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Abstract:
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With recent interest being focused on genome scans using a large number of marker loci, the common approaches of existing statistical methods produce often the inconsistent results. The methodologies using Bayesian model selection via Markov chain Monte Carlo sampling schemes have been developed to remedy the pitfall of classical methods, despite biases that might be introduced in the inferences. In this paper, we propose robust and effective Bayesian genome-wide screening methods of simultaneously identifying loci related to quantitative trait and their possible interaction effects over the entire genome. Our methods are based on Bayesian model selection, and for posterior inferences, we utilize the emerging simulation technique, Perfect sampling algorithm to alleviate any concerns over convergence and sampling mixing, thus leading to a powerful and robust performance.
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