A study of deleterious gene structure in plants using Markov chain Monte Carlo

The characteristics of deleterious genes have been of great interest in bot h theory and practice in genetics. Because of the complex genetic mechanism of these deleterious genes, most current studies try to estimate the overa ll magnitude of mortality effects on a population, which is characterized c lassically by the number of lethal equivalents. This number is a combinatio n of several parameters, each of which has a distinct biological effect on genetic mortality. In conservation and breeding programs, it is important t o be able to distinguish among different combinations of these parameters t hat lead to the same number of lethal equivalents, such as a large number o f mildly deleterious genes or a few lethal genes. The ability to distinguis h such parameter combinations requires more than one generation of mating. We propose a model for survival data from a two-generation mating experimen t on the plant species Brassica rapa, and we enable inference with Markov c hain Monte Carlo. This computational strategy is effective because a vast a mount of missing genotype information must be accounted for. In addition to the lethal equivalents, the two-generation data provide separate informati on on the average intensity of mortality and the average number of deleteri ous genes carried by an individual. In our Markov chain Monte Carlo algorit hm, we use a vector proposal distribution to overcome inefficiency of a sin gle-site Gibbs sampler. Information about environmental effects is obtained from an outcrossing experiment conducted in parallel with the two-generati on mating experiments.