Biology subjects

Heenkenda, E. J.

Publications and source records attributed to Heenkenda, E. J..

2 recordsLinked to original sources

bioRxiv2026

Inferring the demographic history of Chinese and Indian rhesus macaque (Macaca mulatta) populations from PacBio HiFi long-read sequencing data

The rhesus macaque (Macaca mulatta) is one of the most widely used animal models in biomedical research, both as it resembles humans in key biological aspects and as it is characterized by a broad geographic range. Most of the individuals housed in U.S. research colonies have been sampled from either China or India, though notably the source population of these animals has significantly shifted over time. Given the substantial genetic and immunological differences between these populations, a deeper understanding of the underlying population structure is critically important for biomedical interpretation. Despite this, the demographic histories of these two populations remain poorly resolved. Here, we present an analysis of whole-genome, PacBio HiFi long-read sequencing data from ten unrelated individuals of each population, applying four related model- and non-model based demographic inference approaches, in order to reconstruct their ancestral history. We evaluated the fit of the subsequently estimated models against the empirical data, and incorporated underlying uncertainty in the mutation rates used for scaling. We inferred a well-fitting population history characterized by substantial structure between Chinese and Indian populations, with a split time [~]140,000 generations ago from an ancestral population of [~]65,000 individuals. We additionally inferred the subsequent history of size change within, and gene flow between, these populations, reaching the current estimated sizes of [~]220,000 individuals in the Chinese population and [~]14,000 individuals in the Indian population. The robust baseline demographic model established in this study will serve as a valuable resource for future research on this species, including for improved fine-scale recombination mapping, selection inference, and association studies.

evolutionary biology↗

bioRxiv2026

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

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.

evolutionary biology↗