bioRxivEvolutionary Biology
Copy number evolution in simple and complex tandem repeats across the C57BL/6 and C57BL/10 inbred mouse lines
Abstract
Simple sequence tandem repeats are among the most rapidly evolving compartments of the genome. Some repeat expansions are associated with mammalian disease or meiotic segregation distortion, yet the rates of copy number change across generations are not well known. Here, we use 14 distinct sub-lineages of the C57BL/6 and C57BL/10 inbred mouse strains, which have been evolving independently over about 300 generations, to estimate the rates of copy number changes in genome-wide tandem repeats. Rates of change varied across simple repeats and across lines. Notably, CAG, whose expansions in coding regions are associated with many neurological and other genetic disorders, was highly stable in copy number, likely indicating purifying selection. Rates of change were generally positively correlated with copy number, but the direction and magnitude of changes varied across lines. Some mouse lines experienced consistent losses or gains across most genome-wide simple repeats, but this did not correlate with copy number changes in complex repeats. Rates of copy number change were similar between simple repeats and the much more abundant complex repeats once they were normalized by copy number. Finally, the Y-specific centromeric repeat had a 4-fold higher rate of change than the homologous centromeric repeat on other chromosomes. Structural differences in satellite complexity, or restriction to the Y chromosome and the elevated mutation rate of the male germline, may explain the higher rate of change. Overall, our work underscores the mutational fluidity of long tandem arrays of repeats, and the correlations and constraints between genome-wide tandem repeats which suggest that turnover is not a completely neutral process.
- organism: mouse
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- approach: statistical model
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