PuSH - Publication Server of Helmholtz Zentrum München

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1.
Bentley, A.R.* et al.: Multi-ancestry genome-wide gene–smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nat. Genet. 51, 636-648 (2019)
2.
de Vries, P.S.* et al.: Multi-ancestry genome-wide association study of lipid levels incorporating gene-alcohol interactions. Am. J. Epidemiol., accepted (2019)
3.
Erzurumluoglu, A.M.* et al.: Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Mol. Psychiatry, accepted (2019)
4.
Kilpeläinen, T.O.* et al.: Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity. Nat. Commun. 10:376 (2019)
5.
Feitosa, M.F.* et al.: Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570K individuals across multiple ancestries. PLoS ONE 13:e0198166 (2018)
6.
Flannick, J.* et al.: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls. Sci. Data 5:180002 (2018)
7.
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)
8.
Sung, Y.J.* et al.: A large-scale multi-ancestry genome-wide study accounting for smoking behavior identifies multiple significant loci for blood pressure. Am. J. Hum. Genet. 102, 375-400 (2018)
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.
Shungin, D.* et al.: Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions. PLoS Genet. 13:e1006812 (2017)
12.
Fuchsberger, C.* et al.: The genetic architecture of type 2 diabetes. Nature 536, 41-47 (2016)
13.
Kanoni, S.* et al.: Analysis with the exome array identifies multiple new independent variants in lipid loci. Hum. Mol. Genet. 25, 4094-4106 (2016)
14.
Scott, R.A.* et al.: A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. Sci. Transl. Med. 8:341ra76 (2016)
15.
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)
16.
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)
17.
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)
18.
Nead, K.T.* et al.: Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: A systematic review and meta-analysis with evidence from up to 331 175 individuals. Hum. Mol. Genet. 24, 3582-3594 (2015)
19.
Albrechtsen, A.* et al.: Exome sequencing-driven discovery of coding polymorphisms associated with common metabolic phenotypes. Diabetologia 56, 298-310 (2013)
20.
Scott, R.A.* et al.: Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991-1005 (2012)