), Coassin, S. (Stefan), Willeit, J. (Johann), Kiechl, S. (Stefan), Willeit, K. (Karin), Mason, D. (Dan), Wright, J. (Juliet), Morris, R.W. (Richard), Wanamethee, G. (Goya), Whincup, P.H. (Peter), Ben-Shlomo, Y., McLachlan, S. (Stela), Price, J.F. (Jackie F.), Kivimaki, M. (Mika), Welch, C. (Catherine), Sanchez-Galvez, A. (Adelaida), Marques-Vidal, P. (Pedro), Nicolaides, A.N. (Andrew), Panayiotou, A.G. (Andrie), Onland-Moret, N.C. (N Charlotte), Schouw, Y.T. (Yvonne) van der, Matullo, G., Fiorito, G. (Giovanni), Guarrera, S. (Simonetta), Sacerdote, C. (Carlotta), Wareham, N.J. (Nick), Langenberg, C. (Claudia), Scott, R. (Robert), Luan, J. (Jian'an), Bobak, M. (Martin), Malyutina, S., Pajak, A. (Andrzej), Kubinova, R., Tamosiunas, A. (Abdonas), Pikhart, H. (Hynek), Husemoen, L.L.N. (Lise Lotte), Grarup, N. (Niels), Pedersen, O. (Oluf), Hansen, T. (T.), Linneberg, A. (Allan), Simonsen, K.S. (Kenneth Starup), Cooper, J. (Jim), Humphries, S.E. (Steve), Brilliant, M.H. (Murray H.), Kitchner, T.E. (Terrie E.), Hakonarson, H. (Hakon), Carrell, D.S. (David), McCarty, C.A. (Catherine A.), Kirchner, H.L. (H Lester), Larson, E.B. (Eric B.), Crosslin, D.R. (David), de Andrade, M. (Mariza), Roden, D.M. (Dan M.), Denny, J.C. (Joshua C.), Carty, C. (Cara), Hancock, S. (Stephen), Attia, J. (John), Holliday, E.G. (Elizabeth), Donnell, M.O.'. (Martin O'), Yusuf, S. (Salim), Chong, M. (Michael), Pare, G. (Guillame), Harst, P. (Pim) van der, Said, M.A. (M Abdullah), Eppinga, R.N. (Ruben N.), Verweij, N. (Niek), Snieder, H. (Harold), Christen, T. (Tim), Mook-Kanamori, D.O. (Dennis), Gustafsson, S. (Stefan), Kao, W.H.L. (Wen), Ingelsson, E. (Erik), Pazoki, R. (Raha), Franco, O.H. (Oscar), Hofman, A. (Albert), Uitterlinden, A.G. (André), Dehghan, A. (Abbas), Teumer, A. (Alexander), Baumeister, S.E. (Sebastian), Dörr, M. (Marcus), Lerch, M.M. (Markus M.), Völker, U. (Uwe), Völzke, H. (Henry), Ward, J. (Joey), Pell, J.P. (Jill P.), Smith, D.J. (Daniel J.), Meade, T. (Tom), Maitland-van der Zee, A-H. (Anke-Hilse), Baranova, E.V. (Ekaterina V.), Young, R. (Robin), Ford, I. (Ian), Campbell, A. (Archie), Padmanabhan, S. (Sandosh), Bots, M.L. (Michiel), Grobbee, D.E. (Diederick E.), Froguel, P. (Philippe), Thuillier, D. (Dorothee), Balkau, B. (Beverley), Bonnefond, A. (Amélie), Cariou, B. (Bertrand), Smart, M. (Melissa), Bao, Y. (Yanchun), Kumari, M. (Meena), Mahajan, A. (Anubha), Ridker, P.M. (Paul), Chasman, D.I. (Daniel I.), Reiner, A. (Alexander), Lange, L.A. (Leslie), Ritchie, M.D. (Marylyn D.), Asselbergs, F.W. (Folkert), Casas, J.P. (Juan), Keating, J. (John), Preiss, D. (David), Hingorani, A. (Aroon), and Sattar, N. (Naveed)
Abstract: Background: Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductions in both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modest hyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way offsets their substantial benefits. We sought to investigate the associations of LDL cholesterol-lowering . PCSK9 variants with type 2 diabetes and related biomarkers to gauge the likely effects of PCSK9 inhibitors on diabetes risk. Methods: In this mendelian randomisation study, we used data from cohort studies, randomised controlled trials, case control studies, and genetic consortia to estimate associations of PCSK9 genetic variants with LDL cholesterol, fasting blood glucose, HbA1c, fasting insulin, bodyweight, waist-to-hip ratio, BMI, and risk of type 2 diabetes, using a standardised analysis plan, meta-analyses, and weighted gene-centric scores. Findings: Data were available for more than 550 000 individuals and 51 623 cases of type 2 diabetes. Combined analyses of four independent PCSK9 variants (rs11583680, rs11591147, rs2479409, and rs11206510) scaled to 1 mmol/L lower LDL cholesterol showed associations with increased fasting glucose (0·09 mmol/L, 95% CI 0·02 to 0·15), bodyweight (1·03 kg, 0·24 to 1·82), waist-to-hip ratio (0·006, 0·003 to 0·010), and an odds ratio for type diabetes of 1·29 (1·11 to 1·50). Based on the collected data, we did not identify associations with HbA1c (0·03%, -0·01 to 0·08), fasting insulin (0·00%, -0·06 to 0·07), and BMI (0·11 kg/m2, -0·09 to 0·30). Interpretation: . PCSK9 variants associated with lower LDL cholesterol were also associated with circulating higher fasting glucose concentration, bodyweight, and waist-to-hip ratio, and an increased risk of type 2 diabetes. In trials of PCSK9 inhibitor drugs, investigators should carefully assess these safety outcomes and quantify the risks and benefits of PCSK9 inhibi
Published: 2016
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11. Conventional and genetic associations of BMI with major vascular and non-vascular disease incidence and mortality in a relatively lean Chinese population: U-shaped relationship revisited.

Author: Iona A, Bragg F, Fairhurst-Hunter Z, Millwood IY, Wright N, Lin K, Yang L, Du H, Chen Y, Pei P, Cheng L, Schmidt D, Avery D, Yu C, Lv J, Clarke R, Walters R, Li L, Parish S, and Chen Z
Subjects: Humans, Middle Aged, Male, Female, China epidemiology, Adult, Aged, Incidence, Prospective Studies, Cardiovascular Diseases mortality, Cardiovascular Diseases genetics, Risk Factors, Proportional Hazards Models, Thinness genetics, Thinness epidemiology, East Asian People, Body Mass Index, Mendelian Randomization Analysis
Abstract: Background: Higher body mass index (BMI) is associated with higher incidence of cardiovascular and some non-cardiovascular diseases (CVDs/non-CVDs). However, uncertainty remains about its associations with mortality, particularly at lower BMI levels., Methods: The prospective China Kadoorie Biobank recruited >512 000 adults aged 30-79 years in 2004-08 and genotyped a random subset of 76 000 participants. In conventional and Mendelian randomization (MR) analyses, Cox regression yielded adjusted hazard ratios (HRs) associating measured and genetically predicted BMI levels with incident risks of major vascular events (MVEs; conventional/MR 68 431/23 621), ischaemic heart disease (IHD; 50 698/12 177), ischaemic stroke (IS; 42 427/11 897) and intracerebral haemorrhage (ICH; 7644/4712), and with mortality risks of CVD (15 427/6781), non-CVD (26 915/4355) and all causes (42 342/6784), recorded during ∼12 years of follow-up., Results: Overall, the mean BMI was 23.8 (standard deviation: 3.2) kg/m2 and 13% had BMIs of <20 kg/m2. Measured and genetically predicted BMI showed positive log-linear associations with MVE, IHD and IS, but a shallower positive association with ICH in conventional analyses. Adjusted HRs per 5 kg/m2 higher genetically predicted BMI were 1.50 (95% CI 1.41-1.58), 1.49 (1.38-1.61), 1.42 (1.31-1.54) and 1.64 (1.58-1.69) for MVE, IHD, IS and ICH, respectively. These were stronger than associations in conventional analyses [1.21 (1.20-1.23), 1.28 (1.26-1.29), 1.31 (1.29-1.33) and 1.14 (1.10-1.18), respectively]. At BMIs of ≥20 kg/m2, there were stronger positive log-linear associations of BMI with CVD, non-CVD and all-cause mortality in MR than in conventional analyses., Conclusions: Among relatively lean Chinese adults, higher genetically predicted BMI was associated with higher risks of incident CVDs. Excess mortality risks at lower BMI in conventional analyses are likely not causal and may reflect residual reverse causality., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Epidemiological Association.)
Published: 2024
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12. Causal relevance of different blood pressure traits on risk of cardiovascular diseases: GWAS and Mendelian randomisation in 100,000 Chinese adults.

Author: Pozarickij A, Gan W, Lin K, Clarke R, Fairhurst-Hunter Z, Koido M, Kanai M, Okada Y, Kamatani Y, Bennett D, Du H, Chen Y, Yang L, Avery D, Guo Y, Yu M, Yu C, Schmidt Valle D, Lv J, Chen J, Peto R, Collins R, Li L, Chen Z, Millwood IY, and Walters RG
Subjects: Adult, Aged, Female, Humans, Male, Middle Aged, China epidemiology, Genetic Predisposition to Disease, Genome-Wide Association Study, Hypertension genetics, Hypertension epidemiology, Mendelian Randomization Analysis, Polymorphism, Single Nucleotide, Risk Factors, Blood Pressure genetics, Cardiovascular Diseases genetics, Cardiovascular Diseases epidemiology, East Asian People genetics
Abstract: Elevated blood pressure (BP) is major risk factor for cardiovascular diseases (CVD). Genome-wide association studies (GWAS) conducted predominantly in populations of European ancestry have identified >2,000 BP-associated loci, but other ancestries have been less well-studied. We conducted GWAS of systolic, diastolic, pulse, and mean arterial BP in 100,453 Chinese adults. We identified 128 non-overlapping loci associated with one or more BP traits, including 74 newly-reported associations. Despite strong genetic correlations between populations, we identified appreciably higher heritability and larger variant effect sizes in Chinese compared with European or Japanese ancestry populations. Using instruments derived from these GWAS, multivariable Mendelian randomisation demonstrated that BP traits contribute differently to the causal associations of BP with CVD. In particular, only pulse pressure was independently causally associated with carotid plaque. These findings reinforce the need for studies in diverse populations to understand the genetic determinants of BP traits and their roles in disease risk., (© 2024. The Author(s).)
Published: 2024
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13. Protein-truncating variants in BSN are associated with severe adult-onset obesity, type 2 diabetes and fatty liver disease.

Author: Zhao Y, Chukanova M, Kentistou KA, Fairhurst-Hunter Z, Siegert AM, Jia RY, Dowsett GKC, Gardner EJ, Lawler K, Day FR, Kaisinger LR, Tung YL, Lam BYH, Chen HC, Wang Q, Berumen-Campos J, Kuri-Morales P, Tapia-Conyer R, Alegre-Diaz J, Barroso I, Emberson J, Torres JM, Collins R, Saleheen D, Smith KR, Paul DS, Merkle F, Farooqi IS, Wareham NJ, Petrovski S, O'Rahilly S, Ong KK, Yeo GSH, and Perry JRB
Subjects: Adult, Humans, Adaptor Proteins, Signal Transducing genetics, Genetic Predisposition to Disease, Obesity complications, Obesity genetics, Proteomics, Diabetes Mellitus, Type 2 genetics, Induced Pluripotent Stem Cells, Liver Diseases, Nerve Tissue Proteins genetics
Abstract: Obesity is a major risk factor for many common diseases and has a substantial heritable component. To identify new genetic determinants, we performed exome-sequence analyses for adult body mass index (BMI) in up to 587,027 individuals. We identified rare loss-of-function variants in two genes (BSN and APBA1) with effects substantially larger than those of well-established obesity genes such as MC4R. In contrast to most other obesity-related genes, rare variants in BSN and APBA1 were not associated with normal variation in childhood adiposity. Furthermore, BSN protein-truncating variants (PTVs) magnified the influence of common genetic variants associated with BMI, with a common variant polygenic score exhibiting an effect twice as large in BSN PTV carriers than in noncarriers. Finally, we explored the plasma proteomic signatures of BSN PTV carriers as well as the functional consequences of BSN deletion in human induced pluripotent stem cell-derived hypothalamic neurons. Collectively, our findings implicate degenerative processes in synaptic function in the etiology of adult-onset obesity., (© 2024. The Author(s).)
Published: 2024
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14. BMI and well-being in people of East Asian and European ancestry: a Mendelian randomisation study.

Author: O'Loughlin J, Casanova F, Hughes A, Fairhurst-Hunter Z, Li L, Chen Z, Bowden J, Watkins E, Freathy RM, Howe LD, Walters RG, and Tyrrell J
Subjects: Adult, Female, Humans, Male, China, Body Mass Index, East Asian People, European People, Health Status
Abstract: Previous studies have linked higher body mass index (BMI) to lower subjective well-being in adult European ancestry populations. However, our understanding of these relationships across different populations is limited. Here, we investigated the association between BMI and well-being in people of (a) East Asian and (b) European ancestry in the China Kadoorie Biobank (CKB) and UK Biobank (UKB), respectively. Mendelian randomisation (MR) methods were used to test the relationship between BMI with (a) health satisfaction and (b) life satisfaction. One-sample MR enabled us to test effects in men and women separately and to test the role of cultural contexts by stratifying our analyses by urban and rural home location in both China and the UK. Further, we implemented a control function method to test the linearity of the BMI-well-being relationship. We found evidence of different associations between BMI and well-being in individuals of East Asian versus European ancestry. For example, a genetically instrumented higher BMI tentatively associated with higher health satisfaction in people of East Asian ancestry, especially in females (ß: 0.041, 95% CI: 0.002, 0.081). In contrast, there was a robust inverse association between higher genetically instrumented BMI and health satisfaction in all European ancestry UKB participants (ß: -0.183, 95% CI: -0.200, -0.165, P difference < 1.00E-15). We also showed the importance of considering non-linear relationships in the MR framework by providing evidence of non-linear relationships between BMI and health and life satisfaction. Overall, our study suggests potential setting-specific causality in the relationship between BMI and subjective well-being, with robust differences observed between East Asians and Europeans when considering very similar outcomes. We highlight the importance of (a) considering potential non-linear relationships in causal analyses and (b) testing causal relationships in different populations, as the casual nature of relationships, especially relationships influenced by social processes, may be setting-specific., (© 2023. The Author(s).)
Published: 2023
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15. Mendelian randomisation study of body composition and depression in people of East Asian ancestry highlights potential setting-specific causality.

Author: O'Loughlin J, Casanova F, Fairhurst-Hunter Z, Hughes A, Bowden J, Watkins ER, Freathy RM, Millwood IY, Lin K, Chen Z, Li L, Lv J, Walters RG, Howe LD, Kuchenbaecker K, and Tyrrell J
Subjects: Female, Humans, Male, Body Mass Index, Mendelian Randomization Analysis, Obesity genetics, Polymorphism, Single Nucleotide genetics, China, Body Composition genetics, Depression epidemiology, Depression genetics, East Asian People, Genome-Wide Association Study
Abstract: Background: Extensive evidence links higher body mass index (BMI) to higher odds of depression in people of European ancestry. However, our understanding of the relationship across different settings and ancestries is limited. Here, we test the relationship between body composition and depression in people of East Asian ancestry., Methods: Multiple Mendelian randomisation (MR) methods were used to test the relationship between (a) BMI and (b) waist-hip ratio (WHR) with depression. Firstly, we performed two-sample MR using genetic summary statistics from a recent genome-wide association study (GWAS) of depression (with 15,771 cases and 178,777 controls) in people of East Asian ancestry. We selected 838 single nucleotide polymorphisms (SNPs) correlated with BMI and 263 SNPs correlated with WHR as genetic instrumental variables to estimate the causal effect of BMI and WHR on depression using the inverse-variance weighted (IVW) method. We repeated these analyses stratifying by home location status: China versus UK or USA. Secondly, we performed one-sample MR in the China Kadoorie Biobank (CKB) in 100,377 participants. This allowed us to test the relationship separately in (a) males and females and (b) urban and rural dwellers. We also examined (c) the linearity of the BMI-depression relationship., Results: Both MR analyses provided evidence that higher BMI was associated with lower odds of depression. For example, a genetically-instrumented 1-SD higher BMI in the CKB was associated with lower odds of depressive symptoms [OR: 0.77, 95% CI: 0.63, 0.95]. There was evidence of differences according to place of residence. Using the IVW method, higher BMI was associated with lower odds of depression in people of East Asian ancestry living in China but there was no evidence for an association in people of East Asian ancestry living in the USA or UK. Furthermore, higher genetic BMI was associated with differential effects in urban and rural dwellers within China., Conclusions: This study provides the first MR evidence for an inverse relationship between BMI and depression in people of East Asian ancestry. This contrasts with previous findings in European populations and therefore the public health response to obesity and depression is likely to need to differ based on sociocultural factors for example, ancestry and place of residence. This highlights the importance of setting-specific causality when using genetic causal inference approaches and data from diverse populations to test hypotheses. This is especially important when the relationship tested is not purely biological and may involve sociocultural factors., (© 2023. The Author(s).)
Published: 2023
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16. A saturated map of common genetic variants associated with human height.

Author: Yengo L, Vedantam S, Marouli E, Sidorenko J, Bartell E, Sakaue S, Graff M, Eliasen AU, Jiang Y, Raghavan S, Miao J, Arias JD, Graham SE, Mukamel RE, Spracklen CN, Yin X, Chen SH, Ferreira T, Highland HH, Ji Y, Karaderi T, Lin K, Lüll K, Malden DE, Medina-Gomez C, Machado M, Moore A, Rüeger S, Sim X, Vrieze S, Ahluwalia TS, Akiyama M, Allison MA, Alvarez M, Andersen MK, Ani A, Appadurai V, Arbeeva L, Bhaskar S, Bielak LF, Bollepalli S, Bonnycastle LL, Bork-Jensen J, Bradfield JP, Bradford Y, Braund PS, Brody JA, Burgdorf KS, Cade BE, Cai H, Cai Q, Campbell A, Cañadas-Garre M, Catamo E, Chai JF, Chai X, Chang LC, Chang YC, Chen CH, Chesi A, Choi SH, Chung RH, Cocca M, Concas MP, Couture C, Cuellar-Partida G, Danning R, Daw EW, Degenhard F, Delgado GE, Delitala A, Demirkan A, Deng X, Devineni P, Dietl A, Dimitriou M, Dimitrov L, Dorajoo R, Ekici AB, Engmann JE, Fairhurst-Hunter Z, Farmaki AE, Faul JD, Fernandez-Lopez JC, Forer L, Francescatto M, Freitag-Wolf S, Fuchsberger C, Galesloot TE, Gao Y, Gao Z, Geller F, Giannakopoulou O, Giulianini F, Gjesing AP, Goel A, Gordon SD, Gorski M, Grove J, Guo X, Gustafsson S, Haessler J, Hansen TF, Havulinna AS, Haworth SJ, He J, Heard-Costa N, Hebbar P, Hindy G, Ho YA, Hofer E, Holliday E, Horn K, Hornsby WE, Hottenga JJ, Huang H, Huang J, Huerta-Chagoya A, Huffman JE, Hung YJ, Huo S, Hwang MY, Iha H, Ikeda DD, Isono M, Jackson AU, Jäger S, Jansen IE, Johansson I, Jonas JB, Jonsson A, Jørgensen T, Kalafati IP, Kanai M, Kanoni S, Kårhus LL, Kasturiratne A, Katsuya T, Kawaguchi T, Kember RL, Kentistou KA, Kim HN, Kim YJ, Kleber ME, Knol MJ, Kurbasic A, Lauzon M, Le P, Lea R, Lee JY, Leonard HL, Li SA, Li X, Li X, Liang J, Lin H, Lin SY, Liu J, Liu X, Lo KS, Long J, Lores-Motta L, Luan J, Lyssenko V, Lyytikäinen LP, Mahajan A, Mamakou V, Mangino M, Manichaikul A, Marten J, Mattheisen M, Mavarani L, McDaid AF, Meidtner K, Melendez TL, Mercader JM, Milaneschi Y, Miller JE, Millwood IY, Mishra PP, Mitchell RE, Møllehave LT, Morgan A, Mucha S, Munz M, Nakatochi M, Nelson CP, Nethander M, Nho CW, Nielsen AA, Nolte IM, Nongmaithem SS, Noordam R, Ntalla I, Nutile T, Pandit A, Christofidou P, Pärna K, Pauper M, Petersen ERB, Petersen LV, Pitkänen N, Polašek O, Poveda A, Preuss MH, Pyarajan S, Raffield LM, Rakugi H, Ramirez J, Rasheed A, Raven D, Rayner NW, Riveros C, Rohde R, Ruggiero D, Ruotsalainen SE, Ryan KA, Sabater-Lleal M, Saxena R, Scholz M, Sendamarai A, Shen B, Shi J, Shin JH, Sidore C, Sitlani CM, Slieker RC, Smit RAJ, Smith AV, Smith JA, Smyth LJ, Southam L, Steinthorsdottir V, Sun L, Takeuchi F, Tallapragada DSP, Taylor KD, Tayo BO, Tcheandjieu C, Terzikhan N, Tesolin P, Teumer A, Theusch E, Thompson DJ, Thorleifsson G, Timmers PRHJ, Trompet S, Turman C, Vaccargiu S, van der Laan SW, van der Most PJ, van Klinken JB, van Setten J, Verma SS, Verweij N, Veturi Y, Wang CA, Wang C, Wang L, Wang Z, Warren HR, Bin Wei W, Wickremasinghe AR, Wielscher M, Wiggins KL, Winsvold BS, Wong A, Wu Y, Wuttke M, Xia R, Xie T, Yamamoto K, Yang J, Yao J, Young H, Yousri NA, Yu L, Zeng L, Zhang W, Zhang X, Zhao JH, Zhao W, Zhou W, Zimmermann ME, Zoledziewska M, Adair LS, Adams HHH, Aguilar-Salinas CA, Al-Mulla F, Arnett DK, Asselbergs FW, Åsvold BO, Attia J, Banas B, Bandinelli S, Bennett DA, Bergler T, Bharadwaj D, Biino G, Bisgaard H, Boerwinkle E, Böger CA, Bønnelykke K, Boomsma DI, Børglum AD, Borja JB, Bouchard C, Bowden DW, Brandslund I, Brumpton B, Buring JE, Caulfield MJ, Chambers JC, Chandak GR, Chanock SJ, Chaturvedi N, Chen YI, Chen Z, Cheng CY, Christophersen IE, Ciullo M, Cole JW, Collins FS, Cooper RS, Cruz M, Cucca F, Cupples LA, Cutler MJ, Damrauer SM, Dantoft TM, de Borst GJ, de Groot LCPGM, De Jager PL, de Kleijn DPV, Janaka de Silva H, Dedoussis GV, den Hollander AI, Du S, Easton DF, Elders PJM, Eliassen AH, Ellinor PT, Elmståhl S, Erdmann J, Evans MK, Fatkin D, Feenstra B, Feitosa MF, Ferrucci L, Ford I, Fornage M, Franke A, Franks PW, Freedman BI, Gasparini P, Gieger C, Girotto G, Goddard ME, Golightly YM, Gonzalez-Villalpando C, Gordon-Larsen P, Grallert H, Grant SFA, Grarup N, Griffiths L, Gudnason V, Haiman C, Hakonarson H, Hansen T, Hartman CA, Hattersley AT, Hayward C, Heckbert SR, Heng CK, Hengstenberg C, Hewitt AW, Hishigaki H, Hoyng CB, Huang PL, Huang W, Hunt SC, Hveem K, Hyppönen E, Iacono WG, Ichihara S, Ikram MA, Isasi CR, Jackson RD, Jarvelin MR, Jin ZB, Jöckel KH, Joshi PK, Jousilahti P, Jukema JW, Kähönen M, Kamatani Y, Kang KD, Kaprio J, Kardia SLR, Karpe F, Kato N, Kee F, Kessler T, Khera AV, Khor CC, Kiemeney LALM, Kim BJ, Kim EK, Kim HL, Kirchhof P, Kivimaki M, Koh WP, Koistinen HA, Kolovou GD, Kooner JS, Kooperberg C, Köttgen A, Kovacs P, Kraaijeveld A, Kraft P, Krauss RM, Kumari M, Kutalik Z, Laakso M, Lange LA, Langenberg C, Launer LJ, Le Marchand L, Lee H, Lee NR, Lehtimäki T, Li H, Li L, Lieb W, Lin X, Lind L, Linneberg A, Liu CT, Liu J, Loeffler M, London B, Lubitz SA, Lye SJ, Mackey DA, Mägi R, Magnusson PKE, Marcus GM, Vidal PM, Martin NG, März W, Matsuda F, McGarrah RW, McGue M, McKnight AJ, Medland SE, Mellström D, Metspalu A, Mitchell BD, Mitchell P, Mook-Kanamori DO, Morris AD, Mucci LA, Munroe PB, Nalls MA, Nazarian S, Nelson AE, Neville MJ, Newton-Cheh C, Nielsen CS, Nöthen MM, Ohlsson C, Oldehinkel AJ, Orozco L, Pahkala K, Pajukanta P, Palmer CNA, Parra EJ, Pattaro C, Pedersen O, Pennell CE, Penninx BWJH, Perusse L, Peters A, Peyser PA, Porteous DJ, Posthuma D, Power C, Pramstaller PP, Province MA, Qi Q, Qu J, Rader DJ, Raitakari OT, Ralhan S, Rallidis LS, Rao DC, Redline S, Reilly DF, Reiner AP, Rhee SY, Ridker PM, Rienstra M, Ripatti S, Ritchie MD, Roden DM, Rosendaal FR, Rotter JI, Rudan I, Rutters F, Sabanayagam C, Saleheen D, Salomaa V, Samani NJ, Sanghera DK, Sattar N, Schmidt B, Schmidt H, Schmidt R, Schulze MB, Schunkert H, Scott LJ, Scott RJ, Sever P, Shiroma EJ, Shoemaker MB, Shu XO, Simonsick EM, Sims M, Singh JR, Singleton AB, Sinner MF, Smith JG, Snieder H, Spector TD, Stampfer MJ, Stark KJ, Strachan DP, 't Hart LM, Tabara Y, Tang H, Tardif JC, Thanaraj TA, Timpson NJ, Tönjes A, Tremblay A, Tuomi T, Tuomilehto J, Tusié-Luna MT, Uitterlinden AG, van Dam RM, van der Harst P, Van der Velde N, van Duijn CM, van Schoor NM, Vitart V, Völker U, Vollenweider P, Völzke H, Wacher-Rodarte NH, Walker M, Wang YX, Wareham NJ, Watanabe RM, Watkins H, Weir DR, Werge TM, Widen E, Wilkens LR, Willemsen G, Willett WC, Wilson JF, Wong TY, Woo JT, Wright AF, Wu JY, Xu H, Yajnik CS, Yokota M, Yuan JM, Zeggini E, Zemel BS, Zheng W, Zhu X, Zmuda JM, Zonderman AB, Zwart JA, Chasman DI, Cho YS, Heid IM, McCarthy MI, Ng MCY, O'Donnell CJ, Rivadeneira F, Thorsteinsdottir U, Sun YV, Tai ES, Boehnke M, Deloukas P, Justice AE, Lindgren CM, Loos RJF, Mohlke KL, North KE, Stefansson K, Walters RG, Winkler TW, Young KL, Loh PR, Yang J, Esko T, Assimes TL, Auton A, Abecasis GR, Willer CJ, Locke AE, Berndt SI, Lettre G, Frayling TM, Okada Y, Wood AR, Visscher PM, and Hirschhorn JN
Subjects: Humans, Gene Frequency genetics, Genome, Human genetics, Genome-Wide Association Study, Haplotypes genetics, Linkage Disequilibrium genetics, Europe ethnology, Sample Size, Phenotype, Body Height genetics, Polymorphism, Single Nucleotide genetics, Chromosome Mapping
Abstract: 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 sizes 1 . 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 panel 2 ) 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., (© 2022. The Author(s).)
Published: 2022
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17. Adiposity, metabolomic biomarkers, and risk of nonalcoholic fatty liver disease: a case-cohort study.

Author: Pang Y, Kartsonaki C, Lv J, Millwood IY, Fairhurst-Hunter Z, Turnbull I, Bragg F, Hill MR, Yu C, Guo Y, Chen Y, Yang L, Clarke R, Walters RG, Wu M, Chen J, Li L, Chen Z, and Holmes MV
Subjects: Adult, Biomarkers, Cohort Studies, Humans, Obesity metabolism, Prospective Studies, Adiposity, Non-alcoholic Fatty Liver Disease
Abstract: Background: Globally, the burden of obesity and associated nonalcoholic fatty liver disease (NAFLD) are rising, but little is known about the role that circulating metabolomic biomarkers play in mediating their association., Objectives: We aimed to examine the observational and genetic associations of adiposity with metabolomic biomarkers and the observational associations of metabolomic biomarkers with incident NAFLD., Methods: A case-subcohort study within the prospective China Kadoorie Biobank included 176 NAFLD cases and 180 subcohort individuals and measured 1208 metabolites in stored baseline plasma using a Metabolon assay. In the subcohort the observational and genetic associations of BMI with biomarkers were assessed using linear regression, with adjustment for multiple testing. Cox regression was used to estimate adjusted HRs for NAFLD associated with biomarkers., Results: In observational analyses, BMI (kg/m2; mean: 23.9 in the subcohort) was associated with 199 metabolites at a 5% false discovery rate. The effects of genetically elevated BMI with specific metabolites were directionally consistent with the observational associations. Overall, 35 metabolites were associated with NAFLD risk, of which 15 were also associated with BMI, including glutamate (HR per 1-SD higher metabolite: 1.95; 95% CI: 1.48, 2.56), cysteine-glutathione disulfide (0.44; 0.31, 0.62), diaclyglycerol (C32:1) (1.71; 1.24, 2.35), behenoyl dihydrosphingomyelin (C40:0) (1.92; 1.42, 2.59), butyrylcarnitine (C4) (1.91; 1.38, 2.35), 2-hydroxybehenate (1.81; 1.34, 2.45), and 4-cholesten-3-one (1.79; 1.27, 2.54). The discriminatory performance of known risk factors was increased when 28 metabolites were also considered simultaneously in the model (weighted C-statistic: 0.84 to 0.90; P < 0.001)., Conclusions: Among relatively lean Chinese adults, a range of metabolomic biomarkers are associated with NAFLD risk and these biomarkers may lie on the pathway between adiposity and NAFLD., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)
Published: 2022
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18. Associations of Adiposity, Circulating Protein Biomarkers, and Risk of Major Vascular Diseases.

Author: Pang Y, Kartsonaki C, Lv J, Fairhurst-Hunter Z, Millwood IY, Yu C, Guo Y, Chen Y, Bian Z, Yang L, Chen J, Clarke R, Walters RG, Holmes MV, Li L, and Chen Z
Subjects: Body Mass Index, Chemokine CCL2 blood, Chemokine CCL7 blood, China epidemiology, Cohort Studies, Female, Hepatocyte Growth Factor blood, Humans, Interleukin-18 blood, Interleukin-6 blood, Male, Mendelian Randomization Analysis, Middle Aged, TNF-Related Apoptosis-Inducing Ligand blood, Biomarkers blood, Cardiovascular Diseases epidemiology, Obesity epidemiology
Abstract: Importance: Obesity is associated with a higher risk of cardiovascular disease (CVD), but little is known about the role that circulating protein biomarkers play in this association., Objective: To examine the observational and genetic associations of adiposity with circulating protein biomarkers and the observational associations of proteins with incident CVD., Design, Setting, and Participants: This subcohort study included 628 participants from the prospective China Kadoorie Biobank who did not have a history of cancer at baseline. The Olink platform measured 92 protein markers in baseline plasma samples. Data were collected from June 2004 to January 2016 and analyzed from January 2019 to June 2020., Exposures: Measured body mass index (BMI) obtained during the baseline survey and genetically instrumented BMI derived using 571 externally weighted single-nucleotide variants., Main Outcomes and Measures: Cross-sectional associations of adiposity with biomarkers were examined using linear regression. Associations of biomarkers with CVD risk were assessed using Cox regression among those without prior cancer or CVD at baseline. Mendelian randomization was conducted to derive genetically estimated associations of BMI with biomarkers., Findings: In observational analyses of 628 individuals (mean [SD] age, 52.2 [10.5] years; 385 women [61.3%]), BMI (mean [SD], 23.9 [3.6]) was positively associated with 27 proteins (per 1-SD higher BMI; eg, interleukin-6: 0.21 [95% CI, 0.12-0.29] SD; interleukin-18: 0.13 [95% CI, 0.05-0.21] SD; monocyte chemoattractant protein-1: 0.12 [95% CI, 0.04-0.20] SD; hepatocyte growth factor: 0.31 [95% CI, 0.24-0.39] SD), and inversely with 3 proteins (Fas ligand: -0.11 [95% CI, -0.19 to -0.03] SD; TNF-related weak inducer of apoptosis, -0.14 [95% CI, -0.23 to -0.06] SD; and carbonic anhydrase 9: (-0.14 [95% CI, -0.22 to -0.05] SD), with similar associations identified for other adiposity traits (eg, waist circumference [r = 0.96]). In mendelian randomization, the associations of genetically elevated BMI with specific proteins were directionally consistent with the observational associations. In meta-analyses of genetically elevated BMI with 8 proteins, combining present estimates with previous studies, the most robust associations were shown for interleukin-6 (per 1-SD higher BMI; 0.21 [95% CI, 0.13-0.29] SD), interleukin-18 (0.16 [95% CI, 0.06-0.26] SD), monocyte chemoattractant protein-1 (0.21 [95% CI, 0.11-0.30] SD), monocyte chemotactic protein-3 (0.12 [95% CI, 0.03-0.21] SD), TNF-related apoptosis-inducing ligand (0.23 [95% CI, 0.13-0.32] SD), and hepatocyte growth factor (0.14 [95% CI, 0.06-0.22] SD). Of the 30 BMI-associated biomarkers, 10 (including interleukin-6, interleukin-18, and hepatocyte growth factor) were nominally associated with incident CVD., Conclusions and Relevance: Mendelian randomization shows adiposity to be associated with a range of protein biomarkers, with some biomarkers also showing association with CVD risk. Future studies are warranted to validate these findings and assess whether proteins may be mediators between adiposity and CVD.
Published: 2021
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19. Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9.

Author: Schmidt AF, Holmes MV, Preiss D, Swerdlow DI, Denaxas S, Fatemifar G, Faraway R, Finan C, Valentine D, Fairhurst-Hunter Z, Hartwig FP, Horta BL, Hypponen E, Power C, Moldovan M, van Iperen E, Hovingh K, Demuth I, Norman K, Steinhagen-Thiessen E, Demuth J, Bertram L, Lill CM, Coassin S, Willeit J, Kiechl S, Willeit K, Mason D, Wright J, Morris R, Wanamethee G, Whincup P, Ben-Shlomo Y, McLachlan S, Price JF, Kivimaki M, Welch C, Sanchez-Galvez A, Marques-Vidal P, Nicolaides A, Panayiotou AG, Onland-Moret NC, van der Schouw YT, Matullo G, Fiorito G, Guarrera S, Sacerdote C, Wareham NJ, Langenberg C, Scott RA, Luan J, Bobak M, Malyutina S, Pająk A, Kubinova R, Tamosiunas A, Pikhart H, Grarup N, Pedersen O, Hansen T, Linneberg A, Jess T, Cooper J, Humphries SE, Brilliant M, Kitchner T, Hakonarson H, Carrell DS, McCarty CA, Lester KH, Larson EB, Crosslin DR, de Andrade M, Roden DM, Denny JC, Carty C, Hancock S, Attia J, Holliday E, Scott R, Schofield P, O'Donnell M, Yusuf S, Chong M, Pare G, van der Harst P, Said MA, Eppinga RN, Verweij N, Snieder H, Christen T, Mook-Kanamori DO, Gustafsson S, Lind L, Ingelsson E, Pazoki R, Franco O, Hofman A, Uitterlinden A, Dehghan A, Teumer A, Baumeister S, Dörr M, Lerch MM, Völker U, Völzke H, Ward J, Pell JP, Meade T, Christophersen IE, Maitland-van der Zee AH, Baranova EV, Young R, Ford I, Campbell A, Padmanabhan S, Bots ML, Grobbee DE, Froguel P, Thuillier D, Roussel R, Bonnefond A, Cariou B, Smart M, Bao Y, Kumari M, Mahajan A, Hopewell JC, Seshadri S, Dale C, Costa RPE, Ridker PM, Chasman DI, Reiner AP, Ritchie MD, Lange LA, Cornish AJ, Dobbins SE, Hemminki K, Kinnersley B, Sanson M, Labreche K, Simon M, Bondy M, Law P, Speedy H, Allan J, Li N, Went M, Weinhold N, Morgan G, Sonneveld P, Nilsson B, Goldschmidt H, Sud A, Engert A, Hansson M, Hemingway H, Asselbergs FW, Patel RS, Keating BJ, Sattar N, Houlston R, Casas JP, and Hingorani AD
Subjects: Anticholesteremic Agents adverse effects, Biomarkers blood, Brain Ischemia epidemiology, Brain Ischemia prevention & control, Down-Regulation, Dyslipidemias blood, Dyslipidemias epidemiology, Genome-Wide Association Study, Humans, Myocardial Infarction epidemiology, Myocardial Infarction prevention & control, Randomized Controlled Trials as Topic, Risk Assessment, Risk Factors, Serine Proteinase Inhibitors adverse effects, Stroke epidemiology, Stroke prevention & control, Treatment Outcome, Anticholesteremic Agents therapeutic use, Cholesterol, LDL blood, Dyslipidemias drug therapy, Dyslipidemias genetics, PCSK9 Inhibitors, Polymorphism, Single Nucleotide, Proprotein Convertase 9 genetics, Serine Proteinase Inhibitors therapeutic use
Abstract: Background: We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9., Methods: Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Seventeen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration., Results: The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95% CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95% CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95% CI 0.57; 1.22) for the GS, compared to 0.85 (95% CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95% CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer's disease - outcomes for which large-scale trial data were unavailable., Conclusions: Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. While indicating an increased risk of T2DM, no other possible safety concerns were shown; although precision was moderate.
Published: 2019
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20. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study.

Author: Schmidt AF, Swerdlow DI, Holmes MV, Patel RS, Fairhurst-Hunter Z, Lyall DM, Hartwig FP, Horta BL, Hyppönen E, Power C, Moldovan M, van Iperen E, Hovingh GK, Demuth I, Norman K, Steinhagen-Thiessen E, Demuth J, Bertram L, Liu T, Coassin S, Willeit J, Kiechl S, Willeit K, Mason D, Wright J, Morris R, Wanamethee G, Whincup P, Ben-Shlomo Y, McLachlan S, Price JF, Kivimaki M, Welch C, Sanchez-Galvez A, Marques-Vidal P, Nicolaides A, Panayiotou AG, Onland-Moret NC, van der Schouw YT, Matullo G, Fiorito G, Guarrera S, Sacerdote C, Wareham NJ, Langenberg C, Scott R, Luan J, Bobak M, Malyutina S, Pająk A, Kubinova R, Tamosiunas A, Pikhart H, Husemoen LL, Grarup N, Pedersen O, Hansen T, Linneberg A, Simonsen KS, Cooper J, Humphries SE, Brilliant M, Kitchner T, Hakonarson H, Carrell DS, McCarty CA, Kirchner HL, Larson EB, Crosslin DR, de Andrade M, Roden DM, Denny JC, Carty C, Hancock S, Attia J, Holliday E, O'Donnell M, Yusuf S, Chong M, Pare G, van der Harst P, Said MA, Eppinga RN, Verweij N, Snieder H, Christen T, Mook-Kanamori DO, Gustafsson S, Lind L, Ingelsson E, Pazoki R, Franco O, Hofman A, Uitterlinden A, Dehghan A, Teumer A, Baumeister S, Dörr M, Lerch MM, Völker U, Völzke H, Ward J, Pell JP, Smith DJ, Meade T, Maitland-van der Zee AH, Baranova EV, Young R, Ford I, Campbell A, Padmanabhan S, Bots ML, Grobbee DE, Froguel P, Thuillier D, Balkau B, Bonnefond A, Cariou B, Smart M, Bao Y, Kumari M, Mahajan A, Ridker PM, Chasman DI, Reiner AP, Lange LA, Ritchie MD, Asselbergs FW, Casas JP, Keating BJ, Preiss D, Hingorani AD, and Sattar N
Subjects: Blood Glucose metabolism, Case-Control Studies, Cholesterol, LDL blood, Cholesterol, LDL genetics, Cohort Studies, Diabetes Mellitus, Type 2 diagnosis, Humans, Randomized Controlled Trials as Topic methods, Diabetes Mellitus, Type 2 blood, Diabetes Mellitus, Type 2 genetics, Genetic Predisposition to Disease genetics, Genetic Variation genetics, Mendelian Randomization Analysis methods, Proprotein Convertase 9 genetics
Abstract: Background: Statin treatment and variants in the gene encoding HMG-CoA reductase are associated with reductions in both the concentration of LDL cholesterol and the risk of coronary heart disease, but also with modest hyperglycaemia, increased bodyweight, and modestly increased risk of type 2 diabetes, which in no way offsets their substantial benefits. We sought to investigate the associations of LDL cholesterol-lowering PCSK9 variants with type 2 diabetes and related biomarkers to gauge the likely effects of PCSK9 inhibitors on diabetes risk., Methods: In this mendelian randomisation study, we used data from cohort studies, randomised controlled trials, case control studies, and genetic consortia to estimate associations of PCSK9 genetic variants with LDL cholesterol, fasting blood glucose, HbA 1c , fasting insulin, bodyweight, waist-to-hip ratio, BMI, and risk of type 2 diabetes, using a standardised analysis plan, meta-analyses, and weighted gene-centric scores., Findings: Data were available for more than 550 000 individuals and 51 623 cases of type 2 diabetes. Combined analyses of four independent PCSK9 variants (rs11583680, rs11591147, rs2479409, and rs11206510) scaled to 1 mmol/L lower LDL cholesterol showed associations with increased fasting glucose (0·09 mmol/L, 95% CI 0·02 to 0·15), bodyweight (1·03 kg, 0·24 to 1·82), waist-to-hip ratio (0·006, 0·003 to 0·010), and an odds ratio for type diabetes of 1·29 (1·11 to 1·50). Based on the collected data, we did not identify associations with HbA 1c (0·03%, -0·01 to 0·08), fasting insulin (0·00%, -0·06 to 0·07), and BMI (0·11 kg/m 2 , -0·09 to 0·30)., Interpretation: PCSK9 variants associated with lower LDL cholesterol were also associated with circulating higher fasting glucose concentration, bodyweight, and waist-to-hip ratio, and an increased risk of type 2 diabetes. In trials of PCSK9 inhibitor drugs, investigators should carefully assess these safety outcomes and quantify the risks and benefits of PCSK9 inhibitor treatment, as was previously done for statins., Funding: British Heart Foundation, and University College London Hospitals NHS Foundation Trust (UCLH) National Institute for Health Research (NIHR) Biomedical Research Centre., (Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY license. Published by Elsevier Ltd.. All rights reserved.)
Published: 2017
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21. Association of Lipid Fractions With Risks for Coronary Artery Disease and Diabetes.

Author: White J, Swerdlow DI, Preiss D, Fairhurst-Hunter Z, Keating BJ, Asselbergs FW, Sattar N, Humphries SE, Hingorani AD, and Holmes MV
Subjects: Coronary Artery Disease genetics, Coronary Artery Disease prevention & control, Diabetes Mellitus, Type 2 genetics, Genome-Wide Association Study, Humans, Lipids, Polymorphism, Single Nucleotide, Risk Factors, Cholesterol, HDL analysis, Cholesterol, HDL genetics, Cholesterol, LDL analysis, Cholesterol, LDL genetics, Coronary Artery Disease epidemiology, Diabetes Mellitus, Type 2 epidemiology
Abstract: Importance: Low-density lipoprotein cholesterol (LDL-C) is causally related to coronary artery disease (CAD), but the relevance of high-density lipoprotein cholesterol (HDL-C) and triglycerides (TGs) is uncertain. Lowering of LDL-C levels by statin therapy modestly increases the risk of type 2 diabetes, but it is unknown whether this effect is specific to statins., Objective: To investigate the associations of 3 routinely measured lipid fractions with CAD and diabetes through mendelian randomization (MR) using conventional MR and making use of newer approaches, such as multivariate MR and MR-Egger, that address the pleiotropy of genetic instruments where relevant., Design, Setting, and Participants: Published data from genome-wide association studies were used to construct genetic instruments and then applied to investigate associations between lipid fractions and the risk of CAD and diabetes using MR approaches that took into account pleiotropy of genetic instruments. The study was conducted from March 12 to December 31, 2015., Main Outcomes and Measures: Coronary artery disease and diabetes., Results: Genetic instruments composed of 130 single-nucleotide polymorphisms (SNPs) were used for LDL-C (explaining 7.9% of its variance), 140 SNPs for HDL-C (6.6% of variance), and 140 SNPs for TGs (5.9% of variance). A 1-SD genetically instrumented elevation in LDL-C levels (equivalent to 38 mg/dL) and TG levels (equivalent to 89 mg/dL) was associated with higher CAD risk; odds ratios (ORs) were 1.68 (95% CI, 1.51-1.87) for LDL-C and 1.28 (95% CI, 1.13-1.45) for TGs. The corresponding OR for HDL-C (equivalent to a 16-mg/dL increase) was 0.95 (95% CI, 0.85-1.06). All 3 lipid traits were associated with a lower risk of type 2 diabetes. The ORs were 0.79 (95% CI, 0.71-0.88) for LDL-C and 0.83 (95% CI, 0.76-0.90) for HDL-C per 1-SD elevation. For TG, the MR estimates for diabetes were inconsistent, with MR-Egger giving an OR of 0.83 (95%CI, 0.72-0.95) per 1-SD elevation., Conclusions and Relevance: Routinely measured lipid fractions exhibit contrasting associations with the risk of CAD and diabetes. Increased LDL-C, HDL-C, and possibly TG levels are associated with a lower risk of diabetes. This information will be relevant to the design of clinical trials of lipid-modifying agents, which should carefully monitor participants for dysglycemia and the incidence of diabetes.
Published: 2016
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21 results on '"Fairhurst-Hunter Z"'

1. BMI and Well-being in people of East Asian and European Ancestry: a Mendelian randomisation study

2. A saturated map of common genetic variants associated with human height

3. Stroke genetics informs drug discovery and risk prediction across ancestries

4. A saturated map of common genetic variants associated with human height

5. Adiposity, metabolomic biomarkers and risk of nonalcoholic fatty liver disease: a case-cohort study

6. Investigation into the Health Effects of Reduced Chymase Function Using Predicted Loss-of-Function Mutations in CMA1

7. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study

8. PCSK9 genetic variants and risk of type 2 diabetes: a Mendelian randomisation study

9. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study

10. PCSK9 genetic variants and risk of type 2 diabetes: A mendelian randomisation study

11. Conventional and genetic associations of BMI with major vascular and non-vascular disease incidence and mortality in a relatively lean Chinese population: U-shaped relationship revisited.

12. Causal relevance of different blood pressure traits on risk of cardiovascular diseases: GWAS and Mendelian randomisation in 100,000 Chinese adults.

13. Protein-truncating variants in BSN are associated with severe adult-onset obesity, type 2 diabetes and fatty liver disease.

14. BMI and well-being in people of East Asian and European ancestry: a Mendelian randomisation study.

15. Mendelian randomisation study of body composition and depression in people of East Asian ancestry highlights potential setting-specific causality.

16. A saturated map of common genetic variants associated with human height.

17. Adiposity, metabolomic biomarkers, and risk of nonalcoholic fatty liver disease: a case-cohort study.

18. Associations of Adiposity, Circulating Protein Biomarkers, and Risk of Major Vascular Diseases.

19. Phenome-wide association analysis of LDL-cholesterol lowering genetic variants in PCSK9.

20. PCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study.

21. Association of Lipid Fractions With Risks for Coronary Artery Disease and Diabetes.

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21 results on '"Fairhurst-Hunter Z"'

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