Wavelet analysis of human recombination rates demonstrates divergence on fine scales

BackgroundRecombination rates can be estimated across the genome, underpinning genetic analyses such as identification of regions under selection. Accurate recombination mapping requires observing a large number of recombination events, necessitating large sample sizes to achieve high resolution. This can be prohibitive to some analyses, so population-based estimates can also be used, leveraging the increasing population genomic data available to researchers. ObjectiveThis study aimed to determine the extent to which population-based recombination maps from different human populations are similar and assess to what extent they can be used interchangeably. MethodsWavelet analysis was employed to decompose recombination rate signals along a chromosome and evaluate the proportion of variance explained at different scales. This method also enabled the assessment of correlations across scales and identification of regions with high coherence between datasets. The analysis focused on a region of chromosome 22 in human populations of European and African ancestry. ResultsRecombination rates are not closely conserved across populations, with the greatest divergence observed at fine scales. Coherence between populations varied significantly across all scales. ConclusionAs recombination maps differ substantially between human populations, for genetic analyses involving recombination maps, it is recommended to use maps specific to the population under study.