294 related articles for article (PubMed ID: 33326753)
1. Leveraging phenotypic variability to identify genetic interactions in human phenotypes.
Marderstein AR; Davenport ER; Kulm S; Van Hout CV; Elemento O; Clark AG
Am J Hum Genet; 2021 Jan; 108(1):49-67. PubMed ID: 33326753
[TBL] [Abstract][Full Text] [Related]
2. Genome-wide interaction of genotype by erythrocyte n-3 fatty acids contributes to phenotypic variance of diabetes-related traits.
Zheng JS; Lai CQ; Parnell LD; Lee YC; Shen J; Smith CE; Casas-Agustench P; Richardson K; Li D; Noel SE; Tucker KL; Arnett DK; Borecki IB; Ordovás JM
BMC Genomics; 2014 Sep; 15(1):781. PubMed ID: 25213455
[TBL] [Abstract][Full Text] [Related]
3. A quantile integral linear model to quantify genetic effects on phenotypic variability.
Miao J; Lin Y; Wu Y; Zheng B; Schmitz LL; Fletcher JM; Lu Q
Proc Natl Acad Sci U S A; 2022 Sep; 119(39):e2212959119. PubMed ID: 36122202
[TBL] [Abstract][Full Text] [Related]
4. Using Genetic Marginal Effects to Study Gene-Environment Interactions with GWAS Data.
Verhulst B; Pritikin JN; Clifford J; Prom-Wormley E
Behav Genet; 2021 May; 51(3):358-373. PubMed ID: 33899139
[TBL] [Abstract][Full Text] [Related]
5. Genome-wide contribution of genotype by environment interaction to variation of diabetes-related traits.
Zheng JS; Arnett DK; Lee YC; Shen J; Parnell LD; Smith CE; Richardson K; Li D; Borecki IB; Ordovás JM; Lai CQ
PLoS One; 2013; 8(10):e77442. PubMed ID: 24204828
[TBL] [Abstract][Full Text] [Related]
6. Quantitative trait loci, G×E and G×G for glycemic traits: response to metformin and placebo in the Diabetes Prevention Program (DPP).
Maxwell TJ; Franks PW; Kahn SE; Knowler WC; Mather KJ; Florez JC; Jablonski KA;
J Hum Genet; 2022 Aug; 67(8):465-473. PubMed ID: 35260800
[TBL] [Abstract][Full Text] [Related]
7. Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank.
Wang H; Zhang F; Zeng J; Wu Y; Kemper KE; Xue A; Zhang M; Powell JE; Goddard ME; Wray NR; Visscher PM; McRae AF; Yang J
Sci Adv; 2019 Aug; 5(8):eaaw3538. PubMed ID: 31453325
[TBL] [Abstract][Full Text] [Related]
8. Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction.
Domingue BW; Kanopka K; Mallard TT; Trejo S; Tucker-Drob EM
Behav Genet; 2022 Jan; 52(1):56-64. PubMed ID: 34855050
[TBL] [Abstract][Full Text] [Related]
9. Inferring Gene-by-Environment Interactions with a Bayesian Whole-Genome Regression Model.
Kerin M; Marchini J
Am J Hum Genet; 2020 Oct; 107(4):698-713. PubMed ID: 32888427
[TBL] [Abstract][Full Text] [Related]
10. High-throughput allele-specific expression across 250 environmental conditions.
Moyerbrailean GA; Richards AL; Kurtz D; Kalita CA; Davis GO; Harvey CT; Alazizi A; Watza D; Sorokin Y; Hauff N; Zhou X; Wen X; Pique-Regi R; Luca F
Genome Res; 2016 Dec; 26(12):1627-1638. PubMed ID: 27934696
[TBL] [Abstract][Full Text] [Related]
11. Detecting genetic effects on phenotype variability to capture gene-by-environment interactions: a systematic method comparison.
Zhang X; Bell JT
G3 (Bethesda); 2024 Apr; 14(4):. PubMed ID: 38289865
[TBL] [Abstract][Full Text] [Related]
12. GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data.
Shin J; Lee SH
Genome Biol; 2021 Jun; 22(1):183. PubMed ID: 34154633
[TBL] [Abstract][Full Text] [Related]
13. Genome-wide variance quantitative trait locus analysis suggests small interaction effects in blood pressure traits.
Shi G
Sci Rep; 2022 Jul; 12(1):12649. PubMed ID: 35879408
[TBL] [Abstract][Full Text] [Related]
14. Quantitative trait loci and candidate genes underlying genotype by environment interaction in the response of Arabidopsis thaliana to drought.
El-Soda M; Kruijer W; Malosetti M; Koornneef M; Aarts MG
Plant Cell Environ; 2015 Mar; 38(3):585-99. PubMed ID: 25074022
[TBL] [Abstract][Full Text] [Related]
15. Assessing gene-environment interactions for common and rare variants with binary traits using gene-trait similarity regression.
Zhao G; Marceau R; Zhang D; Tzeng JY
Genetics; 2015 Mar; 199(3):695-710. PubMed ID: 25585620
[TBL] [Abstract][Full Text] [Related]
16. Ranking and characterization of established BMI and lipid associated loci as candidates for gene-environment interactions.
Shungin D; Deng WQ; Varga TV; Luan J; Mihailov E; Metspalu A; ; Morris AP; Forouhi NG; Lindgren C; Magnusson PKE; Pedersen NL; Hallmans G; Chu AY; Justice AE; Graff M; Winkler TW; Rose LM; Langenberg C; Cupples LA; Ridker PM; Wareham NJ; Ong KK; Loos RJF; Chasman DI; Ingelsson E; Kilpeläinen TO; Scott RA; Mägi R; Paré G; Franks PW
PLoS Genet; 2017 Jun; 13(6):e1006812. PubMed ID: 28614350
[TBL] [Abstract][Full Text] [Related]
17. Investigation of multi-trait associations using pathway-based analysis of GWAS summary statistics.
Pei G; Sun H; Dai Y; Liu X; Zhao Z; Jia P
BMC Genomics; 2019 Feb; 20(Suppl 1):79. PubMed ID: 30712509
[TBL] [Abstract][Full Text] [Related]
18. Analytical strategies to include the X-chromosome in variance heterogeneity analyses: Evidence for trait-specific polygenic variance structure.
Deng WQ; Mao S; Kalnapenkis A; Esko T; Mägi R; Paré G; Sun L
Genet Epidemiol; 2019 Oct; 43(7):815-830. PubMed ID: 31332826
[TBL] [Abstract][Full Text] [Related]
19. A robust model-free approach for rare variants association studies incorporating gene-gene and gene-environmental interactions.
Fan R; Lo SH
PLoS One; 2013; 8(12):e83057. PubMed ID: 24358248
[TBL] [Abstract][Full Text] [Related]
20. From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time.
Bustos-Korts D; Malosetti M; Chenu K; Chapman S; Boer MP; Zheng B; van Eeuwijk FA
Front Plant Sci; 2019; 10():1540. PubMed ID: 31867027
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
