Nearly all plant and animal populations consist of many populations among which genetic exchange is limited. Understanding the pattern of differences among human populations is of considerable medical importance. Both the epidemiology of complex disorders and the pharmacological effectiveness of certain drugs depend on the genetic background of the population in which alleles are expressed. Commonly used ethnic label often provide insufficient and inaccurate representations of the underlying genetic structure. Thus, we will extend Bayesian methods we have developed to allow investigators to describe patterns of genetic variation in complex, hierarchically structured populations. In addition to the direct medical importance of understanding population genetic structure, geneticists have long been interested in using the pattern of differences among populations to infer population size, migration rates, and mutation rates. Nearly all existing statistical methods allow only the product of population size and migration rate or the product of population size and mutation rate to be estimated. We will develop novel methods for inference that allow these parameters to be separated. Our method is statically novel in that it requires simulation of the first-stage priors in the context of a hierarchical Bayesian model for genetic data. Similar approaches have been used to provide insight into the past demographic history of human populations and for understanding patterns of selection on disease-associated alleles. In both phases of the project we will devote special attention to developing and evaluating methods of models choice. Moreover, we develop refinements to existing user-friendly software implementing the methods we develop and make the software and the associated source code freely available under the GNU General Public license.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM068449-02
Application #
6920003
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Eckstrand, Irene A
Project Start
2004-08-01
Project End
2007-07-31
Budget Start
2005-08-01
Budget End
2006-07-31
Support Year
2
Fiscal Year
2005
Total Cost
$115,112
Indirect Cost
Name
University of Connecticut
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
614209054
City
Storrs-Mansfield
State
CT
Country
United States
Zip Code
06269
Song, Seongho; Dey, Dipak K; Holsinger, Kent E (2011) Genetic diversity of microsatellite loci in hierarchically structured populations. Theor Popul Biol 80:29-37
Guo, Feng; Dey, Dipak K; Holsinger, Kent E (2009) A Bayesian hierarchical model for analysis of SNP diversity in multilocus, multipopulation samples. J Am Stat Assoc 104:142-154
Holsinger, Kent E; Weir, Bruce S (2009) Genetics in geographically structured populations: defining, estimating and interpreting F(ST). Nat Rev Genet 10:639-50
Bhattacharya, Sourabh; Gelfand, Alan E; Holsinger, Kent E (2007) Model fitting and inference under Latent Equilibrium Processes. Stat Comput 17:193-208
Song, Seongho; Dey, Dipak K; Holsinger, Kent E (2006) Differentiation among populations with migration, mutation, and drift: implications for genetic inference. Evolution 60:1-12