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1.
Justice, A.E.* et al.: Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution. Nat. Genet. 51, 452–469 (2019)
2.
Warrington, N.M.* et al.: Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors. Nat. Genet. 51, 804-814 (2019)
3.
Dawed, A.Y.* et al.: GLP-1 receptor variants markedly differentiate glycaemic response to GLP-1 receptor agonists: A direct study. Science 123, 13-14 (2018)
4.
Flannick, J.* et al.: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci. Data 5:180002 (2018)
5.
Mahajan, A.* et al.: Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 50, 559-571 (2018)
6.
Mahajan, A.* et al.: Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505-1513 (2018)
7.
van Zuydam, N.R.* et al.: A genome-wide association study of diabetic kidney disease in subjects with type 2 diabetes. Diabetes 67, 1414-1427 (2018)
8.
Dawed, A.Y.* et al.: GLP-1 receptor variants markedly differentiate glycaemic response to GLP-1 receptor agonists: A DIRECT study. Diabetologia 60, S393-S393 (2017)
9.
Flannick, J.* et al.: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci. Data 4:170179 (2017)
10.
Manning, A.* et al.: A low-frequency inactivating Akt2 variant enriched in the Finnish population is associated with fasting insulin levels and type 2 diabetes risk. Diabetes 66, 2019-2032 (2017)
11.
Wheeler, E.* et al.: Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis. PLoS Med. 14:e1002383 (2017)
12.
Fuchsberger, C.* et al.: The genetic architecture of type 2 diabetes. Nature 536, 41-47 (2016)
13.
Horikoshi, M.* et al.: Genome-wide associations for birth weight and correlations with adult disease. Nature 538, 248-252 (2016)
14.
Stitziel, N.O.* et al.: Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease. N. Engl. J. Med. 374, 1134-1144 (2016)
15.
Stitziel, N.O.* et al.: Coding variation in ANGPTL4, LPL, and SVEP1 and the risk of coronary disease (vol 374, pg 1134, 2016). N. Engl. J. Med. 374, 1898-1898 (2016)
16.
Surendran, P.* et al.: Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat. Genet. 48, 1151-1161 (2016)
17.
Horikoshi, M.* et al.: Discovery and fine-mapping of glycaemic and obesity-related trait loci using high-density imputation. PLoS Genet. 11:e1005230 (2015)
18.
Locke, A.E.* et al.: Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197-206 (2015)
19.
Mahajan, A.* et al.: Identification and functional characterization of G6PC2 coding variants influencing glycemic traits define an effector transcript at the G6PC2-ABCB11 locus. PLoS Genet. 11:e1004876 (2015)
20.
Shungin, D.* et al.: New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187-196 (2015)