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\begin{document}

\title{Validation of machine learning approach for direct mutation rate
estimation}



\author[1]{Katarzyna Burda}%
\author[2]{Mateusz Konczal}%
\affil[1]{Adam Mickiewicz University}%
\affil[2]{Affiliation not available}%


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  \date{May 16, 2024}


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\begin{abstract}
Mutations are the primary source of all genetic variation. Knowledge
about their rates is critical for any evolutionary genetic analyses, but
for a long time, that knowledge has remained elusive and indirectly
inferred. In recent years, parent-offspring comparisons have yielded the
first direct mutation rate estimates. The analyses are, however,
challenging due to high rate of false positives and no consensus
regarding standardized filtering of candidate de novo mutations. Here,
we validate the application of a machine learning approach for such a
task and estimate the mutation rate for the guppy (Poecilia reticulata),
a model species in eco-evolutionary studies. We sequenced 4 parents and
20 offspring, followed by screening their genomes for de novo mutations.
The initial large number of candidate de novo mutations was
hard-filtered to remove false-positive results. These results were
compared with mutation rate estimated with a supervised machine learning
approach. Both approaches were followed by molecular validation of all
candidate de novo mutations and yielded similar results. The ML method
uniquely identified 3 mutations, but overall required more work and had
higher rates of false positives and false negatives. We, thus, recommend
its application if most of the mutations are expected to be identified
or in case of experiment-specific biases. Both methods concordantly
showed that guppy mutation rate is among the lowest directly estimated
mutation rates in vertebrates. Similarly, low estimates were obtained
for two other teleost fishes. We discuss potential explanations for such
a pattern, as well as future utility and limitations of machine-learning
approaches.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Burda_MLmutaitons_MER_FINAL.docx`  available at \url{https://authorea.com/users/498740/articles/626645-validation-of-machine-learning-approach-for-direct-mutation-rate-estimation}\selectlanguage{english}
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