Abstract
Epistasis or the interaction between loci on a genome that controls a quantitative trait is of great interest to geneticists. This work presents a powerful Bayesian method utilizing Markov chain Monte Carlo model composition approach using restricted spaces is developed for identifying epistatic effects in Recombinant Inbred Lines (RIL) in plant studies. This method produces both posterior activation probabilities and posterior conditional activation probabilities. The method is verified through a simulation study and applied to an Arabidopsis thaliana data set with cotyledon as the quantitative trait.
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Boone, E.L., Simmons, S.J., Ricanek, K. (2013). A Restricted Model Space Approach for the Detection of Epistasis in Quantitative Trait Loci Using Markov Chain Monte Carlo Model Composition. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2011. Communications in Computer and Information Science, vol 271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29966-7_7
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DOI: https://doi.org/10.1007/978-3-642-29966-7_7
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