MBE Advance Access published online on May 30, 2003
Molecular Biology and Evolution, doi:10.1093/molbev/msg128
Molecular Biology and Evolution © Society for Molecular Biology and Evolution 2003; all rights reserved
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1 Department of Molecular Biotechnology, Health Sciences Center, University of Washington, Seattle, WA 98195
* To whom correspondence should be addressed. E-mail: jonand{at}u.washington.edu.
The ability to infer relationships between groups of sequences, either by searching for their evolutionary history or by comparing their sequence similarity, can be a crucial step in hypothesis testing. Interpreting relationships of human immunodeficiency virus type 1 (HIV-1) sequences can be challenging due to their rapidly evolving genomes, but may also lead to a better understanding of the underlying biology. Several studies have focused on the evolution of HIV-1, but little information exists linking sequence similarities and evolutionary histories of HIV-1 to the epidemiological information of the infected individual. Our goal was to correlate patterns of HIV-1 genetic diversity with epidemiological information, including risk and demographic factors. These correlations were then used to predict epidemiological information through analyzing short stretches of HIV-1 sequence. Using standard phylogenetic and phenetic techniques on 100 HIV-1 subtype B sequences, we were able to show some correlation between the viral sequences and the geographic area of infection and the risk of men who engage in sex with men. To help identify more subtle relationships between the viral sequences, the method of multidimensional scaling (MDS) was performed. MDS identified statistically significant correlations between the viral sequences and the risk factors of men who engage in sex with men and individuals who engage in sex with injection drug users or use injection drugs themselves. Using tree construction, MDS, and newly developed likelihood assignment methods on the original 100 samples we sequenced and also on a set of blinded samples, we were able to correctly predict demographic/risk group membership at a rate statistically better than by chance alone. Such methods may make it possible to identify viral variants belonging to specific demographic groups by examining only a small portion of the HIV-1 genome. Such predictions of demographic epidemiology based on sequence information may become valuable in assigning different treatment regimens to infected individuals. Key Words:
HIV, Multidimensional scaling, Likelihood assignment, Group prediction
© 2003 Society for Molecular Biology and Evolution
Original Articles
Predicting Demographic Group Structures Based on DNA Sequence Data
2 Department of Microbiology, Health Sciences Center, University of Washington, Seattle, WA 98195
3 Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA 30333
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