The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Online Program Home
Abstract Details
Activity Number:
|
121
|
Type:
|
Topic Contributed
|
Date/Time:
|
Monday, July 30, 2012 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract - #305886 |
Title:
|
Bayesian Coalescent-Based Inference of Population Dynamics from Multiple Loci
|
Author(s):
|
Mandev Gill*+ and Beth Shapiro and Philippe Lemey and Andrew Rambaut and Marc A Suchard
|
Companies:
|
University of California at Los Angeles and Penn State University and Katholieke Universiteit Leuven and University of Edinburgh and University of California at Los Angeles
|
Address:
|
959 Gayley Ave, Los Angeles, CA, 90024, United States
|
Keywords:
|
Bayesian Statistics ;
Phylogenetics ;
Molecular Evolution ;
Statistical Genetics ;
Biostatistics
|
Abstract:
|
Effective population size is fundamental in population genetics and characterizes genetic diversity. To infer past population dynamics from molecular sequence data, coalescent-based models have been developed for Bayesian nonparametric estimation of effective population size over time. Among the most successful is a Gaussian Markov random field (GMRF) model for a single gene locus. Here, we extend the GMRF model to allow for the analysis of multilocus sequence data. Using simulated data, we demonstrate the improved performance of our method to recover true population trajectories and the time to the most recent common ancestor (TMRCA) by incorporating multilocus data. We analyze a multilocus alignment of CRF02_AG gene sequences sampled from Cameroon. Our results are consistent with HIV prevalence data and uncover some aspects of the population history that go undetected in Bayesian parametric estimation. Finally, we recover an older and more reconcilable TMRCA for a classic ancient DNA dataset.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2012 program
|
2012 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.