188 related articles for article (PubMed ID: 24297546)
1. Using the bipartite human phenotype network to reveal pleiotropy and epistasis beyond the gene.
Darabos C; Harmon SH; Moore JH
Pac Symp Biocomput; 2014; ():188-99. PubMed ID: 24297546
[TBL] [Abstract][Full Text] [Related]
2. Genome-wide epistasis and pleiotropy characterized by the bipartite human phenotype network.
Darabos C; Moore JH
Methods Mol Biol; 2015; 1253():269-83. PubMed ID: 25403537
[TBL] [Abstract][Full Text] [Related]
3. Genome-wide genetic interaction analysis of glaucoma using expert knowledge derived from human phenotype networks.
Hu T; Darabos C; Cricco ME; Kong E; Moore JH
Pac Symp Biocomput; 2015; 20():207-18. PubMed ID: 25592582
[TBL] [Abstract][Full Text] [Related]
4. WISH-R- a fast and efficient tool for construction of epistatic networks for complex traits and diseases.
Carmelo VAO; Kogelman LJA; Madsen MB; Kadarmideen HN
BMC Bioinformatics; 2018 Jul; 19(1):277. PubMed ID: 30064383
[TBL] [Abstract][Full Text] [Related]
5. Shadows of complexity: what biological networks reveal about epistasis and pleiotropy.
Tyler AL; Asselbergs FW; Williams SM; Moore JH
Bioessays; 2009 Feb; 31(2):220-7. PubMed ID: 19204994
[TBL] [Abstract][Full Text] [Related]
6. CAPE: an R package for combined analysis of pleiotropy and epistasis.
Tyler AL; Lu W; Hendrick JJ; Philip VM; Carter GW
PLoS Comput Biol; 2013 Oct; 9(10):e1003270. PubMed ID: 24204223
[TBL] [Abstract][Full Text] [Related]
7. Statistical epistasis networks reduce the computational complexity of searching three-locus genetic models.
Hu T; Andrew AS; Karagas MR; Moore JH
Pac Symp Biocomput; 2013; ():397-408. PubMed ID: 23424144
[TBL] [Abstract][Full Text] [Related]
8. The Combined Analysis of Pleiotropy and Epistasis (CAPE).
Tyler AL; Emerson J; El Kassaby B; Wells AE; Philip VM; Carter GW
Methods Mol Biol; 2021; 2212():55-67. PubMed ID: 33733350
[TBL] [Abstract][Full Text] [Related]
9. Convergent downstream candidate mechanisms of independent intergenic polymorphisms between co-classified diseases implicate epistasis among noncoding elements.
Han J; Li J; Achour I; Pesce L; Foster I; Li H; Lussier YA
Pac Symp Biocomput; 2018; 23():524-535. PubMed ID: 29218911
[TBL] [Abstract][Full Text] [Related]
10. High-throughput analysis of epistasis in genome-wide association studies with BiForce.
Gyenesei A; Moody J; Semple CA; Haley CS; Wei WH
Bioinformatics; 2012 Aug; 28(15):1957-64. PubMed ID: 22618535
[TBL] [Abstract][Full Text] [Related]
11. Using C-JAMP to Investigate Epistasis and Pleiotropy.
Konigorski S; Glicksberg BS
Methods Mol Biol; 2021; 2212():225-243. PubMed ID: 33733359
[TBL] [Abstract][Full Text] [Related]
12. Gene-set association and epistatic analyses reveal complex gene interaction networks affecting flowering time in a worldwide barley collection.
He T; Hill CB; Angessa TT; Zhang XQ; Chen K; Moody D; Telfer P; Westcott S; Li C
J Exp Bot; 2019 Oct; 70(20):5603-5616. PubMed ID: 31504706
[TBL] [Abstract][Full Text] [Related]
13. Inferring gene function and network organization in Drosophila signaling by combined analysis of pleiotropy and epistasis.
Carter GW
G3 (Bethesda); 2013 May; 3(5):807-14. PubMed ID: 23550134
[TBL] [Abstract][Full Text] [Related]
14. Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from GWAS data.
Liu Y; Maxwell S; Feng T; Zhu X; Elston RC; Koyutürk M; Chance MR
BMC Syst Biol; 2012; 6 Suppl 3(Suppl 3):S15. PubMed ID: 23281810
[TBL] [Abstract][Full Text] [Related]
15. Characterizing genetic interactions in human disease association studies using statistical epistasis networks.
Hu T; Sinnott-Armstrong NA; Kiralis JW; Andrew AS; Karagas MR; Moore JH
BMC Bioinformatics; 2011 Sep; 12():364. PubMed ID: 21910885
[TBL] [Abstract][Full Text] [Related]
16. Evolution of pleiotropy: epistatic interaction pattern supports a mechanistic model underlying variation in genotype-phenotype map.
Pavlicev M; Norgard EA; Fawcett GL; Cheverud JM
J Exp Zool B Mol Dev Evol; 2011 Jul; 316(5):371-85. PubMed ID: 21462316
[TBL] [Abstract][Full Text] [Related]
17. Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice.
Tyler AL; Ji B; Gatti DM; Munger SC; Churchill GA; Svenson KL; Carter GW
Genetics; 2017 Jun; 206(2):621-639. PubMed ID: 28592500
[TBL] [Abstract][Full Text] [Related]
18. Pleiotropy informed adaptive association test of multiple traits using genome-wide association study summary data.
Masotti M; Guo B; Wu B
Biometrics; 2019 Dec; 75(4):1076-1085. PubMed ID: 31021400
[TBL] [Abstract][Full Text] [Related]
19. A system-level pathway-phenotype association analysis using synthetic feature random forest.
Pan Q; Hu T; Malley JD; Andrew AS; Karagas MR; Moore JH
Genet Epidemiol; 2014 Apr; 38(3):209-19. PubMed ID: 24535726
[TBL] [Abstract][Full Text] [Related]
20. Crop genome-wide association study: a harvest of biological relevance.
Liu HJ; Yan J
Plant J; 2019 Jan; 97(1):8-18. PubMed ID: 30368955
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
