The mutation rate is a fundamental evolutionary parameter with direct and appreciable effects on the health and function of individuals. Here, we examine this important parameter in the domestic cat, a beloved companion animal as well as a valuable biomedical model. We estimate a mutation rate of 0.86 × 10-8 per bp per generation for the domestic cat (at an average age of 3.8 years). We find evidence for a strong paternal age effect, with more mutations transmitted by older sires. Our analyses suggest that the cat and the human have accrued similar numbers of mutations in the germline before reaching sexual maturity. The per-generation mutation rate in the cat is slightly lower than what has been observed in humans, but consistent with the shorter generation time in the cat. Using a model of reproductive longevity, which takes into account differences in the reproductive age and time to sexual maturity, we are able to explain much of the difference in per-generation rates between species. We further apply our reproductive longevity model in a novel analysis of mutation spectra and find that the spectrum for the cat resembles the human mutation spectrum at a younger age of reproduction. Together, these results implicate changes in life-history as a driver of mutation rate evolution between species. As the first direct observation of the paternal age effect outside of primates, our results also suggest a phenomenon that may be universal among mammals.

Recent population studies are ever growing in size of samples to investigate the diversity of a given population or species. These studies reveal ever new polymorphism that lead to important insights into the mechanisms of evolution, but are also important for the interpretation of these variations. Nevertheless, while the full catalog of variations across entire species remains unknown, we can predict which regions harbor additional variations that remain hidden and investigate their properties, thereby enhancing the analysis for potentially missed variants. To achieve this we implemented SVhound (https://github.com/lfpaulin/SVhound), which based on a population level SVs dataset can predict regions that harbor novel SV alleles. We tested SVhound using subsets of the 1000 genomes project data and showed that its correlation (average correlation of 2,800 tests r=0.7136) is high to the full data set. Next, we utilized SVhound to investigate potentially missed or understudied regions across 1KGP and CCDG that included multiple genes. Lastly we show the applicability for SVhound also on a small and novel SV call set for rhesus macaque (Macaca mulatta) and discuss the impact and choice of parameters for SVhound. Overall SVhound is a unique method to identify potential regions that harbor hidden diversity in model and non model organisms and can also be potentially used to ensure high quality of SV call sets.