190 related articles for article (PubMed ID: 35211187)
1. Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies.
Guo Y; Yuan Z; Liang Z; Wang Y; Wang Y; Xu L
Comput Math Methods Med; 2022; 2022():7843990. PubMed ID: 35211187
[TBL] [Abstract][Full Text] [Related]
2. Performance of epistasis detection methods in semi-simulated GWAS.
Chatelain C; Durand G; Thuillier V; Augé F
BMC Bioinformatics; 2018 Jun; 19(1):231. PubMed ID: 29914375
[TBL] [Abstract][Full Text] [Related]
3. MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study.
Wan X; Yang C; Yang Q; Xue H; Tang NL; Yu W
BMC Bioinformatics; 2009 Jan; 10():13. PubMed ID: 19134182
[TBL] [Abstract][Full Text] [Related]
4. Enabling personal genomics with an explicit test of epistasis.
Greene CS; Himmelstein DS; Nelson HH; Kelsey KT; Williams SM; Andrew AS; Karagas MR; Moore JH
Pac Symp Biocomput; 2010; ():327-36. PubMed ID: 19908385
[TBL] [Abstract][Full Text] [Related]
5. Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits.
Crawford L; Zeng P; Mukherjee S; Zhou X
PLoS Genet; 2017 Jul; 13(7):e1006869. PubMed ID: 28746338
[TBL] [Abstract][Full Text] [Related]
6. BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies.
Wan X; Yang C; Yang Q; Xue H; Fan X; Tang NL; Yu W
Am J Hum Genet; 2010 Sep; 87(3):325-40. PubMed ID: 20817139
[TBL] [Abstract][Full Text] [Related]
7. Novel methods for epistasis detection in genome-wide association studies.
Slim L; Chatelain C; Azencott CA; Vert JP
PLoS One; 2020; 15(11):e0242927. PubMed ID: 33253293
[TBL] [Abstract][Full Text] [Related]
8. Cuckoo search epistasis: a new method for exploring significant genetic interactions.
Aflakparast M; Salimi H; Gerami A; Dubé MP; Visweswaran S; Masoudi-Nejad A
Heredity (Edinb); 2014 Jun; 112(6):666-74. PubMed ID: 24549111
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Epistasis Test in Meta-Analysis: A Multi-Parameter Markov Chain Monte Carlo Model for Consistency of Evidence.
Lin C; Chu CM; Su SL
PLoS One; 2016; 11(4):e0152891. PubMed ID: 27045371
[TBL] [Abstract][Full Text] [Related]
11. AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies.
Emily M
Stat Appl Genet Mol Biol; 2016 Apr; 15(2):151-71. PubMed ID: 26913459
[TBL] [Abstract][Full Text] [Related]
12. A novel two-stage approach for epistasis detection in genome-wide case-control studies.
Liao Z; Zeng Q; Liao B; Li X
Biochem Genet; 2014 Oct; 52(9-10):403-14. PubMed ID: 24880910
[TBL] [Abstract][Full Text] [Related]
13. Finding unique filter sets in PLATO: a precursor to efficient interaction analysis in GWAS data.
Grady BJ; Torstenson E; Dudek SM; Giles J; Sexton D; Ritchie MD
Pac Symp Biocomput; 2010; ():315-26. PubMed ID: 19908384
[TBL] [Abstract][Full Text] [Related]
14. Using biological networks to search for interacting loci in genome-wide association studies.
Emily M; Mailund T; Hein J; Schauser L; Schierup MH
Eur J Hum Genet; 2009 Oct; 17(10):1231-40. PubMed ID: 19277065
[TBL] [Abstract][Full Text] [Related]
15. TSGSIS: a high-dimensional grouped variable selection approach for detection of whole-genome SNP-SNP interactions.
Fang YH; Wang JH; Hsiung CA
Bioinformatics; 2017 Nov; 33(22):3595-3602. PubMed ID: 28651334
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. EpiMOGA: An Epistasis Detection Method Based on a Multi-Objective Genetic Algorithm.
Chen Y; Xu F; Pian C; Xu M; Kong L; Fang J; Li Z; Zhang L
Genes (Basel); 2021 Jan; 12(2):. PubMed ID: 33525573
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Mapping the genetic architecture of complex traits in experimental populations.
Yang J; Zhu J; Williams RW
Bioinformatics; 2007 Jun; 23(12):1527-36. PubMed ID: 17459962
[TBL] [Abstract][Full Text] [Related]
20. A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.
Moore JH; Gilbert JC; Tsai CT; Chiang FT; Holden T; Barney N; White BC
J Theor Biol; 2006 Jul; 241(2):252-61. PubMed ID: 16457852
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]