Inference of genetic forces using a Poisson random field model with non-constant population size

摘要: Abstract Aligned DNA sequences have been widely used for quantitatively analyzing and interpreting evolutionary processes. By comparing the information between intraspecific polymorphism with interspecific divergence in two sibling species, Poisson random field (PRF) theory offers a statistical framework which various genetic parameters such as natural selection intensity, mutation rate speciation time can be effectively estimated. A recently developed time-inhomogeneous PRF model has reinforced original method by removing assumption of stationary site frequency, but it keeps that species share same effective population size their ancestral species. This paper explores relaxation this biologically unrealistic hypothesizing each descendant experienced sudden change at times from most recent common ancestor. The newly non-constant is applied to set 91 genes African Drosophila make inference under hierarchical Bayesian carried out multi-layer Markov chain Monte Carlo sampling scheme. In order meet intensive computational demand, R program integrated C++ code parallel executing technique designed run multiple CPU cores.