Population divergence, including speciation and the origins of population structure, is the fundamental evolutionary process leading to the diversity of life. This research project will extend recent advances in a likelihood-based approach to divergence models. The new approach employs analytic integration over prior distributions of model parameters within a Markov chain Monte Carlo framework. The method leads to a joint probability density function, proportional to the likelihood that can be used for parameter estimation and log- likelihood ratio tests of nested demographic models. The new method will be adapted to general multi-population problems in divergence. Such problems have long been appreciated as requiring both a population genetic perspective and a phylogenetic perspective. The research plan outlines how these two can be brought together under a common MCMC simulation. This will be the first such method that does not assume a given phylogeny;that does not assume that gene flow has not occurred;and that makes no assumptions about the relative population sizes of sampled or ancestral populations. By providing estimates of the joint posterior density, proportional to the likelihood, the method will provide direct access to log-likelihood ratio tests and to likelihood-based confidence intervals. The approach will also be extended to problems in sample identification and DNA barcoding. These new methods will be applied to a case study of divergence among species and subspecies of Chimpanzee. The methods will also be applied to large multi-population multi-locus data sets from human populations.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
Project #
Application #
Study Section
Genetic Variation and Evolution Study Section (GVE)
Program Officer
Eckstrand, Irene A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Rutgers University
Other Basic Sciences
Schools of Arts and Sciences
New Brunswick
United States
Zip Code
Hey, Jody; Chung, Yujin; Sethuraman, Arun et al. (2018) Phylogeny Estimation by Integration over Isolation with Migration Models. Mol Biol Evol 35:2805-2818
Price, Nicholas; Moyers, Brook T; Lopez, Lua et al. (2018) Combining population genomics and fitness QTLs to identify the genetics of local adaptation in Arabidopsis thaliana. Proc Natl Acad Sci U S A 115:5028-5033
Kern, Andrew D; Hey, Jody (2017) Exact Calculation of the Joint Allele Frequency Spectrum for Isolation with Migration Models. Genetics 207:241-253
Schrider, Daniel R; Kern, Andrew D (2017) Soft Sweeps Are the Dominant Mode of Adaptation in the Human Genome. Mol Biol Evol 34:1863-1877
Lavington, Erik; Kern, Andrew D (2017) The Effect of Common Inversion Polymorphisms In(2L)t and In(3R)Mo on Patterns of Transcriptional Variation in Drosophila melanogaster. G3 (Bethesda) 7:3659-3668
Knoblauch, Jared; Sethuraman, Arun; Hey, Jody (2017) IMGui-A Desktop GUI Application for Isolation with Migration Analyses. Mol Biol Evol 34:500-504
Chung, Yujin; Hey, Jody (2017) Bayesian Analysis of Evolutionary Divergence with Genomic Data under Diverse Demographic Models. Mol Biol Evol 34:1517-1528
Kern, Andrew D; Schrider, Daniel R (2016) Discoal: flexible coalescent simulations with selection. Bioinformatics 32:3839-3841
Schrider, Daniel R; Kern, Andrew D (2016) S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning. PLoS Genet 12:e1005928
Schrider, Daniel R; Shanku, Alexander G; Kern, Andrew D (2016) Effects of Linked Selective Sweeps on Demographic Inference and Model Selection. Genetics 204:1207-1223

Showing the most recent 10 out of 30 publications