204 related articles for article (PubMed ID: 28626864)
1. Detecting genetic association through shortest paths in a bidirected graph.
Ueki M; Kawasaki Y; Tamiya G;
Genet Epidemiol; 2017 Sep; 41(6):481-497. PubMed ID: 28626864
[TBL] [Abstract][Full Text] [Related]
2. Smooth-Threshold Multivariate Genetic Prediction with Unbiased Model Selection.
Ueki M; Tamiya G;
Genet Epidemiol; 2016 Apr; 40(3):233-43. PubMed ID: 26947266
[TBL] [Abstract][Full Text] [Related]
3. A hidden Markov random field model for genome-wide association studies.
Li H; Wei Z; Maris J
Biostatistics; 2010 Jan; 11(1):139-50. PubMed ID: 19822692
[TBL] [Abstract][Full Text] [Related]
4. Powerful and Adaptive Testing for Multi-trait and Multi-SNP Associations with GWAS and Sequencing Data.
Kim J; Zhang Y; Pan W;
Genetics; 2016 Jun; 203(2):715-31. PubMed ID: 27075728
[TBL] [Abstract][Full Text] [Related]
5. Fast score test with global null estimation regardless of missing genotypes.
Sato S; Ueki M;
PLoS One; 2018; 13(7):e0199692. PubMed ID: 29975732
[TBL] [Abstract][Full Text] [Related]
6. Adaptive testing for multiple traits in a proportional odds model with applications to detect SNP-brain network associations.
Kim J; Pan W;
Genet Epidemiol; 2017 Apr; 41(3):259-277. PubMed ID: 28191669
[TBL] [Abstract][Full Text] [Related]
7. A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies.
Sinoquet C
BMC Bioinformatics; 2018 Mar; 19(1):106. PubMed ID: 29587628
[TBL] [Abstract][Full Text] [Related]
8. Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association Study.
Zhang P; Lewinger JP; Conti D; Morrison JL; Gauderman WJ
Genet Epidemiol; 2016 Jul; 40(5):394-403. PubMed ID: 27230133
[TBL] [Abstract][Full Text] [Related]
9. Comparison of multimarker logistic regression models, with application to a genomewide scan of schizophrenia.
Wason JM; Dudbridge F
BMC Genet; 2010 Sep; 11():80. PubMed ID: 20828390
[TBL] [Abstract][Full Text] [Related]
10. Identifying Candidate Genetic Associations with MRI-Derived AD-Related ROI via Tree-Guided Sparse Learning.
Hao X; Yao X; Risacher SL; Saykin AJ; Yu J; Wang H; Tan L; Shen L; Zhang D
IEEE/ACM Trans Comput Biol Bioinform; 2019; 16(6):1986-1996. PubMed ID: 29993890
[TBL] [Abstract][Full Text] [Related]
11. Performance of random forest when SNPs are in linkage disequilibrium.
Meng YA; Yu Y; Cupples LA; Farrer LA; Lunetta KL
BMC Bioinformatics; 2009 Mar; 10():78. PubMed ID: 19265542
[TBL] [Abstract][Full Text] [Related]
12. Performance of a blockwise approach in variable selection using linkage disequilibrium information.
Dehman A; Ambroise C; Neuvial P
BMC Bioinformatics; 2015 May; 16():148. PubMed ID: 25951947
[TBL] [Abstract][Full Text] [Related]
13. Further investigation of linkage disequilibrium SNPs and their ability to identify associated susceptibility loci.
North BV; Curtis D; Martin ER; Lai EH; Roses AD; Sham PC
Ann Hum Genet; 2004 May; 68(Pt 3):240-8. PubMed ID: 15180704
[TBL] [Abstract][Full Text] [Related]
14. An efficient weighted tag SNP-set analytical method in genome-wide association studies.
Yan B; Wang S; Jia H; Liu X; Wang X
BMC Genet; 2015 Mar; 16():25. PubMed ID: 25879733
[TBL] [Abstract][Full Text] [Related]
15. Establishing an adjusted p-value threshold to control the family-wide type 1 error in genome wide association studies.
Duggal P; Gillanders EM; Holmes TN; Bailey-Wilson JE
BMC Genomics; 2008 Oct; 9():516. PubMed ID: 18976480
[TBL] [Abstract][Full Text] [Related]
16. Contributions of linkage disequilibrium and co-segregation information to the accuracy of genomic prediction.
Sun X; Fernando R; Dekkers J
Genet Sel Evol; 2016 Oct; 48(1):77. PubMed ID: 27729012
[TBL] [Abstract][Full Text] [Related]
17. Tagging SNP-set selection with maximum information based on linkage disequilibrium structure in genome-wide association studies.
Wang S; He S; Yuan F; Zhu X
Bioinformatics; 2017 Jul; 33(14):2078-2081. PubMed ID: 28334342
[TBL] [Abstract][Full Text] [Related]
18. Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection.
Lu ZH; Zhu H; Knickmeyer RC; Sullivan PF; Williams SN; Zou F;
Genet Epidemiol; 2015 Dec; 39(8):664-77. PubMed ID: 26515609
[TBL] [Abstract][Full Text] [Related]
19. Structured Genome-Wide Association Studies with Bayesian Hierarchical Variable Selection.
Zhao Y; Zhu H; Lu Z; Knickmeyer RC; Zou F
Genetics; 2019 Jun; 212(2):397-415. PubMed ID: 31010934
[TBL] [Abstract][Full Text] [Related]
20. Strategies for selecting subsets of single-nucleotide polymorphisms to genotype in association studies.
Butler JM; Bishop DT; Barrett JH
BMC Genet; 2005 Dec; 6 Suppl 1(Suppl 1):S72. PubMed ID: 16451686
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
