Title page for ETD etd-06292007-162133

Type of Document

Thesis

Author

Shi, Yanfeng

Author's Email Address

yanfeng.shi@gmail.com

URN

etd-06292007-162133

Title

Evolutionary Forces of H3N2 type Influenza A Virus

Degree

Master of Science

Department

Biological Science, Department of

Advisory Committee

Advisor Name	Title
David Swofford	Committee Chair
Hengli Tang	Committee Member
Peter Beerli	Committee Member

Keywords

Evolutionary Force
Influenza A Virus

Date of Defense

2007-06-18

Availability

unrestricted

Abstract

With more influenza A virus sequence data available than before, researchers have paid more attention to how to utilize these data to predict the influenza A virus evolution trend. There are diverse opinions on that. Some researchers believe that using positive selection information can advise which strain will be the next season’s predominant strain. Some researchers consider that reassortment is the main evolutionary force of influenza A virus and positive selection only has minor effects. Still some other researchers reason that for a certain geography region like New York State, virus immigration is the main cause for the emergences of new influenza strains. In our study we proposed another evolutionary force, seasonal reassortment which is the gene reassortment between the same subtype influenza viruses of different seasons. We used three datasets (the USA dataset, the Australian dataset, and the New Zealand dataset) to investigate the effect of this evolutionary force. Also we examined the phylogenetic predictability of influenza A virus evolution using the positive selection information. Our results mainly include 1) seasonal reassortment occurs frequently; 2) the USA dataset has different evolutionary pathway from the other two datasets; 3) the Haemagglutinin and Neuraminidase genes show similar evolutionary pathway in the USA dataset; 4) mutation rate and positive selection strength seem not strongly related to the influenza virus fitness; 5) few positive selection sites were detected in the USA dataset and the prediction power using positive selection is weak.

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