Germ Line Mutation Rates in Old World Monkeys

Abstract

Mutations that occur in the germ line are the ultimate source of genetic variation upon which natural selection can act. As such, an understanding of the germ line mutation rate is crucial for interpreting evolutionary processes and products, whether in response to emerging infectious disease or climate change. Even in closely related species such as primates, substantial variation exists in both mutation rates as well as the mutational spectra, partially owing to differences in life history traits. Though considerable attention has been given to humans and other great apes in these characterisations, Old World monkeys represent an excellent and underutilised model system to investigate the extent and time scale at which mutation rates have evolved across the primate clade. Key Concepts Mutation rates are influenced by a variety of genetic factors, evolutionary processes and life history traits – many of which differ between primate species. As a consequence, studying the evolution of mutation rate itself across the primate clade has arisen as a key research question in the field of evolutionary and population genomics.

The Effects of Mutation and Recombination Rate Heterogeneity on the Inference of Demography and the Distribution of Fitness Effects

“…Similarly, the rate at which mutations appear throughout the genome varies considerably at every scale examined, from individual sites in a genome to differences observed between individuals, populations, and species (see reviews of Baer et al 2007 ; Lynch 2010 ; Hodgkinson and Eyre-Walker 2011 ; Pfeifer 2020 ). Thereby, numerous factors can affect the mutation rate, including local nucleotide composition (such as GC-content; Hwang and Green 2004 ), genomic factors (such as chromatin accessibility, replication timing, and recombination; Agarwal and Przeworski 2019 ), as well as life history traits (see discussion in Tran and Pfeifer 2018 ). Methods that employ different site categories for demographic and/or DFE inference ( Piganeau and Eyre-Walker 2003 ; Eyre-Walker and Keightley 2009 ; Gutenkunst et al 2009 ; Galtier 2016 ; Tataru et al 2017 ; Tataru and Bataillon 2020 ) may thus be biased by neglecting this heterogeneity, as underlying rates may differ systematically between different site classes.…”

The effects of mutation and recombination rate heterogeneity on the inference of demography and the distribution of fitness effects

“…Similarly, the rate at which mutations appear throughout the genome varies considerably at every scale examined, from individual sites in a genome to differences observed between individuals, populations, and species (see reviews of Baer et al 2007; Lynch 2010; Hodgkinson and Eyre-Walker 2011; Pfeifer 2020). Thereby, numerous factors can affect the mutation rate, including: local nucleotide composition (such as GC-content; Hwang and Green 2004), genomic factors (such as chromatin accessibility, replication timing, and recombination; Agarwal and Przeworski 2019), as well as life history traits (see discussion in Tran and Pfeifer 2018). Methods that employ different site categories for demographic and/or DFE inference (Piganeau and Eyre-Walker 2003; Eyre-Walker and Keightley 2009; Gutenkunst et al 2009; Galtier 2016; Tataru et al 2017; Tataru and Bataillon 2020) may thus be biased by neglecting this heterogeneity, as underlying rates may differ systematically between different site classes.…”