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Finite adaptation and multistep moves in the metropolis-hastings algorithm for variable selection in genome-wide association analysis.

Peltola T, Marttinen P, Vehtari A.

PLoS One. 2012;7(11):e49445. doi: 10.1371/journal.pone.0049445. Epub 2012 Nov 15.


Bayesian variable selection in searching for additive and dominant effects in genome-wide data.

Peltola T, Marttinen P, Jula A, Salomaa V, Perola M, Vehtari A.

PLoS One. 2012;7(1):e29115. doi: 10.1371/journal.pone.0029115. Epub 2012 Jan 3.


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 1;28(15):1957-64. doi: 10.1093/bioinformatics/bts304. Epub 2012 May 21. Erratum in: Bioinformatics. 2013 Oct 15;29(20):2667-8.


Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers.

Shepherd RK, Meuwissen TH, Woolliams JA.

BMC Bioinformatics. 2010 Oct 22;11:529. doi: 10.1186/1471-2105-11-529.


Genetic studies of complex human diseases: characterizing SNP-disease associations using Bayesian networks.

Han B, Chen XW, Talebizadeh Z, Xu H.

BMC Syst Biol. 2012;6 Suppl 3:S14. doi: 10.1186/1752-0509-6-S3-S14. Epub 2012 Dec 17.


SNP selection and classification of genome-wide SNP data using stratified sampling random forests.

Wu Q, Ye Y, Liu Y, Ng MK.

IEEE Trans Nanobioscience. 2012 Sep;11(3):216-27. doi: 10.1109/TNB.2012.2214232.


A new permutation strategy of pathway-based approach for genome-wide association study.

Guo YF, Li J, Chen Y, Zhang LS, Deng HW.

BMC Bioinformatics. 2009 Dec 18;10:429. doi: 10.1186/1471-2105-10-429.


Genome-wide tagging SNPs with entropy-based Monte Carlo method.

Liu Z, Lin S, Tan M.

J Comput Biol. 2006 Nov;13(9):1606-14.


Bayesian variable and model selection methods for genetic association studies.

Fridley BL.

Genet Epidemiol. 2009 Jan;33(1):27-37. doi: 10.1002/gepi.20353.


Enhancing the power to detect low-frequency variants in genome-wide screens.

Lin CY, Xing G, Ku HC, Elston RC, Xing C.

Genetics. 2014 Apr;196(4):1293-302. doi: 10.1534/genetics.113.160739. Epub 2014 Feb 4.


An application of Random Forests to a genome-wide association dataset: methodological considerations & new findings.

Goldstein BA, Hubbard AE, Cutler A, Barcellos LF.

BMC Genet. 2010 Jun 14;11:49. doi: 10.1186/1471-2156-11-49.


A multivariate regression approach to association analysis of a quantitative trait network.

Kim S, Sohn KA, Xing EP.

Bioinformatics. 2009 Jun 15;25(12):i204-12. doi: 10.1093/bioinformatics/btp218.


Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets.

Xiong Q, Ancona N, Hauser ER, Mukherjee S, Furey TS.

Genome Res. 2012 Feb;22(2):386-97. doi: 10.1101/gr.124370.111. Epub 2011 Sep 22. Erratum in: Genome Res. 2013 May;23(5):905.


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 9;10:13. doi: 10.1186/1471-2105-10-13.


Use of wrapper algorithms coupled with a random forests classifier for variable selection in large-scale genomic association studies.

Rodin AS, Litvinenko A, Klos K, Morrison AC, Woodage T, Coresh J, Boerwinkle E.

J Comput Biol. 2009 Dec;16(12):1705-18. doi: 10.1089/cmb.2008.0037.


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, Koyut├╝rk M, Chance MR.

BMC Syst Biol. 2012;6 Suppl 3:S15. doi: 10.1186/1752-0509-6-S3-S15. Epub 2012 Dec 17.


Gene prioritization based on biological plausibility over genome wide association studies renders new loci associated with type 2 diabetes.

Sookoian S, Gianotti TF, Schuman M, Pirola CJ.

Genet Med. 2009 May;11(5):338-43. doi: 10.1097/GIM.0b013e31819995ca.


The SNP ratio test: pathway analysis of genome-wide association datasets.

O'Dushlaine C, Kenny E, Heron EA, Segurado R, Gill M, Morris DW, Corvin A.

Bioinformatics. 2009 Oct 15;25(20):2762-3. doi: 10.1093/bioinformatics/btp448. Epub 2009 Jul 20.


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 10;87(3):325-40. doi: 10.1016/j.ajhg.2010.07.021.


COE: a general approach for efficient genome-wide two-locus epistasis test in disease association study.

Zhang X, Pan F, Xie Y, Zou F, Wang W.

J Comput Biol. 2010 Mar;17(3):401-15. doi: 10.1089/cmb.2009.0155.

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