Maximum likelihood and Bayesian methods for estimating the distribution of selective effects among classes of mutations using DNA polymorphism data.
Maximum likelihood and Bayesian approaches are presented for analyzing hierarchical statistical models of natural selection operating on DNA polymorphism within a panmictic population. For analyzing Bayesian models, we present Markov chain Monte-Carlo (MCMC) methods for sampling from the joint posterior distribution of parameters. For frequentist analysis, an Expectation-Maximization (EM) algorithm is presented for finding the maximum likelihood estimate of the genome wide mean and variance in selection intensity among classes of mutations. The framework presented here provides an ideal setting for modeling mutations dispersed through the genome and, in particular, for the analysis of how natural selection operates on different classes of single nucleotide polymorphisms (SNPs).
Bustamante
Carlos D
CD
Mathematical Genetics Group, Department of Statistics, University of Oxford, 1 South Parks Road, Oxford, UK OX1 3TG. cdb28@cornell.edu
Nielsen
Rasmus
R
Hartl
Daniel L
DL
D001499:Bayes Theorem; D004247:DNA / Q000235:genetics*; D005828:Genetics, Population; D016013:Likelihood Functions; D009154:Mutation; D016012:Poisson Distribution; D020641:Polymorphism, Single Nucleotide; D012641:Selection, Genetic; D014481:United States
D004247:DNA