title: Methods for estimating gene frequencies and detecting selection in bacterial populations.
abstract: Recent breakthroughs in molecular technology, most significantly the polymerase chain reaction (PCR) and in situ hybridization, have allowed the detection of genetic variation in bacterial communities without prior cultivation. These methods often produce data in the form of the presence or absence of alleles or genotypes, however, rather than counts of alleles. Using relative allele frequencies from presence-absence data as estimates of population allele frequencies tends to underestimate the frequencies of common alleles and overestimate those of rare ones, potentially biasing the results of a test of neutrality in favor of balancing selection. In this study, a maximum-likelihood estimator (MLE) of bacterial allele frequencies designed for use with presence-absence data is derived using an explicit stochastic model of the host infection (or bacterial sampling) process. The performance of the MLE is evaluated using computer simulation and a method is presented for evaluating the fit of estimated allele frequencies to the neutral infinite alleles model (IAM). The methods are applied to estimate allele frequencies at two outer surface protein loci (ospA and ospC) of the Lyme disease spirochete, Borrelia burgdorferi, infecting local populations of deer ticks (Ixodes scapularis) and to test the fit to a neutral IAM.
authors.affiliation: Department of Ecology and Evolution, State University of New York, Stony Brook 11754-5245, USA. rannala@life.bio.sunysb.edu
authors.forename: B
authors.initials: B
authors.lastname: Rannala
authors.forename: W G
authors.initials: WG
authors.lastname: Qiu
authors.forename: D E
authors.initials: DE
authors.lastname: Dykhuizen
mesh_terms: D000483:Alleles; D015748:Borrelia burgdorferi Group / Q000235:genetics*; D005787:Gene Frequency; D008957:Models, Genetic; D009010:Monte Carlo Method; D012641:Selection, Genetic