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34 results on '"Veturi, Y."'

1. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Author: Kanoni, S., Graham, S.E., Wang, Yuxuan, Surakka, I., Ramdas, S., Zhu, X, Clarke, S.L., Bhatti, K.F., Vedantam, S., Winkler, T.W., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Zeyuan, Xue, C., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, Wei, Weir, D.R., Turman, C., Huang, H., Graff, M, Choudhury, A., Sengupta, D., Mahajan, A., Brown, M.R., Zhang, W, Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J, Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Yousri, N.A., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Hwu, C.M., Hung, Y.J., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Sabater-Lleal, M., Noordam, R., Mauro, P., Galesloot, T.E., Lores-Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Graaf, J. de, Assimes, T.L., Peloso, G.M., Kanoni, S., Graham, S.E., Wang, Yuxuan, Surakka, I., Ramdas, S., Zhu, X, Clarke, S.L., Bhatti, K.F., Vedantam, S., Winkler, T.W., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Zeyuan, Xue, C., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, Wei, Weir, D.R., Turman, C., Huang, H., Graff, M, Choudhury, A., Sengupta, D., Mahajan, A., Brown, M.R., Zhang, W, Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J, Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Yousri, N.A., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Hwu, C.M., Hung, Y.J., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Sabater-Lleal, M., Noordam, R., Mauro, P., Galesloot, T.E., Lores-Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Graaf, J. de, Assimes, T.L., and Peloso, G.M.
Abstract: Item does not contain fulltext, BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
Published: 2022

2. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Author: Ramdas, S., Judd, J., Graham, S.E., Kanoni, S., Wang, Y, Surakka, I., Wenz, B., Clarke, S.L., Chesi, A., Wells, A., Bhatti, K.F., Vedantam, S., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Z, Xue, C., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, W, Weir, D.R., Turman, C., Huang, H., Graff, Mariaelisa, Choudhury, A., Sengupta, D., Mahajan, A., Brown, M.R., Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J, Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Lee, W.J., Hsiung, C.A., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Galesloot, T.E., Lores de Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Zhu, Xiang, Brown, C.D., Ramdas, S., Judd, J., Graham, S.E., Kanoni, S., Wang, Y, Surakka, I., Wenz, B., Clarke, S.L., Chesi, A., Wells, A., Bhatti, K.F., Vedantam, S., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Z, Xue, C., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, W, Weir, D.R., Turman, C., Huang, H., Graff, Mariaelisa, Choudhury, A., Sengupta, D., Mahajan, A., Brown, M.R., Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J, Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Lee, W.J., Hsiung, C.A., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Galesloot, T.E., Lores de Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Zhu, Xiang, and Brown, C.D.
Abstract: Item does not contain fulltext, A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.
Published: 2022

3. 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

4. The power of genetic diversity in genome-wide association studies of lipids

Author: Graham, S.E., Clarke, S.L., Wu, K.H., Kanoni, S., Zajac, G.J.M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T.W., Locke, A.E., Marouli, E., Hwang, M.Y., Han, S., Narita, A., Choudhury, A., Bentley, A.R., Ekoru, K., Verma, A., Trivedi, B., Martin, H.C., Hunt, K.A., Hui, Q., Klarin, D., Zhu, X, Thorleifsson, G., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W., Brumpton, B.M., Rasheed, H., Ruotsalainen, S.E., Havulinna, A.S., Veturi, Y., Feng, Q., Rosenthal, E.A., Lingren, T., Pacheco, J.A., Pendergrass, S.A., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Hindy, G., Rasheed, A., Faul, J.D., Zhao, W., Weir, D.R., Turman, C., Huang, H., Graff, M, Mahajan, A., Brown, M.R., Zhang, W., Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Willemsen, G., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Mitchell, R.E., Chai, J.F., Aadahl, M., Yao, J., Manichaikul, A., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Sabater-Lleal, M., Noordam, R., Sidore, C., Fiorillo, E., McDaid, A.F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Galesloot, T.E., Kiemeney, B., Graaf, J. de, Lores de Motta, L., Pauper, M., Hollander, A.I. den, Sun, Y.V., Willer, C.J., Graham, S.E., Clarke, S.L., Wu, K.H., Kanoni, S., Zajac, G.J.M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T.W., Locke, A.E., Marouli, E., Hwang, M.Y., Han, S., Narita, A., Choudhury, A., Bentley, A.R., Ekoru, K., Verma, A., Trivedi, B., Martin, H.C., Hunt, K.A., Hui, Q., Klarin, D., Zhu, X, Thorleifsson, G., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W., Brumpton, B.M., Rasheed, H., Ruotsalainen, S.E., Havulinna, A.S., Veturi, Y., Feng, Q., Rosenthal, E.A., Lingren, T., Pacheco, J.A., Pendergrass, S.A., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Hindy, G., Rasheed, A., Faul, J.D., Zhao, W., Weir, D.R., Turman, C., Huang, H., Graff, M, Mahajan, A., Brown, M.R., Zhang, W., Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Willemsen, G., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Mitchell, R.E., Chai, J.F., Aadahl, M., Yao, J., Manichaikul, A., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Sabater-Lleal, M., Noordam, R., Sidore, C., Fiorillo, E., McDaid, A.F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Galesloot, T.E., Kiemeney, B., Graaf, J. de, Lores de Motta, L., Pauper, M., Hollander, A.I. den, Sun, Y.V., and Willer, C.J.
Abstract: Item does not contain fulltext, Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use(1). Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels(2), heart disease remains the leading cause of death worldwide(3). Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS(4-23) have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns(24). Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine(25), we anticipate that increased diversity of participants will lead to more accurate and equitable(26) application of polygenic scores in clinical practice.
Published: 2021

5. The power of genetic diversity in genome-wide association studies of lipids

Author: Graham, S. E. (Sarah E.), Clarke, S. L. (Shoa L.), Wu, K. H. (Kuan-Han H.), Kanoni, S. (Stavroula), Zajac, G. J. (Greg J. M.), Ramdas, S. (Shweta), Surakka, I. (Ida), Ntalla, I. (Ioanna), Vedantam, S. (Sailaja), Winkler, T. W. (Thomas W.), Locke, A. E. (Adam E.), Marouli, E. (Eirini), Hwang, M. Y. (Mi Yeong), Han, S. (Sohee), Narita, A. (Akira), Choudhury, A. (Ananyo), Bentley, A. R. (Amy R.), Ekoru, K. (Kenneth), Verma, A. (Anurag), Trivedi, B. (Bhavi), Martin, H. C. (Hilary C.), Hunt, K. A. (Karen A.), Hui, Q. (Qin), Klarin, D. (Derek), Zhu, X. (Xiang), Thorleifsson, G. (Gudmar), Helgadottir, A. (Anna), Gudbjartsson, D. F. (Daniel F.), Holm, H. (Hilma), Olafsson, I. (Isleifur), Akiyama, M. (Masato), Sakaue, S. (Saori), Terao, C. (Chikashi), Kanai, M. (Masahiro), Zhou, W. (Wei), Brumpton, B. M. (Ben M.), Rasheed, H. (Humaira), Ruotsalainen, S. E. (Sanni E.), Havulinna, A. S. (Aki S.), Veturi, Y. (Yogasudha), Feng, Q. (QiPing), Rosenthal, E. A. (Elisabeth A.), Lingren, T. (Todd), Pacheco, J. A. (Jennifer Allen), Pendergrass, S. A. (Sarah A.), Haessler, J. (Jeffrey), Giulianini, F. (Franco), Bradford, Y. (Yuki), Miller, J. E. (Jason E.), Campbell, A. (Archie), Lin, K. (Kuang), Millwood, I. Y. (Iona Y.), Hindy, G. (George), Rasheed, A. (Asif), Faul, J. D. (Jessica D.), Zhao, W. (Wei), Weir, D. R. (David R.), Turman, C. (Constance), Huang, H. (Hongyan), Graff, M. (Mariaelisa), Mahajan, A. (Anubha), Brown, M. R. (Michael R.), Zhang, W. (Weihua), Yu, K. (Ketian), Schmidt, E. M. (Ellen M.), Pandit, A. (Anita), Gustafsson, S. (Stefan), Yin, X. (Xianyong), Luan, J. (Jian'an), Zhao, J.-H. (Jing-Hua), Matsuda, F. (Fumihiko), Jang, H.-M. (Hye-Mi), Yoon, K. (Kyungheon), Medina-Gomez, C. (Carolina), Pitsillides, A. (Achilleas), Hottenga, J. J. (Jouke Jan), Willemsen, G. (Gonneke), Wood, A. R. (Andrew R.), Ji, Y. (Yingji), Gao, Z. (Zishan), Haworth, S. (Simon), Mitchell, R. E. (Ruth E.), Chai, J. F. (Jin Fang), Aadahl, M. (Mette), Yao, J. (Jie), Manichaikul, A. (Ani), Warren, H. R. (Helen R.), Ramirez, J. (Julia), Bork-Jensen, J. (Jette), Karhus, L. L. (Line L.), Goel, A. (Anuj), Sabater-Lleal, M. (Maria), Noordam, R. (Raymond), Sidore, C. (Carlo), Fiorillo, E. (Edoardo), McDaid, A. F. (Aaron F.), Marques-Vidal, P. (Pedro), Wielscher, M. (Matthias), Trompet, S. (Stella), Sattar, N. (Naveed), Mollehave, L. T. (Line T.), Thuesen, B. H. (Betina H.), Munz, M. (Matthias), Zeng, L. (Lingyao), Huang, J. (Jianfeng), Yang, B. (Bin), Poveda, A. (Alaitz), Kurbasic, A. (Azra), Lamina, C. (Claudia), Forer, L. (Lukas), Scholz, M. (Markus), Galesloot, T. E. (Tessel E.), Bradfield, J. P. (Jonathan P.), Daw, E. W. (E. Warwick), Zmuda, J. M. (Joseph M.), Mitchell, J. S. (Jonathan S.), Fuchsberger, C. (Christian), Christensen, H. (Henry), Brody, J. A. (Jennifer A.), Feitosa, M. F. (Mary F.), Wojczynski, M. K. (Mary K.), Preuss, M. (Michael), Mangino, M. (Massimo), Christofidou, P. (Paraskevi), Verweij, N. (Niek), Benjamins, J. W. (Jan W.), Engmann, J. (Jorgen), Kember, R. L. (Rachel L.), Slieker, R. C. (Roderick C.), Lo, K. S. (Ken Sin), Zilhao, N. R. (Nuno R.), Kleber, M. E. (Marcus E.), Delgado, G. E. (Graciela E.), Huo, S. (Shaofeng), Ikeda, D. D. (Daisuke D.), Iha, H. (Hiroyuki), Yang, J. (Jian), Liu, J. (Jun), Leonard, H. L. (Hampton L.), Marten, J. (Jonathan), Schmidt, B. (Borge), Arendt, M. (Marina), Smyth, L. J. (Laura J.), Canadas-Garre, M. (Marisa), Wang, C. (Chaolong), Nakatochi, M. (Masahiro), Wong, A. (Andrew), Hutri-Kahonen, N. (Nina), Sim, X. (Xueling), Xia, R. (Rui), Huerta-Chagoya, A. (Alicia), Fernandez-Lopez, J. C. (Juan Carlos), Lyssenko, V. (Valeriya), Ahmed, M. (Meraj), Jackson, A. U. (Anne U.), Irvin, M. R. (Marguerite R.), Oldmeadow, C. (Christopher), Kim, H.-N. (Han-Na), Ryu, S. (Seungho), Timmers, P. R. (Paul R. H. J.), Arbeeva, L. (Liubov), Dorajoo, R. (Rajkumar), Lange, L. A. (Leslie A.), Chai, X. (Xiaoran), Prasad, G. (Gauri), Lores-Motta, L. (Laura), Pauper, M. (Marc), Long, J. (Jirong), Li, X. (Xiaohui), Theusch, E. (Elizabeth), Takeuchi, F. (Fumihiko), Spracklen, C. N. (Cassandra N.), Loukola, A. (Anu), Bollepalli, S. (Sailalitha), Warner, S. C. (Sophie C.), Wang, Y. X. (Ya Xing), Wei, W. B. (Wen B.), Nutile, T. (Teresa), Ruggiero, D. (Daniela), Sung, Y. J. (Yun Ju), Hung, Y.-J. (Yi-Jen), Chen, S. (Shufeng), Liu, F. (Fangchao), Yang, J. (Jingyun), Kentistou, K. A. (Katherine A.), Gorski, M. (Mathias), Brumat, M. (Marco), Meidtner, K. (Karina), Bielak, L. F. (Lawrence F.), Smith, J. A. (Jennifer A.), Hebbar, P. (Prashantha), Farmaki, A.-E. (Aliki-Eleni), Hofer, E. (Edith), Lin, M. (Maoxuan), Xue, C. (Chao), Zhang, J. (Jifeng), Concas, M. P. (Maria Pina), Vaccargiu, S. (Simona), van der Most, P. J. (Peter J.), Pitkanen, N. (Niina), Cade, B. E. (Brian E.), Lee, J. (Jiwon), van Der Laan, S. W. (Sander W.), Chitrala, K. N. (Kumaraswamy Naidu), Weiss, S. (Stefan), Zimmermann, M. E. (Martina E.), Lee, J. Y. (Jong Young), Choi, H. S. (Hyeok Sun), Nethander, M. (Maria), Freitag-Wolf, S. (Sandra), Southam, L. (Lorraine), Rayner, N. W. (Nigel W.), Wang, C. A. (Carol A.), Lin, S.-Y. (Shih-Yi), Wang, J.-S. (Jun-Sing), Couture, C. (Christian), Lyytikainen, L.-P. (Leo-Pekka), Nikus, K. (Kjell), Cuellar-Partida, G. (Gabriel), Vestergaard, H. (Henrik), Hildalgo, B. (Bertha), Giannakopoulou, O. (Olga), Cai, Q. (Qiuyin), Obura, M. O. (Morgan O.), van Setten, J. (Jessica), Li, X. (Xiaoyin), Schwander, K. (Karen), Terzikhan, N. (Natalie), Shin, J. H. (Jae Hun), Jackson, R. D. (Rebecca D.), Reiner, A. P. (Alexander P.), Martin, L. W. (Lisa Warsinger), Chen, Z. (Zhengming), Li, L. (Liming), Highland, H. M. (Heather M.), Young, K. L. (Kristin L.), Kawaguchi, T. (Takahisa), Thiery, J. (Joachim), Bis, J. C. (Joshua C.), Nadkarni, G. N. (Girish N.), Launer, L. J. (Lenore J.), Li, H. (Huaixing), Nalls, M. A. (Mike A.), Raitakari, O. T. (Olli T.), Ichihara, S. (Sahoko), Wild, S. H. (Sarah H.), Nelson, C. P. (Christopher P.), Campbell, H. (Harry), Jager, S. (Susanne), Nabika, T. (Toru), Al-Mulla, F. (Fahd), Niinikoski, H. (Harri), Braund, P. S. (Peter S.), Kolcic, I. (Ivana), Kovacs, P. (Peter), Giardoglou, T. (Tota), Katsuya, T. (Tomohiro), Bhatti, F. (Fatima), de Kleijn, D. (Dominique), de Borst, G. J. (Gert J.), Kim, E. K. (Eung Kweon), Adams, H. H. (Hieab H. H.), Ikram, M. A. (M. Arfan), Zhu, X. (Xiaofeng), Asselbergs, F. W. (Folkert W.), Kraaijeveld, A. O. (Adriaan O.), Beulens, J. W. (Joline W. J.), Shu, X.-O. (Xiao-Ou), Rallidis, L. S. (Loukianos S.), Pedersen, O. (Oluf), Hansen, T. (Torben), Mitchell, P. (Paul), Hewitt, A. W. (Alex W.), Kahonen, M. (Mika), Perusse, L. (Louis), Bouchard, C. (Claude), Tonjes, A. (Anke), Chen, Y. I. (Yii-Der Ida), Pennell, C. E. (Craig E.), Mori, T. A. (Trevor A.), Lieb, W. (Wolfgang), Franke, A. (Andre), Ohlsson, C. (Claes), Mellstrom, D. (Dan), Cho, Y. S. (Yoon Shin), Lee, H. (Hyejin), Yuan, J.-M. (Jian-Min), Koh, W.-P. (Woon-Puay), Rhee, S. Y. (Sang Youl), Woo, J.-T. (Jeong-Taek), Heid, I. M. (Iris M.), Stark, K. J. (Klaus J.), Volzke, H. (Henry), Homuth, G. (Georg), Evans, M. K. (Michele K.), Zonderman, A. B. (Alan B.), Polasek, O. (Ozren), Pasterkamp, G. (Gerard), Hoefer, I. E. (Imo E.), Redline, S. (Susan), Pahkala, K. (Katja), Oldehinkel, A. J. (Albertine J.), Snieder, H. (Harold), Biino, G. (Ginevra), Schmidt, R. (Reinhold), Schmidt, H. (Helena), Chen, Y. E. (Y. Eugene), Bandinelli, S. (Stefania), Dedoussis, G. (George), Thanaraj, T. A. (Thangavel Alphonse), Kardia, S. L. (Sharon L. R.), Kato, N. (Norihiro), Schulze, M. B. (Matthias B.), Girotto, G. (Giorgia), Jung, B. (Bettina), Boger, C. A. (Carsten A.), Joshi, P. K. (Peter K.), Bennett, D. A. (David A.), De Jager, P. L. (Philip L.), Lu, X. (Xiangfeng), Mamakou, V. (Vasiliki), Brown, M. (Morris), Caulfield, M. J. (Mark J.), Munroe, P. B. (Patricia B.), Guo, X. (Xiuqing), Ciullo, M. (Marina), Jonas, J. B. (Jost B.), Samani, N. J. (Nilesh J.), Kaprio, J. (Jaakko), Pajukanta, P. (Paivi), Adair, L. S. (Linda S.), Bechayda, S. A. (Sonny Augustin), de Silva, H. J. (H. Janaka), Wickremasinghe, A. R. (Ananda R.), Krauss, R. M. (Ronald M.), Wu, J.-Y. (Jer-Yuarn), Zheng, W. (Wei), den Hollander, A. I. (Anneke, I), Bharadwaj, D. (Dwaipayan), Correa, A. (Adolfo), Wilson, J. G. (James G.), Lind, L. (Lars), Heng, C.-K. (Chew-Kiat), Nelson, A. E. (Amanda E.), Golightly, Y. M. (Yvonne M.), Wilson, J. F. (James F.), Penninx, B. (Brenda), Kim, H.-L. (Hyung-Lae), Attia, J. (John), Scott, R. J. (Rodney J.), Rao, D. C. (D. C.), Arnett, D. K. (Donna K.), Walker, M. (Mark), Koistinen, H. A. (Heikki A.), Chandak, G. R. (Giriraj R.), Yajnik, C. S. (Chittaranjan S.), Mercader, J. M. (Josep M.), Tusie-Luna, T. (Teresa), Aguilar-Salinas, C. A. (Carlos A.), Villalpando, C. G. (Clicerio Gonzalez), Orozco, L. (Lorena), Fornage, M. (Myriam), Tai, E. S. (E. Shyong), van Dam, R. M. (Rob M.), Lehtimaki, T. (Terho), Chaturvedi, N. (Nish), Yokota, M. (Mitsuhiro), Liu, J. (Jianjun), Reilly, D. F. (Dermot F.), McKnight, A. J. (Amy Jayne), Kee, F. (Frank), Jockel, K.-H. (Karl-Heinz), McCarthy, M. I. (Mark, I), Palmer, C. N. (Colin N. A.), Vitart, V. (Veronique), Hayward, C. (Caroline), Simonsick, E. (Eleanor), van Duijn, C. M. (Cornelia M.), Lu, F. (Fan), Qu, J. (Jia), Hishigaki, H. (Haretsugu), Lin, X. (Xu), Marz, W. (Winfried), Parra, E. J. (Esteban J.), Cruz, M. (Miguel), Gudnason, V. (Vilmundur), Tardif, J.-C. (Jean-Claude), Lettre, G. (Guillaume), Elders, P. J. (Petra J. M.), Damrauer, S. M. (Scott M.), Kumari, M. (Meena), Kivimaki, M. (Mika), van der Harst, P. (Pim), Spector, T. D. (Tim D.), Loos, R. J. (Ruth J. F.), Province, M. A. (Michael A.), Psaty, B. M. (Bruce M.), Brandslund, I. (Ivan), Pramstaller, P. P. (Peter P.), Christensen, K. (Kaare), Ripatti, S. (Samuli), Widen, E. (Elisabeth), Hakonarson, H. (Hakon), Grant, S. F. (Struan F. A.), Kiemeney, L. A. (Lambertus A. L. M.), de Graaf, J. (Jacqueline), Loeffler, M. (Markus), Kronenberg, F. (Florian), Gu, D. (Dongfeng), Erdmann, J. (Jeanette), Schunkert, H. (Heribert), Franks, P. W. (Paul W.), Linneberg, A. (Allan), Jukema, J. W. (J. Wouter), Khera, A. V. (Amit, V), Männikkö, M. (Minna), Järvelin, M.-R. (Marjo-Riitta), Kutalik, Z. (Zoltan), Cucca, F. (Francesco), Mook-Kanamori, D. O. (Dennis O.), van Dijk, K. W. (Ko Willems), Watkins, H. (Hugh), Strachan, D. P. (David P.), Grarup, N. (Niels), Sever, P. (Peter), Poulter, N. (Neil), Rotter, J. I. (Jerome, I), Dantoft, T. M. (Thomas M.), Karpe, F. (Fredrik), Neville, M. J. (Matt J.), Timpson, N. J. (Nicholas J.), Cheng, C.-Y. (Ching-Yu), Wong, T.-Y. (Tien-Yin), Khor, C. C. (Chiea Chuen), Sabanayagam, C. (Charumathi), Peters, A. (Annette), Gieger, C. (Christian), Hattersley, A. T. (Andrew T.), Pedersen, N. L. (Nancy L.), Magnusson, P. K. (Patrik K. E.), Boomsma, D. I. (Dorret, I), de Geus, E. J. (Eco J. C.), Cupples, L. A. (L. Adrienne), van Meurs, J. B. (Joyce B. J.), Ghanbari, M. (Mohsen), Rsen, P. G. (Penny Gordon-La), Huang, W. (Wei), Kim, Y. J. (Young Jin), Tabara, Y. (Yasuharu), Wareham, N. J. (Nicholas J.), Langenberg, C. (Claudia), Zeggini, E. (Eleftheria), Kuusisto, J. (Johanna), Laakso, M. (Markku), Ingelsson, E. (Erik), Abecasis, G. (Goncalo), Chambers, J. C. (John C.), Kooner, J. S. (Jaspal S.), de Vries, P. S. (Paul S.), Morrison, A. C. (Alanna C.), North, K. E. (Kari E.), Daviglus, M. (Martha), Kraft, P. (Peter), Martin, N. G. (Nicholas G.), Whitfield, J. B. (John B.), Abbas, S. (Shahid), Saleheen, D. (Danish), Walters, R. G. (Robin G.), Holmes, M. V. (Michael, V), Black, C. (Corri), Smith, B. H. (Blair H.), Justice, A. E. (Anne E.), Baras, A. (Aris), Buring, J. E. (Julie E.), Ridker, P. M. (Paul M.), Chasman, D. I. (Daniel, I), Kooperberg, C. (Charles), Wei, W.-Q. (Wei-Qi), Jarvik, G. P. (Gail P.), Namjou, B. (Bahram), Hayes, M. G. (M. Geoffrey), Ritchie, M. D. (Marylyn D.), Jousilahti, P. (Pekka), Salomaa, V. (Veikko), Hveem, K. (Kristian), Asvold, B. O. (Bjorn Olav), Kubo, M. (Michiaki), Kamatani, Y. (Yoichiro), Okada, Y. (Yukinori), Murakami, Y. (Yoshinori), Thorsteinsdottir, U. (Unnur), Stefansson, K. (Kari), Ho, Y.-L. (Yuk-Lam), Lynch, J. A. (Julie A.), Rader, D. J. (Daniel J.), Tsao, P. S. (Philip S.), Chang, K.-M. (Kyong-Mi), Cho, K. (Kelly), O'Donnell, C. J. (Christopher J.), Gaziano, J. M. (John M.), Wilson, P. (Peter), Rotimi, C. N. (Charles N.), Hazelhurst, S. (Scott), Ramsay, M. (Michele), Trembath, R. C. (Richard C.), van Heel, D. A. (David A.), Tamiya, G. (Gen), Yamamoto, M. (Masayuki), Kim, B.-J. (Bong-Jo), Mohlke, K. L. (Karen L.), Frayling, T. M. (Timothy M.), Hirschhorn, J. N. (Joel N.), Kathiresan, S. (Sekar), Boehnke, M. (Michael), Natarajan, P. (Pradeep), Peloso, G. M. (Gina M.), Brown, C. D. (Christopher D.), Morris, A. P. (Andrew P.), Assimes, T. L. (Themistocles L.), Deloukas, P. (Panos), Sun, Y. V. (Yan, V), Willer, C. J. (Cristen J.), Graham, S. E. (Sarah E.), Clarke, S. L. (Shoa L.), Wu, K. H. (Kuan-Han H.), Kanoni, S. (Stavroula), Zajac, G. J. (Greg J. M.), Ramdas, S. (Shweta), Surakka, I. (Ida), Ntalla, I. (Ioanna), Vedantam, S. (Sailaja), Winkler, T. W. (Thomas W.), Locke, A. E. (Adam E.), Marouli, E. (Eirini), Hwang, M. Y. (Mi Yeong), Han, S. (Sohee), Narita, A. (Akira), Choudhury, A. (Ananyo), Bentley, A. R. (Amy R.), Ekoru, K. (Kenneth), Verma, A. (Anurag), Trivedi, B. (Bhavi), Martin, H. C. (Hilary C.), Hunt, K. A. (Karen A.), Hui, Q. (Qin), Klarin, D. (Derek), Zhu, X. (Xiang), Thorleifsson, G. (Gudmar), Helgadottir, A. (Anna), Gudbjartsson, D. F. (Daniel F.), Holm, H. (Hilma), Olafsson, I. (Isleifur), Akiyama, M. (Masato), Sakaue, S. (Saori), Terao, C. (Chikashi), Kanai, M. (Masahiro), Zhou, W. (Wei), Brumpton, B. M. (Ben M.), Rasheed, H. (Humaira), Ruotsalainen, S. E. (Sanni E.), Havulinna, A. S. (Aki S.), Veturi, Y. (Yogasudha), Feng, Q. (QiPing), Rosenthal, E. A. (Elisabeth A.), Lingren, T. (Todd), Pacheco, J. A. (Jennifer Allen), Pendergrass, S. A. (Sarah A.), Haessler, J. (Jeffrey), Giulianini, F. (Franco), Bradford, Y. (Yuki), Miller, J. E. (Jason E.), Campbell, A. (Archie), Lin, K. (Kuang), Millwood, I. Y. (Iona Y.), Hindy, G. (George), Rasheed, A. (Asif), Faul, J. D. (Jessica D.), Zhao, W. (Wei), Weir, D. R. (David R.), Turman, C. (Constance), Huang, H. (Hongyan), Graff, M. (Mariaelisa), Mahajan, A. (Anubha), Brown, M. R. (Michael R.), Zhang, W. (Weihua), Yu, K. (Ketian), Schmidt, E. M. (Ellen M.), Pandit, A. (Anita), Gustafsson, S. (Stefan), Yin, X. (Xianyong), Luan, J. (Jian'an), Zhao, J.-H. (Jing-Hua), Matsuda, F. (Fumihiko), Jang, H.-M. (Hye-Mi), Yoon, K. (Kyungheon), Medina-Gomez, C. (Carolina), Pitsillides, A. (Achilleas), Hottenga, J. J. (Jouke Jan), Willemsen, G. (Gonneke), Wood, A. R. (Andrew R.), Ji, Y. (Yingji), Gao, Z. (Zishan), Haworth, S. (Simon), Mitchell, R. E. (Ruth E.), Chai, J. F. (Jin Fang), Aadahl, M. (Mette), Yao, J. (Jie), Manichaikul, A. (Ani), Warren, H. R. (Helen R.), Ramirez, J. (Julia), Bork-Jensen, J. (Jette), Karhus, L. L. (Line L.), Goel, A. (Anuj), Sabater-Lleal, M. (Maria), Noordam, R. (Raymond), Sidore, C. (Carlo), Fiorillo, E. (Edoardo), McDaid, A. F. (Aaron F.), Marques-Vidal, P. (Pedro), Wielscher, M. (Matthias), Trompet, S. (Stella), Sattar, N. (Naveed), Mollehave, L. T. (Line T.), Thuesen, B. H. (Betina H.), Munz, M. (Matthias), Zeng, L. (Lingyao), Huang, J. (Jianfeng), Yang, B. (Bin), Poveda, A. (Alaitz), Kurbasic, A. (Azra), Lamina, C. (Claudia), Forer, L. (Lukas), Scholz, M. (Markus), Galesloot, T. E. (Tessel E.), Bradfield, J. P. (Jonathan P.), Daw, E. W. (E. Warwick), Zmuda, J. M. (Joseph M.), Mitchell, J. S. (Jonathan S.), Fuchsberger, C. (Christian), Christensen, H. (Henry), Brody, J. A. (Jennifer A.), Feitosa, M. F. (Mary F.), Wojczynski, M. K. (Mary K.), Preuss, M. (Michael), Mangino, M. (Massimo), Christofidou, P. (Paraskevi), Verweij, N. (Niek), Benjamins, J. W. (Jan W.), Engmann, J. (Jorgen), Kember, R. L. (Rachel L.), Slieker, R. C. (Roderick C.), Lo, K. S. (Ken Sin), Zilhao, N. R. (Nuno R.), Kleber, M. E. (Marcus E.), Delgado, G. E. (Graciela E.), Huo, S. (Shaofeng), Ikeda, D. D. (Daisuke D.), Iha, H. (Hiroyuki), Yang, J. (Jian), Liu, J. (Jun), Leonard, H. L. (Hampton L.), Marten, J. (Jonathan), Schmidt, B. (Borge), Arendt, M. (Marina), Smyth, L. J. (Laura J.), Canadas-Garre, M. (Marisa), Wang, C. (Chaolong), Nakatochi, M. (Masahiro), Wong, A. (Andrew), Hutri-Kahonen, N. (Nina), Sim, X. (Xueling), Xia, R. (Rui), Huerta-Chagoya, A. (Alicia), Fernandez-Lopez, J. C. (Juan Carlos), Lyssenko, V. (Valeriya), Ahmed, M. (Meraj), Jackson, A. U. (Anne U.), Irvin, M. R. (Marguerite R.), Oldmeadow, C. (Christopher), Kim, H.-N. (Han-Na), Ryu, S. (Seungho), Timmers, P. R. (Paul R. H. J.), Arbeeva, L. (Liubov), Dorajoo, R. (Rajkumar), Lange, L. A. (Leslie A.), Chai, X. (Xiaoran), Prasad, G. (Gauri), Lores-Motta, L. (Laura), Pauper, M. (Marc), Long, J. (Jirong), Li, X. (Xiaohui), Theusch, E. (Elizabeth), Takeuchi, F. (Fumihiko), Spracklen, C. N. (Cassandra N.), Loukola, A. (Anu), Bollepalli, S. (Sailalitha), Warner, S. C. (Sophie C.), Wang, Y. X. (Ya Xing), Wei, W. B. (Wen B.), Nutile, T. (Teresa), Ruggiero, D. (Daniela), Sung, Y. J. (Yun Ju), Hung, Y.-J. (Yi-Jen), Chen, S. (Shufeng), Liu, F. (Fangchao), Yang, J. (Jingyun), Kentistou, K. A. (Katherine A.), Gorski, M. (Mathias), Brumat, M. (Marco), Meidtner, K. (Karina), Bielak, L. F. (Lawrence F.), Smith, J. A. (Jennifer A.), Hebbar, P. (Prashantha), Farmaki, A.-E. (Aliki-Eleni), Hofer, E. (Edith), Lin, M. (Maoxuan), Xue, C. (Chao), Zhang, J. (Jifeng), Concas, M. P. (Maria Pina), Vaccargiu, S. (Simona), van der Most, P. J. (Peter J.), Pitkanen, N. (Niina), Cade, B. E. (Brian E.), Lee, J. (Jiwon), van Der Laan, S. W. (Sander W.), Chitrala, K. N. (Kumaraswamy Naidu), Weiss, S. (Stefan), Zimmermann, M. E. (Martina E.), Lee, J. Y. (Jong Young), Choi, H. S. (Hyeok Sun), Nethander, M. (Maria), Freitag-Wolf, S. (Sandra), Southam, L. (Lorraine), Rayner, N. W. (Nigel W.), Wang, C. A. (Carol A.), Lin, S.-Y. (Shih-Yi), Wang, J.-S. (Jun-Sing), Couture, C. (Christian), Lyytikainen, L.-P. (Leo-Pekka), Nikus, K. (Kjell), Cuellar-Partida, G. (Gabriel), Vestergaard, H. (Henrik), Hildalgo, B. (Bertha), Giannakopoulou, O. (Olga), Cai, Q. (Qiuyin), Obura, M. O. (Morgan O.), van Setten, J. (Jessica), Li, X. (Xiaoyin), Schwander, K. (Karen), Terzikhan, N. (Natalie), Shin, J. H. (Jae Hun), Jackson, R. D. (Rebecca D.), Reiner, A. P. (Alexander P.), Martin, L. W. (Lisa Warsinger), Chen, Z. (Zhengming), Li, L. (Liming), Highland, H. M. (Heather M.), Young, K. L. (Kristin L.), Kawaguchi, T. (Takahisa), Thiery, J. (Joachim), Bis, J. C. (Joshua C.), Nadkarni, G. N. (Girish N.), Launer, L. J. (Lenore J.), Li, H. (Huaixing), Nalls, M. A. (Mike A.), Raitakari, O. T. (Olli T.), Ichihara, S. (Sahoko), Wild, S. H. (Sarah H.), Nelson, C. P. (Christopher P.), Campbell, H. (Harry), Jager, S. (Susanne), Nabika, T. (Toru), Al-Mulla, F. (Fahd), Niinikoski, H. (Harri), Braund, P. S. (Peter S.), Kolcic, I. (Ivana), Kovacs, P. (Peter), Giardoglou, T. (Tota), Katsuya, T. (Tomohiro), Bhatti, F. (Fatima), de Kleijn, D. (Dominique), de Borst, G. J. (Gert J.), Kim, E. K. (Eung Kweon), Adams, H. H. (Hieab H. H.), Ikram, M. A. (M. Arfan), Zhu, X. (Xiaofeng), Asselbergs, F. W. (Folkert W.), Kraaijeveld, A. O. (Adriaan O.), Beulens, J. W. (Joline W. J.), Shu, X.-O. (Xiao-Ou), Rallidis, L. S. (Loukianos S.), Pedersen, O. (Oluf), Hansen, T. (Torben), Mitchell, P. (Paul), Hewitt, A. W. (Alex W.), Kahonen, M. (Mika), Perusse, L. (Louis), Bouchard, C. (Claude), Tonjes, A. (Anke), Chen, Y. I. (Yii-Der Ida), Pennell, C. E. (Craig E.), Mori, T. A. (Trevor A.), Lieb, W. (Wolfgang), Franke, A. (Andre), Ohlsson, C. (Claes), Mellstrom, D. (Dan), Cho, Y. S. (Yoon Shin), Lee, H. (Hyejin), Yuan, J.-M. (Jian-Min), Koh, W.-P. (Woon-Puay), Rhee, S. Y. (Sang Youl), Woo, J.-T. (Jeong-Taek), Heid, I. M. (Iris M.), Stark, K. J. (Klaus J.), Volzke, H. (Henry), Homuth, G. (Georg), Evans, M. K. (Michele K.), Zonderman, A. B. (Alan B.), Polasek, O. (Ozren), Pasterkamp, G. (Gerard), Hoefer, I. E. (Imo E.), Redline, S. (Susan), Pahkala, K. (Katja), Oldehinkel, A. J. (Albertine J.), Snieder, H. (Harold), Biino, G. (Ginevra), Schmidt, R. (Reinhold), Schmidt, H. (Helena), Chen, Y. E. (Y. Eugene), Bandinelli, S. (Stefania), Dedoussis, G. (George), Thanaraj, T. A. (Thangavel Alphonse), Kardia, S. L. (Sharon L. R.), Kato, N. (Norihiro), Schulze, M. B. (Matthias B.), Girotto, G. (Giorgia), Jung, B. (Bettina), Boger, C. A. (Carsten A.), Joshi, P. K. (Peter K.), Bennett, D. A. (David A.), De Jager, P. L. (Philip L.), Lu, X. (Xiangfeng), Mamakou, V. (Vasiliki), Brown, M. (Morris), Caulfield, M. J. (Mark J.), Munroe, P. B. (Patricia B.), Guo, X. (Xiuqing), Ciullo, M. (Marina), Jonas, J. B. (Jost B.), Samani, N. J. (Nilesh J.), Kaprio, J. (Jaakko), Pajukanta, P. (Paivi), Adair, L. S. (Linda S.), Bechayda, S. A. (Sonny Augustin), de Silva, H. J. (H. Janaka), Wickremasinghe, A. R. (Ananda R.), Krauss, R. M. (Ronald M.), Wu, J.-Y. (Jer-Yuarn), Zheng, W. (Wei), den Hollander, A. I. (Anneke, I), Bharadwaj, D. (Dwaipayan), Correa, A. (Adolfo), Wilson, J. G. (James G.), Lind, L. (Lars), Heng, C.-K. (Chew-Kiat), Nelson, A. E. (Amanda E.), Golightly, Y. M. (Yvonne M.), Wilson, J. F. (James F.), Penninx, B. (Brenda), Kim, H.-L. (Hyung-Lae), Attia, J. (John), Scott, R. J. (Rodney J.), Rao, D. C. (D. C.), Arnett, D. K. (Donna K.), Walker, M. (Mark), Koistinen, H. A. (Heikki A.), Chandak, G. R. (Giriraj R.), Yajnik, C. S. (Chittaranjan S.), Mercader, J. M. (Josep M.), Tusie-Luna, T. (Teresa), Aguilar-Salinas, C. A. (Carlos A.), Villalpando, C. G. (Clicerio Gonzalez), Orozco, L. (Lorena), Fornage, M. (Myriam), Tai, E. S. (E. Shyong), van Dam, R. M. (Rob M.), Lehtimaki, T. (Terho), Chaturvedi, N. (Nish), Yokota, M. (Mitsuhiro), Liu, J. (Jianjun), Reilly, D. F. (Dermot F.), McKnight, A. J. (Amy Jayne), Kee, F. (Frank), Jockel, K.-H. (Karl-Heinz), McCarthy, M. I. (Mark, I), Palmer, C. N. (Colin N. A.), Vitart, V. (Veronique), Hayward, C. (Caroline), Simonsick, E. (Eleanor), van Duijn, C. M. (Cornelia M.), Lu, F. (Fan), Qu, J. (Jia), Hishigaki, H. (Haretsugu), Lin, X. (Xu), Marz, W. (Winfried), Parra, E. J. (Esteban J.), Cruz, M. (Miguel), Gudnason, V. (Vilmundur), Tardif, J.-C. (Jean-Claude), Lettre, G. (Guillaume), Elders, P. J. (Petra J. M.), Damrauer, S. M. (Scott M.), Kumari, M. (Meena), Kivimaki, M. (Mika), van der Harst, P. (Pim), Spector, T. D. (Tim D.), Loos, R. J. (Ruth J. F.), Province, M. A. (Michael A.), Psaty, B. M. (Bruce M.), Brandslund, I. (Ivan), Pramstaller, P. P. (Peter P.), Christensen, K. (Kaare), Ripatti, S. (Samuli), Widen, E. (Elisabeth), Hakonarson, H. (Hakon), Grant, S. F. (Struan F. A.), Kiemeney, L. A. (Lambertus A. L. M.), de Graaf, J. (Jacqueline), Loeffler, M. (Markus), Kronenberg, F. (Florian), Gu, D. (Dongfeng), Erdmann, J. (Jeanette), Schunkert, H. (Heribert), Franks, P. W. (Paul W.), Linneberg, A. (Allan), Jukema, J. W. (J. Wouter), Khera, A. V. (Amit, V), Männikkö, M. (Minna), Järvelin, M.-R. (Marjo-Riitta), Kutalik, Z. (Zoltan), Cucca, F. (Francesco), Mook-Kanamori, D. O. (Dennis O.), van Dijk, K. W. (Ko Willems), Watkins, H. (Hugh), Strachan, D. P. (David P.), Grarup, N. (Niels), Sever, P. (Peter), Poulter, N. (Neil), Rotter, J. I. (Jerome, I), Dantoft, T. M. (Thomas M.), Karpe, F. (Fredrik), Neville, M. J. (Matt J.), Timpson, N. J. (Nicholas J.), Cheng, C.-Y. (Ching-Yu), Wong, T.-Y. (Tien-Yin), Khor, C. C. (Chiea Chuen), Sabanayagam, C. (Charumathi), Peters, A. (Annette), Gieger, C. (Christian), Hattersley, A. T. (Andrew T.), Pedersen, N. L. (Nancy L.), Magnusson, P. K. (Patrik K. E.), Boomsma, D. I. (Dorret, I), de Geus, E. J. (Eco J. C.), Cupples, L. A. (L. Adrienne), van Meurs, J. B. (Joyce B. J.), Ghanbari, M. (Mohsen), Rsen, P. G. (Penny Gordon-La), Huang, W. (Wei), Kim, Y. J. (Young Jin), Tabara, Y. (Yasuharu), Wareham, N. J. (Nicholas J.), Langenberg, C. (Claudia), Zeggini, E. (Eleftheria), Kuusisto, J. (Johanna), Laakso, M. (Markku), Ingelsson, E. (Erik), Abecasis, G. (Goncalo), Chambers, J. C. (John C.), Kooner, J. S. (Jaspal S.), de Vries, P. S. (Paul S.), Morrison, A. C. (Alanna C.), North, K. E. (Kari E.), Daviglus, M. (Martha), Kraft, P. (Peter), Martin, N. G. (Nicholas G.), Whitfield, J. B. (John B.), Abbas, S. (Shahid), Saleheen, D. (Danish), Walters, R. G. (Robin G.), Holmes, M. V. (Michael, V), Black, C. (Corri), Smith, B. H. (Blair H.), Justice, A. E. (Anne E.), Baras, A. (Aris), Buring, J. E. (Julie E.), Ridker, P. M. (Paul M.), Chasman, D. I. (Daniel, I), Kooperberg, C. (Charles), Wei, W.-Q. (Wei-Qi), Jarvik, G. P. (Gail P.), Namjou, B. (Bahram), Hayes, M. G. (M. Geoffrey), Ritchie, M. D. (Marylyn D.), Jousilahti, P. (Pekka), Salomaa, V. (Veikko), Hveem, K. (Kristian), Asvold, B. O. (Bjorn Olav), Kubo, M. (Michiaki), Kamatani, Y. (Yoichiro), Okada, Y. (Yukinori), Murakami, Y. (Yoshinori), Thorsteinsdottir, U. (Unnur), Stefansson, K. (Kari), Ho, Y.-L. (Yuk-Lam), Lynch, J. A. (Julie A.), Rader, D. J. (Daniel J.), Tsao, P. S. (Philip S.), Chang, K.-M. (Kyong-Mi), Cho, K. (Kelly), O'Donnell, C. J. (Christopher J.), Gaziano, J. M. (John M.), Wilson, P. (Peter), Rotimi, C. N. (Charles N.), Hazelhurst, S. (Scott), Ramsay, M. (Michele), Trembath, R. C. (Richard C.), van Heel, D. A. (David A.), Tamiya, G. (Gen), Yamamoto, M. (Masayuki), Kim, B.-J. (Bong-Jo), Mohlke, K. L. (Karen L.), Frayling, T. M. (Timothy M.), Hirschhorn, J. N. (Joel N.), Kathiresan, S. (Sekar), Boehnke, M. (Michael), Natarajan, P. (Pradeep), Peloso, G. M. (Gina M.), Brown, C. D. (Christopher D.), Morris, A. P. (Andrew P.), Assimes, T. L. (Themistocles L.), Deloukas, P. (Panos), Sun, Y. V. (Yan, V), and Willer, C. J. (Cristen J.)
Abstract: Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4‐23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.
Published: 2021

6. The power of genetic diversity in genome-wide association studies of lipids

Author: Graham, Sarah E, Clarke, Shoa L, Wu, Kuan-Han H, Kanoni, Stavroula, Zajac, Greg JM, Ramdas, Shweta, Surakka, Ida, Ntalla, Ioanna, Vedantam, Sailaja, Winkler, Thomas W, Locke, Adam E, Marouli, Eirini, Hwang, Mi Yeong, Han, Sohee, Narita, Akira, Choudhury, Ananyo, Bentley, Amy R, Ekoru, Kenneth, Verma, Anurag, Trivedi, Bhavi, Martin, Hilary C, Hunt, Karen A, Hui, Qin, Klarin, Derek, Zhu, Xiang, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F, Holm, Hilma, Olafsson, Isleifur, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M, Rasheed, Humaira, Ruotsalainen, Sanni E, Havulinna, Aki S, Veturi, Yogasudha, Feng, QiPing, Rosenthal, Elisabeth A, Lingren, Todd, Pacheco, Jennifer Allen, Pendergrass, Sarah A, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E, Campbell, Archie, Lin, Kuang, Millwood, Iona Y, Hindy, George, Rasheed, Asif, Faul, Jessica D, Zhao, Wei, Weir, David R, Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Mahajan, Anubha, Brown, Michael R, Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M, Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian’an, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achilleas, Hottenga, Jouke Jan, Willemsen, Gonneke, Wood, Andrew R, Ji, Yingji, Gao, Zishan, Haworth, Simon, Mitchell, Ruth E, Chai, Jin Fang, Aadahl, Mette, Yao, Jie, Manichaikul, Ani, Warren, Helen R, Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L, Goel, Anuj, Sabater-Lleal, Maria, Noordam, Raymond, Sidore, Carlo, Fiorillo, Edoardo, McDaid, Aaron F, Marques-Vidal, Pedro, Wielscher, Matthias, Trompet, Stella, Sattar, Naveed, Møllehave, Line T, Thuesen, Betina H, Munz, Matthias, Zeng, Lingyao, Huang, Jianfeng, Yang, Bin, Poveda, Alaitz, Kurbasic, Azra, Lamina, Claudia, Forer, Lukas, Scholz, Markus, Galesloot, Tessel E., Bradfield, Jonathan P., Daw, E Warwick, Zmuda, Joseph M, Mitchell, Jonathan S, Fuchsberger, Christian, Christensen, Henry, Brody, Jennifer A, Feitosa, Mary F, Wojczynski, Mary K, Preuss, Michael, Mangino, Massimo, Christofidou, Paraskevi, Verweij, Niek, Benjamins, Jan W, Engmann, Jorgen, Kember, Rachel L, Slieker, Roderick C, Lo, Ken Sin, Zilhao, Nuno R, Le, Phuong, Kleber, Marcus E, Delgado, Graciela E, Huo, Shaofeng, Ikeda, Daisuke D, Iha, Hiroyuki, Yang, Jian, Liu, Jun, Leonard, Hampton L, Marten, Jonathan, Schmidt, Börge, Arendt, Marina, Smyth, Laura J, Cañadas-Garre, Marisa, Wang, Chaolong, Nakatochi, Masahiro, Wong, Andrew, Hutri-Kähönen, Nina, Sim, Xueling, Xia, Rui, Huerta-Chagoya, Alicia, Fernandez-Lopez, Juan Carlos, Lyssenko, Valeriya, Ahmed, Meraj, Jackson, Anne U, Irvin, Marguerite R, Oldmeadow, Christopher, Kim, Han-Na, Ryu, Seungho, Timmers, Paul RHJ, Arbeeva, Liubov, Dorajoo, Rajkumar, Lange, Leslie A, Chai, Xiaoran, Prasad, Gauri, Lorés-Motta, Laura, Pauper, Marc, Long, Jirong, Li, Xiaohui, Theusch, Elizabeth, Takeuchi, Fumihiko, Spracklen, Cassandra N, Loukola, Anu, Bollepalli, Sailalitha, Warner, Sophie C, Wang, Ya Xing, Wei, Wen B., Nutile, Teresa, Ruggiero, Daniela, Sung, Yun Ju, Hung, Yi-Jen, Chen, Shufeng, Liu, Fangchao, Yang, Jingyun, Kentistou, Katherine A, Gorski, Mathias, Brumat, Marco, Meidtner, Karina, Bielak, Lawrence F, Smith, Jennifer A, Hebbar, Prashantha, Farmaki, Aliki-Eleni, Hofer, Edith, Lin, Maoxuan, Xue, Chao, Zhang, Jifeng, Concas, Maria Pina, Vaccargiu, Simona, van der Most, Peter J, Pitkänen, Niina, Cade, Brian E, Lee, Jiwon, van der Laan, Sander W., Chitrala, Kumaraswamy Naidu, Weiss, Stefan, Zimmermann, Martina E, Lee, Jong Young, Choi, Hyeok Sun, Nethander, Maria, Freitag-Wolf, Sandra, Southam, Lorraine, Rayner, Nigel W, Wang, Carol A, Lin, Shih-Yi, Wang, Jun-Sing, Couture, Christian, Lyytikäinen, Leo-Pekka, Nikus, Kjell, Cuellar-Partida, Gabriel, Vestergaard, Henrik, Hildalgo, Bertha, Giannakopoulou, Olga, Cai, Qiuyin, Obura, Morgan O, van Setten, Jessica, Li, Xiaoyin, Schwander, Karen, Terzikhan, Natalie, Shin, Jae Hun, Jackson, Rebecca D, Reiner, Alexander P, Martin, Lisa Warsinger, Chen, Zhengming, Li, Liming, Highland, Heather M, Young, Kristin L, Kawaguchi, Takahisa, Thiery, Joachim, Bis, Joshua C, Nadkarni, Girish N., Launer, Lenore J, Li, Huaixing, Nalls, Mike A, Raitakari, Olli T, Ichihara, Sahoko, Wild, Sarah H, Nelson, Christopher P, Campbell, Harry, Jäger, Susanne, Nabika, Toru, Al-Mulla, Fahd, Niinikoski, Harri, Braund, Peter S, Kolcic, Ivana, Kovacs, Peter, Giardoglou, Tota, Katsuya, Tomohiro, Bhatti, Konain Fatima, de Kleijn, Dominique, de Borst, Gert J., Kim, Eung Kweon, Adams, Hieab H.H., Ikram, M. Arfan, Zhu, Xiaofeng, Asselbergs, Folkert W, Kraaijeveld, Adriaan O, Beulens, Joline WJ, Shu, Xiao-Ou, Rallidis, Loukianos S, Pedersen, Oluf, Hansen, Torben, Mitchell, Paul, Hewitt, Alex W, Kähönen, Mika, Pérusse, Louis, Bouchard, Claude, Tönjes, Anke, Chen, Yii-Der Ida, Pennell, Craig E, Mori, Trevor A, Lieb, Wolfgang, Franke, Andre, Ohlsson, Claes, Mellström, Dan, Cho, Yoon Shin, Lee, Hyejin, Yuan, Jian-Min, Koh, Woon-Puay, Rhee, Sang Youl, Woo, Jeong-Taek, Heid, Iris M, Stark, Klaus J, Völzke, Henry, Homuth, Georg, Evans, Michele K, Zonderman, Alan B, Polasek, Ozren, Pasterkamp, Gerard, Hoefer, Imo E, Redline, Susan, Pahkala, Katja, Oldehinkel, Albertine J, Snieder, Harold, Biino, Ginevra, Schmidt, Reinhold, Schmidt, Helena, Chen, Y Eugene, Bandinelli, Stefania, Dedoussis, George, Thanaraj, Thangavel Alphonse, Kardia, Sharon LR, Kato, Norihiro, Schulze, Matthias B, Girotto, Giorgia, Jung, Bettina, Böger, Carsten A, Joshi, Peter K, Bennett, David A, De Jager, Philip L, Lu, Xiangfeng, Mamakou, Vasiliki, Brown, Morris, Caulfield, Mark J, Munroe, Patricia B, Guo, Xiuqing, Ciullo, Marina, Jonas, Jost B., Samani, Nilesh J, Chasman, Daniel I., Kaprio, Jaakko, Pajukanta, Päivi, Tusié-Luna, Teresa, Aguilar-Salinas, Carlos A, Adair, Linda S, Bechayda, Sonny Augustin, de Silva, H. Janaka, Wickremasinghe, Ananda R, Krauss, Ronald M, Wu, Jer-Yuarn, Zheng, Wei, den Hollander, Anneke I, Bharadwaj, Dwaipayan, Correa, Adolfo, Wilson, James G, Lind, Lars, Heng, Chew-Kiat, Nelson, Amanda E, Golightly, Yvonne M, Wilson, James F, Penninx, Brenda, Kim, Hyung-Lae, Attia, John, Scott, Rodney J, Rao, D C, Arnett, Donna K, Walker, Mark, Koistinen, Heikki A, Chandak, Giriraj R, Yajnik, Chittaranjan S, Mercader, Josep M, Tusie-Luna, Teresa, Aguilar-Salinas, Carlos, Villalpando, Clicerio Gonzalez, Orozco, Lorena, Fornage, Myriam, Tai, E Shyong, van Dam, Rob M, Lehtimäki, Terho, Chaturvedi, Nish, Yokota, Mitsuhiro, Liu, Jianjun, Reilly, Dermot F, McKnight, Amy Jayne, Kee, Frank, Jöckel, Karl-Heinz, McCarthy, Mark I, Palmer, Colin NA, Vitart, Veronique, Hayward, Caroline, Simonsick, Eleanor, van Duijn, Cornelia M, Lu, Fan, Qu, Jia, Hishigaki, Haretsugu, Lin, Xu, März, Winfried, Parra, Esteban J, Cruz, Miguel, Gudnason, Vilmundur, Tardif, Jean-Claude, Lettre, Guillaume, Hart, Leen M t, Elders, Petra JM, Rader, Daniel J, Damrauer, Scott M, Kumari, Meena, Kivimaki, Mika, van der Harst, Pim, Spector, Tim D, Loos, Ruth J.F., Province, Michael A, Psaty, Bruce M, Brandslund, Ivan, Pramstaller, Peter P, Christensen, Kaare, Ripatti, Samuli, Widén, Elisabeth, Hakonarson, Hakon, Grant, Struan F.A., Kiemeney, Lambertus ALM, de Graaf, Jacqueline, Loeffler, Markus, Kronenberg, Florian, Gu, Dongfeng, Erdmann, Jeanette, Schunkert, Heribert, Franks, Paul W, Linneberg, Allan, Jukema, J. Wouter, Khera, Amit V, Männikkö, Minna, Jarvelin, Marjo-Riitta, Kutalik, Zoltan, Cucca, Francesco, Mook-Kanamori, Dennis O, van Dijk, Ko Willems, Watkins, Hugh, Strachan, David P, Grarup, Niels, Sever, Peter, Poulter, Neil, Rotter, Jerome I, Dantoft, Thomas M, Karpe, Fredrik, Neville, Matt J, Timpson, Nicholas J, Cheng, Ching-Yu, Wong, Tien-Yin, Khor, Chiea Chuen, Sabanayagam, Charumathi, Peters, Annette, Gieger, Christian, Hattersley, Andrew T, Pedersen, Nancy L, Magnusson, Patrik KE, Boomsma, Dorret I, de Geus, Eco JC, Cupples, L Adrienne, van Meurs, Joyce B.J., Ghanbari, Mohsen, Gordon-Larsen, Penny, Huang, Wei, Kim, Young Jin, Tabara, Yasuharu, Wareham, Nicholas J, Langenberg, Claudia, Zeggini, Eleftheria, Kuusisto, Johanna, Laakso, Markku, Ingelsson, Erik, Abecasis, Goncalo, Chambers, John C, Kooner, Jaspal S, de Vries, Paul S, Morrison, Alanna C, North, Kari E., Daviglus, Martha, Kraft, Peter, Martin, Nicholas G, Whitfield, John B, Abbas, Shahid, Saleheen, Danish, Walters, Robin G, Holmes, Michael V, Black, Corri, Smith, Blair H, Justice, Anne E, Baras, Aris, Buring, Julie E, Ridker, Paul M, Chasman, Daniel I, Kooperberg, Charles, Wei, Wei-Qi, Jarvik, Gail P, Namjou, Bahram, Hayes, M. Geoffrey, Ritchie, Marylyn D, Jousilahti, Pekka, Salomaa, Veikko, Hveem, Kristian, Åsvold, Bjørn Olav, Kubo, Michiaki, Kamatani, Yoichiro, Okada, Yukinori, Murakami, Yoshinori, Thorsteinsdottir, Unnur, Stefansson, Kari, Ho, Yuk-Lam, Lynch, Julie A, Rader, Daniel, Tsao, Phil S, Chang, Kyong-Mi, Cho, Kelly, O’Donnell, Christopher J, Gaziano, John M, Wilson, Peter, Rotimi, Charles N, Hazelhurst, Scott, Ramsay, Michèle, Trembath, Richard C, van Heel, David A, Tamiya, Gen, Yamamoto, Masayuki, Kim, Bong-Jo, Mohlke, Karen L, Frayling, Timothy M, Hirschhorn, Joel N, Kathiresan, Sekar, Boehnke, Michael, Natarajan, Pradeep, Peloso, Gina M, Brown, Christopher D, Morris, Andrew P, Assimes, Themistocles L, Deloukas, Panos, Sun, Yan V, Willer, Cristen J, VA Million Veteran Program, Global Lipids Genetics Consortium, Internal Medicine, Epidemiology, Radiology & Nuclear Medicine, Graham, S. E., Clarke, S. L., Wu, K. -H. H., Kanoni, S., Zajac, G. J. M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T. W., Locke, A. E., Marouli, E., Hwang, M. Y., Han, S., Narita, A., Choudhury, A., Bentley, A. R., Ekoru, K., Verma, A., Trivedi, B., Martin, H. C., Hunt, K. A., Hui, Q., Klarin, D., Zhu, X., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D. F., Holm, H., Olafsson, I., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W., Brumpton, B. M., Rasheed, H., Ruotsalainen, S. E., Havulinna, A. S., Veturi, Y., Feng, Q. P., Rosenthal, E. A., Lingren, T., Pacheco, J. A., Pendergrass, S. A., Haessler, J., Giulianini, F., Bradford, Y., Miller, J. E., Campbell, A., Lin, K., Millwood, I. Y., Hindy, G., Rasheed, A., Faul, J. D., Zhao, W., Weir, D. R., Turman, C., Huang, H., Graff, M., Mahajan, A., Brown, M. R., Zhang, W., Yu, K., Schmidt, E. M., Pandit, A., Gustafsson, S., Yin, X., Luan, J., Zhao, J. -H., Matsuda, F., Jang, H. -M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J. J., Willemsen, G., Wood, A. R., Ji, Y., Gao, Z., Haworth, S., Mitchell, R. E., Chai, J. F., Aadahl, M., Yao, J., Manichaikul, A., Warren, H. R., Ramirez, J., Bork-Jensen, J., Karhus, L. L., Goel, A., Sabater-Lleal, M., Noordam, R., Sidore, C., Fiorillo, E., Mcdaid, A. F., Marques-Vidal, P., Wielscher, M., Trompet, S., Sattar, N., Mollehave, L. T., Thuesen, B. H., Munz, M., Zeng, L., Huang, J., Yang, B., Poveda, A., Kurbasic, A., Lamina, C., Forer, L., Scholz, M., Galesloot, T. E., Bradfield, J. P., Daw, E. W., Zmuda, J. M., Mitchell, J. S., Fuchsberger, C., Christensen, H., Brody, J. A., Feitosa, M. F., Wojczynski, M. K., Preuss, M., Mangino, M., Christofidou, P., Verweij, N., Benjamins, J. W., Engmann, J., Kember, R. L., Slieker, R. C., Lo, K. S., Zilhao, N. R., Le, P., Kleber, M. E., Delgado, G. E., Huo, S., Ikeda, D. D., Iha, H., Yang, J., Liu, J., Leonard, H. L., Marten, J., Schmidt, B., Arendt, M., Smyth, L. J., Canadas-Garre, M., Wang, C., Nakatochi, M., Wong, A., Hutri-Kahonen, N., Sim, X., Xia, R., Huerta-Chagoya, A., Fernandez-Lopez, J. C., Lyssenko, V., Ahmed, M., Jackson, A. U., Irvin, M. R., Oldmeadow, C., Kim, H. -N., Ryu, S., Timmers, P. R. H. J., Arbeeva, L., Dorajoo, R., Lange, L. A., Chai, X., Prasad, G., Lores-Motta, L., Pauper, M., Long, J., Li, X., Theusch, E., Takeuchi, F., Spracklen, C. N., Loukola, A., Bollepalli, S., Warner, S. C., Wang, Y. X., Wei, W. B., Nutile, T., Ruggiero, D., Sung, Y. J., Hung, Y. -J., Chen, S., Liu, F., Kentistou, K. A., Gorski, M., Brumat, M., Meidtner, K., Bielak, L. F., Smith, J. A., Hebbar, P., Farmaki, A. -E., Hofer, E., Lin, M., Xue, C., Zhang, J., Concas, M. P., Vaccargiu, S., van der Most, P. J., Pitkanen, N., Cade, B. E., Lee, J., van der Laan, S. W., Chitrala, K. N., Weiss, S., Zimmermann, M. E., Lee, J. Y., Choi, H. S., Nethander, M., Freitag-Wolf, S., Southam, L., Rayner, N. W., Wang, C. A., Lin, S. -Y., Wang, J. -S., Couture, C., Lyytikainen, L. -P., Nikus, K., Cuellar-Partida, G., Vestergaard, H., Hildalgo, B., Giannakopoulou, O., Cai, Q., Obura, M. O., van Setten, J., Schwander, K., Terzikhan, N., Shin, J. H., Jackson, R. D., Reiner, A. P., Martin, L. W., Chen, Z., Li, L., Highland, H. M., Young, K. L., Kawaguchi, T., Thiery, J., Bis, J. C., Nadkarni, G. N., Launer, L. J., Li, H., Nalls, M. A., Raitakari, O. T., Ichihara, S., Wild, S. H., Nelson, C. P., Campbell, H., Jager, S., Nabika, T., Al-Mulla, F., Niinikoski, H., Braund, P. S., Kolcic, I., Kovacs, P., Giardoglou, T., Katsuya, T., Bhatti, K. F., de Kleijn, D., de Borst, G. J., Kim, E. K., Adams, H. H. H., Ikram, M. A., Asselbergs, F. W., Kraaijeveld, A. O., Beulens, J. W. J., Shu, X. -O., Rallidis, L. S., Pedersen, O., Hansen, T., Mitchell, P., Hewitt, A. W., Kahonen, M., Perusse, L., Bouchard, C., Tonjes, A., Chen, Y. -D. I., Pennell, C. E., Mori, T. A., Lieb, W., Franke, A., Ohlsson, C., Mellstrom, D., Cho, Y. S., Lee, H., Yuan, J. -M., Koh, W. -P., Rhee, S. Y., Woo, J. -T., Heid, I. M., Stark, K. J., Volzke, H., Homuth, G., Evans, M. K., Zonderman, A. B., Polasek, O., Pasterkamp, G., Hoefer, I. E., Redline, S., Pahkala, K., Oldehinkel, A. J., Snieder, H., Biino, G., Schmidt, R., Schmidt, H., Chen, Y. E., Bandinelli, S., Dedoussis, G., Thanaraj, T. A., Kardia, S. L. R., Kato, N., Schulze, M. B., Girotto, G., Jung, B., Boger, C. A., Joshi, P. K., Bennett, D. A., De Jager, P. L., Lu, X., Mamakou, V., Brown, M., Caulfield, M. J., Munroe, P. B., Guo, X., Ciullo, M., Jonas, J. B., Samani, N. J., Kaprio, J., Pajukanta, P., Adair, L. S., Bechayda, S. A., de Silva, H. J., Wickremasinghe, A. R., Krauss, R. M., Wu, J. -Y., Zheng, W., den Hollander, A. I., Bharadwaj, D., Correa, A., Wilson, J. G., Lind, L., Heng, C. -K., Nelson, A. E., Golightly, Y. M., Wilson, J. F., Penninx, B., Kim, H. -L., Attia, J., Scott, R. J., Rao, D. C., Arnett, D. K., Walker, M., Koistinen, H. A., Chandak, G. R., Yajnik, C. S., Mercader, J. M., Tusie-Luna, T., Aguilar-Salinas, C. A., Villalpando, C. G., Orozco, L., Fornage, M., Tai, E. S., van Dam, R. M., Lehtimaki, T., Chaturvedi, N., Yokota, M., Reilly, D. F., Mcknight, A. J., Kee, F., Jockel, K. -H., Mccarthy, M. I., Palmer, C. N. A., Vitart, V., Hayward, C., Simonsick, E., van Duijn, C. M., Lu, F., Qu, J., Hishigaki, H., Lin, X., Marz, W., Parra, E. J., Cruz, M., Gudnason, V., Tardif, J. -C., Lettre, G., 't Hart, L. M., Elders, P. J. M., Damrauer, S. M., Kumari, M., Kivimaki, M., van der Harst, P., Spector, T. D., Loos, R. J. F., Province, M. A., Psaty, B. M., Brandslund, I., Pramstaller, P. P., Christensen, K., Ripatti, S., Widen, E., Hakonarson, H., Grant, S. F. A., Kiemeney, L. A. L. M., de Graaf, J., Loeffler, M., Kronenberg, F., Gu, D., Erdmann, J., Schunkert, H., Franks, P. W., Linneberg, A., Jukema, J. W., Khera, A. V., Mannikko, M., Jarvelin, M. -R., Kutalik, Z., Cucca, F., Mook-Kanamori, D. O., van Dijk, K. W., Watkins, H., Strachan, D. P., Grarup, N., Sever, P., Poulter, N., Rotter, J. I., Dantoft, T. M., Karpe, F., Neville, M. J., Timpson, N. J., Cheng, C. -Y., Wong, T. -Y., Khor, C. C., Sabanayagam, C., Peters, A., Gieger, C., Hattersley, A. T., Pedersen, N. L., Magnusson, P. K. E., Boomsma, D. I., de Geus, E. J. C., Cupples, L. A., van Meurs, J. B. J., Ghanbari, M., Gordon-Larsen, P., Huang, W., Kim, Y. J., Tabara, Y., Wareham, N. J., Langenberg, C., Zeggini, E., Kuusisto, J., Laakso, M., Ingelsson, E., Abecasis, G., Chambers, J. C., Kooner, J. S., de Vries, P. S., Morrison, A. C., North, K. E., Daviglus, M., Kraft, P., Martin, N. G., Whitfield, J. B., Abbas, S., Saleheen, D., Walters, R. G., Holmes, M. V., Black, C., Smith, B. H., Justice, A. E., Baras, A., Buring, J. E., Ridker, P. M., Chasman, D. I., Kooperberg, C., Wei, W. -Q., Jarvik, G. P., Namjou, B., Hayes, M. G., Ritchie, M. D., Jousilahti, P., Salomaa, V., Hveem, K., Asvold, B. O., Kubo, M., Kamatani, Y., Okada, Y., Murakami, Y., Thorsteinsdottir, U., Stefansson, K., Ho, Y. -L., Lynch, J. A., Rader, D. J., Tsao, P. S., Chang, K. -M., Cho, K., O'Donnell, C. J., Gaziano, J. M., Wilson, P., Rotimi, C. N., Hazelhurst, S., Ramsay, M., Trembath, R. C., van Heel, D. A., Tamiya, G., Yamamoto, M., Kim, B. -J., Mohlke, K. L., Frayling, T. M., Hirschhorn, J. N., Kathiresan, S., Boehnke, M., Natarajan, P., Peloso, G. M., Brown, C. D., Morris, A. P., Assimes, T. L., Deloukas, P., Sun, Y. V., Willer, C. J., Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Life Course Epidemiology (LCE), Cardiovascular Centre (CVC), Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, APH - Mental Health, APH - Methodology, AMS - Ageing & Vitality, and AMS - Sports
Subjects: blood lipid level, Multifactorial Inheritance, GWAS, blood lipid levels, cardiovascular disease, Vascular damage Radboud Institute for Health Sciences [Radboudumc 16], Medizin, LOCI, ANCESTRY, VARIANTS, Polymorphism, Single Nucleotide, Linkage Disequilibrium, Sensory disorders Donders Center for Medical Neuroscience [Radboudumc 12], Article, Population Groups, SDG 3 - Good Health and Well-being, Humans, Genetic Predisposition to Disease, METAANALYSIS, POLYMORPHISMS, RISK, Multidisciplinary, Cardiovascular Diseases, Genome-Wide Association Study, Cardiovascular Diseases/genetics, Genetic Predisposition to Disease/genetics, Genome-Wide Association Study/methods, Polymorphism, Single Nucleotide/genetics, CHOLESTEROL, Human Genetics, INDIVIDUALS, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], DISCOVERY, LOW-FREQUENCY, Delivery of Health Care
Abstract: Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use 1 . Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels 2 , heart disease remains the leading cause of death worldwide 3 . Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS 4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns 24 . Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine 25 , we anticipate that increased diversity of participants will lead to more accurate and equitable 26 application of polygenic scores in clinical practice.
Published: 2021
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7. The genetic architecture of multimodal human brain age.

Author: Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, and Davatzikos C
Subjects: Humans, Brain, Gray Matter, Magnetic Resonance Imaging methods, Mendelian Randomization Analysis, Diabetes Mellitus, Type 2, White Matter physiology
Abstract: The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10 -8 ). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine ., (© 2024. The Author(s).)
Published: 2024
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8. Genomic loci influence patterns of structural covariance in the human brain.

Author: Wen J, Nasrallah IM, Abdulkadir A, Satterthwaite TD, Yang Z, Erus G, Robert-Fitzgerald T, Singh A, Sotiras A, Boquet-Pujadas A, Mamourian E, Doshi J, Cui Y, Srinivasan D, Skampardoni I, Chen J, Hwang G, Bergman M, Bao J, Veturi Y, Zhou Z, Yang S, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Gur RC, Gur RE, Koutsouleris N, Wolf DH, Saykin AJ, Ritchie MD, Shen L, Thompson PM, Colliot O, Wittfeld K, Grabe HJ, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Espeland M, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Resnick SM, Fan Y, Habes M, Wolk D, Shou H, and Davatzikos C
Subjects: Humans, Brain pathology, Brain Mapping methods, Genomics, Magnetic Resonance Imaging methods, Brain Neoplasms pathology
Abstract: Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain., Competing Interests: Competing interests statement:D.H.W. served as Site PI for studies by Biogen, Merck, and Eli Lilly/Avid. He has received consulting fees from GE Healthcare and Neuronix. He is on the DSMB for a trial sponsored by Functional Neuromodulation. A.J.S. receives support from multiple NIH grants (P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, U01 AG024904, R01 LM013463, R01 AG068193, T32 AG071444, U01 AG068057, and U01 AG072177). He has also received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in-kind contribution of PET tracer precursor); Bayer Oncology (Scientific Advisory Board); Eisai (Scientific Advisory Board); Siemens Medical Solutions, Inc. (Dementia Advisory Board); and Springer-Nature Publishing (Editorial Office Support as Editor-in-Chief, Brain Imaging, and Behavior). O.C. reports having received consulting fees from AskBio (2020) and Therapanacea (2022), having received payments for writing a lay audience short paper from Expression Santé (2019), and that his laboratory has received grants (paid to the institution) from Qynapse (2017 to present). Members of his laboratory have co-supervised a Ph.D. thesis with myBrainTechnologies (2016 to 2019) and with Qynapse (2017 to present). O.C.’s spouse is an employee and holds stock options of myBrainTechnologies (2015 to present). O.C. has a patent registered at the International Bureau of the World Intellectual Property Organization (PCT/IB2016/0526993, J.-B. Schiratti, S. Allassonniere, O.C., S. Durrleman, A method for determining the temporal progression of a biological phenomenon and associated methods and devices) (2017). M.E. receives support from multiple NIH grants, the Alzheimer’s Association, and the Alzheimer’s Therapeutic Research Institute. M.V.Z. serves as a consultant and/or speaker for the following pharmaceutical companies: Eurofarma, Lundbeck, Abbott, Greencare, Myralis, and Elleven Healthcare.
Published: 2023
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9. The Genetic Architecture of Multimodal Human Brain Age.

Author: Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, and Davatzikos C
Abstract: The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N =31,557 European ancestry), white matter microstructure (WM-BAG, N =31,674), and functional connectivity (FC-BAG, N =32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10 -8 ). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10 -2 ) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10 -5 ). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine., Competing Interests: Competing Interests None
Published: 2023
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10. Author Correction: The power of genetic diversity in genome-wide association studies of lipids.

Author: Graham SE, Clarke SL, Wu KH, Kanoni S, Zajac GJM, Ramdas S, Surakka I, Ntalla I, Vedantam S, Winkler TW, Locke AE, Marouli E, Hwang MY, Han S, Narita A, Choudhury A, Bentley AR, Ekoru K, Verma A, Trivedi B, Martin HC, Hunt KA, Hui Q, Klarin D, Zhu X, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Ruotsalainen SE, Havulinna AS, Veturi Y, Feng Q, Rosenthal EA, Lingren T, Pacheco JA, Pendergrass SA, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Hindy G, Rasheed A, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao JH, Matsuda F, Jang HM, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Willemsen G, Wood AR, Ji Y, Gao Z, Haworth S, Mitchell RE, Chai JF, Aadahl M, Yao J, Manichaikul A, Warren HR, Ramirez J, Bork-Jensen J, Kårhus LL, Goel A, Sabater-Lleal M, Noordam R, Sidore C, Fiorillo E, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Møllehave LT, Thuesen BH, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Lamina C, Forer L, Scholz M, Galesloot TE, Bradfield JP, Daw EW, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Feitosa MF, Wojczynski MK, Preuss M, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Kember RL, Slieker RC, Lo KS, Zilhao NR, Le P, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Leonard HL, Marten J, Schmidt B, Arendt M, Smyth LJ, Cañadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kähönen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Ahmed M, Jackson AU, Yousri NA, Irvin MR, Oldmeadow C, Kim HN, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Chai X, Prasad G, Lorés-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Hung YJ, Chen S, Liu F, Yang J, Kentistou KA, Gorski M, Brumat M, Meidtner K, Bielak LF, Smith JA, Hebbar P, Farmaki AE, Hofer E, Lin M, Xue C, Zhang J, Concas MP, Vaccargiu S, van der Most PJ, Pitkänen N, Cade BE, Lee J, van der Laan SW, Chitrala KN, Weiss S, Zimmermann ME, Lee JY, Choi HS, Nethander M, Freitag-Wolf S, Southam L, Rayner NW, Wang CA, Lin SY, Wang JS, Couture C, Lyytikäinen LP, Nikus K, Cuellar-Partida G, Vestergaard H, Hildalgo B, Giannakopoulou O, Cai Q, Obura MO, van Setten J, Li X, Schwander K, Terzikhan N, Shin JH, Jackson RD, Reiner AP, Martin LW, Chen Z, Li L, Highland HM, Young KL, Kawaguchi T, Thiery J, Bis JC, Nadkarni GN, Launer LJ, Li H, Nalls MA, Raitakari OT, Ichihara S, Wild SH, Nelson CP, Campbell H, Jäger S, Nabika T, Al-Mulla F, Niinikoski H, Braund PS, Kolcic I, Kovacs P, Giardoglou T, Katsuya T, Bhatti KF, de Kleijn D, de Borst GJ, Kim EK, Adams HHH, Ikram MA, Zhu X, Asselbergs FW, Kraaijeveld AO, Beulens JWJ, Shu XO, Rallidis LS, Pedersen O, Hansen T, Mitchell P, Hewitt AW, Kähönen M, Pérusse L, Bouchard C, Tönjes A, Chen YI, Pennell CE, Mori TA, Lieb W, Franke A, Ohlsson C, Mellström D, Cho YS, Lee H, Yuan JM, Koh WP, Rhee SY, Woo JT, Heid IM, Stark KJ, Völzke H, Homuth G, Evans MK, Zonderman AB, Polasek O, Pasterkamp G, Hoefer IE, Redline S, Pahkala K, Oldehinkel AJ, Snieder H, Biino G, Schmidt R, Schmidt H, Chen YE, Bandinelli S, Dedoussis G, Thanaraj TA, Kardia SLR, Kato N, Schulze MB, Girotto G, Jung B, Böger CA, Joshi PK, Bennett DA, De Jager PL, Lu X, Mamakou V, Brown M, Caulfield MJ, Munroe PB, Guo X, Ciullo M, Jonas JB, Samani NJ, Kaprio J, Pajukanta P, Adair LS, Bechayda SA, de Silva HJ, Wickremasinghe AR, Krauss RM, Wu JY, Zheng W, den Hollander AI, Bharadwaj D, Correa A, Wilson JG, Lind L, Heng CK, Nelson AE, Golightly YM, Wilson JF, Penninx B, Kim HL, Attia J, Scott RJ, Rao DC, Arnett DK, Hunt SC, Walker M, Koistinen HA, Chandak GR, Yajnik CS, Mercader JM, Tusié-Luna T, Aguilar-Salinas CA, Villalpando CG, Orozco L, Fornage M, Tai ES, van Dam RM, Lehtimäki T, Chaturvedi N, Yokota M, Liu J, Reilly DF, McKnight AJ, Kee F, Jöckel KH, McCarthy MI, Palmer CNA, Vitart V, Hayward C, Simonsick E, van Duijn CM, Lu F, Qu J, Hishigaki H, Lin X, März W, Parra EJ, Cruz M, Gudnason V, Tardif JC, Lettre G, 't Hart LM, Elders PJM, Damrauer SM, Kumari M, Kivimaki M, van der Harst P, Spector TD, Loos RJF, Province MA, Psaty BM, Brandslund I, Pramstaller PP, Christensen K, Ripatti S, Widén E, Hakonarson H, Grant SFA, Kiemeney LALM, de Graaf J, Loeffler M, Kronenberg F, Gu D, Erdmann J, Schunkert H, Franks PW, Linneberg A, Jukema JW, Khera AV, Männikkö M, Jarvelin MR, Kutalik Z, Cucca F, Mook-Kanamori DO, van Dijk KW, Watkins H, Strachan DP, Grarup N, Sever P, Poulter N, Rotter JI, Dantoft TM, Karpe F, Neville MJ, Timpson NJ, Cheng CY, Wong TY, Khor CC, Sabanayagam C, Peters A, Gieger C, Hattersley AT, Pedersen NL, Magnusson PKE, Boomsma DI, de Geus EJC, Cupples LA, van Meurs JBJ, Ghanbari M, Gordon-Larsen P, Huang W, Kim YJ, Tabara Y, Wareham NJ, Langenberg C, Zeggini E, Kuusisto J, Laakso M, Ingelsson E, Abecasis G, Chambers JC, Kooner JS, de Vries PS, Morrison AC, North KE, Daviglus M, Kraft P, Martin NG, Whitfield JB, Abbas S, Saleheen D, Walters RG, Holmes MV, Black C, Smith BH, Justice AE, Baras A, Buring JE, Ridker PM, Chasman DI, Kooperberg C, Wei WQ, Jarvik GP, Namjou B, Hayes MG, Ritchie MD, Jousilahti P, Salomaa V, Hveem K, Åsvold BO, Kubo M, Kamatani Y, Okada Y, Murakami Y, Thorsteinsdottir U, Stefansson K, Ho YL, Lynch JA, Rader DJ, Tsao PS, Chang KM, Cho K, O'Donnell CJ, Gaziano JM, Wilson P, Rotimi CN, Hazelhurst S, Ramsay M, Trembath RC, van Heel DA, Tamiya G, Yamamoto M, Kim BJ, Mohlke KL, Frayling TM, Hirschhorn JN, Kathiresan S, Boehnke M, Natarajan P, Peloso GM, Brown CD, Morris AP, Assimes TL, Deloukas P, Sun YV, and Willer CJ
Published: 2023
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11. Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets.

Author: Singhal P, Veturi Y, Dudek SM, Lucas A, Frase A, van Steen K, Schrodi SJ, Fasel D, Weng C, Pendergrass R, Schaid DJ, Kullo IJ, Dikilitas O, Sleiman PMA, Hakonarson H, Moore JH, Williams SM, Ritchie MD, and Verma SS
Subjects: Linkage Disequilibrium genetics, Genotype, Biological Specimen Banks, United Kingdom, Polymorphism, Single Nucleotide genetics, Genome-Wide Association Study, Epistasis, Genetic
Abstract: Leveraging linkage disequilibrium (LD) patterns as representative of population substructure enables the discovery of additive association signals in genome-wide association studies (GWASs). Standard GWASs are well-powered to interrogate additive models; however, new approaches are required for invesigating other modes of inheritance such as dominance and epistasis. Epistasis, or non-additive interaction between genes, exists across the genome but often goes undetected because of a lack of statistical power. Furthermore, the adoption of LD pruning as customary in standard GWASs excludes detection of sites that are in LD but might underlie the genetic architecture of complex traits. We hypothesize that uncovering long-range interactions between loci with strong LD due to epistatic selection can elucidate genetic mechanisms underlying common diseases. To investigate this hypothesis, we tested for associations between 23 common diseases and 5,625,845 epistatic SNP-SNP pairs (determined by Ohta's D statistics) in long-range LD (>0.25 cM). Across five disease phenotypes, we identified one significant and four near-significant associations that replicated in two large genotype-phenotype datasets (UK Biobank and eMERGE). The genes that were most likely involved in the replicated associations were (1) members of highly conserved gene families with complex roles in multiple pathways, (2) essential genes, and/or (3) genes that were associated in the literature with complex traits that display variable expressivity. These results support the highly pleiotropic and conserved nature of variants in long-range LD under epistatic selection. Our work supports the hypothesis that epistatic interactions regulate diverse clinical mechanisms and might especially be driving factors in conditions with a wide range of phenotypic outcomes., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)
Published: 2023
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12. Session Introduction: SALUD: Scalable Applications of cLinical risk Utility and preDiction.

Author: Singhal P, Veturi Y, Judy R, Park Y, Vujkovic M, Veatch O, Kember R, and Verma SS
Subjects: Humans, Risk Factors, Genetic Predisposition to Disease, Computational Biology, Multifactorial Inheritance
Abstract: This PSB 2023 session discusses challenges in clinical implication and application of risk prediction models, which includes but is not limited to: implementation of risk models, responsible use of polygenic risk scores (PGS), and other risk prediction strategies. We focus on the development and use of new, scalable methods for harmonizing and refining risk prediction models by incorporating genetic and non-genetic risk factors, applying new phenotyping strategies, and integrating clinical factors and biomarkers. Lastly, we will discuss innovation in expanding the utility of these prediction models to underrepresented populations. This session focuses on the overarching theme of enabling early diagnosis, and treatment and preventive measures related to complex diseases and comorbidities.
Published: 2023

13. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

Author: Kanoni S, Graham SE, Wang Y, Surakka I, Ramdas S, Zhu X, Clarke SL, Bhatti KF, Vedantam S, Winkler TW, Locke AE, Marouli E, Zajac GJM, Wu KH, Ntalla I, Hui Q, Klarin D, Hilliard AT, Wang Z, Xue C, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Hwang MY, Han S, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Havulinna AS, Veturi Y, Pacheco JA, Rosenthal EA, Lingren T, Feng Q, Kullo IJ, Narita A, Takayama J, Martin HC, Hunt KA, Trivedi B, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Rasheed A, Hindy G, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Choudhury A, Sengupta D, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao JH, Matsuda F, Jang HM, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Wood AR, Ji Y, Gao Z, Haworth S, Yousri NA, Mitchell RE, Chai JF, Aadahl M, Bjerregaard AA, Yao J, Manichaikul A, Hwu CM, Hung YJ, Warren HR, Ramirez J, Bork-Jensen J, Kårhus LL, Goel A, Sabater-Lleal M, Noordam R, Mauro P, Matteo F, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Møllehave LT, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Lamina C, Forer L, Scholz M, Galesloot TE, Bradfield JP, Ruotsalainen SE, Daw E, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Vazquez-Moreno M, Feitosa MF, Wojczynski MK, Wang Z, Preuss MH, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Tsao NL, Verma A, Slieker RC, Lo KS, Zilhao NR, Le P, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Demirkan A, Leonard HL, Marten J, Frank M, Schmidt B, Smyth LJ, Cañadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kähönen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Nongmaithem SS, Bayyana S, Stringham HM, Irvin MR, Oldmeadow C, Kim HN, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Prasad G, Lorés-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Chen S, Liu F, Yang J, Kentistou KA, Banas B, Nardone GG, Meidtner K, Bielak LF, Smith JA, Hebbar P, Farmaki AE, Hofer E, Lin M, Concas MP, Vaccargiu S, van der Most PJ, Pitkänen N, Cade BE, van der Laan SW, Chitrala KN, Weiss S, Bentley AR, Doumatey AP, Adeyemo AA, Lee JY, Petersen ERB, Nielsen AA, Choi HS, Nethander M, Freitag-Wolf S, Southam L, Rayner NW, Wang CA, Lin SY, Wang JS, Couture C, Lyytikäinen LP, Nikus K, Cuellar-Partida G, Vestergaard H, Hidalgo B, Giannakopoulou O, Cai Q, Obura MO, van Setten J, Li X, Liang J, Tang H, Terzikhan N, Shin JH, Jackson RD, Reiner AP, Martin LW, Chen Z, Li L, Kawaguchi T, Thiery J, Bis JC, Launer LJ, Li H, Nalls MA, Raitakari OT, Ichihara S, Wild SH, Nelson CP, Campbell H, Jäger S, Nabika T, Al-Mulla F, Niinikoski H, Braund PS, Kolcic I, Kovacs P, Giardoglou T, Katsuya T, de Kleijn D, de Borst GJ, Kim EK, Adams HHH, Ikram MA, Zhu X, Asselbergs FW, Kraaijeveld AO, Beulens JWJ, Shu XO, Rallidis LS, Pedersen O, Hansen T, Mitchell P, Hewitt AW, Kähönen M, Pérusse L, Bouchard C, Tönjes A, Chen YI, Pennell CE, Mori TA, Lieb W, Franke A, Ohlsson C, Mellström D, Cho YS, Lee H, Yuan JM, Koh WP, Rhee SY, Woo JT, Heid IM, Stark KJ, Zimmermann ME, Völzke H, Homuth G, Evans MK, Zonderman AB, Polasek O, Pasterkamp G, Hoefer IE, Redline S, Pahkala K, Oldehinkel AJ, Snieder H, Biino G, Schmidt R, Schmidt H, Bandinelli S, Dedoussis G, Thanaraj TA, Kardia SLR, Peyser PA, Kato N, Schulze MB, Girotto G, Böger CA, Jung B, Joshi PK, Bennett DA, De Jager PL, Lu X, Mamakou V, Brown M, Caulfield MJ, Munroe PB, Guo X, Ciullo M, Jonas JB, Samani NJ, Kaprio J, Pajukanta P, Tusié-Luna T, Aguilar-Salinas CA, Adair LS, Bechayda SA, de Silva HJ, Wickremasinghe AR, Krauss RM, Wu JY, Zheng W, Hollander AI, Bharadwaj D, Correa A, Wilson JG, Lind L, Heng CK, Nelson AE, Golightly YM, Wilson JF, Penninx B, Kim HL, Attia J, Scott RJ, Rao DC, Arnett DK, Hunt SC, Walker M, Koistinen HA, Chandak GR, Mercader JM, Costanzo MC, Jang D, Burtt NP, Villalpando CG, Orozco L, Fornage M, Tai E, van Dam RM, Lehtimäki T, Chaturvedi N, Yokota M, Liu J, Reilly DF, McKnight AJ, Kee F, Jöckel KH, McCarthy MI, Palmer CNA, Vitart V, Hayward C, Simonsick E, van Duijn CM, Jin ZB, Qu J, Hishigaki H, Lin X, März W, Gudnason V, Tardif JC, Lettre G, Hart LM', Elders PJM, Damrauer SM, Kumari M, Kivimaki M, van der Harst P, Spector TD, Loos RJF, Province MA, Parra EJ, Cruz M, Psaty BM, Brandslund I, Pramstaller PP, Rotimi CN, Christensen K, Ripatti S, Widén E, Hakonarson H, Grant SFA, Kiemeney LALM, de Graaf J, Loeffler M, Kronenberg F, Gu D, Erdmann J, Schunkert H, Franks PW, Linneberg A, Jukema JW, Khera AV, Männikkö M, Jarvelin MR, Kutalik Z, Francesco C, Mook-Kanamori DO, van Dijk KW, Watkins H, Strachan DP, Grarup N, Sever P, Poulter N, Chuang LM, Rotter JI, Dantoft TM, Karpe F, Neville MJ, Timpson NJ, Cheng CY, Wong TY, Khor CC, Li H, Sabanayagam C, Peters A, Gieger C, Hattersley AT, Pedersen NL, Magnusson PKE, Boomsma DI, Willemsen AHM, Cupples L, van Meurs JBJ, Ghanbari M, Gordon-Larsen P, Huang W, Kim YJ, Tabara Y, Wareham NJ, Langenberg C, Zeggini E, Kuusisto J, Laakso M, Ingelsson E, Abecasis G, Chambers JC, Kooner JS, de Vries PS, Morrison AC, Hazelhurst S, Ramsay M, North KE, Daviglus M, Kraft P, Martin NG, Whitfield JB, Abbas S, Saleheen D, Walters RG, Holmes MV, Black C, Smith BH, Baras A, Justice AE, Buring JE, Ridker PM, Chasman DI, Kooperberg C, Tamiya G, Yamamoto M, van Heel DA, Trembath RC, Wei WQ, Jarvik GP, Namjou B, Hayes MG, Ritchie MD, Jousilahti P, Salomaa V, Hveem K, Åsvold BO, Kubo M, Kamatani Y, Okada Y, Murakami Y, Kim BJ, Thorsteinsdottir U, Stefansson K, Zhang J, Chen Y, Ho YL, Lynch JA, Rader DJ, Tsao PS, Chang KM, Cho K, O'Donnell CJ, Gaziano JM, Wilson PWF, Frayling TM, Hirschhorn JN, Kathiresan S, Mohlke KL, Sun YV, Morris AP, Boehnke M, Brown CD, Natarajan P, Deloukas P, Willer CJ, Assimes TL, and Peloso GM
Subjects: Humans, Sex Characteristics, Phenotype, Lipids genetics, Polymorphism, Single Nucleotide, Genetic Pleiotropy, Genome-Wide Association Study, Genetic Predisposition to Disease
Abstract: Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery., Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism., Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk., (© 2022. The Author(s).)
Published: 2022
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14. Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

Author: Zhou W, Kanai M, Wu KH, Rasheed H, Tsuo K, Hirbo JB, Wang Y, Bhattacharya A, Zhao H, Namba S, Surakka I, Wolford BN, Lo Faro V, Lopera-Maya EA, Läll K, Favé MJ, Partanen JJ, Chapman SB, Karjalainen J, Kurki M, Maasha M, Brumpton BM, Chavan S, Chen TT, Daya M, Ding Y, Feng YA, Guare LA, Gignoux CR, Graham SE, Hornsby WE, Ingold N, Ismail SI, Johnson R, Laisk T, Lin K, Lv J, Millwood IY, Moreno-Grau S, Nam K, Palta P, Pandit A, Preuss MH, Saad C, Setia-Verma S, Thorsteinsdottir U, Uzunovic J, Verma A, Zawistowski M, Zhong X, Afifi N, Al-Dabhani KM, Al Thani A, Bradford Y, Campbell A, Crooks K, de Bock GH, Damrauer SM, Douville NJ, Finer S, Fritsche LG, Fthenou E, Gonzalez-Arroyo G, Griffiths CJ, Guo Y, Hunt KA, Ioannidis A, Jansonius NM, Konuma T, Lee MTM, Lopez-Pineda A, Matsuda Y, Marioni RE, Moatamed B, Nava-Aguilar MA, Numakura K, Patil S, Rafaels N, Richmond A, Rojas-Muñoz A, Shortt JA, Straub P, Tao R, Vanderwerff B, Vernekar M, Veturi Y, Barnes KC, Boezen M, Chen Z, Chen CY, Cho J, Smith GD, Finucane HK, Franke L, Gamazon ER, Ganna A, Gaunt TR, Ge T, Huang H, Huffman J, Katsanis N, Koskela JT, Lajonchere C, Law MH, Li L, Lindgren CM, Loos RJF, MacGregor S, Matsuda K, Olsen CM, Porteous DJ, Shavit JA, Snieder H, Takano T, Trembath RC, Vonk JM, Whiteman DC, Wicks SJ, Wijmenga C, Wright J, Zheng J, Zhou X, Awadalla P, Boehnke M, Bustamante CD, Cox NJ, Fatumo S, Geschwind DH, Hayward C, Hveem K, Kenny EE, Lee S, Lin YF, Mbarek H, Mägi R, Martin HC, Medland SE, Okada Y, Palotie AV, Pasaniuc B, Rader DJ, Ritchie MD, Sanna S, Smoller JW, Stefansson K, van Heel DA, Walters RG, Zöllner S, Martin AR, Willer CJ, Daly MJ, and Neale BM
Abstract: Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits., Competing Interests: M.J.D. is a founder of Maze Therapeutics. B.M.N. is a member of the scientific advisory board at Deep Genomics and a consultant for Camp4 Therapeutics, Takeda Pharmaceutical, and Biogen. The spouse of C.J.W. works at Regeneron Pharmaceuticals. C.-Y.C. is employed by Biogen. C.R.G. owns stock in 23andMe, Inc. T.R.G. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. E.E.K. has received speaker fees from Regeneron, Illumina, and 23andMe and is a member of the advisory board for Galateo Bio. R.E.M. has received speaker fees from Illumina and is a scientific advisor to the Epigenetic Clock Development Foundation. G.D.S. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. K.S. and U.T. are employed by deCODE Genetics/Amgen, Inc. J.Z. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. S.M. is a co-founder of and holds stock in Seonix Bio., (© 2022.)
Published: 2022
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15. 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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16. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.

Author: Ramdas S, Judd J, Graham SE, Kanoni S, Wang Y, Surakka I, Wenz B, Clarke SL, Chesi A, Wells A, Bhatti KF, Vedantam S, Winkler TW, Locke AE, Marouli E, Zajac GJM, Wu KH, Ntalla I, Hui Q, Klarin D, Hilliard AT, Wang Z, Xue C, Thorleifsson G, Helgadottir A, Gudbjartsson DF, Holm H, Olafsson I, Hwang MY, Han S, Akiyama M, Sakaue S, Terao C, Kanai M, Zhou W, Brumpton BM, Rasheed H, Havulinna AS, Veturi Y, Pacheco JA, Rosenthal EA, Lingren T, Feng Q, Kullo IJ, Narita A, Takayama J, Martin HC, Hunt KA, Trivedi B, Haessler J, Giulianini F, Bradford Y, Miller JE, Campbell A, Lin K, Millwood IY, Rasheed A, Hindy G, Faul JD, Zhao W, Weir DR, Turman C, Huang H, Graff M, Choudhury A, Sengupta D, Mahajan A, Brown MR, Zhang W, Yu K, Schmidt EM, Pandit A, Gustafsson S, Yin X, Luan J, Zhao JH, Matsuda F, Jang HM, Yoon K, Medina-Gomez C, Pitsillides A, Hottenga JJ, Wood AR, Ji Y, Gao Z, Haworth S, Mitchell RE, Chai JF, Aadahl M, Bjerregaard AA, Yao J, Manichaikul A, Lee WJ, Hsiung CA, Warren HR, Ramirez J, Bork-Jensen J, Kårhus LL, Goel A, Sabater-Lleal M, Noordam R, Mauro P, Matteo F, McDaid AF, Marques-Vidal P, Wielscher M, Trompet S, Sattar N, Møllehave LT, Munz M, Zeng L, Huang J, Yang B, Poveda A, Kurbasic A, Schönherr S, Forer L, Scholz M, Galesloot TE, Bradfield JP, Ruotsalainen SE, Daw EW, Zmuda JM, Mitchell JS, Fuchsberger C, Christensen H, Brody JA, Le P, Feitosa MF, Wojczynski MK, Hemerich D, Preuss M, Mangino M, Christofidou P, Verweij N, Benjamins JW, Engmann J, Noah TL, Verma A, Slieker RC, Lo KS, Zilhao NR, Kleber ME, Delgado GE, Huo S, Ikeda DD, Iha H, Yang J, Liu J, Demirkan A, Leonard HL, Marten J, Emmel C, Schmidt B, Smyth LJ, Cañadas-Garre M, Wang C, Nakatochi M, Wong A, Hutri-Kähönen N, Sim X, Xia R, Huerta-Chagoya A, Fernandez-Lopez JC, Lyssenko V, Nongmaithem SS, Sankareswaran A, Irvin MR, Oldmeadow C, Kim HN, Ryu S, Timmers PRHJ, Arbeeva L, Dorajoo R, Lange LA, Prasad G, Lorés-Motta L, Pauper M, Long J, Li X, Theusch E, Takeuchi F, Spracklen CN, Loukola A, Bollepalli S, Warner SC, Wang YX, Wei WB, Nutile T, Ruggiero D, Sung YJ, Chen S, Liu F, Yang J, Kentistou KA, Banas B, Morgan A, Meidtner K, Bielak LF, Smith JA, Hebbar P, Farmaki AE, Hofer E, Lin M, Concas MP, Vaccargiu S, van der Most PJ, Pitkänen N, Cade BE, van der Laan SW, Chitrala KN, Weiss S, Bentley AR, Doumatey AP, Adeyemo AA, Lee JY, Petersen ERB, Nielsen AA, Choi HS, Nethander M, Freitag-Wolf S, Southam L, Rayner NW, Wang CA, Lin SY, Wang JS, Couture C, Lyytikäinen LP, Nikus K, Cuellar-Partida G, Vestergaard H, Hidalgo B, Giannakopoulou O, Cai Q, Obura MO, van Setten J, He KY, Tang H, Terzikhan N, Shin JH, Jackson RD, Reiner AP, Martin LW, Chen Z, Li L, Kawaguchi T, Thiery J, Bis JC, Launer LJ, Li H, Nalls MA, Raitakari OT, Ichihara S, Wild SH, Nelson CP, Campbell H, Jäger S, Nabika T, Al-Mulla F, Niinikoski H, Braund PS, Kolcic I, Kovacs P, Giardoglou T, Katsuya T, de Kleijn D, de Borst GJ, Kim EK, Adams HHH, Ikram MA, Zhu X, Asselbergs FW, Kraaijeveld AO, Beulens JWJ, Shu XO, Rallidis LS, Pedersen O, Hansen T, Mitchell P, Hewitt AW, Kähönen M, Pérusse L, Bouchard C, Tönjes A, Ida Chen YD, Pennell CE, Mori TA, Lieb W, Franke A, Ohlsson C, Mellström D, Cho YS, Lee H, Yuan JM, Koh WP, Rhee SY, Woo JT, Heid IM, Stark KJ, Zimmermann ME, Völzke H, Homuth G, Evans MK, Zonderman AB, Polasek O, Pasterkamp G, Hoefer IE, Redline S, Pahkala K, Oldehinkel AJ, Snieder H, Biino G, Schmidt R, Schmidt H, Bandinelli S, Dedoussis G, Thanaraj TA, Peyser PA, Kato N, Schulze MB, Girotto G, Böger CA, Jung B, Joshi PK, Bennett DA, De Jager PL, Lu X, Mamakou V, Brown M, Caulfield MJ, Munroe PB, Guo X, Ciullo M, Jonas JB, Samani NJ, Kaprio J, Pajukanta P, Tusié-Luna T, Aguilar-Salinas CA, Adair LS, Bechayda SA, de Silva HJ, Wickremasinghe AR, Krauss RM, Wu JY, Zheng W, den Hollander AI, Bharadwaj D, Correa A, Wilson JG, Lind L, Heng CK, Nelson AE, Golightly YM, Wilson JF, Penninx B, Kim HL, Attia J, Scott RJ, Rao DC, Arnett DK, Walker M, Scott LJ, Koistinen HA, Chandak GR, Mercader JM, Villalpando CG, Orozco L, Fornage M, Tai ES, van Dam RM, Lehtimäki T, Chaturvedi N, Yokota M, Liu J, Reilly DF, McKnight AJ, Kee F, Jöckel KH, McCarthy MI, Palmer CNA, Vitart V, Hayward C, Simonsick E, van Duijn CM, Jin ZB, Lu F, Hishigaki H, Lin X, März W, Gudnason V, Tardif JC, Lettre G, T Hart LM, Elders PJM, Rader DJ, Damrauer SM, Kumari M, Kivimaki M, van der Harst P, Spector TD, Loos RJF, Province MA, Parra EJ, Cruz M, Psaty BM, Brandslund I, Pramstaller PP, Rotimi CN, Christensen K, Ripatti S, Widén E, Hakonarson H, Grant SFA, Kiemeney L, de Graaf J, Loeffler M, Kronenberg F, Gu D, Erdmann J, Schunkert H, Franks PW, Linneberg A, Jukema JW, Khera AV, Männikkö M, Jarvelin MR, Kutalik Z, Francesco C, Mook-Kanamori DO, Willems van Dijk K, Watkins H, Strachan DP, Grarup N, Sever P, Poulter N, Huey-Herng Sheu W, Rotter JI, Dantoft TM, Karpe F, Neville MJ, Timpson NJ, Cheng CY, Wong TY, Khor CC, Li H, Sabanayagam C, Peters A, Gieger C, Hattersley AT, Pedersen NL, Magnusson PKE, Boomsma DI, de Geus EJC, Cupples LA, van Meurs JBJ, Ikram A, Ghanbari M, Gordon-Larsen P, Huang W, Kim YJ, Tabara Y, Wareham NJ, Langenberg C, Zeggini E, Tuomilehto J, Kuusisto J, Laakso M, Ingelsson E, Abecasis G, Chambers JC, Kooner JS, de Vries PS, Morrison AC, Hazelhurst S, Ramsay M, North KE, Daviglus M, Kraft P, Martin NG, Whitfield JB, Abbas S, Saleheen D, Walters RG, Holmes MV, Black C, Smith BH, Baras A, Justice AE, Buring JE, Ridker PM, Chasman DI, Kooperberg C, Tamiya G, Yamamoto M, van Heel DA, Trembath RC, Wei WQ, Jarvik GP, Namjou B, Hayes MG, Ritchie MD, Jousilahti P, Salomaa V, Hveem K, Åsvold BO, Kubo M, Kamatani Y, Okada Y, Murakami Y, Kim BJ, Thorsteinsdottir U, Stefansson K, Zhang J, Chen YE, Ho YL, Lynch JA, Tsao PS, Chang KM, Cho K, O'Donnell CJ, Gaziano JM, Wilson P, Mohlke KL, Frayling TM, Hirschhorn JN, Kathiresan S, Boehnke M, Struan Grant, Natarajan P, Sun YV, Morris AP, Deloukas P, Peloso G, Assimes TL, Willer CJ, Zhu X, and Brown CD
Subjects: Chromatin genetics, Genomics, Humans, Lipids genetics, Genome-Wide Association Study, Polymorphism, Single Nucleotide genetics
Abstract: A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology., Competing Interests: Declaration of interests G.C.-P. is currently an employee of 23andMe Inc. M.J.C. is the Chief Scientist for Genomics England, a UK Government company. B.M. Psaty serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. G. Thorleifsson, A.H., D.F.G., H. Holm, U.T., and K.S. are employees of deCODE/Amgen Inc. V.S. has received honoraria for consultations from Novo Nordisk and Sanofi and has an ongoing research collaboration with Bayer Ltd. M. McCarthy has served on advisory panels for Pfizer, NovoNordisk, and Zoe Global and has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. M. McCarthy and A. Mahajan are employees of Genentech and holders of Roche stock. M.S. receives funding from Pfizer Inc. for a project unrelated to this work. M.E.K. is employed by SYNLAB MVZ Mannheim GmbH. W.M. has received grants from Siemens Healthineers, grants and personal fees from Aegerion Pharmaceuticals, grants and personal fees from AMGEN, grants from Astrazeneca, grants and personal fees from Sanofi, grants and personal fees from Alexion Pharmaceuticals, grants and personal fees from BASF, grants and personal fees from Abbott Diagnostics, grants and personal fees from Numares AG, grants and personal fees from Berlin-Chemie, grants and personal fees from Akzea Therapeutics, grants from Bayer Vital GmbH , grants from bestbion dx GmbH, grants from Boehringer Ingelheim Pharma GmbH Co KG, grants from Immundiagnostik GmbH, grants from Merck Chemicals GmbH, grants from MSD Sharp and Dohme GmbH, grants from Novartis Pharma GmbH, grants from Olink Proteomics, and other from Synlab Holding Deutschland GmbH, all outside the submitted work. A.V.K. has served as a consultant to Sanofi, Medicines Company, Maze Pharmaceuticals, Navitor Pharmaceuticals, Verve Therapeutics, Amgen, and Color Genomics; received speaking fees from Illumina and the Novartis Institute for Biomedical Research; received sponsored research agreements from the Novartis Institute for Biomedical Research and IBM Research, and reports a patent related to a genetic risk predictor (20190017119). S. Kathiresan is an employee of Verve Therapeutics and holds equity in Verve Therapeutics, Maze Therapeutics, Catabasis, and San Therapeutics. He is a member of the scientific advisory boards for Regeneron Genetics Center and Corvidia Therapeutics; he has served as a consultant for Acceleron, Eli Lilly, Novartis, Merck, Novo Nordisk, Novo Ventures, Ionis, Alnylam, Aegerion, Haug Partners, Noble Insights, Leerink Partners, Bayer Healthcare, Illumina, Color Genomics, MedGenome, Quest, and Medscape; and he reports patents related to a method of identifying and treating a person having a predisposition to or afflicted with cardiometabolic disease (20180010185) and a genetics risk predictor (20190017119). D.K. accepts consulting fees from Regeneron Pharmaceuticals. D.O.M.-K. is a part-time clinical research consultant for Metabolon, Inc. D. Saleheen has received support from the British Heart Foundation, Pfizer, Regeneron, Genentech, and Eli Lilly pharmaceuticals. P.N. reports investigator-initated grants from Amgen, Apple, AstraZeneca, Boston Scientific, and Novartis, personal fees from Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, Novartis, Roche / Genentech, is a co-founder of TenSixteen Bio, is a scientific advisory board member of Esperion Therapeutics, geneXwell, and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work. The spouse of C.J.W. is employed by Regeneron., (Copyright © 2022 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)
Published: 2022
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17. Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders.

Author: Zhang X, Lucas AM, Veturi Y, Drivas TG, Bone WP, Verma A, Chung WK, Crosslin D, Denny JC, Hebbring S, Jarvik GP, Kullo I, Larson EB, Rasmussen-Torvik LJ, Schaid DJ, Smoller JW, Stanaway IB, Wei WQ, Weng C, and Ritchie MD
Subjects: Genetic Pleiotropy, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Polymorphism, Single Nucleotide, Cardiovascular Diseases genetics, Nervous System Diseases genetics
Abstract: Clinical and epidemiological studies have shown that circulatory system diseases and nervous system disorders often co-occur in patients. However, genetic susceptibility factors shared between these disease categories remain largely unknown. Here, we characterized pleiotropy across 107 circulatory system and 40 nervous system traits using an ensemble of methods in the eMERGE Network and UK Biobank. Using a formal test of pleiotropy, five genomic loci demonstrated statistically significant evidence of pleiotropy. We observed region-specific patterns of direction of genetic effects for the two disease categories, suggesting potential antagonistic and synergistic pleiotropy. Our findings provide insights into the relationship between circulatory system diseases and nervous system disorders which can provide context for future prevention and treatment strategies., (© 2022. The Author(s).)
Published: 2022
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18. Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression.

Author: Wen J, Fu CHY, Tosun D, Veturi Y, Yang Z, Abdulkadir A, Mamourian E, Srinivasan D, Skampardoni I, Singh A, Nawani H, Bao J, Erus G, Shou H, Habes M, Doshi J, Varol E, Mackin RS, Sotiras A, Fan Y, Saykin AJ, Sheline YI, Shen L, Ritchie MD, Wolk DA, Albert M, Resnick SM, and Davatzikos C
Subjects: Brain diagnostic imaging, Cognition, Depression, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging methods, Male, Neuroimaging, Alzheimer Disease diagnostic imaging, Alzheimer Disease genetics, Depressive Disorder, Major diagnostic imaging, Depressive Disorder, Major genetics
Abstract: Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine., Objective: To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles., Design, Setting, and Participants: The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021., Main Outcomes and Measures: Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics., Results: A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×108) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant-based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f2 = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f2 = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets)., Conclusions and Relevance: This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.
Published: 2022
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19. A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts.

Author: Veturi Y, Lucas A, Bradford Y, Hui D, Dudek S, Theusch E, Verma A, Miller JE, Kullo I, Hakonarson H, Sleiman P, Schaid D, Stein CM, Edwards DRV, Feng Q, Wei WQ, Medina MW, Krauss RM, Hoffmann TJ, Risch N, Voight BF, Rader DJ, and Ritchie MD
Subjects: Alleles, Biological Specimen Banks, Genetic Association Studies, Genetic Predisposition to Disease, Humans, Polymorphism, Single Nucleotide, Public Health Surveillance, Quantitative Trait, Heritable, United Kingdom, Biomarkers, Disease Susceptibility, Electronic Health Records, Lipids blood
Abstract: Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify new lipid-associated genes and study the relationships among lipids, genotypes, gene expression and hundreds of complex human diseases from the Electronic Medical Records and Genomics (347 traits) and the UK Biobank (549 traits). Aside from 67 new lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)
Published: 2021
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20. Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults.

Author: Li B, Veturi Y, Verma A, Bradford Y, Daar ES, Gulick RM, Riddler SA, Robbins GK, Lennox JL, Haas DW, and Ritchie MD
Subjects: Computer Simulation, Gene Expression Regulation genetics, Humans, Organ Specificity genetics, Polymorphism, Single Nucleotide genetics, Genetic Predisposition to Disease, Genome-Wide Association Study, Quantitative Trait Loci genetics, Transcriptome genetics
Abstract: As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, often found significant associations in the wrong tissues as well (therefore had high false positive rates). Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyltransferase enzyme) and total bilirubin levels (p = 3.59×10-12), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49×10-12). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: G.K.R. has been on principal investigator on research grants to Massachusetts General Hospital from Gilead Sciences, Citius Pharmaceuticals, Emergent Biosolutions, Pfizer and Leonard Meron Bioscience -all research support paid to the institution. E.S.D. has received research grant support from Gilead, Merck and ViiV and has been consultant for Abbvie, Gilead, Merck and Genentech. S.A.R. has been an investigator on research grants to the University of Pittsburgh from Gilead, Merck, and Bristol Myers Squibb and she has been a consultant to Novimab. M.D.R. is on the scientific advisory board for Cipherome and Goldfinch Bio. All other authors have no conflicts to disclose.
Published: 2021
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21. Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes.

Author: Miller JE, Veturi Y, and Ritchie MD
Abstract: Characterizing how variation at the level of individual nucleotides contributes to traits and diseases has been an area of growing interest since the completion of sequencing the first human genome. Our understanding of how a single nucleotide polymorphism (SNP) leads to a pathogenic phenotype on a genome-wide scale is a fruitful endeavor for anyone interested in developing diagnostic tests, therapeutics, or simply wanting to understand the etiology of a disease or trait. To this end, many datasets and algorithms have been developed as resources/tools to annotate SNPs. One of the most common practices is to annotate coding SNPs that affect the protein sequence. Synonymous variants are often grouped as one type of variant, however there are in fact many tools available to dissect their effects on gene expression. More recently, large consortiums like ENCODE and GTEx have made it possible to annotate non-coding regions. Although annotating variants is a common technique among human geneticists, the constant advances in tools and biology surrounding SNPs requires an updated summary of what is known and the trajectory of the field. This review will discuss the history behind SNP annotation, commonly used tools, and newer strategies for SNP annotation. Additionally, we will comment on the caveats that distinguish approaches from one another, along with gaps in the current state of knowledge, and potential future directions. We do not intend for this to be a comprehensive review for any specific area of SNP annotation, but rather it will be an excellent resource for those unfamiliar with computational tools used to functionally characterize SNPs. In summary, this review will help illustrate how each SNP annotation method impacts the way in which the genetic and molecular etiology of a disease is explored in-silico ., Competing Interests: Not applicable.Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Published: 2019
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22. Modeling Heterogeneity in the Genetic Architecture of Ethnically Diverse Groups Using Random Effect Interaction Models.

Author: Veturi Y, de Los Campos G, Yi N, Huang W, Vazquez AI, and Kühnel B
Subjects: Genome-Wide Association Study methods, Genome-Wide Association Study standards, Humans, Polymorphism, Single Nucleotide, Ethnicity genetics, Genetic Heterogeneity, Models, Genetic, Population genetics, Quantitative Trait, Heritable
Abstract: In humans, most genome-wide association studies have been conducted using data from Caucasians and many of the reported findings have not replicated in other populations. This lack of replication may be due to statistical issues (small sample sizes or confounding) or perhaps more fundamentally to differences in the genetic architecture of traits between ethnically diverse subpopulations. What aspects of the genetic architecture of traits vary between subpopulations and how can this be quantified? We consider studying effect heterogeneity using Bayesian random effect interaction models. The proposed methodology can be applied using shrinkage and variable selection methods, and produces useful information about effect heterogeneity in the form of whole-genome summaries ( e.g. , the proportions of variance of a complex trait explained by a set of SNPs and the average correlation of effects) as well as SNP-specific attributes. Using simulations, we show that the proposed methodology yields (nearly) unbiased estimates when the sample size is not too small relative to the number of SNPs used. Subsequently, we used the methodology for the analyses of four complex human traits (standing height, high-density lipoprotein, low-density lipoprotein, and serum urate levels) in European-Americans (EAs) and African-Americans (AAs). The estimated correlations of effects between the two subpopulations were well below unity for all the traits, ranging from 0.73 to 0.50. The extent of effect heterogeneity varied between traits and SNP sets. Height showed less differences in SNP effects between AAs and EAs whereas HDL, a trait highly influenced by lifestyle, exhibited a greater extent of effect heterogeneity. For all the traits, we observed substantial variability in effect heterogeneity across SNPs, suggesting that effect heterogeneity varies between regions of the genome., (Copyright © 2019 by the Genetics Society of America.)
Published: 2019
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23. Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies.

Author: Li B, Veturi Y, Bradford Y, Verma SS, Verma A, Lucas AM, Haas DW, and Ritchie MD
Subjects: Anti-HIV Agents therapeutic use, Computational Biology, Gene Expression Profiling methods, Genetic Predisposition to Disease, Genome-Wide Association Study methods, Genotype, HIV Infections drug therapy, HIV Infections genetics, Humans, Pharmacogenomic Variants, Polymorphism, Single Nucleotide, Gene Expression Profiling statistics & numerical data, Genome-Wide Association Study statistics & numerical data, Organ Specificity genetics, Quantitative Trait Loci, Transcriptome
Abstract: Transcriptome-wide association studies (TWAS) have recently gained great attention due to their ability to prioritize complex trait-associated genes and promote potential therapeutics development for complex human diseases. TWAS integrates genotypic data with expression quantitative trait loci (eQTLs) to predict genetically regulated gene expression components and associates predictions with a trait of interest. As such, TWAS can prioritize genes whose differential expressions contribute to the trait of interest and provide mechanistic explanation of complex trait(s). Tissue-specific eQTL information grants TWAS the ability to perform association analysis on tissues whose gene expression profiles are otherwise hard to obtain, such as liver and heart. However, as eQTLs are tissue context-dependent, whether and how the tissue-specificity of eQTLs influences TWAS gene prioritization has not been fully investigated. In this study, we addressed this question by adopting two distinct TWAS methods, PrediXcan and UTMOST, which assume single tissue and integrative tissue effects of eQTLs, respectively. Thirty-eight baseline laboratory traits in 4,360 antiretroviral treatment-naïve individuals from the AIDS Clinical Trials Group (ACTG) studies comprised the input dataset for TWAS. We performed TWAS in a tissue-specific manner and obtained a total of 430 significant gene-trait associations (q-value < 0.05) across multiple tissues. Single tissue-based analysis by PrediXcan contributed 116 of the 430 associations including 64 unique gene-trait pairs in 28 tissues. Integrative tissue-based analysis by UTMOST found the other 314 significant associations that include 50 unique gene-trait pairs across all 44 tissues. Both analyses were able to replicate some associations identified in past variant-based genome-wide association studies (GWAS), such as high-density lipoprotein (HDL) and CETP (PrediXcan, q-value = 3.2e-16). Both analyses also identified novel associations. Moreover, single tissue-based and integrative tissuebased analysis shared 11 of 103 unique gene-trait pairs, for example, PSRC1-low-density lipoprotein (PrediXcan's lowest q-value = 8.5e-06; UTMOST's lowest q-value = 1.8e-05). This study suggests that single tissue-based analysis may have performed better at discovering gene-trait associations when combining results from all tissues. Integrative tissue-based analysis was better at prioritizing genes in multiple tissues and in trait-related tissue. Additional exploration is needed to confirm this conclusion. Finally, although single tissue-based and integrative tissue-based analysis shared significant novel discoveries, tissue context-dependency of eQTLs impacted TWAS gene prioritization. This study provides preliminary data to support continued work on tissue contextdependency of eQTL studies and TWAS.
Published: 2019

24. Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network.

Author: Zhang X, Veturi Y, Verma S, Bone W, Verma A, Lucas A, Hebbring S, Denny JC, Stanaway IB, Jarvik GP, Crosslin D, Larson EB, Rasmussen-Torvik L, Pendergrass SA, Smoller JW, Hakonarson H, Sleiman P, Weng C, Fasel D, Wei WQ, Kullo I, Schaid D, Chung WK, and Ritchie MD
Subjects: Adult, Aged, Computational Biology, Electronic Health Records, Female, Genetic Association Studies, Genetic Predisposition to Disease, Humans, Male, Middle Aged, Multivariate Analysis, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Cardiovascular Diseases genetics, Genetic Pleiotropy, Nervous System Diseases genetics
Abstract: The link between cardiovascular diseases and neurological disorders has been widely observed in the aging population. Disease prevention and treatment rely on understanding the potential genetic nexus of multiple diseases in these categories. In this study, we were interested in detecting pleiotropy, or the phenomenon in which a genetic variant influences more than one phenotype. Marker-phenotype association approaches can be grouped into univariate, bivariate, and multivariate categories based on the number of phenotypes considered at one time. Here we applied one statistical method per category followed by an eQTL colocalization analysis to identify potential pleiotropic variants that contribute to the link between cardiovascular and neurological diseases. We performed our analyses on ~530,000 common SNPs coupled with 65 electronic health record (EHR)-based phenotypes in 43,870 unrelated European adults from the Electronic Medical Records and Genomics (eMERGE) network. There were 31 variants identified by all three methods that showed significant associations across late onset cardiac- and neurologic- diseases. We further investigated functional implications of gene expression on the detected "lead SNPs" via colocalization analysis, providing a deeper understanding of the discovered associations. In summary, we present the framework and landscape for detecting potential pleiotropy using univariate, bivariate, multivariate, and colocalization methods. Further exploration of these potentially pleiotropic genetic variants will work toward understanding disease causing mechanisms across cardiovascular and neurological diseases and may assist in considering disease prevention as well as drug repositioning in future research.
Published: 2019

25. Author Correction: Rare variants in drug target genes contributing to complex diseases, phenome-wide.

Author: Verma SS, Josyula N, Verma A, Zhang X, Veturi Y, Dewey FE, Hartzel DN, Lavage DR, Leader J, Ritchie MD, and Pendergrass SA
Abstract: A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.
Published: 2018
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26. Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery.

Author: Cha EDK, Veturi Y, Agarwal C, Patel A, Arbabshirani MR, and Pendergrass SA
Subjects: Adiposity, Adolescent, Adult, Body Mass Index, Data Collection, Female, Humans, Intra-Abdominal Fat pathology, Male, Metabolic Syndrome genetics, Metabolic Syndrome pathology, Middle Aged, Obesity genetics, Obesity pathology, Risk Factors, Young Adult, Genome-Wide Association Study, Health Personnel, Intra-Abdominal Fat diagnostic imaging, Metabolic Syndrome diagnostic imaging, Obesity diagnostic imaging, Tomography, X-Ray Computed
Abstract: The location and type of adipose tissue is an important factor in metabolic syndrome. A database of picture archiving and communication system (PACS) derived abdominal computerized tomography (CT) images from a large health care provider, Geisinger, was used for large-scale research of the relationship of volume of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) with obesity-related diseases and clinical laboratory measures. Using a "greedy snake" algorithm and 2,545 CT images from the Geisinger PACS, we measured levels of VAT, SAT, total adipose tissue (TAT), and adipose ratio volumes. Sex-combined and sex-stratified association testing was done between adipose measures and 1,233 disease diagnoses and 37 clinical laboratory measures. A genome-wide association study (GWAS) for adipose measures was also performed. SAT was strongly associated with obesity and morbid obesity. VAT levels were strongly associated with type 2 diabetes-related diagnoses ( p = 1.5 × 10 -58 ), obstructive sleep apnea ( p = 7.7 × 10 -37 ), high-density lipoprotein (HDL) levels ( p = 1.42 × 10 -36 ), triglyceride levels ( p = 1.44 × 10 -43 ), and white blood cell (WBC) counts ( p = 7.37 × 10 -9 ). Sex-stratified tests revealed stronger associations among women, indicating the increased influence of VAT on obesity-related disease outcomes particularly among women. The GWAS identified some suggestive associations. This study supports the utility of pursuing future clinical and genetic discoveries with existing imaging data-derived adipose tissue measures deployed at a larger scale.
Published: 2018
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27. Collective feature selection to identify crucial epistatic variants.

Author: Verma SS, Lucas A, Zhang X, Veturi Y, Dudek S, Li B, Li R, Urbanowicz R, Moore JH, Kim D, and Ritchie MD
Abstract: Background: Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called "short fat data" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach., Results: Through our simulation study we propose a collective feature selection approach to select features that are in the "union" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger's MyCode Community Health Initiative (on behalf of DiscovEHR collaboration)., Conclusions: In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity., Competing Interests: The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Published: 2018
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28. Rare variants in drug target genes contributing to complex diseases, phenome-wide.

Author: Verma SS, Josyula N, Verma A, Zhang X, Veturi Y, Dewey FE, Hartzel DN, Lavage DR, Leader J, Ritchie MD, and Pendergrass SA
Subjects: Algorithms, Computational Biology methods, Genetic Association Studies, Genome, Human, Genotype, Humans, Pharmaceutical Preparations metabolism, Biomarkers analysis, Databases, Pharmaceutical, Disease genetics, Genome-Wide Association Study statistics & numerical data, Pharmaceutical Preparations analysis, Phenotype, Polymorphism, Single Nucleotide
Abstract: The DrugBank database consists of ~800 genes that are well characterized drug targets. This list of genes is a useful resource for association testing. For example, loss of function (LOF) genetic variation has the potential to mimic the effect of drugs, and high impact variation in these genes can impact downstream traits. Identifying novel associations between genetic variation in these genes and a range of diseases can also uncover new uses for the drugs that target these genes. Phenome Wide Association Studies (PheWAS) have been successful in identifying genetic associations across hundreds of thousands of diseases. We have conducted a novel gene based PheWAS to test the effect of rare variants in DrugBank genes, evaluating associations between these genes and more than 500 quantitative and dichotomous phenotypes. We used whole exome sequencing data from 38,568 samples in Geisinger MyCode Community Health Initiative. We evaluated the results of this study when binning rare variants using various filters based on potential functional impact. We identified multiple novel associations, and the majority of the significant associations were driven by functionally annotated variation. Overall, this study provides a sweeping exploration of rare variant associations within functionally relevant genes across a wide range of diagnoses.
Published: 2018
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29. How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures?

Author: Veturi Y and Ritchie MD
Subjects: Bayes Theorem, Computational Biology methods, Computer Simulation, Databases, Genetic statistics & numerical data, Genetic Variation, Genome-Wide Association Study statistics & numerical data, Humans, Machine Learning, Male, Models, Genetic, Models, Statistical, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Transcriptome, Gene Expression Profiling statistics & numerical data
Abstract: Transcriptome-wide association studies (TWAS) have recently been employed as an approach that can draw upon the advantages of genome-wide association studies (GWAS) and gene expression studies to identify genes associated with complex traits. Unlike standard GWAS, summary level data suffices for TWAS and offers improved statistical power. Two popular TWAS methods include either (a) imputing the cis genetic component of gene expression from smaller sized studies (using multi-SNP prediction or MP) into much larger effective sample sizes afforded by GWAS - TWAS-MP or (b) using summary-based Mendelian randomization - TWAS-SMR. Although these methods have been effective at detecting functional variants, it remains unclear how extensive variability in the genetic architecture of complex traits and diseases impacts TWAS results. Our goal was to investigate the different scenarios under which these methods yielded enough power to detect significant expression-trait associations. In this study, we conducted extensive simulations based on 6000 randomly chosen, unrelated Caucasian males from Geisinger's MyCode population to compare the power to detect cis expression-trait associations (within 500 kb of a gene) using the above-described approaches. To test TWAS across varying genetic backgrounds we simulated gene expression and phenotype using different quantitative trait loci per gene and cis-expression /trait heritability under genetic models that differentiate the effect of causality from that of pleiotropy. For each gene, on a training set ranging from 100 to 1000 individuals, we either (a) estimated regression coefficients with gene expression as the response using five different methods: LASSO, elastic net, Bayesian LASSO, Bayesian spike-slab, and Bayesian ridge regression or (b) performed eQTL analysis. We then sampled with replacement 50,000, 150,000, and 300,000 individuals respectively from the testing set of the remaining 5000 individuals and conducted GWAS on each set. Subsequently, we integrated the GWAS summary statistics derived from the testing set with the weights (or eQTLs) derived from the training set to identify expression-trait associations using (a) TWAS-MP (b) TWAS-SMR (c) eQTL-based GWAS, or (d) standalone GWAS. Finally, we examined the power to detect functionally relevant genes using the different approaches under the considered simulation scenarios. In general, we observed great similarities among TWAS-MP methods although the Bayesian methods resulted in improved power in comparison to LASSO and elastic net as the trait architecture grew more complex while training sample sizes and expression heritability remained small. Finally, we observed high power under causality but very low to moderate power under pleiotropy.
Published: 2018

30. Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles.

Author: Vazquez AI, Veturi Y, Behring M, Shrestha S, Kirst M, Resende MF Jr, and de Los Campos G
Subjects: Breast Neoplasms diagnosis, Breast Neoplasms pathology, Disease Progression, Female, Gene Expression Regulation, Neoplastic genetics, Genomics, Humans, Models, Genetic, Neoplasm Proteins genetics, Proteomics, Software, Survival Analysis, Breast Neoplasms genetics, DNA Methylation genetics, Genome, Human, Neoplasm Proteins biosynthesis
Abstract: Whole-genome multiomic profiles hold valuable information for the analysis and prediction of disease risk and progression. However, integrating high-dimensional multilayer omic data into risk-assessment models is statistically and computationally challenging. We describe a statistical framework, the Bayesian generalized additive model ((BGAM), and present software for integrating multilayer high-dimensional inputs into risk-assessment models. We used BGAM and data from The Cancer Genome Atlas for the analysis and prediction of survival after diagnosis of breast cancer. We developed a sequence of studies to (1) compare predictions based on single omics with those based on clinical covariates commonly used for the assessment of breast cancer patients (COV), (2) evaluate the benefits of combining COV and omics, (3) compare models based on (a) COV and gene expression profiles from oncogenes with (b) COV and whole-genome gene expression (WGGE) profiles, and (4) evaluate the impacts of combining multiple omics and their interactions. We report that (1) WGGE profiles and whole-genome methylation (METH) profiles offer more predictive power than any of the COV commonly used in clinical practice (e.g., subtype and stage), (2) adding WGGE or METH profiles to COV increases prediction accuracy, (3) the predictive power of WGGE profiles is considerably higher than that based on expression from large-effect oncogenes, and (4) the gain in prediction accuracy when combining multiple omics is consistent. Our results show the feasibility of omic integration and highlight the importance of WGGE and METH profiles in breast cancer, achieving gains of up to 7 points area under the curve (AUC) over the COV in some cases., (Copyright © 2016 by the Genetics Society of America.)
Published: 2016
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31. Effectiveness of shrinkage and variable selection methods for the prediction of complex human traits using data from distantly related individuals.

Author: Berger S, Pérez-Rodríguez P, Veturi Y, Simianer H, and de los Campos G
Subjects: Computer Simulation, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Regression Analysis, Disease genetics, Genome, Human, Models, Genetic, Quantitative Trait Loci
Abstract: Genome-wide association studies (GWAS) have detected large numbers of variants associated with complex human traits and diseases. However, the proportion of variance explained by GWAS-significant single nucleotide polymorphisms has been usually small. This brought interest in the use of whole-genome regression (WGR) methods. However, there has been limited research on the factors that affect prediction accuracy (PA) of WGRs when applied to human data of distantly related individuals. Here, we examine, using real human genotypes and simulated phenotypes, how trait complexity, marker-quantitative trait loci (QTL) linkage disequilibrium (LD), and the model used affect the performance of WGRs. Our results indicated that the estimated rate of missing heritability is dependent on the extent of marker-QTL LD. However, this parameter was not greatly affected by trait complexity. Regarding PA our results indicated that: (a) under perfect marker-QTL LD WGR can achieve moderately high prediction accuracy, and with simple genetic architectures variable selection methods outperform shrinkage procedures and (b) under imperfect marker-QTL LD, variable selection methods can achieved reasonably good PA with simple or moderately complex genetic architectures; however, the PA of these methods deteriorated as trait complexity increases and with highly complex traits variable selection and shrinkage methods both performed poorly. This was confirmed with an analysis of human height., (© 2015 The Authors. Annals of Human Genetics published by University College London (UCL) and John Wiley & Sons Ltd.)
Published: 2015
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32. Incorporating Genetic Heterogeneity in Whole-Genome Regressions Using Interactions.

Author: de Los Campos G, Veturi Y, Vazquez AI, Lehermeier C, and Pérez-Rodríguez P
Abstract: Naturally and artificially selected populations usually exhibit some degree of stratification. In Genome-Wide Association Studies and in Whole-Genome Regressions (WGR) analyses, population stratification has been either ignored or dealt with as a potential confounder. However, systematic differences in allele frequency and in patterns of linkage disequilibrium can induce sub-population-specific effects. From this perspective, structure acts as an effect modifier rather than as a confounder. In this article, we extend WGR models commonly used in plant and animal breeding to allow for sub-population-specific effects. This is achieved by decomposing marker effects into main effects and interaction components that describe group-specific deviations. The model can be used both with variable selection and shrinkage methods and can be implemented using existing software for genomic selection. Using a wheat and a pig breeding data set, we compare parameter estimates and the prediction accuracy of the interaction WGR model with WGR analysis ignoring population stratification (across-group analysis) and with a stratified (i.e., within-sub-population) WGR analysis. The interaction model renders trait-specific estimates of the average correlation of effects between sub-populations; we find that such correlation not only depends on the extent of genetic differentiation in allele frequencies between groups but also varies among traits. The evaluation of prediction accuracy shows a modest superiority of the interaction model relative to the other two approaches. This superiority is the result of better stability in performance of the interaction models across data sets and traits; indeed, in almost all cases, the interaction model was either the best performing model or it performed close to the best performing model., Electronic Supplementary Material: Supplementary materials for this article are available at 10.1007/s13253-015-0222-5.
Published: 2015
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33. Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing plant disease resistance.

Author: Veturi Y, Kump K, Walsh E, Ott O, Poland J, Kolkman JM, Balint-Kurti PJ, Holland JB, and Wisser RJ
Subjects: Ascomycota physiology, Computer Simulation, Data Interpretation, Statistical, Genotype, Longitudinal Studies, Multivariate Analysis, Plant Diseases microbiology, Research Design, Software, Zea mays microbiology, Ascomycota immunology, Disease Resistance, Linear Models, Plant Diseases immunology, Zea mays immunology
Abstract: ABSTRACT The mixed linear model (MLM) is an advanced statistical technique applicable to many fields of science. The multivariate MLM can be used to model longitudinal data, such as repeated ratings of disease resistance taken across time. In this study, using an example data set from a multi-environment trial of northern leaf blight disease on 290 maize lines with diverse levels of resistance, multivariate MLM analysis was performed and its utility was examined. In the population and environments tested, genotypic effects were highly correlated across disease ratings and followed an autoregressive pattern of correlation decay. Because longitudinal data are often converted to the univariate measure of area under the disease progress curve (AUDPC), comparisons between univariate MLM analysis of AUDPC and multivariate MLM analysis of longitudinal data were made. Univariate analysis had the advantage of simplicity and reduced computational demand, whereas multivariate analysis enabled a comprehensive perspective on disease development, providing the opportunity for unique insights into disease resistance. To aid in the application of multivariate MLM analysis of longitudinal data on disease resistance, annotated program syntax for model fitting is provided for the software ASReml.
Published: 2012
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34. A Database Developed with Information Extracted from Chemotherapy Drug Package Inserts to Enhance Future Prescriptions.

Author: D'Souza MJ, Alabed GJ, Wheatley JM, Roberts N, Veturi Y, Bi X, and Continisio CH
Abstract: Package inserts of Food and Drug Administration (FDA) approved prescription drugs, including chemotherapy drugs, must follow a specific format imposed by the FDA. These inserts are created by unrelated pharmaceutical companies and as a result tend to be very different in the way the required information is reported. Chemical and pharmacokinetic properties including absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) are crucial elements to a prescribing information packet and are often missing from the reported data. This undergraduate research project analyzes the information packets of 85 randomly chosen chemically diverse chemotherapy drugs for four parameters important to patient care; viz, volume of distribution (V D ), elimination half-life (t 1/2 ), bioavailability, and water solubility. The prescribing information from the package inserts of each was analyzed in detail and pertinent information was consequently tabulated into a database using a commercial informatics platform. Then using a substructure search-tool, sixty-five chemotherapy drugs containing a carbonyl group in their chemical structure were selected and as hypothesized, it was found that many of these packets were significantly lacking in the reporting of the four parameters of interest. To further enhance this cataloged data, a freely available online database was consequently developed (http://annotation.dbi.udel.edu/CancerDB/) with the intention that the chemical, biological, and clinical community will now add some of the missing parameters.
Published: 2011

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34 results on '"Veturi, Y."'

1. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

2. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

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

4. The power of genetic diversity in genome-wide association studies of lipids

5. The power of genetic diversity in genome-wide association studies of lipids

6. The power of genetic diversity in genome-wide association studies of lipids

7. The genetic architecture of multimodal human brain age.

8. Genomic loci influence patterns of structural covariance in the human brain.

9. The Genetic Architecture of Multimodal Human Brain Age.

10. Author Correction: The power of genetic diversity in genome-wide association studies of lipids.

11. Evidence of epistasis in regions of long-range linkage disequilibrium across five complex diseases in the UK Biobank and eMERGE datasets.

12. Session Introduction: SALUD: Scalable Applications of cLinical risk Utility and preDiction.

13. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.

14. Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease.

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

16. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.

17. Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders.

18. Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression.

19. A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts.

20. Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults.

21. Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes.

22. Modeling Heterogeneity in the Genetic Architecture of Ethnically Diverse Groups Using Random Effect Interaction Models.

23. Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies.

24. Detecting potential pleiotropy across cardiovascular and neurological diseases using univariate, bivariate, and multivariate methods on 43,870 individuals from the eMERGE network.

25. Author Correction: Rare variants in drug target genes contributing to complex diseases, phenome-wide.

26. Using Adipose Measures from Health Care Provider-Based Imaging Data for Discovery.

27. Collective feature selection to identify crucial epistatic variants.

28. Rare variants in drug target genes contributing to complex diseases, phenome-wide.

29. How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures?

30. Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles.

31. Effectiveness of shrinkage and variable selection methods for the prediction of complex human traits using data from distantly related individuals.

32. Incorporating Genetic Heterogeneity in Whole-Genome Regressions Using Interactions.

33. Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing plant disease resistance.

34. A Database Developed with Information Extracted from Chemotherapy Drug Package Inserts to Enhance Future Prescriptions.

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