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A novel method distinguishes between mutation rates and fixation biases in patterns of single-nucleotide substitution

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons50074

Arndt,  Peter F.
Evolutionary Genomics (Peter Arndt), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Zitation

Lipatov1, M., Arndt, P. F., Hwa, T., & Petrov, D. A. (2006). A novel method distinguishes between mutation rates and fixation biases in patterns of single-nucleotide substitution. Journal of Molecular Evolution: the Journal of the International Society of Molecular Evolution, 62(2), 168-175. doi:10.1007/s00239-005-0207-z.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-84B5-A
Zusammenfassung
Analysis of the genome-wide patterns of single-nucleotide substitution reveals that the human GC content structure is out of equilibrium. The substitutions are decreasing the overall GC content (GC), at the same time making its range narrower. Investigation of single-nucleotide polymorphisms (SNPs) revealed that presently the decrease in GC content is due to a uniform mutational preference for A:T pairs, while its projected range is due to a variability in the fixation preference for G:C pairs. However, it is important to determine whether lessons learned about evolutionary processes operating at the present time (that is reflected in the SNP data) can be extended back into the evolutionary past. We describe here a new approach to this problem that utilizes the juxtaposition of forward and reverse substitution rates to determine the relative importance of variability in mutation rates and fixation probabilities in shaping long-term substitutional patterns. We use this approach to demonstrate that the forces shaping GC content structure over the recent past (since the appearance of the SNPs) extend all the way back to the mammalian radiation ∼90 million years ago. In addition, we find a small but significant effect that has not been detected in the SNP data—relatively high rates of C:G→A:T germline mutation in low-GC regions of the genome.