Project Title:
Unveiling the Past: Analysis of Evolutionary and Demographic History

Project Description (short):
Refine Tajima's statistical test to infer a population's evolutionary and demographic history from the current distribution of genetic variation.

Skills needed:
The biology student will need BIOL 107 and 108, and optimally Genetics or Bioinformatics (or a strong interest in these fields). The mathematics student will need CS 180 (and preferably 185), STAT 290 and a willingness to learn about bioinformatics. Some linear and abstract algebra would be very useful for working with abstract distances between sequences.

Start Date:
January 2007

End Date:
December 2009

Mentors:
Prof. Anton Weisstein (Biology) weisstae@truman.edu
Prof. Pam Ryan (Mathematics), pjryan@truman.edu

Current Students:

• Amanda Hamilton (Mathematics)
• Karen O'Connell (Biology)
• Dianne Kopp (Biology)

About Prof. Weisstein:
Anton (Tony) Weisstein received his B.A. in Math and Chemistry from Washington University in St. Louis, and his Ph.D. in Evolutionary and Population Biology from the same institution. After postdoctoral positions in New Zealand and with the BioQUEST Curriculum Consortium in Wisconsin, he moved to Kirksville in 2004. He is currently an Assistant Professor of Biology at Truman State University, where he serves as the curriculum coordinator for the introductory course sequence. A mathematical biologist at heart, Tony’s current research with undergraduates focuses on viral phylogenetics and epidemiological modeling. He is also interested in developing new curricular materials for biology education, and serves as Biology Editor for BioQUEST's ESTEEM Collection, a suite of open-source Excel-based modules for computational and mathematical biology.

About Prof. Ryan:
Biographical and Personal Sketch

Project Description (long):
Many different processes leave characteristic signatures on the genetic variation present in a population. For example, purifying selection and population bottlenecks both dramatically reduce a population's genetic diversity. This observation has led to the development of summary statistics, such as Tajma's D, that can be used to infer a population's history and/or selective pressures from its current levels of genetic variation.

Existing statistics can determine whether diversity-reducing or diversity-enhancing processes prevail in a given population; however, they generally have limited ability to discriminate between demographic and evolutionary processes. Continuing the previous example, the detection of limited diversity in a population may indicate the presence of either purifying selection or historical population bottlenecks (or both).

Our research is aimed at modifying Tajima's D statistic to better distinguish between present selective forces and past historical events. This is accomplished by analyzing synonymous and nonsynonymous substitutions separately and applying Tajima's test to each. Under the assumption that selective forces affect primarily nonsynonymous sites, while demographic events affect both synonymous and nonsynonymous sites, we can potentially detect both kinds of forces simultaneously.

The first part of our project consists of using computer simulations to determine the expected distribution of the synonymous and nonsynonymous D statistics under a neutral model. Further simulations of specific and known evolutionary/demographic processes will be conducted to determine the modified test's power and specificity. Finally, we will attempt to demonstrate the application of the new procedure to a specific data set from the literature. One specific gene region we hope to analyze is the V3 region of the envelope gene from HIV. This region is under very strong selection from the host's immune system, and the high mutation rate and large population size of HIV within a patient make it a promising candidate for further study.

References:

• Allman, E. and Rhodes, J. Mathematical Models in Biology: An Introduction. Cambridge University Press, 2004.
• Allman, E. S. and Rhodes, J.A. Phylogenetic invariants for the general Markov model of sequence mutation, Math.Biosci. 186 (2003), no. 2, 113-144.
• Tajima, F. Statistical Method for Testing the Neutral Mutation Hypothesis by DNA Polymorhphism. Genetics 123: 585-595 (November, 1989)
• Nei, M. Selectionism and Neutralism in Molecular Evolution. Mol. Biol. Evol. 22(12)2318-2342, 2005.
• Nei, M and Gojobori, T. Simple Methods for Estimating the Numbers of Synonymous and Nonsynonymous Nucleotide Substitutions. Mol. Biol. Evol. 3(5):418-426. 1986.