Statistical Methods for Analyzing Drosophila Germline Mutation Rates

Abstract

Abstract Most studies of mutation rates implicitly assume that they remain constant throughout development of the germline. However, researchers recently used a novel statistical framework to reveal that mutation rates differ dramatically during sperm development in Drosophila melanogaster. Here a general framework is described for the inference of germline mutation patterns, generated from either mutation screening experiments or DNA sequence polymorphism data, that enables analysis of more than two mutations per family. The inference is made more rigorous and flexible by providing a better approximation of the probabilities of patterns of mutations and an improved coalescent algorithm within a single host with realistic assumptions. The properties of the inference framework, both the estimation and the hypothesis testing, were investigated by simulation. The refined inference framework is shown to provide (1) nearly unbiased maximum-likelihood estimates of mutation rates and (2) robust hypothesis testing using the standard asymptotic distribution of the likelihood-ratio tests. It is readily applicable to data sets in which multiple mutations in the same family are common.