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1. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI

Author

Couto Alves, A, De Silva, NMG, Karhunen, V, Sovio, U, Das, S, Taal, HR, Warrington, NM, Lewin, AM, Kaakinen, M, Cousminer, DL, Thiering, E, Timpson, NJ, Bond, TA, Lowry, E, Brown, CD, Estivill, X, Lindi, V, Bradfield, JP, Geller, F, Speed, D, Coin, LJM, Loh, M, Barton, SJ, Beilin, LJ, Bisgaard, H, Bønnelykke, K, Alili, R, Hatoum, IJ, Schramm, K, Cartwright, R, Charles, M, Salerno, V, Clément, K, Claringbould, AAJ, BIOS Consortium, van Duijn, CM, Moltchanova, E, Eriksson, JG, Elks, C, Feenstra, B, Flexeder, C, Franks, S, Frayling, TM, Freathy, RM, Elliott, P, Widén, E, Hakonarson, H, Hattersley, AT, Rodriguez, A, Banterle, M, Heinrich, J, Heude, B, Holloway, JW, Hofman, A, Hyppönen, E, Inskip, H, Kaplan, LM, Hedman, AK, Läärä, E, Prokisch, H, Grallert, H, Lakka, TA, Lawlor, DA, Melbye, M, Ahluwalia, TS, Marinelli, M, Millwood, IY, Palmer, LJ, Pennell, CE, Perry, JR, Ring, SM, Savolainen, MJ, Rivadeneira, F, Standl, M, Sunyer, J, Tiesler, CMT, Uitterlinden, AG, Schierding, W, O’Sullivan, JM, Prokopenko, I, Herzig, K, Smith, GD, O'Reilly, P, Felix, JF, Buxton, JL, Blakemore, AIF, Ong, KK, Jaddoe, VWV, Grant, SFA, Sebert, S, McCarthy, MI, Järvelin, M., and Early Growth Genetics (EGG) Consortium

Abstract

Copyright © 2019 The Authors. Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

Published
2019

2. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI

Author

Alves, AC, De Silva, NMG, Karhunen, V, Sovio, U, Das, S, Taal, HR, Warrington, NM, Lewin, AM, Kaakinen, M, Cousminer, DL, Thiering, E, Timpson, NJ, Bond, TA, Lowry, E, Brown, CD, Estivill, X, Lindi, V, Bradfield, JP, Geller, F, Speed, D, Coin, LJM, Loh, M, Barton, SJ, Beilin, LJ, Bisgaard, H, Bonnelykke, K, Alili, R, Hatoum, IJ, Schramm, K, Cartwright, R, Charles, M-A, Salerno, V, Clement, K, Claringbould, AAJ, van Duijn, CM, Moltchanova, E, Eriksson, JG, Elks, C, Feenstra, B, Flexeder, C, Franks, S, Frayling, TM, Freathy, RM, Elliott, P, Widen, E, Hakonarson, H, Hattersley, AT, Rodriguez, A, Banterle, M, Heinrich, J, Heude, B, Holloway, JW, Hofman, A, Hypponen, E, Inskip, H, Kaplan, LM, Hedman, AK, Laara, E, Prokisch, H, Grallert, H, Lakka, TA, Lawlor, DA, Melbye, M, Ahluwalia, TS, Marinelli, M, Millwood, IY, Palmer, LJ, Pennell, CE, Perry, JR, Ring, SM, Savolainen, MJ, Rivadeneira, F, Standl, M, Sunyer, J, Tiesler, CMT, Uitterlinden, AG, Schierding, W, O'Sullivan, JM, Prokopenko, I, Herzig, K-H, Smith, GD, O'Reilly, P, Felix, JF, Buxton, JL, Blakemore, AIF, Ong, KK, Jaddoe, VWV, Grant, SFA, Sebert, S, McCarthy, MI, Jarvelin, M-R, Alves, AC, De Silva, NMG, Karhunen, V, Sovio, U, Das, S, Taal, HR, Warrington, NM, Lewin, AM, Kaakinen, M, Cousminer, DL, Thiering, E, Timpson, NJ, Bond, TA, Lowry, E, Brown, CD, Estivill, X, Lindi, V, Bradfield, JP, Geller, F, Speed, D, Coin, LJM, Loh, M, Barton, SJ, Beilin, LJ, Bisgaard, H, Bonnelykke, K, Alili, R, Hatoum, IJ, Schramm, K, Cartwright, R, Charles, M-A, Salerno, V, Clement, K, Claringbould, AAJ, van Duijn, CM, Moltchanova, E, Eriksson, JG, Elks, C, Feenstra, B, Flexeder, C, Franks, S, Frayling, TM, Freathy, RM, Elliott, P, Widen, E, Hakonarson, H, Hattersley, AT, Rodriguez, A, Banterle, M, Heinrich, J, Heude, B, Holloway, JW, Hofman, A, Hypponen, E, Inskip, H, Kaplan, LM, Hedman, AK, Laara, E, Prokisch, H, Grallert, H, Lakka, TA, Lawlor, DA, Melbye, M, Ahluwalia, TS, Marinelli, M, Millwood, IY, Palmer, LJ, Pennell, CE, Perry, JR, Ring, SM, Savolainen, MJ, Rivadeneira, F, Standl, M, Sunyer, J, Tiesler, CMT, Uitterlinden, AG, Schierding, W, O'Sullivan, JM, Prokopenko, I, Herzig, K-H, Smith, GD, O'Reilly, P, Felix, JF, Buxton, JL, Blakemore, AIF, Ong, KK, Jaddoe, VWV, Grant, SFA, Sebert, S, McCarthy, MI, and Jarvelin, M-R

Abstract

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

Published
2019

3. A DNA methylation biomarker of alcohol consumption

Author

Liu, Chang, Marioni, RE, Hedman, AK, Pfeiffer, L, Tsai, P C, Reynolds, LM, Just, AC, Duan, Q, Boer, Cindy, Tanaka, T, Elks, CE, Aslibekyan, S, Brody, JA, Kuhnel, B, Herder, C, Almli, LM, Zhi, D, Wang, Y, Huan, T, Yao, C, Mendelson, MM, Joehanes, R, Liang, L, Love, SA, Guan, W, Shah, S, Mcrae, AF, Kretschmer, A, Prokisch, H, Strauch, K, Peters, A, Visscher, PM, Wray, NR, Guo, X, Wiggins, KL, Smith, AK, Binder, EB, Ressler, KJ, Irvin, M R, Absher, DM, Hernandez, D, Ferrucci, L, Bandinelli, S, Lohman, K, Ding, J, Trevisi, L, Gustafsson, S, Sandling, JH, Stolk, Lisette, Uitterlinden, André, Yet, I, Castillo-Fernandez, JE, Spector, TD, Schwartz, JD, Vokonas, P, Lind, L, Li, Y, Fornage, M, Arnett, DK, Wareham, NJ, Sotoodehnia, N, Ong, KK, van Meurs, Joyce, Conneely, KN, Baccarelli, AA, Deary, IJ, Bell, JT, North, KE, Liu, Y, Waldenberger, M, London, SJ, Ingelsson, E, Levy, D, Liu, Chang, Marioni, RE, Hedman, AK, Pfeiffer, L, Tsai, P C, Reynolds, LM, Just, AC, Duan, Q, Boer, Cindy, Tanaka, T, Elks, CE, Aslibekyan, S, Brody, JA, Kuhnel, B, Herder, C, Almli, LM, Zhi, D, Wang, Y, Huan, T, Yao, C, Mendelson, MM, Joehanes, R, Liang, L, Love, SA, Guan, W, Shah, S, Mcrae, AF, Kretschmer, A, Prokisch, H, Strauch, K, Peters, A, Visscher, PM, Wray, NR, Guo, X, Wiggins, KL, Smith, AK, Binder, EB, Ressler, KJ, Irvin, M R, Absher, DM, Hernandez, D, Ferrucci, L, Bandinelli, S, Lohman, K, Ding, J, Trevisi, L, Gustafsson, S, Sandling, JH, Stolk, Lisette, Uitterlinden, André, Yet, I, Castillo-Fernandez, JE, Spector, TD, Schwartz, JD, Vokonas, P, Lind, L, Li, Y, Fornage, M, Arnett, DK, Wareham, NJ, Sotoodehnia, N, Ong, KK, van Meurs, Joyce, Conneely, KN, Baccarelli, AA, Deary, IJ, Bell, JT, North, KE, Liu, Y, Waldenberger, M, London, SJ, Ingelsson, E, and Levy, D

Published
2018

4. Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels

Author

Kilpelainen, TO, Carli, J F M, Skowronski, A A, Sun, Q, Kriebel, J, Feitosa, MF, Hedman, AK, Drong, AW, Hayes, J E, Zhao, JH, Pers, TH, Schick, U, Grarup, N, Kutalik, Z, Trompet, S, Mangino, M, Kristiansson, K, Beekman, M, Lyytikainen, LP, Eriksson, J, Henneman, Peter, Lahti, J, Tanaka, T, Luan, JA, Del Greco, F, Pasko, D, Renstrom, F, Willems, SM, Mahajan, A, Rose, LM, Guo, XQ, Liu, YM, Kleber, ME, Perusse, L, Gaunt, T, Ahluwalia, TS, Sung, YJ, Ramos, YF, Amin, Najaf, Amuzu, A, Barroso, I, Bellis, C, Blangero, J, Buckley, BM, Bohringer, S, Chen, YDI, de Craen, A J N, Crosslin, DR, Dale, CE, Dastani, Z, Day, FR, Deelen, J, Delgado, GE, Demirkan, Ayse, Finucane, FM, Ford, I, Garcia, ME, Gieger, C, Gustafsson, S, Hallmans, G, Hankinson, SE, Havulinna, AS, Herder, Cindy, Hernandez, D, Hicks, AA, Hunter, DJ, Illig, T, Ingelsson, E, Ioan-Facsinay, A, Jansson, JO, Jenny, NS, Jorgensen, ME, Jorgensen, T, Karlsson, M, Koenig, W, Kraft, P, Kwekkeboom, J, Laatikainen, T, Ladwig, KH, LeDuc, C A, Lowe, G, Lu, YC, Marques-Vidal, P, Meisinger, C, Menni, C, Morris, AP, Myers, RH, Mannisto, S, Nalls, MA, Paternoster, L, Peters, A, Pradhan, A D, Rankinen, T, Rasmussen-Torvik, LJ, Rathmann, W, Rice, TK, Richards, JB, Ridker, PM, Sattar, N, Savage, DB, Soderberg, S, Timpson, NJ, Vandenput, L, van Heemst, D, Uh, HW, Vohl, MC, Walker, M, Wichmann, HE, Widen, E, Wood, AR, Yao, J, Zeller, T, Zhang, YY, Meulenbelt, I, Kloppenburg, M, Astrup, A, Sorensen, TIA, Sarzynski, MA, Rao, DC, Jousilahti, P, Vartiainen, E, Hofman, Bert, Rivadeneira, Fernando, Uitterlinden, André, Kajantie, E, Osmond, C, Palotie, A, Eriksson, JG, Heliovaara, M, Knekt, PB, Koskinen, S, Jula, A, Perola, M, Huupponen, R K, Viikari, JS, Kahonen, M, Lehtimaki, T, Raitakari, OT, Mellstrom, D, Lorentzon, M, Casas, JP, Bandinelli, S, Marz, W, Isaacs, Aaron, van Dijk, KW, Duijn, Cornelia, Harris, TB, Bouchard, C, Allison, MA, Chasman, DI, Ohlsson, C, Lind, L, Scott, RA, Langenberg, C, Wareham, NJ, Ferrucci, L, Frayling, TM, Pramstaller, PP, Borecki, IB, Waterworth, DM, Bergmann, S, Waeber, G, Vollenweider, P, Vestergaard, H, Hansen, T, Pedersen, O, Hu, FB, Slagboom, PE (Eline), Grallert, H, Spector, TD, Jukema, JW, Klein, RJ, Schadt, EE, Franks, PW, Lindgren, CM, Leibel, R L, Loos, RJF, Kilpelainen, TO, Carli, J F M, Skowronski, A A, Sun, Q, Kriebel, J, Feitosa, MF, Hedman, AK, Drong, AW, Hayes, J E, Zhao, JH, Pers, TH, Schick, U, Grarup, N, Kutalik, Z, Trompet, S, Mangino, M, Kristiansson, K, Beekman, M, Lyytikainen, LP, Eriksson, J, Henneman, Peter, Lahti, J, Tanaka, T, Luan, JA, Del Greco, F, Pasko, D, Renstrom, F, Willems, SM, Mahajan, A, Rose, LM, Guo, XQ, Liu, YM, Kleber, ME, Perusse, L, Gaunt, T, Ahluwalia, TS, Sung, YJ, Ramos, YF, Amin, Najaf, Amuzu, A, Barroso, I, Bellis, C, Blangero, J, Buckley, BM, Bohringer, S, Chen, YDI, de Craen, A J N, Crosslin, DR, Dale, CE, Dastani, Z, Day, FR, Deelen, J, Delgado, GE, Demirkan, Ayse, Finucane, FM, Ford, I, Garcia, ME, Gieger, C, Gustafsson, S, Hallmans, G, Hankinson, SE, Havulinna, AS, Herder, Cindy, Hernandez, D, Hicks, AA, Hunter, DJ, Illig, T, Ingelsson, E, Ioan-Facsinay, A, Jansson, JO, Jenny, NS, Jorgensen, ME, Jorgensen, T, Karlsson, M, Koenig, W, Kraft, P, Kwekkeboom, J, Laatikainen, T, Ladwig, KH, LeDuc, C A, Lowe, G, Lu, YC, Marques-Vidal, P, Meisinger, C, Menni, C, Morris, AP, Myers, RH, Mannisto, S, Nalls, MA, Paternoster, L, Peters, A, Pradhan, A D, Rankinen, T, Rasmussen-Torvik, LJ, Rathmann, W, Rice, TK, Richards, JB, Ridker, PM, Sattar, N, Savage, DB, Soderberg, S, Timpson, NJ, Vandenput, L, van Heemst, D, Uh, HW, Vohl, MC, Walker, M, Wichmann, HE, Widen, E, Wood, AR, Yao, J, Zeller, T, Zhang, YY, Meulenbelt, I, Kloppenburg, M, Astrup, A, Sorensen, TIA, Sarzynski, MA, Rao, DC, Jousilahti, P, Vartiainen, E, Hofman, Bert, Rivadeneira, Fernando, Uitterlinden, André, Kajantie, E, Osmond, C, Palotie, A, Eriksson, JG, Heliovaara, M, Knekt, PB, Koskinen, S, Jula, A, Perola, M, Huupponen, R K, Viikari, JS, Kahonen, M, Lehtimaki, T, Raitakari, OT, Mellstrom, D, Lorentzon, M, Casas, JP, Bandinelli, S, Marz, W, Isaacs, Aaron, van Dijk, KW, Duijn, Cornelia, Harris, TB, Bouchard, C, Allison, MA, Chasman, DI, Ohlsson, C, Lind, L, Scott, RA, Langenberg, C, Wareham, NJ, Ferrucci, L, Frayling, TM, Pramstaller, PP, Borecki, IB, Waterworth, DM, Bergmann, S, Waeber, G, Vollenweider, P, Vestergaard, H, Hansen, T, Pedersen, O, Hu, FB, Slagboom, PE (Eline), Grallert, H, Spector, TD, Jukema, JW, Klein, RJ, Schadt, EE, Franks, PW, Lindgren, CM, Leibel, R L, and Loos, RJF

Published
2016

5. y New loci for body fat percentage reveal link between adiposity and cardiometabolic disease risk

Author

Lu, YC, Day, FR, Gustafsson, S, Buchkovich, ML, Na, JB, Bataille, V, Cousminer, DL, Dastani, Z, Drong, AW, Esko, T, Evans, DM, Falchi, M, Feitosa, MF, Ferreira, T, Hedman, AK, Haring, R, Hysi, PG, Iles, MM, Justice, AE, Kanoni, S, Lagou, V, Li, R, Li, X, Locke, A, Lu, C, Magi, R, Perry, JRB, Pers, TH, Qi, QB, Sanna, M, Schmidt, EM, Scott, WR, Shungin, D, Teumer, A, Vinkhuyzen, AAE, Walker, RW, Westra, HJ, Zhang, MF, Zhang, WH, Zhao, JH, Zhu, ZH, Afzal, U, Ahluwalia, TS, Bakker, SJL, Bellis, C, Bonnefond, A, Borodulin, K, Buchman, AS, Cederholm, T, Choh, AC, Choi, HJ, Curran, JE, de Groot, LCPGM (Lisette), De Jager, PL, Dhonukshe-Rutten, RAM, Enneman, Anke, Eury, E, Evans, DS, Forsen, T, Friedrich, N, Fumeron, F, Garcia, ME, Gartner, S, Han, BG, Havulinna, AS, Hayward, C, Hernandez, D, Hillege, H, Ittermann, T, Kent, JW, Kolcic, I, Laatikainen, T, Lahti, J, Leach, IM, Lee, CG, Lee, JY, Liu, T, Liu, YF, Lobbens, S, Loh, M, Lyytikainen, LP, Medina-Gomez, C, Michaelsson, K, Nalls, MA, Nielson, CM, Oozageer, L, Pascoe, L, Paternoster, L, Polasek, O, Ripatti, S, Sarzynski, MA, Shin, CS, Narancic, NS, Spira, D, Srikanth, P, Steinhagen-Thiessen, E, Sung, YJ, Swart, KMA, Taittonen, L, Tanaka, T, Tikkanen, E, van der Velde, Nathalie, Schoor, NM, Verweij, N (Niek), Wright, AF, Yu, L, Zmuda, JM, Eklund, N, Forrester, T, Grarup, N, Jackson, AU, Kristiansson, K, Kuulasmaa, T, Kuusisto, J, Lichtner, P, Luan, JA, Mahajan, A, Mannisto, S, Palmer, CD, Ried, JS, Scott, RA, Stancakova, A, Wagner, PJ, Demirkan, Ayse, Doring, A, Gudnason, V, Kiel, DP, Kuhnel, B, Mangino, M, McKnight, B, Menni, C, O'Connell, JR, Oostra, Ben, Shuldiner, AR, Song, KJ, Vandenput, L, Duijn, Cornelia, Vollenweider, P, White, CC, Boehnke, M, Boettcher, Y, Cooper, RS, Forouhi, NG, Gieger, C, Grallert, H, Hingorani, A, Jorgensen, T, Jousilahti, P, Kivimaki, M, Kumari, M, Laakso, M, Langenberg, C, Linneberg, A, Luke, A, McKenzie, CA, Palotie, A, Pedersen, O, Peters, A, Strauch, K, Tayo, BO, Wareham, NJ, Bennett, DA, Bertram, L, Blangero, J, Bluher, M, Bouchard, C, Campbell, H, Cho, NH, Cummings, SR, Czerwinski, SA, Demuth, I, Eckardt, R, Eriksson, JG, Ferrucci, L, Franco Duran, OH, Froguel, P, Gansevoort, RT, Hansen, T, Harris, TB, Hastie, N, Heliovaara, M, Hofman, Bert, Jordan, JM, Jula, A, Kahonen, M, Kajantie, E, Knekt, PB, Koskinen, S, Kovacs, P, Lehtimaki, T, Lind, L, Liu, YM, Orwoll, ES, Osmond, C, Perola, M, Perusse, L, Raitakari, OT, Rankinen, T, Rao, DC, Rice, TK, Rivadeneira, Fernando, Rudan, I, Salomaa, V, Sorensen, TIA, Stumvoll, M, Tonjes, A, Towne, B, Tranah, GJ, Tremblay, A, Uitterlinden, André, van der Harst, P, Vartiainen, E, Viikari, JS, Vitart, V, Vohl, MC, Volzke, H, Walker, M, Wallaschofski, H, Wild, S, Wilson, JF, Yengo, L, Bishop, DT, Borecki, IB, Chambers, JC, Cupples, LA, Dehghan, Abbas, Deloukas, P, Fatemifar, G, Fox, C, Furey, TS, Franke, L, Han, JL, Hunter, DJ, Karjalainen, J, Karpe, F, Kaplan, RC, Kooner, JS, McCarthy, MI, Murabito, JM, Morris, AP, Bishop, JAN, North, KE, Ohlsson, C, Ong, KK, Prokopenko, I, Richards, JB, Schadt, EE, Spector, TD, Widen, E, Willer, CJ, Yang, Jiaqi, Ingelsson, E, Mohlke, KL, Hirschhorn, JN, Pospisilik, JA, Zillikens, M.C., Lindgren, C, Kilpelainen, TO, Loos, RJF, Lu, YC, Day, FR, Gustafsson, S, Buchkovich, ML, Na, JB, Bataille, V, Cousminer, DL, Dastani, Z, Drong, AW, Esko, T, Evans, DM, Falchi, M, Feitosa, MF, Ferreira, T, Hedman, AK, Haring, R, Hysi, PG, Iles, MM, Justice, AE, Kanoni, S, Lagou, V, Li, R, Li, X, Locke, A, Lu, C, Magi, R, Perry, JRB, Pers, TH, Qi, QB, Sanna, M, Schmidt, EM, Scott, WR, Shungin, D, Teumer, A, Vinkhuyzen, AAE, Walker, RW, Westra, HJ, Zhang, MF, Zhang, WH, Zhao, JH, Zhu, ZH, Afzal, U, Ahluwalia, TS, Bakker, SJL, Bellis, C, Bonnefond, A, Borodulin, K, Buchman, AS, Cederholm, T, Choh, AC, Choi, HJ, Curran, JE, de Groot, LCPGM (Lisette), De Jager, PL, Dhonukshe-Rutten, RAM, Enneman, Anke, Eury, E, Evans, DS, Forsen, T, Friedrich, N, Fumeron, F, Garcia, ME, Gartner, S, Han, BG, Havulinna, AS, Hayward, C, Hernandez, D, Hillege, H, Ittermann, T, Kent, JW, Kolcic, I, Laatikainen, T, Lahti, J, Leach, IM, Lee, CG, Lee, JY, Liu, T, Liu, YF, Lobbens, S, Loh, M, Lyytikainen, LP, Medina-Gomez, C, Michaelsson, K, Nalls, MA, Nielson, CM, Oozageer, L, Pascoe, L, Paternoster, L, Polasek, O, Ripatti, S, Sarzynski, MA, Shin, CS, Narancic, NS, Spira, D, Srikanth, P, Steinhagen-Thiessen, E, Sung, YJ, Swart, KMA, Taittonen, L, Tanaka, T, Tikkanen, E, van der Velde, Nathalie, Schoor, NM, Verweij, N (Niek), Wright, AF, Yu, L, Zmuda, JM, Eklund, N, Forrester, T, Grarup, N, Jackson, AU, Kristiansson, K, Kuulasmaa, T, Kuusisto, J, Lichtner, P, Luan, JA, Mahajan, A, Mannisto, S, Palmer, CD, Ried, JS, Scott, RA, Stancakova, A, Wagner, PJ, Demirkan, Ayse, Doring, A, Gudnason, V, Kiel, DP, Kuhnel, B, Mangino, M, McKnight, B, Menni, C, O'Connell, JR, Oostra, Ben, Shuldiner, AR, Song, KJ, Vandenput, L, Duijn, Cornelia, Vollenweider, P, White, CC, Boehnke, M, Boettcher, Y, Cooper, RS, Forouhi, NG, Gieger, C, Grallert, H, Hingorani, A, Jorgensen, T, Jousilahti, P, Kivimaki, M, Kumari, M, Laakso, M, Langenberg, C, Linneberg, A, Luke, A, McKenzie, CA, Palotie, A, Pedersen, O, Peters, A, Strauch, K, Tayo, BO, Wareham, NJ, Bennett, DA, Bertram, L, Blangero, J, Bluher, M, Bouchard, C, Campbell, H, Cho, NH, Cummings, SR, Czerwinski, SA, Demuth, I, Eckardt, R, Eriksson, JG, Ferrucci, L, Franco Duran, OH, Froguel, P, Gansevoort, RT, Hansen, T, Harris, TB, Hastie, N, Heliovaara, M, Hofman, Bert, Jordan, JM, Jula, A, Kahonen, M, Kajantie, E, Knekt, PB, Koskinen, S, Kovacs, P, Lehtimaki, T, Lind, L, Liu, YM, Orwoll, ES, Osmond, C, Perola, M, Perusse, L, Raitakari, OT, Rankinen, T, Rao, DC, Rice, TK, Rivadeneira, Fernando, Rudan, I, Salomaa, V, Sorensen, TIA, Stumvoll, M, Tonjes, A, Towne, B, Tranah, GJ, Tremblay, A, Uitterlinden, André, van der Harst, P, Vartiainen, E, Viikari, JS, Vitart, V, Vohl, MC, Volzke, H, Walker, M, Wallaschofski, H, Wild, S, Wilson, JF, Yengo, L, Bishop, DT, Borecki, IB, Chambers, JC, Cupples, LA, Dehghan, Abbas, Deloukas, P, Fatemifar, G, Fox, C, Furey, TS, Franke, L, Han, JL, Hunter, DJ, Karjalainen, J, Karpe, F, Kaplan, RC, Kooner, JS, McCarthy, MI, Murabito, JM, Morris, AP, Bishop, JAN, North, KE, Ohlsson, C, Ong, KK, Prokopenko, I, Richards, JB, Schadt, EE, Spector, TD, Widen, E, Willer, CJ, Yang, Jiaqi, Ingelsson, E, Mohlke, KL, Hirschhorn, JN, Pospisilik, JA, Zillikens, M.C., Lindgren, C, Kilpelainen, TO, and Loos, RJF

Abstract

To increase our understanding of the genetic basis of adiposity and its links to cardiometabolic disease risk, we conducted a genome-wide association meta-analysis of body fat percentage (BF%) in up to 100,716 individuals. Twelve loci reached genome-wide significance (P < 5 x 10(-8)), of which eight were previously associated with increased overall adiposity (BMI, BF%) and four (in or near COBLL1/GRB14, IGF2BP1, PLA2G6, CRTC1) were novel associations with BF%. Seven loci showed a larger effect on BF% than on BMI, suggestive of a primary association with adiposity, while five loci showed larger effects on BMI than on BF%, suggesting association with both fat and lean mass. In particular, the loci more strongly associated with BF% showed distinct cross-phenotype association signatures with a range of cardiometabolic traits revealing new insights in the link between adiposity and disease risk.

Published
2016

6. Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility

Author

Mahajan, A, Go, MJ, Zhang, W, Below, JE, Gaulton, KJ, Ferreira, T, Horikoshi, M, Johnson, AD, Ng, MCY, Prokopenko, I, Saleheen, D, Wang, X, Zeggini, E, Abecasis, GR, Adair, LS, Almgren, P, Atalay, M, Aung, T, Baldassarre, D, Balkau, B, Bao, Y, Barnett, AH, Barroso, I, Basit, A, Been, LF, Beilby, J, Bell, GI, Benediktsson, R, Bergman, RN, Boehm, BO, Boerwinkle, E, Bonnycastle, LL, Burtt, N, Cai, Q, Campbell, H, Carey, J, Cauchi, S, Caulfield, M, Chan, JCN, Chang, L-C, Chang, T-J, Chang, Y-C, Charpentier, G, Chen, C-H, Chen, H, Chen, Y-T, Chia, K-S, Chidambaram, M, Chines, PS, Cho, NH, Cho, YM, Chuang, L-M, Collins, FS, Cornelis, MC, Couper, DJ, Crenshaw, AT, van Dam, RM, Danesh, J, Das, D, de Faire, U, Dedoussis, G, Deloukas, P, Dimas, AS, Dina, C, Doney, ASF, Donnelly, PJ, Dorkhan, M, van Duijn, C, Dupuis, J, Edkins, S, Elliott, P, Emilsson, V, Erbel, R, Eriksson, JG, Escobedo, J, Esko, T, Eury, E, Florez, JC, Fontanillas, P, Forouhi, NG, Forsen, T, Fox, C, Fraser, RM, Frayling, TM, Froguel, P, Frossard, P, Gao, Y, Gertow, K, Gieger, C, Gigante, B, Grallert, H, Grant, GB, Groop, LC, Groves, CJ, Grundberg, E, Guiducci, C, Hamsten, A, Han, B-G, Hara, K, Hassanali, N, Hattersley, AT, Hayward, C, Hedman, AK, Herder, C, Hofman, A, Holmen, OL, Hovingh, K, Hreidarsson, AB, Hu, C, Hu, FB, Hui, J, Humphries, SE, Hunt, SE, Hunter, DJ, Hveem, K, Hydrie, ZI, Ikegami, H, Illig, T, Ingelsson, E, Islam, M, Isomaa, B, Jackson, AU, Jafar, T, James, A, Jia, W, Joeckel, K-H, Jonsson, A, Jowett, JBM, Kadowaki, T, Kang, HM, Kanoni, S, Kao, WHL, Kathiresan, S, Kato, N, Katulanda, P, Keinanen-Kiukaanniemi, SM, Kelly, AM, Khan, H, Khaw, K-T, Khor, C-C, Kim, H-L, Kim, S, Kim, YJ, Kinnunen, L, Klopp, N, Kong, A, Korpi-Hyovalti, E, Kowlessur, S, Kraft, P, Kravic, J, Kristensen, MM, Krithika, S, Kumar, A, Kumate, J, Kuusisto, J, Kwak, SH, Laakso, M, Lagou, V, Lakka, TA, Langenberg, C, Langford, C, Lawrence, R, Leander, K, Lee, J-M, Lee, NR, Li, M, Li, X, Li, Y, Liang, J, Liju, S, Lim, W-Y, Lind, L, Lindgren, CM, Lindholm, E, Liu, C-T, Liu, JJ, Lobbens, S, Long, J, Loos, RJF, Lu, W, Luan, J, Lyssenko, V, Ma, RCW, Maeda, S, Maegi, R, Mannisto, S, Matthews, DR, Meigs, JB, Melander, O, Metspalu, A, Meyer, J, Mirza, G, Mihailov, E, Moebus, S, Mohan, V, Mohlke, KL, Morris, AD, Muehleisen, TW, Mueller-Nurasyid, M, Musk, B, Nakamura, J, Nakashima, E, Navarro, P, Peng-Keat, N, Nica, AC, Nilsson, PM, Njolstad, I, Noethen, MM, Ohnaka, K, Ong, TH, Owen, KR, Palmer, CNA, Pankow, JS, Park, KS, Parkin, M, Pechlivanis, S, Pedersen, NL, Peltonen, L, Perry, JRB, Peters, A, Pinidiyapathirage, JM, Platou, CGP, Potter, S, Price, JF, Qi, L, Radha, V, Rallidis, L, Rasheed, A, Rathmann, W, Rauramaa, R, Raychaudhuri, S, Rayner, NW, Rees, SD, Rehnberg, E, Ripatti, S, Robertson, N, Roden, M, Rossin, EJ, Rudan, I, Rybin, D, Saaristo, TE, Salomaa, V, Saltevo, J, Samuel, M, Sanghera, DK, Saramies, J, Scott, J, Scott, LJ, Scott, RA, Segre, AV, Sehmi, J, Sennblad, B, Shah, N, Shah, S, Shera, AS, Shu, XO, Shuldiner, AR, Sigurdsson, G, Sijbrands, E, Silveira, A, Sim, X, Sivapalaratnam, S, Small, KS, So, WY, Stancakova, A, Stefansson, K, Steinbach, G, Steinthorsdottir, V, Stirrups, K, Strawbridge, RJ, Stringham, HM, Sun, Q, Suo, C, Syvanen, A-C, Takayanagi, R, Takeuchi, F, Tay, WT, Teslovich, TM, Thorand, B, Thorleifsson, G, Thorsteinsdottir, U, Tikkanen, E, Trakalo, J, Tremoli, E, Trip, MD, Tsai, FJ, Tuomi, T, Tuomilehto, J, Uitterlinden, AG, Valladares-Salgado, A, Vedantam, S, Veglia, F, Voight, BF, Wang, C, Wareham, NJ, Wennauer, R, Wickremasinghe, AR, Wilsgaard, T, Wilson, JF, Wiltshire, S, Winckler, W, Wong, TY, Wood, AR, Wu, J-Y, Wu, Y, Yamamoto, K, Yamauchi, T, Yang, M, Yengo, L, Yokota, M, Young, R, Zabaneh, D, Zhang, F, Zhang, R, Zheng, W, Zimmet, PZ, Altshuler, D, Bowden, DW, Cho, YS, Cox, NJ, Cruz, M, Hanis, CL, Kooner, J, Lee, J-Y, Seielstad, M, Teo, YY, Boehnke, M, Parra, EJ, Chambers, JC, Tai, ES, McCarthy, MI, Morris, AP, Mahajan, A, Go, MJ, Zhang, W, Below, JE, Gaulton, KJ, Ferreira, T, Horikoshi, M, Johnson, AD, Ng, MCY, Prokopenko, I, Saleheen, D, Wang, X, Zeggini, E, Abecasis, GR, Adair, LS, Almgren, P, Atalay, M, Aung, T, Baldassarre, D, Balkau, B, Bao, Y, Barnett, AH, Barroso, I, Basit, A, Been, LF, Beilby, J, Bell, GI, Benediktsson, R, Bergman, RN, Boehm, BO, Boerwinkle, E, Bonnycastle, LL, Burtt, N, Cai, Q, Campbell, H, Carey, J, Cauchi, S, Caulfield, M, Chan, JCN, Chang, L-C, Chang, T-J, Chang, Y-C, Charpentier, G, Chen, C-H, Chen, H, Chen, Y-T, Chia, K-S, Chidambaram, M, Chines, PS, Cho, NH, Cho, YM, Chuang, L-M, Collins, FS, Cornelis, MC, Couper, DJ, Crenshaw, AT, van Dam, RM, Danesh, J, Das, D, de Faire, U, Dedoussis, G, Deloukas, P, Dimas, AS, Dina, C, Doney, ASF, Donnelly, PJ, Dorkhan, M, van Duijn, C, Dupuis, J, Edkins, S, Elliott, P, Emilsson, V, Erbel, R, Eriksson, JG, Escobedo, J, Esko, T, Eury, E, Florez, JC, Fontanillas, P, Forouhi, NG, Forsen, T, Fox, C, Fraser, RM, Frayling, TM, Froguel, P, Frossard, P, Gao, Y, Gertow, K, Gieger, C, Gigante, B, Grallert, H, Grant, GB, Groop, LC, Groves, CJ, Grundberg, E, Guiducci, C, Hamsten, A, Han, B-G, Hara, K, Hassanali, N, Hattersley, AT, Hayward, C, Hedman, AK, Herder, C, Hofman, A, Holmen, OL, Hovingh, K, Hreidarsson, AB, Hu, C, Hu, FB, Hui, J, Humphries, SE, Hunt, SE, Hunter, DJ, Hveem, K, Hydrie, ZI, Ikegami, H, Illig, T, Ingelsson, E, Islam, M, Isomaa, B, Jackson, AU, Jafar, T, James, A, Jia, W, Joeckel, K-H, Jonsson, A, Jowett, JBM, Kadowaki, T, Kang, HM, Kanoni, S, Kao, WHL, Kathiresan, S, Kato, N, Katulanda, P, Keinanen-Kiukaanniemi, SM, Kelly, AM, Khan, H, Khaw, K-T, Khor, C-C, Kim, H-L, Kim, S, Kim, YJ, Kinnunen, L, Klopp, N, Kong, A, Korpi-Hyovalti, E, Kowlessur, S, Kraft, P, Kravic, J, Kristensen, MM, Krithika, S, Kumar, A, Kumate, J, Kuusisto, J, Kwak, SH, Laakso, M, Lagou, V, Lakka, TA, Langenberg, C, Langford, C, Lawrence, R, Leander, K, Lee, J-M, Lee, NR, Li, M, Li, X, Li, Y, Liang, J, Liju, S, Lim, W-Y, Lind, L, Lindgren, CM, Lindholm, E, Liu, C-T, Liu, JJ, Lobbens, S, Long, J, Loos, RJF, Lu, W, Luan, J, Lyssenko, V, Ma, RCW, Maeda, S, Maegi, R, Mannisto, S, Matthews, DR, Meigs, JB, Melander, O, Metspalu, A, Meyer, J, Mirza, G, Mihailov, E, Moebus, S, Mohan, V, Mohlke, KL, Morris, AD, Muehleisen, TW, Mueller-Nurasyid, M, Musk, B, Nakamura, J, Nakashima, E, Navarro, P, Peng-Keat, N, Nica, AC, Nilsson, PM, Njolstad, I, Noethen, MM, Ohnaka, K, Ong, TH, Owen, KR, Palmer, CNA, Pankow, JS, Park, KS, Parkin, M, Pechlivanis, S, Pedersen, NL, Peltonen, L, Perry, JRB, Peters, A, Pinidiyapathirage, JM, Platou, CGP, Potter, S, Price, JF, Qi, L, Radha, V, Rallidis, L, Rasheed, A, Rathmann, W, Rauramaa, R, Raychaudhuri, S, Rayner, NW, Rees, SD, Rehnberg, E, Ripatti, S, Robertson, N, Roden, M, Rossin, EJ, Rudan, I, Rybin, D, Saaristo, TE, Salomaa, V, Saltevo, J, Samuel, M, Sanghera, DK, Saramies, J, Scott, J, Scott, LJ, Scott, RA, Segre, AV, Sehmi, J, Sennblad, B, Shah, N, Shah, S, Shera, AS, Shu, XO, Shuldiner, AR, Sigurdsson, G, Sijbrands, E, Silveira, A, Sim, X, Sivapalaratnam, S, Small, KS, So, WY, Stancakova, A, Stefansson, K, Steinbach, G, Steinthorsdottir, V, Stirrups, K, Strawbridge, RJ, Stringham, HM, Sun, Q, Suo, C, Syvanen, A-C, Takayanagi, R, Takeuchi, F, Tay, WT, Teslovich, TM, Thorand, B, Thorleifsson, G, Thorsteinsdottir, U, Tikkanen, E, Trakalo, J, Tremoli, E, Trip, MD, Tsai, FJ, Tuomi, T, Tuomilehto, J, Uitterlinden, AG, Valladares-Salgado, A, Vedantam, S, Veglia, F, Voight, BF, Wang, C, Wareham, NJ, Wennauer, R, Wickremasinghe, AR, Wilsgaard, T, Wilson, JF, Wiltshire, S, Winckler, W, Wong, TY, Wood, AR, Wu, J-Y, Wu, Y, Yamamoto, K, Yamauchi, T, Yang, M, Yengo, L, Yokota, M, Young, R, Zabaneh, D, Zhang, F, Zhang, R, Zheng, W, Zimmet, PZ, Altshuler, D, Bowden, DW, Cho, YS, Cox, NJ, Cruz, M, Hanis, CL, Kooner, J, Lee, J-Y, Seielstad, M, Teo, YY, Boehnke, M, Parra, EJ, Chambers, JC, Tai, ES, McCarthy, MI, and Morris, AP

Abstract

To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.

Published
2014

7. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population.

Author

Li, J, Bell, JT, Tsai, P-C, Yang, T-P, Pidsley, R, Nisbet, J, Glass, D, Mangino, M, Zhai, G, Zhang, F, Valdes, A, Shin, S-Y, Dempster, EL, Murray, RM, Grundberg, E, Hedman, AK, Nica, A, Small, KS, MuTHER Consortium, Dermitzakis, ET, McCarthy, MI, Mill, J, Spector, TD, Deloukas, P, Li, J, Bell, JT, Tsai, P-C, Yang, T-P, Pidsley, R, Nisbet, J, Glass, D, Mangino, M, Zhai, G, Zhang, F, Valdes, A, Shin, S-Y, Dempster, EL, Murray, RM, Grundberg, E, Hedman, AK, Nica, A, Small, KS, MuTHER Consortium, Dermitzakis, ET, McCarthy, MI, Mill, J, Spector, TD, and Deloukas, P

Abstract

Age-related changes in DNA methylation have been implicated in cellular senescence and longevity, yet the causes and functional consequences of these variants remain unclear. To elucidate the role of age-related epigenetic changes in healthy ageing and potential longevity, we tested for association between whole-blood DNA methylation patterns in 172 female twins aged 32 to 80 with age and age-related phenotypes. Twin-based DNA methylation levels at 26,690 CpG-sites showed evidence for mean genome-wide heritability of 18%, which was supported by the identification of 1,537 CpG-sites with methylation QTLs in cis at FDR 5%. We performed genome-wide analyses to discover differentially methylated regions (DMRs) for sixteen age-related phenotypes (ap-DMRs) and chronological age (a-DMRs). Epigenome-wide association scans (EWAS) identified age-related phenotype DMRs (ap-DMRs) associated with LDL (STAT5A), lung function (WT1), and maternal longevity (ARL4A, TBX20). In contrast, EWAS for chronological age identified hundreds of predominantly hyper-methylated age DMRs (490 a-DMRs at FDR 5%), of which only one (TBX20) was also associated with an age-related phenotype. Therefore, the majority of age-related changes in DNA methylation are not associated with phenotypic measures of healthy ageing in later life. We replicated a large proportion of a-DMRs in a sample of 44 younger adult MZ twins aged 20 to 61, suggesting that a-DMRs may initiate at an earlier age. We next explored potential genetic and environmental mechanisms underlying a-DMRs and ap-DMRs. Genome-wide overlap across cis-meQTLs, genotype-phenotype associations, and EWAS ap-DMRs identified CpG-sites that had cis-meQTLs with evidence for genotype-phenotype association, where the CpG-site was also an ap-DMR for the same phenotype. Monozygotic twin methylation difference analyses identified one potential environmentally-mediated ap-DMR associated with total cholesterol and LDL (CSMD1). Our results suggest that in a smal

Published
2012

8. Eight Common Genetic Variants Associated with Serum DHEAS Levels Suggest a Key Role in Ageing Mechanisms

Author

Zhai, GJ, Teumer, A, Stolk, Lisette, Perry, JRB, Vandenput, L, Coviello, AD, Koster, A, Bell, JT, Bhasin, S, Eriksson, J, Eriksson, A, Ernst, F, Ferrucci, L, Frayling, TM, Glass, D, Grundberg, E, Haring, R, Hedman, AK, Hofman, Bert, Kiel, DP, Kroemer, HK, Liu, YM, Lunetta, KL, Maggio, M, Lorentzon, M, Mangino, M, Melzer, D, Miljkovic, I, Nica, A, Penninx, BWJH, Vasan, RS, Rivadeneira, Fernando, Small, KS, Soranzo, N, Uitterlinden, André, Volzke, H, Wilson, SG, Xi, L, Zhuang, WV, Harris, TB, Murabito, JM, Ohlsson, C, Murray, A, Jong, Frank, Spector, TD, Wallaschofski, H, Zhai, GJ, Teumer, A, Stolk, Lisette, Perry, JRB, Vandenput, L, Coviello, AD, Koster, A, Bell, JT, Bhasin, S, Eriksson, J, Eriksson, A, Ernst, F, Ferrucci, L, Frayling, TM, Glass, D, Grundberg, E, Haring, R, Hedman, AK, Hofman, Bert, Kiel, DP, Kroemer, HK, Liu, YM, Lunetta, KL, Maggio, M, Lorentzon, M, Mangino, M, Melzer, D, Miljkovic, I, Nica, A, Penninx, BWJH, Vasan, RS, Rivadeneira, Fernando, Small, KS, Soranzo, N, Uitterlinden, André, Volzke, H, Wilson, SG, Xi, L, Zhuang, WV, Harris, TB, Murabito, JM, Ohlsson, C, Murray, A, Jong, Frank, Spector, TD, and Wallaschofski, H

Abstract

Dehydroepiandrosterone sulphate (DHEAS) is the most abundant circulating steroid secreted by adrenal glands-yet its function is unknown. Its serum concentration declines significantly with increasing age, which has led to speculation that a relative DHEAS deficiency may contribute to the development of common age-related diseases or diminished longevity. We conducted a meta-analysis of genome-wide association data with 14,846 individuals and identified eight independent common SNPs associated with serum DHEAS concentrations. Genes at or near the identified loci include ZKSCAN5 (rs11761528; p = 3.15610(-36)), SULT2A1 (rs2637125; p = 2.61610(-19)), ARPC1A (rs740160; p = 1.56610(-16)), TRIM4 (rs17277546; p = 4.50610(-11)), BMF (rs7181230; p = 5.44610(-11)), HHEX (rs2497306; p = 4.64610(-9)), BCL2L11 ( rs6738028; p = 1.72610(-8)), and CYP2C9 (rs2185570; p = 2.29610(-8)). These genes are associated with type 2 diabetes, lymphoma, actin filament assembly, drug and xenobiotic metabolism, and zinc finger proteins. Several SNPs were associated with changes in gene expression levels, and the related genes are connected to biological pathways linking DHEAS with ageing. This study provides much needed insight into the function of DHEAS.

Published
2011

9. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI.

Author

Couto Alves A, De Silva NMG, Karhunen V, Sovio U, Das S, Taal HR, Warrington NM, Lewin AM, Kaakinen M, Cousminer DL, Thiering E, Timpson NJ, Bond TA, Lowry E, Brown CD, Estivill X, Lindi V, Bradfield JP, Geller F, Speed D, Coin LJM, Loh M, Barton SJ, Beilin LJ, Bisgaard H, Bønnelykke K, Alili R, Hatoum IJ, Schramm K, Cartwright R, Charles MA, Salerno V, Clément K, Claringbould AAJ, van Duijn CM, Moltchanova E, Eriksson JG, Elks C, Feenstra B, Flexeder C, Franks S, Frayling TM, Freathy RM, Elliott P, Widén E, Hakonarson H, Hattersley AT, Rodriguez A, Banterle M, Heinrich J, Heude B, Holloway JW, Hofman A, Hyppönen E, Inskip H, Kaplan LM, Hedman AK, Läärä E, Prokisch H, Grallert H, Lakka TA, Lawlor DA, Melbye M, Ahluwalia TS, Marinelli M, Millwood IY, Palmer LJ, Pennell CE, Perry JR, Ring SM, Savolainen MJ, Rivadeneira F, Standl M, Sunyer J, Tiesler CMT, Uitterlinden AG, Schierding W, O'Sullivan JM, Prokopenko I, Herzig KH, Smith GD, O'Reilly P, Felix JF, Buxton JL, Blakemore AIF, Ong KK, Jaddoe VWV, Grant SFA, Sebert S, McCarthy MI, and Järvelin MR

Subjects
Adaptor Proteins, Signal Transducing genetics, Adult, Child, Female, Genetic Predisposition to Disease, Genomics, Growth Charts, Humans, Infant, Intracellular Signaling Peptides and Proteins, Longitudinal Studies, Male, Pharmacogenomic Variants, Polymorphism, Single Nucleotide, Receptors, Leptin genetics, Body Mass Index, Genetic Association Studies, Genome-Wide Association Study, Quantitative Trait Loci, Quantitative Trait, Heritable
Abstract

Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).)

Published
2019
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10. Large-scale metabolomic profiling identifies novel biomarkers for incident coronary heart disease.

Author

Ganna A, Salihovic S, Sundström J, Broeckling CD, Hedman AK, Magnusson PK, Pedersen NL, Larsson A, Siegbahn A, Zilmer M, Prenni J, Arnlöv J, Lind L, Fall T, and Ingelsson E

Subjects
Aged, C-Reactive Protein metabolism, Coronary Disease genetics, Female, Follow-Up Studies, Gene Expression Profiling, Genetic Association Studies, Humans, Incidence, Linear Models, Longitudinal Studies, Lysophosphatidylcholines blood, Male, Metabolomics, Middle Aged, Monoglycerides blood, Polymorphism, Single Nucleotide, Prospective Studies, Reproducibility of Results, Risk Assessment, Risk Factors, Sphingomyelins blood, Sweden, Biomarkers blood, Coronary Disease blood, Coronary Disease epidemiology
Abstract

Analyses of circulating metabolites in large prospective epidemiological studies could lead to improved prediction and better biological understanding of coronary heart disease (CHD). We performed a mass spectrometry-based non-targeted metabolomics study for association with incident CHD events in 1,028 individuals (131 events; 10 y. median follow-up) with validation in 1,670 individuals (282 events; 3.9 y. median follow-up). Four metabolites were replicated and independent of main cardiovascular risk factors [lysophosphatidylcholine 18∶1 (hazard ratio [HR] per standard deviation [SD] increment = 0.77, P-value<0.001), lysophosphatidylcholine 18∶2 (HR = 0.81, P-value<0.001), monoglyceride 18∶2 (MG 18∶2; HR = 1.18, P-value = 0.011) and sphingomyelin 28∶1 (HR = 0.85, P-value = 0.015)]. Together they contributed to moderate improvements in discrimination and re-classification in addition to traditional risk factors (C-statistic: 0.76 vs. 0.75; NRI: 9.2%). MG 18∶2 was associated with CHD independently of triglycerides. Lysophosphatidylcholines were negatively associated with body mass index, C-reactive protein and with less evidence of subclinical cardiovascular disease in additional 970 participants; a reverse pattern was observed for MG 18∶2. MG 18∶2 showed an enrichment (P-value = 0.002) of significant associations with CHD-associated SNPs (P-value = 1.2×10-7 for association with rs964184 in the ZNF259/APOA5 region) and a weak, but positive causal effect (odds ratio = 1.05 per SD increment in MG 18∶2, P-value = 0.05) on CHD, as suggested by Mendelian randomization analysis. In conclusion, we identified four lipid-related metabolites with evidence for clinical utility, as well as a causal role in CHD development.

Published
2014
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11. Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements.

Author

Grundberg E, Meduri E, Sandling JK, Hedman AK, Keildson S, Buil A, Busche S, Yuan W, Nisbet J, Sekowska M, Wilk A, Barrett A, Small KS, Ge B, Caron M, Shin SY, Lathrop M, Dermitzakis ET, McCarthy MI, Spector TD, Bell JT, and Deloukas P

Subjects
Body Mass Index, Chromosome Mapping, Epigenomics, Female, Gene Expression Profiling, Gene Expression Regulation, Genome, Human, Humans, Hybridization, Genetic, Oligonucleotide Array Sequence Analysis, Phenotype, Quantitative Trait Loci, Sequence Analysis, DNA, Sulfites metabolism, Twins genetics, Adipose Tissue, DNA Methylation, Polymorphism, Single Nucleotide, Regulatory Sequences, Nucleic Acid
Abstract

Epigenetic modifications such as DNA methylation play a key role in gene regulation and disease susceptibility. However, little is known about the genome-wide frequency, localization, and function of methylation variation and how it is regulated by genetic and environmental factors. We utilized the Multiple Tissue Human Expression Resource (MuTHER) and generated Illumina 450K adipose methylome data from 648 twins. We found that individual CpGs had low variance and that variability was suppressed in promoters. We noted that DNA methylation variation was highly heritable (h(2)median = 0.34) and that shared environmental effects correlated with metabolic phenotype-associated CpGs. Analysis of methylation quantitative-trait loci (metQTL) revealed that 28% of CpGs were associated with nearby SNPs, and when overlapping them with adipose expression quantitative-trait loci (eQTL) from the same individuals, we found that 6% of the loci played a role in regulating both gene expression and DNA methylation. These associations were bidirectional, but there were pronounced negative associations for promoter CpGs. Integration of metQTL with adipose reference epigenomes and disease associations revealed significant enrichment of metQTL overlapping metabolic-trait or disease loci in enhancers (the strongest effects were for high-density lipoprotein cholesterol and body mass index [BMI]). We followed up with the BMI SNP rs713586, a cg01884057 metQTL that overlaps an enhancer upstream of ADCY3, and used bisulphite sequencing to refine this region. Our results showed widespread population invariability yet sequence dependence on adipose DNA methylation but that incorporating maps of regulatory elements aid in linking CpG variation to gene regulation and disease risk in a tissue-dependent manner., (Copyright © 2013 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Published
2013
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12. Gene expression changes with age in skin, adipose tissue, blood and brain.

Author

Glass D, Viñuela A, Davies MN, Ramasamy A, Parts L, Knowles D, Brown AA, Hedman AK, Small KS, Buil A, Grundberg E, Nica AC, Di Meglio P, Nestle FO, Ryten M, Durbin R, McCarthy MI, Deloukas P, Dermitzakis ET, Weale ME, Bataille V, and Spector TD

Subjects
Adult, Age Distribution, Aged, Aged, 80 and over, Cell Line, Databases, Genetic, Female, Genome-Wide Association Study, Genotype, Humans, Middle Aged, Polymorphism, Single Nucleotide genetics, Quantitative Trait Loci genetics, Transcription, Genetic, Adipose Tissue metabolism, Aging blood, Aging genetics, Brain metabolism, Gene Expression Regulation, Developmental, Skin metabolism
Abstract

Background: Previous studies have demonstrated that gene expression levels change with age. These changes are hypothesized to influence the aging rate of an individual. We analyzed gene expression changes with age in abdominal skin, subcutaneous adipose tissue and lymphoblastoid cell lines in 856 female twins in the age range of 39-85 years. Additionally, we investigated genotypic variants involved in genotype-by-age interactions to understand how the genomic regulation of gene expression alters with age., Results: Using a linear mixed model, differential expression with age was identified in 1,672 genes in skin and 188 genes in adipose tissue. Only two genes expressed in lymphoblastoid cell lines showed significant changes with age. Genes significantly regulated by age were compared with expression profiles in 10 brain regions from 100 postmortem brains aged 16 to 83 years. We identified only one age-related gene common to the three tissues. There were 12 genes that showed differential expression with age in both skin and brain tissue and three common to adipose and brain tissues., Conclusions: Skin showed the most age-related gene expression changes of all the tissues investigated, with many of the genes being previously implicated in fatty acid metabolism, mitochondrial activity, cancer and splicing. A significant proportion of age-related changes in gene expression appear to be tissue-specific with only a few genes sharing an age effect in expression across tissues. More research is needed to improve our understanding of the genetic influences on aging and the relationship with age-related diseases.

Published
2013
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13. The presence of methylation quantitative trait loci indicates a direct genetic influence on the level of DNA methylation in adipose tissue.

Author

Drong AW, Nicholson G, Hedman AK, Meduri E, Grundberg E, Small KS, Shin SY, Bell JT, Karpe F, Soranzo N, Spector TD, McCarthy MI, Deloukas P, Rantalainen M, and Lindgren CM

Subjects
Adult, Aged, Cohort Studies, CpG Islands, Female, Gene Frequency, Genome-Wide Association Study, Genotype, Humans, Male, Metabolic Syndrome genetics, Middle Aged, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, RNA, Messenger genetics, RNA, Messenger metabolism, Transcriptome, DNA Methylation, Quantitative Trait Loci, Subcutaneous Fat, Abdominal metabolism
Abstract

Genetic variants that associate with DNA methylation at CpG sites (methylation quantitative trait loci, meQTLs) offer a potential biological mechanism of action for disease associated SNPs. We investigated whether meQTLs exist in abdominal subcutaneous adipose tissue (SAT) and if CpG methylation associates with metabolic syndrome (MetSyn) phenotypes. We profiled 27,718 genomic regions in abdominal SAT samples of 38 unrelated individuals using differential methylation hybridization (DMH) together with genotypes at 5,227,243 SNPs and expression of 17,209 mRNA transcripts. Validation and replication of significant meQTLs was pursued in an independent cohort of 181 female twins. We find that, at 5% false discovery rate, methylation levels of 149 DMH regions associate with at least one SNP in a ±500 kilobase cis-region in our primary study. We sought to validate 19 of these in the replication study and find that five of these significantly associate with the corresponding meQTL SNPs from the primary study. We find that none of the 149 meQTL top SNPs is a significant expression quantitative trait locus in our expression data, but we observed association between expression levels of two mRNA transcripts and cis-methylation status. Our results indicate that DNA CpG methylation in abdominal SAT is partly under genetic control. This study provides a starting point for future investigations of DNA methylation in adipose tissue.

Published
2013
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14. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population.

Author

Bell JT, Tsai PC, Yang TP, Pidsley R, Nisbet J, Glass D, Mangino M, Zhai G, Zhang F, Valdes A, Shin SY, Dempster EL, Murray RM, Grundberg E, Hedman AK, Nica A, Small KS, Dermitzakis ET, McCarthy MI, Mill J, Spector TD, and Deloukas P

Subjects
Adult, Aged, Aged, 80 and over, Cellular Senescence genetics, CpG Islands genetics, Female, Gene-Environment Interaction, Genetic Association Studies, Genome, Human, Genome-Wide Association Study, Humans, Middle Aged, Twins, Monozygotic genetics, Aging genetics, DNA Methylation genetics, Epigenesis, Genetic genetics, Longevity genetics, Quantitative Trait Loci genetics
Abstract

Age-related changes in DNA methylation have been implicated in cellular senescence and longevity, yet the causes and functional consequences of these variants remain unclear. To elucidate the role of age-related epigenetic changes in healthy ageing and potential longevity, we tested for association between whole-blood DNA methylation patterns in 172 female twins aged 32 to 80 with age and age-related phenotypes. Twin-based DNA methylation levels at 26,690 CpG-sites showed evidence for mean genome-wide heritability of 18%, which was supported by the identification of 1,537 CpG-sites with methylation QTLs in cis at FDR 5%. We performed genome-wide analyses to discover differentially methylated regions (DMRs) for sixteen age-related phenotypes (ap-DMRs) and chronological age (a-DMRs). Epigenome-wide association scans (EWAS) identified age-related phenotype DMRs (ap-DMRs) associated with LDL (STAT5A), lung function (WT1), and maternal longevity (ARL4A, TBX20). In contrast, EWAS for chronological age identified hundreds of predominantly hyper-methylated age DMRs (490 a-DMRs at FDR 5%), of which only one (TBX20) was also associated with an age-related phenotype. Therefore, the majority of age-related changes in DNA methylation are not associated with phenotypic measures of healthy ageing in later life. We replicated a large proportion of a-DMRs in a sample of 44 younger adult MZ twins aged 20 to 61, suggesting that a-DMRs may initiate at an earlier age. We next explored potential genetic and environmental mechanisms underlying a-DMRs and ap-DMRs. Genome-wide overlap across cis-meQTLs, genotype-phenotype associations, and EWAS ap-DMRs identified CpG-sites that had cis-meQTLs with evidence for genotype-phenotype association, where the CpG-site was also an ap-DMR for the same phenotype. Monozygotic twin methylation difference analyses identified one potential environmentally-mediated ap-DMR associated with total cholesterol and LDL (CSMD1). Our results suggest that in a small set of genes DNA methylation may be a candidate mechanism of mediating not only environmental, but also genetic effects on age-related phenotypes., Competing Interests: The authors have declared that no competing interests exist.

Published
2012
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15. Transcriptional profiling of serogroup B Neisseria meningitidis growing in human blood: an approach to vaccine antigen discovery.

Author

Hedman ÅK, Li MS, Langford PR, and Kroll JS

Subjects
Adult, Flow Cytometry, Humans, Meningococcal Infections blood, Meningococcal Infections microbiology, Oligonucleotide Array Sequence Analysis, Principal Component Analysis, Reverse Transcriptase Polymerase Chain Reaction, Neisseria meningitidis, Serogroup B genetics, Transcriptome genetics
Abstract

Neisseria meningitidis is a nasopharyngeal commensal of humans which occasionally invades the blood to cause septicaemia. The transcriptome of N. meningitidis strain MC58 grown in human blood for up to 4 hours was determined and around 10% of the genome was found to be differentially regulated. The nuo, pet and atp operons, involved in energy metabolism, were up-regulated, while many house-keeping genes were down-regulated. Genes encoding protein chaperones and proteases, involved in the stress response; complement resistant genes encoding enzymes for LOS sialylation and biosynthesis; and fHbp (NMB1870) and nspA (NMB0663), encoding vaccine candidates, were all up-regulated. Genes for glutamate uptake and metabolism, and biosynthesis of purine and pyrimidine were also up-regulated. Blood grown meningococci are under stress and undergo a metabolic adaptation and energy conservation strategy. The localisation of four putative outer membrane proteins encoded by genes found to be up-regulated in blood was assessed by FACS using polyclonal mouse antisera, and one (NMB0390) showed evidence of surface expression, supporting its vaccine candidacy.

Published
2012
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16. Eight common genetic variants associated with serum DHEAS levels suggest a key role in ageing mechanisms.

Author

Zhai G, Teumer A, Stolk L, Perry JR, Vandenput L, Coviello AD, Koster A, Bell JT, Bhasin S, Eriksson J, Eriksson A, Ernst F, Ferrucci L, Frayling TM, Glass D, Grundberg E, Haring R, Hedman AK, Hofman A, Kiel DP, Kroemer HK, Liu Y, Lunetta KL, Maggio M, Lorentzon M, Mangino M, Melzer D, Miljkovic I, Nica A, Penninx BW, Vasan RS, Rivadeneira F, Small KS, Soranzo N, Uitterlinden AG, Völzke H, Wilson SG, Xi L, Zhuang WV, Harris TB, Murabito JM, Ohlsson C, Murray A, de Jong FH, Spector TD, and Wallaschofski H

Subjects
Actin-Related Protein 2-3 Complex genetics, Actin-Related Protein 2-3 Complex metabolism, Adaptor Proteins, Signal Transducing genetics, Adaptor Proteins, Signal Transducing metabolism, Adult, Aged, Aging genetics, Apoptosis Regulatory Proteins genetics, Aryl Hydrocarbon Hydroxylases genetics, Aryl Hydrocarbon Hydroxylases metabolism, Bcl-2-Like Protein 11, Cytochrome P-450 CYP2C9, DNA-Binding Proteins, Female, Gene Expression, Genome-Wide Association Study, Homeodomain Proteins genetics, Homeodomain Proteins metabolism, Humans, Kruppel-Like Transcription Factors genetics, Kruppel-Like Transcription Factors metabolism, Linkage Disequilibrium, Liver metabolism, Male, Membrane Proteins genetics, Middle Aged, Proto-Oncogene Proteins genetics, Sulfotransferases genetics, Sulfotransferases metabolism, Transcription Factors genetics, Transcription Factors metabolism, White People genetics, Young Adult, Aging blood, Apoptosis Regulatory Proteins metabolism, Dehydroepiandrosterone Sulfate blood, Membrane Proteins metabolism, Polymorphism, Single Nucleotide, Proto-Oncogene Proteins metabolism
Abstract

Dehydroepiandrosterone sulphate (DHEAS) is the most abundant circulating steroid secreted by adrenal glands--yet its function is unknown. Its serum concentration declines significantly with increasing age, which has led to speculation that a relative DHEAS deficiency may contribute to the development of common age-related diseases or diminished longevity. We conducted a meta-analysis of genome-wide association data with 14,846 individuals and identified eight independent common SNPs associated with serum DHEAS concentrations. Genes at or near the identified loci include ZKSCAN5 (rs11761528; p = 3.15 × 10(-36)), SULT2A1 (rs2637125; p =  2.61 × 10(-19)), ARPC1A (rs740160; p =  1.56 × 10(-16)), TRIM4 (rs17277546; p =  4.50 × 10(-11)), BMF (rs7181230; p = 5.44 × 10(-11)), HHEX (rs2497306; p =  4.64 × 10(-9)), BCL2L11 (rs6738028; p = 1.72 × 10(-8)), and CYP2C9 (rs2185570; p = 2.29 × 10(-8)). These genes are associated with type 2 diabetes, lymphoma, actin filament assembly, drug and xenobiotic metabolism, and zinc finger proteins. Several SNPs were associated with changes in gene expression levels, and the related genes are connected to biological pathways linking DHEAS with ageing. This study provides much needed insight into the function of DHEAS., Competing Interests: The authors have declared that no competing interests exist.

Published
2011
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17. The architecture of gene regulatory variation across multiple human tissues: the MuTHER study.

Author

Nica AC, Parts L, Glass D, Nisbet J, Barrett A, Sekowska M, Travers M, Potter S, Grundberg E, Small K, Hedman AK, Bataille V, Tzenova Bell J, Surdulescu G, Dimas AS, Ingle C, Nestle FO, di Meglio P, Min JL, Wilk A, Hammond CJ, Hassanali N, Yang TP, Montgomery SB, O'Rahilly S, Lindgren CM, Zondervan KT, Soranzo N, Barroso I, Durbin R, Ahmadi K, Deloukas P, McCarthy MI, Dermitzakis ET, and Spector TD

Subjects
Cell Line, Cells, Cultured, Data Interpretation, Statistical, Female, Gene Expression Profiling, Genotype, Humans, Organ Specificity genetics, Phenotype, Twins, Adipose Tissue metabolism, Genes, Regulator genetics, Quantitative Trait Loci genetics, Skin metabolism
Abstract

While there have been studies exploring regulatory variation in one or more tissues, the complexity of tissue-specificity in multiple primary tissues is not yet well understood. We explore in depth the role of cis-regulatory variation in three human tissues: lymphoblastoid cell lines (LCL), skin, and fat. The samples (156 LCL, 160 skin, 166 fat) were derived simultaneously from a subset of well-phenotyped healthy female twins of the MuTHER resource. We discover an abundance of cis-eQTLs in each tissue similar to previous estimates (858 or 4.7% of genes). In addition, we apply factor analysis (FA) to remove effects of latent variables, thus more than doubling the number of our discoveries (1,822 eQTL genes). The unique study design (Matched Co-Twin Analysis--MCTA) permits immediate replication of eQTLs using co-twins (93%-98%) and validation of the considerable gain in eQTL discovery after FA correction. We highlight the challenges of comparing eQTLs between tissues. After verifying previous significance threshold-based estimates of tissue-specificity, we show their limitations given their dependency on statistical power. We propose that continuous estimates of the proportion of tissue-shared signals and direct comparison of the magnitude of effect on the fold change in expression are essential properties that jointly provide a biologically realistic view of tissue-specificity. Under this framework we demonstrate that 30% of eQTLs are shared among the three tissues studied, while another 29% appear exclusively tissue-specific. However, even among the shared eQTLs, a substantial proportion (10%-20%) have significant differences in the magnitude of fold change between genotypic classes across tissues. Our results underline the need to account for the complexity of eQTL tissue-specificity in an effort to assess consequences of such variants for complex traits., Competing Interests: The authors have declared that no competing interests exist.

Published
2011
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