186 related articles for article (PubMed ID: 30219835)
1. Polygenic approaches to detect gene-environment interactions when external information is unavailable.
Lin WY; Huang CC; Liu YL; Tsai SJ; Kuo PH
Brief Bioinform; 2019 Nov; 20(6):2236-2252. PubMed ID: 30219835
[TBL] [Abstract][Full Text] [Related]
2. Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests.
Lin WY; Huang CC; Liu YL; Tsai SJ; Kuo PH
Front Genet; 2018; 9():715. PubMed ID: 30693016
[TBL] [Abstract][Full Text] [Related]
3. Comparison of weighting approaches for genetic risk scores in gene-environment interaction studies.
Hüls A; Krämer U; Carlsten C; Schikowski T; Ickstadt K; Schwender H
BMC Genet; 2017 Dec; 18(1):115. PubMed ID: 29246113
[TBL] [Abstract][Full Text] [Related]
4. Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression.
Hüls A; Ickstadt K; Schikowski T; Krämer U
BMC Genet; 2017 Jun; 18(1):55. PubMed ID: 28606108
[TBL] [Abstract][Full Text] [Related]
5. Gene-environment interactions related to blood pressure traits in two community-based Korean cohorts.
Lim JE; Kim HO; Rhee SY; Kim MK; Kim YJ; Oh B
Genet Epidemiol; 2019 Jun; 43(4):402-413. PubMed ID: 30770579
[TBL] [Abstract][Full Text] [Related]
6. Gene-Environment Interactions and the Risk of Barrett's Esophagus in Three US Cohorts.
Crous-Bou M; Jovani M; De Vivo I; Jacobson BC
Am J Gastroenterol; 2019 Jun; 114(6):893-899. PubMed ID: 30950840
[TBL] [Abstract][Full Text] [Related]
7. The case-only test for gene-environment interaction is not uniformly powerful: an empirical example.
Wu C; Chang J; Ma B; Miao X; Zhou Y; Liu Y; Li Y; Wu T; Hu Z; Shen H; Jia W; Zeng Y; Lin D; Kraft P
Genet Epidemiol; 2013 May; 37(4):402-7. PubMed ID: 23595356
[TBL] [Abstract][Full Text] [Related]
8. Joint Effects of GWAS SNPs in Coagulation System Confer Risk to Hypertensive Intracerebral Hemorrhage.
Cao Y; Tian M; Fang Q; Wen Z; Wang W; Ding H; Wang DW
Neuromolecular Med; 2017 Sep; 19(2-3):395-405. PubMed ID: 28718048
[TBL] [Abstract][Full Text] [Related]
9. Using Genetic Risk Score Approaches to Infer Whether an Environmental Factor Attenuates or Exacerbates the Adverse Influence of a Candidate Gene.
Lin WY; Lin YS; Chan CC; Liu YL; Tsai SJ; Kuo PH
Front Genet; 2020; 11():331. PubMed ID: 32457790
[TBL] [Abstract][Full Text] [Related]
10. Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using biofilter, and gene-environment interactions using the Phenx Toolkit*.
Pendergrass SA; Verma SS; Hall MA; Holzinger ER; Moore CB; Wallace JR; Dudek SM; Huggins W; Kitchner T; Waudby C; Berg R; Mccarty CA; Ritchie MD
Pac Symp Biocomput; 2015; ():495-505. PubMed ID: 25741542
[TBL] [Abstract][Full Text] [Related]
11. Using Bayes model averaging to leverage both gene main effects and G × E interactions to identify genomic regions in genome-wide association studies.
Moss LC; Gauderman WJ; Lewinger JP; Conti DV
Genet Epidemiol; 2019 Mar; 43(2):150-165. PubMed ID: 30456811
[TBL] [Abstract][Full Text] [Related]
12. The effect of leptin on blood pressure considering smoking status: a Mendelian randomization study.
Shen L; Cordero JF; Wang JS; Shen Y; Zhang R; Qi Y; Li C
Hypertens Res; 2020 Apr; 43(4):342-349. PubMed ID: 31831886
[TBL] [Abstract][Full Text] [Related]
13. Interactions of established risk factors and a GWAS-based genetic risk score on the risk of venous thromboembolism.
Crous-Bou M; De Vivo I; Camargo CA; Varraso R; Grodstein F; Jensen MK; Kraft P; Goldhaber SZ; Lindström S; Kabrhel C
Thromb Haemost; 2016 Sep; 116(4):705-13. PubMed ID: 27305932
[TBL] [Abstract][Full Text] [Related]
14. Deciphering Genome Environment Wide Interactions Using Exposed Subjects Only.
Zhao LP; Fan W; Goodman G; Radich J; Martin P
Genet Epidemiol; 2015 Jul; 39(5):334-46. PubMed ID: 25694100
[TBL] [Abstract][Full Text] [Related]
15. Influence of obesity on association between genetic variants identified by genome-wide association studies and hypertension risk in Chinese children.
Xi B; Zhao X; Chandak GR; Shen Y; Cheng H; Hou D; Wang X; Mi J
Am J Hypertens; 2013 Aug; 26(8):990-6. PubMed ID: 23591986
[TBL] [Abstract][Full Text] [Related]
16. Efficient two-step testing of gene-gene interactions in genome-wide association studies.
Lewinger JP; Morrison JL; Thomas DC; Murcray CE; Conti DV; Li D; Gauderman WJ
Genet Epidemiol; 2013 Jul; 37(5):440-51. PubMed ID: 23633124
[TBL] [Abstract][Full Text] [Related]
17. Enhanced polygenic risk score incorporating gene-environment interaction suggests the association of major depressive disorder with cardiac and lung function.
Pan C; Cheng B; Qin X; Cheng S; Liu L; Yang X; Meng P; Zhang N; He D; Cai Q; Wei W; Hui J; Wen Y; Jia Y; Liu H; Zhang F
Brief Bioinform; 2024 Jan; 25(2):. PubMed ID: 38436562
[TBL] [Abstract][Full Text] [Related]
18. A combination test for detection of gene-environment interaction in cohort studies.
Coombes B; Basu S; McGue M
Genet Epidemiol; 2017 Jul; 41(5):396-412. PubMed ID: 28370330
[TBL] [Abstract][Full Text] [Related]
19. Joint Analysis of Multiple Interaction Parameters in Genetic Association Studies.
Kim J; Ziyatdinov A; Laville V; Hu FB; Rimm E; Kraft P; Aschard H
Genetics; 2019 Feb; 211(2):483-494. PubMed ID: 30578273
[TBL] [Abstract][Full Text] [Related]
20. High genetic risk scores of SLIT3, PLEKHA5 and PPP2R2C variants increased insulin resistance and interacted with coffee and caffeine consumption in middle-aged adults.
Daily JW; Liu M; Park S
Nutr Metab Cardiovasc Dis; 2019 Jan; 29(1):79-89. PubMed ID: 30454882
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
