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Bayesian inference of epistatic interactions in case-control studies


Epistatic interactions among multiple genetic variants in the human genome may be important in determining individual susceptibility to common diseases. Although some existing computational methods for identifying genetic interactions have been effective for small-scale studies, we here propose a method, denoted 'bayesian epistasis association mapping' (BEAM), for genome-wide case-control studies. BEAM treats the disease-associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. Testing this on an age-related macular degeneration genome-wide association data set, we demonstrate that the method is significantly more powerful than existing approaches and that genome-wide case-control epistasis mapping with many thousands of markers is both computationally and statistically feasible.

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Figure 1: Comparison between BEAM (B), the stepwise B-stat (S), the stepwise logistic regression (L) and the 2-d.f. χ2 test (C) on six disease models.
Figure 2: Impact of MAF discrepancy and LD on the powers of BEAM (B), the stepwise B-stat (S), the stepwise logistic regression (L) and the 2-d.f. χ2 test (C).
Figure 3: Posterior probabilities of association for each marker in the AMD data set, obtained by running BEAM for 108 iterations and taking samples at every 105 iterations.
Figure 4: Comparison of BEAM (B), the stepwise B-stat (S), the stepwise logistic regression (L) and the 2-d.f. χ2 test (C) on the 100,000-SNP data sets.
Figure 5: Comparison of BEAM (B), MDR (M), logic regression (R), BGTA (T) and the 2-d.f. χ2 test (C) on model 4.


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This work was supported in part by US National Institutes of Health grant R01HG002518-06, US National Science Foundation grant DMS-0204674 and a grant from the National Science Foundation of China (10228102). We thank J. Hoh for providing us the AMD data set and T. Niu for discussions.

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Y.Z. and J.S.L. designed the statistical models and simulation studies together. Y.Z. implemented the method and wrote the software. Both authors contributed to the writing of the manuscript.

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Correspondence to Jun S Liu.

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The authors declare no competing financial interests.

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Supplementary Methods, Supplementary Figures 1–4, Supplementary Table 1 (PDF 367 kb)

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Zhang, Y., Liu, J. Bayesian inference of epistatic interactions in case-control studies. Nat Genet 39, 1167–1173 (2007).

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