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PLoS One. 2013 Jun 6;8(6):e64698. doi: 10.1371/journal.pone.0064698. Print 2013.

The evolutionary rates of HCV estimated with subtype 1a and 1b sequences over the ORF length and in different genomic regions.

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Department of Pathology and Laboratory Medicine, Center for Viral Oncology, University of Kansas Medical Center, Kansas City, Kansas, United States of America.



Considerable progress has been made in the HCV evolutionary analysis, since the software BEAST was released. However, prior information, especially the prior evolutionary rate, which plays a critical role in BEAST analysis, is always difficult to ascertain due to various uncertainties. Providing a proper prior HCV evolutionary rate is thus of great importance.


176 full-length sequences of HCV subtype 1a and 144 of 1b were assembled by taking into consideration the balance of the sampling dates and the even dispersion in phylogenetic trees. According to the HCV genomic organization and biological functions, each dataset was partitioned into nine genomic regions and two routinely amplified regions. A uniform prior rate was applied to the BEAST analysis for each region and also the entire ORF. All the obtained posterior rates for 1a are of a magnitude of 10(-3) substitutions/site/year and in a bell-shaped distribution. Significantly lower rates were estimated for 1b and some of the rate distribution curves resulted in a one-sided truncation, particularly under the exponential model. This indicates that some of the rates for subtype 1b are less accurate, so they were adjusted by including more sequences to improve the temporal structure.


Among the various HCV subtypes and genomic regions, the evolutionary patterns are dissimilar. Therefore, an applied estimation of the HCV epidemic history requires the proper selection of the rate priors, which should match the actual dataset so that they can fit for the subtype, the genomic region and even the length. By referencing the findings here, future evolutionary analysis of the HCV subtype 1a and 1b datasets may become more accurate and hence prove useful for tracing their patterns.

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