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Strong Evidence for Lineage and Sequence Specificity of Substitution Rates and Patterns in Drosophila

Summary: Strong Evidence for Lineage and Sequence Specificity of Substitution Rates and
Patterns in Drosophila
Nadia D. Singh,* Peter F. Arndt, Andrew G. Clark,* and Charles F. Aquadro*
*Department of Molecular Biology and Genetics, Cornell University; and Department of Computational Molecular Biology, Max
Planck Institute for Molecular Genetics, Berlin
Rates of single nucleotide substitution in Drosophila are highly variable within the genome, and several examples
illustrate that evolutionary rates differ among Drosophila species as well. Here, we use a maximum likelihood method to
quantify lineage-specific substitutional patterns and apply this method to 4-fold degenerate synonymous sites and introns
from more than 8,000 genes aligned in the Drosophila melanogaster group. We find that within species, different classes
of sequence evolve at different rates, with long introns evolving most slowly and short introns evolving most rapidly.
Relative rates of individual single nucleotide substitutions vary ;3-fold among lineages, yielding patterns of substitution
that are comparatively less GC-biased in the melanogaster species complex relative to Drosophila yakuba and
Drosophila erecta. These results are consistent with a model coupling a mutational shift toward reduced GC content, or
a shift in mutation­selection balance, in the D. melanogaster species complex, with variation in selective constraint
among different classes of DNA sequence. Finally, base composition of coding and intronic sequences is not at
equilibrium with respect to substitutional patterns, which primarily reflects the slow rate of the substitutional process.
These results thus support the view that mutational and/or selective processes are labile on an evolutionary timescale and
that if the process is indeed selection driven, then the distribution of selective constraint is variable across the genome.
Rates and patterns of molecular evolution are gov-


Source: Arndt, Peter - Max-Planck-Institut für molekulare Genetik
Spang, Rainer - Computational Molecular Biology Group, Max-Planck-Institut für molekulare Genetik


Collections: Biology and Medicine; Biotechnology; Computer Technologies and Information Sciences; Physics