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Comparing fine-scale mutation and recombination landscapes in rhesus macaque (Macaca mulatta) populations of Chinese and Indian descent inferred from both short- and long-read sequencing data

Spatola, G. J.; Versoza, C. J.; Soni, V.; Heenkenda, E. J.; Jensen, J. D.; Pfeifer, S. P.

2026-05-26 evolutionary biology
10.64898/2026.05.26.727910 bioRxiv
Show abstract

Genomic diversity amongst primates is fundamentally shaped by species- and population-specific rates of mutation and recombination. In this study, we infer fine-scale mutation and recombination rate maps for the rhesus macaque (Macaca mulatta) -- the most widely used non-human primate model in biomedical research -- leveraging both short-(Illumina) and long-(PacBio HiFi) read sequencing data from two distinct populations of Chinese and Indian descent. Thereby, we draw comparisons between the rates estimated from each dataset, highlighting both biologically meaningful variation between these populations as well as artefactual discrepancies likely arising from systematic biases and differences in the utilized sequencing technologies. Consistent with previous observations in humans, broad-scale features of the recombination landscape are well-conserved between the two populations, but significant differences exist at the finer scales. Notably, we find evidence for a high rate of turnover in recombination hotspots over a short evolutionary time span, resulting in population-specific recombination maps in which the vast majority of the >30,000 identified recombination hotspots in one population are inactive in the other population. Given that mutation and recombination rates are necessary components for the interpretation of other diversity-shaping processes and events, including those characterizing both the underlying demographic and selective histories, the incorporation of these population-specific maps into future models will improve our understanding of the evolutionary genomics of the species. Additionally, these maps will serve as a fundamental component of future genome-wide association and fine-mapping studies of disease traits in this biomedical model system.

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