We present a Bayesian method for characterizing the mating system of populations reproducing through a mixture of self-fertilization and random outcrossing. Our method uses patterns of genetic variation across the genome as a basis for inference about reproduction under pure hermaphroditism, gynodioecy, and a model developed to describe the self-fertilizing killifish Kryptolebias marmoratus. We extend the standard coalescence model to accommodate these mating systems, accounting explicitly for multilocus identity disequilibrium, inbreeding depression, and variation in fertility among mating types. We incorporate the Ewens sampling formula (ESF) under the infinite-alleles model of mutation to obtain a novel expression for the likelihood of mating system parameters. Our Markov chain Monte Carlo (MCMC) algorithm assigns locus-specific mutation rates, drawn from a common mutation rate distribution that is itself estimated from the data using a Dirichlet process prior model. Our sampler is designed to accommodate additional information, including observations pertaining to the sex ratio, the intensity of inbreeding depression, and other aspects of reproduction. It can provide joint posterior distributions for the population-wide proportion of uniparental individuals, locus-specific mutation rates, and the number of generations since the most recent outcrossing event for each sampled individual. Further, estimation of all basic parameters of a given model permits estimation of functions of those parameters, including the proportion of the gene pool contributed by each sex and relative effective numbers.

We present a Bayesian method for characterizing the mating system of populations reproducing through a mixture of self-fertilization and random outcrossing. Our method uses patterns of genetic variation across the genome as a basis for inference about pure hermaphroditism, androdioecy, and gynodioecy. We extend the standard coalescence model to accommodate these mating systems, accounting explicitly for multilocus identity disequilibrium, inbreeding depression, and variation in fertility among mating types. We incorporate the Ewens Sampling Formula (ESF) under the infinite-alleles model of mutation to obtain a novel expression for the likelihood of mating system parameters. Our Markov chain Monte Carlo (MCMC) algorithm assigns locus-specific mutation rates, drawn from a common mutation rate distribution that is itself estimated from the data using a Dirichlet Process Prior (DPP) model. Among the parameters jointly inferred are the population-wide rate of self-fertilization, locus-specific mutation rates, and the number of generations since the most recent outcrossing event for each sampled individual.

Risk, severity, and outcome of infection depend on the interplay of pathogen virulence and host susceptibility. Systematic identification of genetic susceptibility to infection is being undertaken through genome-wide association studies, but how to expeditiously move from genetic differences to functional mechanisms is unclear. Here, we use genetic association of molecular, cellular, and human disease traits and experimental validation to demonstrate that genetic variation affects expression of VAC14, a phosphoinositide-regulating protein, to influence susceptibility to Salmonella enterica serovar Typhi (S Typhi) infection. Decreased VAC14 expression increased plasma membrane cholesterol, facilitating Salmonella docking and invasion. This increased susceptibility at the cellular level manifests as increased susceptibility to typhoid fever in a Vietnamese population. Furthermore, treating zebrafish with a cholesterol-lowering agent, ezetimibe, reduced susceptibility to S Typhi. Thus, coupling multiple genetic association studies with mechanistic dissection revealed how VAC14 regulates Salmonella invasion and typhoid fever susceptibility and may open doors to new prophylactic/therapeutic approaches.

Risk, severity, and outcome of infection depend on the interplay of pathogen virulence and host susceptibility. Systematic identification of genetic susceptibility to infection is being undertaken through genome-wide association studies, but how to expeditiously move from genetic differences to functional mechanisms is unclear. Here, we use genetic association of molecular, cellular, and human disease traits and experimental validation to demonstrate that genetic variation affects expression of VAC14, a phosphoinositide-regulating protein, to influence susceptibility to Salmonella enterica serovar Typhi (S Typhi) infection. Decreased VAC14 expression increased plasma membrane cholesterol, facilitating Salmonella docking and invasion. This increased susceptibility at the cellular level manifests as increased susceptibility to typhoid fever in a Vietnamese population. Furthermore, treating zebrafish with a cholesterol-lowering agent, ezetimibe, reduced susceptibility to S Typhi. Thus, coupling multiple genetic association studies with mechanistic dissection revealed how VAC14 regulates Salmonella invasion and typhoid fever susceptibility and may open doors to new prophylactic/therapeutic approaches.