title: Efficient Bayesian approach for multilocus association mapping including gene-gene interactions.
abstract: BACKGROUND
  since the introduction of large-scale genotyping methods that can be utilized in genome-wide association (GWA) studies for deciphering complex diseases, statistical genetics has been posed with a tremendous challenge of how to most appropriately analyze such data. A plethora of advanced model-based methods for genetic mapping of traits has been available for more than 10 years in animal and plant breeding. However, most such methods are computationally intractable in the context of genome-wide studies. Therefore, it is hardly surprising that GWA analyses have in practice been dominated by simple statistical tests concerned with a single marker locus at a time, while the more advanced approaches have appeared only relatively recently in the biomedical and statistical literature.
  
  
  RESULTS
  we introduce a novel Bayesian modeling framework for association mapping which enables the detection of multiple loci and their interactions that influence a dichotomous phenotype of interest. The method is shown to perform well in a simulation study when compared to widely used standard alternatives and its computational complexity is typically considerably smaller than that of a maximum likelihood based approach. We also discuss in detail the sensitivity of the Bayesian inferences with respect to the choice of prior distributions in the GWA context.
  
  
  CONCLUSIONS
  our results show that the Bayesian model averaging approach which explicitly considers gene-gene interactions may improve the detection of disease associated genetic markers in two respects: first, by providing better estimates of the locations of the causal loci; second, by reducing the number of false positives. The benefits are most apparent when the interacting genes exhibit no main effects. However, our findings also illustrate that such an approach is somewhat sensitive to the prior distribution assigned on the model structure.
authors.affiliation: Department of Biomedical Engineering and Computational Science, FI-02015 Helsinki University of Technology, Finland. pekka.marttinen@hut.fi
authors.forename: Pekka
authors.initials: P
authors.lastname: Marttinen
authors.forename: Jukka
authors.initials: J
authors.lastname: Corander
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D002874:Chromosome Mapping; D004843:Epistasis, Genetic / Q000235:genetics*; D056426:Genetic Loci; D016678:Genome; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D010641:Phenotype
references.citation: Am J Hum Genet. 2008 Dec;83(6):787-94
references.pmid: 19061986
references.citation: Am J Hum Genet. 2006 Jul;79(1):100-12
references.pmid: 16773569
references.citation: Bioinformatics. 2006 Oct 15;22(20):2466-74
references.pmid: 16870932
references.citation: Nat Rev Genet. 2009 Oct;10(10):681-90
references.pmid: 19763151
references.citation: PLoS Genet. 2008 Oct;4(10):e1000231
references.pmid: 18949033
references.citation: Hum Hered. 2007;63(2):67-84
references.pmid: 17283436
references.citation: PLoS Genet. 2008 Jul 25;4(7):e1000130
references.pmid: 18654633
references.citation: Nature. 2009 Oct 8;461(7265):747-53
references.pmid: 19812666
references.citation: Nat Genet. 2007 Jul;39(7):906-13
references.pmid: 17572673
references.citation: J Mol Med (Berl). 2009 Aug;87(8):825-35
references.pmid: 19554302
references.citation: Hum Hered. 2008;65(1):47-56
references.pmid: 17652960
references.citation: BMC Bioinformatics. 2009 Mar 18;10:90
references.pmid: 19296858
references.citation: Nat Rev Genet. 2006 Oct;7(10):781-91
references.pmid: 16983374
references.citation: Nat Genet. 2005 Apr;37(4):413-7
references.pmid: 15793588
references.citation: Genet Epidemiol. 2005 Feb;28(2):157-70
references.pmid: 15532037