Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis

BioData Mining

Volumn 6, Issue 1, 2013, Pages

  Collins, Ryan L ^a   Hu, Ting ^b   Wejse, Christian ^c,d   Sirugo, Giorgio ^e   Williams, Scott M ^a,b   Moore, Jason H ^a,b  

^a DARTMOUTH COLLEGE   (United States)

^b DARTMOUTH MEDICAL SCHOOL   (United States)

^c BANDIM HEALTH PROJECT   (Guinea-Bissau)

^d AARHUS UNIVERSITY   (Denmark)

^e FATEBENEFRATELLI HOSPITAL   (Italy)

Author keywords

Epistasis; Gene gene interactions; Machine learning; Pulmonary tuberculosis

Indexed keywords

ACCURACY; ALGORITHM; ARTICLE; CASE CONTROL STUDY; CONTROLLED STUDY; GENETIC ASSOCIATION; GENETIC EPISTASIS; GENETIC SUSCEPTIBILITY; GENOTYPE; HUMAN; LUNG TUBERCULOSIS; MACHINE LEARNING; MAJOR CLINICAL STUDY; MUTATIONAL ANALYSIS; POPULATION GENETICS; PRIORITY JOURNAL; SINGLE NUCLEOTIDE POLYMORPHISM;

EID: 84873939918     PISSN: None     EISSN: 17560381     Source Type: Journal    
DOI: 10.1186/1756-0381-6-4     Document Type: Article

References (10)

1. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD, Parl FF, Moore JH, Am J Hum Genetics 2001 69 138 147 10.1086/321276
2. Gene-gene interaction between tuberculosis candidate genes in a South African population. De Wit E, van der Merwe L, van Helden PD, Hoal EG, Mamm Genome 2011 22 1-2 100 110 20799037
3. DC-SIGN (CD209), pentraxin 3 and vitamin D receptor gene variants associate with pulmonary tuberculosis risk in West Africans. Olesen R, Wejse C, Velez DR, Bisseye C, Sodemann M, Aaby P, Rabna P, Worwui A, Chapman H, Diatta M, Adegbola RA, Hill PC, Østergaard L, Williams SM, Sirugo G, Genes Immun 2007 8 suppl 6 456 467 17611589
4. 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 C, Chiang F, Holden T, Barney N, White BC, J Theor Biol 2006 241 252 261 10.1016/j.jtbi.2005.11.036 16457852
5. Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Ritchie MD, Hahn LW, Moore JH, Genet Epidemiol 2003 24 2 150 157 10.1002/gepi.10218 12548676
6. A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH, Genet Epidemiol 2007 31 4 306 315 10.1002/gepi.20211 17323372
7. An application of conditional logistic regression and multifactor dimensionality reduction for detecting gene-gene interactions on risk of myocardial infarction: the importance of model validation. Coffey CS, Hebert PR, Ritchie MD, Krumholz HM, Gaziano JM, Ridker PM, Brown NJ, Vaughan DE, Moore JH, BMC Bioinformatics 2004 5 49 10.1186/1471-2105-5-49 15119966
8. 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 327 336
9. Spatially uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions. Greene CS, Penrod NM, Kiralis J, Moore JH, Biodata Min 2009 2 5 13 10.1186/1756-0381-2-5 19772641
10. An information-gain approach to detecting three-way epistatic interactions in genetic association studies. Hu T, Chen Y, Kiralis JW, Collins RL, Wejse C, Sirugo G, Williams SM, Moore JH, J Am Med Inform Assoc 2013