EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units

European Journal of Human Genetics

Volumn 19, Issue 4, 2011, Pages 465-471

  Kam Thong, Tony ^a   Czamara, Darina ^a   Tsuda, Koji ^b,c,d   Borgwardt, Karsten ^e   Lewis, Cathryn M ^f,g   Erhardt Lehmann, Angelika ^a   Hemmer, Bernhard ^h   Rieckmann, Peter ⁱ   Daake, Markus ^a   Weber, Frank ^a   Wolf, Christiane ^a   Ziegler, Andreas ^j   Pütz, Benno ^a   Holsboer, Florian ^a   Schölkopf, Bernhard ^c   Müller Myhsok, Bertram ^a  

^a MAX PLANCK INSTITUTE OF PSYCHIATRY   (Germany)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^c NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY AIST   (Japan)

^d JAPAN SCIENCE AND TECHNOLOGY AGENCY   (Japan)

^e Machine Learning and Computational Biology Research Group   (Germany)

^f MEDICAL RESEARCH COUNCIL   (United Kingdom)

^g KING'S COLLEGE LONDON   (United Kingdom)

^h TECHNICAL UNIVERSITY OF MUNICH   (Germany)

ⁱ UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^j UNIVERSITY OF LÜBECK   (Germany)

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Author keywords

Epistasis; genome wide interaction analysis; graphical processing unit

Indexed keywords

ALGORITHM; ARTICLE; CASE CONTROL STUDY; COMPUTER PROGRAM; CONTROLLED STUDY; GENE INTERACTION; GENE LOCUS; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC EPISTASIS; GRAPHICAL PROCESSING UNIT; HUMAN; MULTIPLE SCLEROSIS; PANIC; PRIORITY JOURNAL; SINGLE NUCLEOTIDE POLYMORPHISM;

ALGORITHMS; CASE-CONTROL STUDIES; COMPUTER GRAPHICS; EPISTASIS, GENETIC; GENETIC PREDISPOSITION TO DISEASE; GENOME-WIDE ASSOCIATION STUDY; HUMANS; LOGISTIC MODELS; POLYMORPHISM, SINGLE NUCLEOTIDE; SOFTWARE;

EID: 79952740772     PISSN: 10184813     EISSN: 14765438     Source Type: Journal    
DOI: 10.1038/ejhg.2010.196     Document Type: Article

References (13)

1. Marchini J, Donelly P, Cardon LR: Genome-wide strategies for detecting multiple loci that influence complex diseases. Nat Genet 2005; 37: 413-417
2. Wellek S, Ziegler A: A genotype-based approach to assessing the association between single nucleotide polymorphisms. Hum Hered 2009; 67: 128-139
3. Gretton A, Borgwardt K, Rasch B, Schölkopf B, Smola A: A kernel method for the two-sample-problem. NIPS 2006; 513-520
4. R Development Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2009. ISBN 3-900051-07-0, http://www.R-project.org
5. Buckner J, Wilson J, Seligman M, Athey B, Watson S, Meng F: The gputools package enables GPU computing in R. Bioinformatics 2010; 26: 134-135
6. Erhardt A, Czibere L, Roeske D et al: TMEM132D, a new candidate for anxiety phenotypes: evidence from human and mouse studies. Mol Psychiatry 2010, e-pub ahead of print 6 April 2010; doi:10.1038/mp.2010.41 2010.41
7. Nischwitz S, Cepok S, Kroner A et al: Evidence for VAV2 and ZNF433 as susceptibility genes for multiple sclerosis. J Neuroimmunol 2010; 227: 162-166
8. Schüpbach T, Xenarios I, Bergmann S, Kapur K: FastEpistasis: a high performance computing solution for quantitative trait epistasis. Bioinformatics 2010; 26: 1468-1469
9. Herold C, Steffens M, Brockschmidt F, Baur MP, Becker T: INTERSNP: genome-wide interaction analysis guided by a priori information. Bioinformatics 2009; 25: 3275-3281
10. Hoh J, Ott J: Mathematical multi-locus approaches to localizing complex human trait genes. Nat Rev Genet 2003; 4: 701-709 (Pubitemid 37046137)
11. Zhao J, Xiong M: Test for interaction between two unlinked loci. Am J Hum Genet 2006; 79: 831-845 (Pubitemid 44763398)
12. Zaykin DV, Meng Z, Ehm MG: Contrasting linkage-disequilibrium patterns between cases and controls as a novel association-mapping method. Am J Hum Genet 2006; 78: 737-746
13. Hu X, Liu Q, Zhang Z et al: SHEsisEpi, a GPU-enhanced genome-wide SNP-SNP interaction scanning algorithm, efficiently reveals the risk genetic epistasis in bipolar disorder. Cell Res 2010; 20: 854-857.