This study compares two mutation models: one that incorporates recurrent mutations and another that allows only boundary mutations and shows that while tree topologies remain mostly unaffected, branch lengths and estimates of mutation bias and selection are substantially compromised.
Abstract
A common assumption in mathematical models of molecular evolution is that mutations are rare. One example is the boundary mutation assumption, which posits that mutations occur so infrequently that, by the time a new one arises, the previous mutation has already been fixed or lost in the population. This assumption, however, ignores recurrent mutations and can be problematic for highly diverse organisms such as bacteria and viruses. In this study, we challenge the assumption of infrequent mutation in phylogenetic inference. To do so, we compare two mutation models: one that incorporates recurrent mutations and another that allows only boundary mutations. Our results show that while tree topologies remain mostly unaffected, branch lengths and estimates of mutation bias and selection are substantially compromised. These patterns hold across both simulations and empirical case studies, including HIV, HCV and IAV viruses. Overall, this study highlights the importance of accounting for recurrent mutations in phylogenetic analyses of highly diverse organisms, many of which have significant epidemiological and medical relevance.
The results suggest that modest numbers of mutations suffice to reconstruct clonal tree topologies for typical numbers of clones, supporting subsampling as a general strategy for managing the challenges of ever-growing data.
N. Bristy, Russell Schwartz· Bioinformatics Advances· 0 citations
Recent findings in humans and other species have revealed the presence of “mutator” alleles that increase germline mutation rate across the genome. Such mutators are expected to be selected against because of the additional deleterious alleles that they generate, to a degree that will depend on how much they increase t...
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Julie M. Grosse-Sommer, Dhobasheni Newman, J. Hadfield· bioRxiv· 0 citations
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The findings show that mutation rate heterogeneity systematically biases current variant effect prediction frameworks, highlight the need to model mutation probabilities explicitly in future VEPs, and reveal a genuine biological signal of mutational robustness.
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