ABSTRACT
A total of 969 isolates of Campylobacter jejuni originating in the Preston, Lancashire postcode district over a 3-year period were characterized using multi-locus sequence typing. Recently developed statistical methods and a genetic model were used to investigate temporal, spatial, spatio-temporal and genetic variation in human C. jejuni infections. The analysis of the data showed statistically significant seasonal variation, spatial clustering, small-scale spatio-temporal clustering and spatio-temporal interaction in the overall pattern of incidence, and spatial segregation in cases classified according to their most likely species-of-origin.
Subject(s)
Campylobacter Infections/epidemiology , Campylobacter jejuni/isolation & purification , Enteritis/epidemiology , Adolescent , Adult , Aged , Aged, 80 and over , Bacterial Typing Techniques , Campylobacter Infections/microbiology , Campylobacter jejuni/classification , Campylobacter jejuni/genetics , Child , Child, Preschool , Cluster Analysis , DNA Fingerprinting , DNA, Bacterial/chemistry , DNA, Bacterial/genetics , England/epidemiology , Enteritis/microbiology , Female , Foodborne Diseases/epidemiology , Foodborne Diseases/microbiology , Genotype , Humans , Infant , Infant, Newborn , Male , Middle Aged , Molecular Epidemiology , Seasons , Sequence Analysis, DNA , Time Factors , Young AdultABSTRACT
We introduce a new method for estimating recombination rates from population genetic data. The method uses a computationally intensive statistical procedure (importance sampling) to calculate the likelihood under a coalescent-based model. Detailed comparisons of the new algorithm with two existing methods (the importance sampling method of Griffiths and Marjoram and the MCMC method of Kuhner and colleagues) show it to be substantially more efficient. (The improvement over the existing importance sampling scheme is typically by four orders of magnitude.) The existing approaches not infrequently led to misleading results on the problems we investigated. We also performed a simulation study to look at the properties of the maximum-likelihood estimator of the recombination rate and its robustness to misspecification of the demographic model.
Subject(s)
Recombination, Genetic , Genetics, Population , Likelihood Functions , Microsatellite Repeats , Models, Genetic , Models, Statistical , Models, Theoretical , Monte Carlo MethodABSTRACT
We consider using the ancestral selection graph (ASG) to simulate samples from population genetic models with selection. Currently the use of the ASG to simulate samples is limited. This is because the computational requirement for simulating samples increases exponentially with the selection rate and also due to needing to simulate a sample of size one from the population at equilibrium. For the only case where the distribution of a sample of size one is known, that of parent-independent mutations, more efficient simulation algorithms exist. We will show that by applying the idea of coupling from the past to the ASG, samples can be simulated from a general K-allele model without knowledge of the distribution of a sample of size one. Furthermore, the computation involved in generating such samples appears to be less than that of simulating the ASG until its ultimate ancestor. In particular, in the case of genic selection with parent-independent mutations, the computational requirement increases only quadratically with the selection rate. The algorithm is demonstrated by simulating samples at a microsatellite locus.
