| Type of Document |
Dissertation |
| Author |
Mannino, Frank Vincent, |
| URN |
etd-03272006-140300 |
| Title |
Site-to-site rate variation in protein coding genes |
| Degree |
PhD |
| Graduate Program |
Bioinformatics |
| Advisory Committee |
| Advisor Name |
Title |
| Spencer Muse |
Committee Chair |
| Bruce Weir |
Committee Member |
| Jeffrey Thorne |
Committee Member |
| William Atchley |
Committee Member |
|
| Keywords |
- codon model
- synonymous and nonsynonymous substitution rates
- site-to-site rate heterogeneity
- molecular evolution
- gamma distribution
- rate correlation
|
| Date of Defense |
2006-03-23 |
| Availability |
unrestricted |
Abstract
The ability to realistically model gene evolution improved dramatically with the rejection of the assumption that rates are constant across sites. Rate heterogeneity models allow for better estimates of parameters and site specific inferences such as the detection of positive selection. Recently developed models of codon evolution allow for both synonymous and nonsynonymous rates to vary independently according to discretized gamma distributions. I applied this model to mitochondrial genomes and concluded that synonymous rate variation is present in many genes, and is of appreciable magnitude relative to the amount of nonsynonymous heterogeneity. I then extending this model to allow for the two rates to vary according to a dependent bivariate distribution, permitting tests for the significance of correlation of rates within a gene. I present here the algorithm to discretize this bivariate distribution and the application of the model to many real data sets. Significant correlation between synonymous and nonsynonymous rates exists in roughly half of the data sets that I examined, and the correlation is typically positive. These data sets range over a wide group of taxa and genes, implying that the trend of correlation is general. Finally, I performed a thorough investigation of the statistical properties of using discretized gamma distributions to model rate variation, looking at the bias and variance in parameter estimates. These discretized distributions are common in modeling heterogeneity, but have weaknesses that must be well understood before making inferences.
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| Files |
| Filename |
Size |
Approximate Download Time
(Hours:Minutes:Seconds)
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| 28.8 Modem |
56K Modem |
ISDN (64 Kb) |
ISDN (128 Kb) |
Higher-speed Access |
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etd.pdf |
1.87 Mb |
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