Biology subjects

Jody Hey

Publications and source records attributed to Jody Hey.

3 recordsLinked to original sources

bioRxiv2016

Bayesian Analysis of Evolutionary Divergence with Genomic Data Under Diverse Demographic Models

We present a new Bayesian method for estimating demographic and phylogenetic history using population genomic data. Several key innovations are introduced that allow the study of diverse models within an Isolation with Migration framework. For the Markov chain Monte Carlo (MCMC) phase of the analysis, we use a reduced state space, consisting of simple coalescent trees without migration paths, and a simple importance sampling distribution without demography. Migration paths are analytically integrated using a Markov chain as a representation of genealogy. The new method is scalable to a large number of loci with excellent MCMC mixing properties. Once obtained, a single sample of trees is used to calculate the joint posterior density for model parameters under multiple diverse demographic models, without having to repeat MCMC runs. As implemented in the computer program MIST, we demonstrate the accuracy, scalability and other advantages of the new method using simulated data and DNA sequences of two common chimpanzee subspecies: Pan troglodytes troglodytes (P. t.) and P. t. verus.

Evolutionary Biology↗

bioRxiv2016

Exact calculation of the joint allele frequency spectrum for generalized isolation with migration models

Population genomic datasets collected over the past decade have spurred interest in developing methods that can utilize massive numbers of loci for inference of demographic and selective histories of populations. The allele frequency spectrum (AFS) provides a convenient statistic for such analysis and accordingly much attention has been paid to predicting theoretical expectations of the AFS under a number of different models. However, to date, exact solutions for the joint AFS of two or more populations under models of migration and divergence have not been found. Here we present a novel Markov chain representation of the coalescent on the state space of the joint AFS that allows for rapid, exact calculation of the joint AFS under isolation with migration (IM) models. In turn, we show how our Markov chain method, in the context of composite likelihood estimation, can be used for accurate inference of parameters of the IM model using SNP data. Lastly, we apply our method to recent whole genome datasets from African Drosophila melanogaster.

Evolutionary Biology↗

bioRxiv2014

A Hidden Markov Model for Investigating Recent Positive Selection through Haplotype Structure

Recent positive selection can increase the frequency of an advantageous mutant rapidly enough that a relatively long ancestral haplotype will be remained intact around it. We present a hidden Markov model (HMM) to identify such haplotype structures. With HMM identified haplotype structures, a population genetic model for the extent of ancestral haplotypes is then adopted for parameter inference of the selection intensity and the allele age. Simulations show that this method can detect selection under a wide range of conditions and has higher power than the existing frequency spectrum-based method. In addition, it provides good estimate of the selection coefficients and allele ages for strong selection. The method analyzes large data sets in a reasonable amount of running time. This method is applied to HapMap III data for a genome scan, and identifies a list of candidate regions putatively under recent positive selection. It is also applied to several genes known to be under recent positive selection, including the LCT, KITLG and TYRP1 genes in Northern Europeans, and OCA2 in East Asians, to estimate their allele ages and selection coefficients.

Evolutionary Biology↗