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\begin{document}

\title{Missing genotype imputation in non-model species using Self-Organizing
Maps}



\author[1]{Fernando Mora-Márquez}%
\author[1]{Juan Carlos Nuño}%
\author[1]{Álvaro Soto}%
\author[1]{Unai López de Heredia}%
\affil[1]{Universidad Politécnica de Madrid}%


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  \date{May 22, 2024}


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\begin{abstract}
Current methodologies of genome-wide Single Nucleotide Polymorphism
(SNP) genotyping produce large amounts of missing data that may affect
statistical inference and bias the outcome of experiments. Genotype
imputation is routinely used in well-studied species to buffer the
impact in downstream analysis and several algorithms are available to
fill in missing genotypes. The lack of reference haplotype panels
precludes the use of these methods in genomic studies on non-model
organisms. As an alternative, machine learning algorithms are employed
to explore the genotype data and to estimate the missing genotypes.
Here, we propose an imputation method based on Self-Organizing Maps
(SOM), a widely used neural networks formed by spatially distributed
neurons that cluster similar inputs into close neurons. We follow a
classical approach that explores genotype datasets to select SNP loci
for each query missing SNP genotype to build training sets, and that
initializes and trains the neural networks to finally use the
SOM-derived clustering to impute the best genotype. To automate the
imputation process, we have implemented GTIMPUTATION, an open source
application programmed in Python3 and with a user-friendly GUI to
facilitate the whole process. The method performance was validated by
comparing its accuracy, precision and sensitivity on several benchmark
genotype datasets with other available imputation algorithms. Our
approach produced highly accurate and precise genotype imputations and
outperformed other algorithms, especially for datasets from mixed
populations with unrelated individuals.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Mora-Marquezetal-SOMbasedImputation.docx`  available at \url{https://authorea.com/users/347523/articles/647482-missing-genotype-imputation-in-non-model-species-using-self-organizing-maps}

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