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1. Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity.

Author

Hägg S, Ganna A, Van Der Laan SW, Esko T, Pers TH, Locke AE, Berndt SI, Justice AE, Kahali B, Siemelink MA, Pasterkamp G, Strachan DP, Speliotes EK, North KE, Loos RJ, Hirschhorn JN, Pawitan Y, and Ingelsson E

Subjects
Female, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, White People genetics, Body Mass Index, Genetic Loci, Genetic Predisposition to Disease, Obesity genetics
Abstract

To date, genome-wide association studies (GWASs) have identified >100 loci with single variants associated with body mass index (BMI). This approach may miss loci with high allelic heterogeneity; therefore, the aim of the present study was to use gene-based meta-analysis to identify regions with high allelic heterogeneity to discover additional obesity susceptibility loci. We included GWAS data from 123 865 individuals of European descent from 46 cohorts in Stage 1 and Metabochip data from additional 103 046 individuals from 43 cohorts in Stage 2, all within the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Each cohort was tested for association between ∼2.4 million (Stage 1) or ∼200 000 (Stage 2) imputed or genotyped single variants and BMI, and summary statistics were subsequently meta-analyzed in 17 941 genes. We used the 'VErsatile Gene-based Association Study' (VEGAS) approach to assign variants to genes and to calculate gene-based P-values based on simulations. The VEGAS method was applied to each cohort separately before a gene-based meta-analysis was performed. In Stage 1, two known (FTO and TMEM18) and six novel (PEX2, MTFR2, SSFA2, IARS2, CEP295 and TXNDC12) loci were associated with BMI (P < 2.8 × 10(-6) for 17 941 gene tests). We confirmed all loci, and six of them were gene-wide significant in Stage 2 alone. We provide biological support for the loci by pathway, expression and methylation analyses. Our results indicate that gene-based meta-analysis of GWAS provides a useful strategy to find loci of interest that were not identified in standard single-marker analyses due to high allelic heterogeneity., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Published
2015
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2. Genome-wide meta-analysis of 241,258 adults accounting for smoking behaviour identifies novel loci for obesity traits

Author

Justice, AE, Winkler, TW, Feitosa, MF, Graff, M, Fisher, VA, Young, K, Barata, L, Deng, X, Czajkowski, J, Hadley, D, Ngwa, JS, Ahluwalia, TS, Chu, AY, Heard-Costa, NL, Lim, E, Perez, J, Eicher, JD, Kutalik, Z, Xue, L, Mahajan, A, Renström, F, Wu, J, Qi, Q, Ahmad, S, Alfred, T, Amin, N, Bielak, LF, Bonnefond, A, Bragg, J, Cadby, G, Chittani, M, Coggeshall, S, Corre, T, Direk, N, Eriksson, J, Fischer, K, Gorski, M, Neergaard Harder, M, Horikoshi, M, Huang, T, Huffman, JE, Jackson, AU, Justesen, JM, Kanoni, S, Kinnunen, L, Kleber, ME, Komulainen, P, Kumari, M, Lim, U, Luan, J, Lyytikäinen, L-P, Mangino, M, Manichaikul, A, Marten, J, Middelberg, RPS, Müller-Nurasyid, M, Navarro, P, Pérusse, L, Pervjakova, N, Sarti, C, Smith, AV, Smith, JA, Stančáková, A, Strawbridge, RJ, Stringham, HM, Sung, YJ, Tanaka, T, Teumer, A, Trompet, S, Van Der Laan, SW, Van Der Most, PJ, Van Vliet-Ostaptchouk, JV, Vedantam, SL, Verweij, N, Vink, JM, Vitart, V, Wu, Y, Yengo, L, Zhang, W, Hua Zhao, J, Zimmermann, ME, Zubair, N, Abecasis, GR, Adair, LS, Afaq, S, Afzal, U, Bakker, SJL, Bartz, TM, Beilby, J, Bergman, RN, Bergmann, S, Biffar, R, Blangero, J, Boerwinkle, E, Bonnycastle, LL, Bottinger, E, Braga, D, Buckley, BM, Buyske, S, Campbell, H, Chambers, JC, Collins, FS, Curran, JE, De Borst, GJ, De Craen, AJM, De Geus, EJC, Dedoussis, G, Delgado, GE, Den Ruijter, HM, Eiriksdottir, G, Eriksson, AL, Esko, T, Faul, JD, Ford, I, Forrester, T, Gertow, K, Gigante, B, Glorioso, N, Gong, J, Grallert, H, Grammer, TB, Grarup, N, Haitjema, S, Hallmans, G, Hamsten, A, Hansen, T, Harris, TB, Hartman, CA, Hassinen, M, Hastie, ND, Heath, AC, Hernandez, D, Hindorff, L, Hocking, LJ, Hollensted, M, Holmen, OL, Homuth, G, Jan Hottenga, J, Huang, J, Hung, J, Hutri-Kähönen, N, Ingelsson, E, James, AL, Jansson, J-O, Jarvelin, M-R, Jhun, MA, Jørgensen, ME, Juonala, M, Kähönen, M, Karlsson, M, Koistinen, HA, Kolcic, I, Kolovou, G, Kooperberg, C, Krämer, BK, Kuusisto, J, Kvaløy, K, Lakka, TA, Langenberg, C, Launer, LJ, Leander, K, Lee, NR, Lind, L, Lindgren, CM, Linneberg, A, Lobbens, S, Loh, M, Lorentzon, M, Luben, R, Lubke, G, Ludolph-Donislawski, A, Lupoli, S, Madden, PAF, Männikkö, R, Marques-Vidal, P, Martin, NG, McKenzie, CA, McKnight, B, Mellström, D, Menni, C, Montgomery, GW, Musk, AB, Narisu, N, Nauck, M, Nolte, IM, Oldehinkel, AJ, Olden, M, Ong, KK, Padmanabhan, S, Peyser, PA, Pisinger, C, Porteous, DJ, Raitakari, OT, Rankinen, T, Rao, DC, Rasmussen-Torvik, LJ, Rawal, R, Rice, T, Ridker, PM, Rose, LM, Bien, SA, Rudan, I, Sanna, S, Sarzynski, MA, Sattar, N, Savonen, K, Schlessinger, D, Scholtens, S, Schurmann, C, Scott, RA, Sennblad, B, Siemelink, MA, Silbernagel, G, Slagboom, PE, Snieder, H, Staessen, JA, Stott, DJ, Swertz, MA, Swift, AJ, Taylor, KD, Tayo, BO, Thorand, B, Thuillier, D, Tuomilehto, J, Uitterlinden, AG, Vandenput, L, Vohl, M-C, Völzke, H, Vonk, JM, Waeber, G, Waldenberger, M, Westendorp, RGJ, Wild, S, Willemsen, G, Wolffenbuttel, BHR, Wong, A, Wright, AF, Zhao, W, Zillikens, MC, Baldassarre, D, Balkau, B, Bandinelli, S, Böger, CA, Boomsma, DI, Bouchard, C, Bruinenberg, M, Chasman, DI, Chen, Y-D, Chines, PS, Cooper, RS, Cucca, F, Cusi, D, Faire, UD, Ferrucci, L, Franks, PW, Froguel, P, Gordon-Larsen, P, Grabe, H-J, Gudnason, V, Haiman, CA, Hayward, C, Hveem, K, Johnson, AD, Wouter Jukema, J, Kardia, SLR, Kivimaki, M, Kooner, JS, Kuh, D, Laakso, M, Lehtimäki, T, Marchand, LL, März, W, McCarthy, MI, Metspalu, A, Morris, AP, Ohlsson, C, Palmer, LJ, Pasterkamp, G, Pedersen, O, Peters, A, Peters, U, Polasek, O, Psaty, BM, Qi, L, Rauramaa, R, Smith, BH, Sørensen, TIA, Strauch, K, Tiemeier, H, Tremoli, E, Van Der Harst, P, Vestergaard, H, Vollenweider, P, Wareham, NJ, Weir, DR, Whitfield, JB, Wilson, JF, Tyrrell, J, Frayling, TM, Barroso, I, Boehnke, M, Deloukas, P, Fox, CS, Hirschhorn, JN, Hunter, DJ, Spector, TD, Strachan, DP, Van Duijn, CM, Heid, IM, Mohlke, KL, Marchini, J, Loos, RJF, Kilpeläinen, TO, Liu, C-T, Borecki, IB, North, KE, and Cupples, LA

Subjects
Adult, Waist-Hip Ratio, Quantitative Trait Loci, Smoking, Epistasis, Genetic, Polymorphism, Single Nucleotide, 3. Good health, Body Mass Index, Phenotype, Body Fat Distribution, Humans, Genetic Predisposition to Disease, Obesity, Waist Circumference, Adiposity, Genome-Wide Association Study
Abstract

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution.

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