Efficient Bayesian approach for multilocus association mapping including gene-gene interactions.
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.
Marttinen
Pekka
P
Department of Biomedical Engineering and Computational Science, FI-02015 Helsinki University of Technology, Finland. pekka.marttinen@hut.fi
Corander
Jukka
J
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
Am J Hum Genet. 2008 Dec;83(6):787-94
19061986
Am J Hum Genet. 2006 Jul;79(1):100-12
16773569
Bioinformatics. 2006 Oct 15;22(20):2466-74
16870932
Nat Rev Genet. 2009 Oct;10(10):681-90
19763151
PLoS Genet. 2008 Oct;4(10):e1000231
18949033
Hum Hered. 2007;63(2):67-84
17283436
PLoS Genet. 2008 Jul 25;4(7):e1000130
18654633
Nature. 2009 Oct 8;461(7265):747-53
19812666
Nat Genet. 2007 Jul;39(7):906-13
17572673
J Mol Med (Berl). 2009 Aug;87(8):825-35
19554302
Hum Hered. 2008;65(1):47-56
17652960
BMC Bioinformatics. 2009 Mar 18;10:90
19296858
Nat Rev Genet. 2006 Oct;7(10):781-91
16983374
Nat Genet. 2005 Apr;37(4):413-7
15793588
Genet Epidemiol. 2005 Feb;28(2):157-70
15532037