Title | A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. |
Publication Type | Journal Article |
Year of Publication | 2016 |
Authors | Scott, RA, Freitag, DF, Li, L, Chu, AY, Surendran, P, Young, R, Grarup, N, Stančáková, A, Chen, Y, Varga, TV, Yaghootkar, H, Luan, J'an, Zhao, JHua, Willems, SM, Wessel, J, Wang, S, Maruthur, N, Michailidou, K, Pirie, A, van der Lee, SJ, Gillson, C, Olama, AAmin Al, Amouyel, P, Arriola, L, Arveiler, D, Aviles-Olmos, I, Balkau, B, Barricarte, A, Barroso, I, Garcia, SBenlloch, Bis, JC, Blankenberg, S, Boehnke, M, Boeing, H, Boerwinkle, E, Borecki, IB, Bork-Jensen, J, Bowden, S, Caldas, C, Caslake, M, L Cupples, A, Cruchaga, C, Czajkowski, J, Hoed, Mden, Dunn, JA, Earl, HM, Ehret, GB, Ferrannini, E, Ferrieres, J, Foltynie, T, Ford, I, Forouhi, NG, Gianfagna, F, Gonzalez, C, Grioni, S, Hiller, L, Jansson, J-H, Jørgensen, ME, J Jukema, W, Kaaks, R, Kee, F, Kerrison, ND, Key, TJ, Kontto, J, Kote-Jarai, Z, Kraja, AT, Kuulasmaa, K, Kuusisto, J, Linneberg, A, Liu, C, Marenne, G, Mohlke, KL, Morris, AP, Muir, K, Müller-Nurasyid, M, Munroe, PB, Navarro, C, Nielsen, SF, Nilsson, PM, Nordestgaard, BG, Packard, CJ, Palli, D, Panico, S, Peloso, GM, Perola, M, Peters, A, Poole, CJ, J Quirós, R, Rolandsson, O, Sacerdote, C, Salomaa, V, Sánchez, M-J, Sattar, N, Sharp, SJ, Sims, R, Slimani, N, Smith, JA, Thompson, DJ, Trompet, S, Tumino, R, van der A, DL, van der Schouw, YT, Virtamo, J, Walker, M, Walter, K, Abraham, JE, Amundadottir, LT, Aponte, JL, Butterworth, AS, Dupuis, J, Easton, DF, Eeles, RA, Erdmann, J, Franks, PW, Frayling, TM, Hansen, T, Howson, JMM, Jørgensen, T, Kooner, J, Laakso, M, Langenberg, C, McCarthy, MI, Pankow, JS, Pedersen, O, Riboli, E, Rotter, JI, Saleheen, D, Samani, NJ, Schunkert, H, Vollenweider, P, O'Rahilly, S, Deloukas, P, Danesh, J, Goodarzi, MO, Kathiresan, S, Meigs, JB, Ehm, MG, Wareham, NJ, Waterworth, DM |
Corporate Authors | CVD50 consortium, GERAD_EC Consortium, Neurology Working Group of the Cohorts for Heart, Aging Research in Genomic Epidemiology (CHARGE), Alzheimer’s Disease Genetics Consortium, Pancreatic Cancer Cohort Consortium, European Prospective Investigation into Cancer and Nutrition–Cardiovascular Disease (EPIC-CVD), EPIC-InterAct, CHARGE Consortium, CHD Exome+ Consortium, CARDIOGRAM Exome Consortium |
Journal | Sci Transl Med |
Volume | 8 |
Issue | 341 |
Pagination | 341ra76 |
Date Published | 2016 Jun 01 |
ISSN | 1946-6242 |
Abstract | Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11,806 individuals by targeted exome sequencing and follow-up in 39,979 individuals by targeted genotyping, with additional in silico follow-up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process. |
DOI | 10.1126/scitranslmed.aad3744 |
Alternate Journal | Sci Transl Med |
PubMed ID | 27252175 |
Grant List | R01 DK078616 / DK / NIDDK NIH HHS / United States UL1 RR025005 / RR / NCRR NIH HHS / United States U01 AG032984 / AG / NIA NIH HHS / United States R01 HL059367 / HL / NHLBI NIH HHS / United States R01 HL086694 / HL / NHLBI NIH HHS / United States U01 HG004402 / HG / NHGRI NIH HHS / United States UL1 TR000124 / TR / NCATS NIH HHS / United States P30 DK063491 / DK / NIDDK NIH HHS / United States R01 AG033193 / AG / NIA NIH HHS / United States K24 DK080140 / DK / NIDDK NIH HHS / United States U01 DK078616 / DK / NIDDK NIH HHS / United States R01 HL087641 / HL / NHLBI NIH HHS / United States |