The propose research will address a great challenge of modern human genetics which is to understand the recent evolutionary history of our species. This is important not only as key basic knowledge, but also for many medical genetic questions, such as when and where have mutations occurred that contribute to diseases. The backdrop for medical questions like these is the demographic history of human populations, which includes our history of population sizes, population founding events, migrations and admixture, and population size changes. Two new methods for estimating the demographic history of a species will be developed. Both of these have been designed to address three major challenges that limit current methods. First, methods that are intended to study the history of gene exchange must include recombination as part of the model because of the strong interaction affect that recombination and gene exchange have on patterns of genetic variation. Second, methods must be able to handle large, complex demographic models with human populations. This is especially true for histories with gene exchange. Third, methods must be able to handle very large data sets with speed and without introducing biases. One new method will provide estimates of the Allele Frequency Spectrum (AFS) for pairs of linked single-nucleotide- polymorphisms (SNPs). By including recombination for pairs of SNPs, the 2 SNP AFS is far larger and holds much more information on gene exchange than the conventional single SNP-based AFS. The second new method will be a new genealogy sampling approach that uses a greatly reduced representation of genealogies (gene trees). This method does not give up any information, and yet unlike most current genealogy samplers will be able to be used for models with recombination and for large data sets. The third goal is to apply these methods to the study of human demographic history among African populations. African populations have ancient and complex genetic histories that include all of the major demographic processes considered here. Both methods will be applied to a large high-quality population genomic data set that currently includes 33 genomes from 7 populations, including three hunter-gatherer populations. The goal is an accurate and rich multi- population portrait of human history in Africa.

Public Health Relevance

Identifying and understanding the genetic contribution to human diseases depends in part upon having a backdrop of detailed knowledge of demographic history our species. The propose research will provide new methods capable of revealing the demographic history of human populations and will apply these methods to population genomic data from African populations.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM078204-07
Application #
8640190
Study Section
Special Emphasis Panel (ZRG1-GGG-H (02))
Program Officer
Eckstrand, Irene A
Project Start
2007-09-01
Project End
2017-01-31
Budget Start
2014-02-01
Budget End
2015-01-31
Support Year
7
Fiscal Year
2014
Total Cost
$220,301
Indirect Cost
$64,861
Name
Temple University
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
057123192
City
Philadelphia
State
PA
Country
United States
Zip Code
19122
Kern, Andrew D; Hey, Jody (2017) Exact Calculation of the Joint Allele Frequency Spectrum for Isolation with Migration Models. Genetics 207:241-253
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
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
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
Sethuraman, Arun; Hey, Jody (2016) IMa2p--parallel MCMC and inference of ancient demography under the Isolation with migration (IM) model. Mol Ecol Resour 16:206-15
Schrider, Daniel R; Mendes, Fábio K; Hahn, Matthew W et al. (2015) Soft shoulders ahead: spurious signatures of soft and partial selective sweeps result from linked hard sweeps. Genetics 200:267-84

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