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Predicting Dog Phenotypes from Genotypes

View ORCID ProfileEmily R. Bartusiak, Míriam Barrabés, Aigerim Rymbekova, Julia Gimbernat-Mayol, Cayetana López, Lorenzo Barberis, View ORCID ProfileDaniel Mas Montserrat, Xavier Giró-i-Nieto, View ORCID ProfileAlexander G. Ioannidis
doi: https://doi.org/10.1101/2022.04.13.488108
Emily R. Bartusiak
1Purdue University
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Míriam Barrabés
2Universitat Politècnica de Catalunya
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Aigerim Rymbekova
3University of Bologna
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Julia Gimbernat-Mayol
4Imperial College of London
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Cayetana López
2Universitat Politècnica de Catalunya
5Stanford University
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Lorenzo Barberis
5Stanford University
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Daniel Mas Montserrat
5Stanford University
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Xavier Giró-i-Nieto
2Universitat Politècnica de Catalunya
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Alexander G. Ioannidis
5Stanford University
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  • For correspondence: ioannidis@stanford.edu
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Abstract

We analyze dog genotypes (i.e., positions of dog DNA sequences that often vary between different dogs) in order to predict the corresponding phenotypes (i.e., unique observed characteristics). More specifically, given chromosome data from a dog, we aim to predict the breed, height, and weight. We explore a variety of linear and non-linear classification and regression techniques to accomplish these three tasks. We also investigate the use of a neural network (both in linear and non-linear modes) for breed classification and compare the performance to traditional statistical methods. We show that linear methods generally outperform or match the performance of non-linear methods for breed classification. However, we show that the reverse is true for height and weight regression. Finally, we evaluate the results of all of these methods based on the number of input features used in the analysis. We conduct experiments using different fractions of the full genomic sequences, resulting in input sequences ranging from 20 SNPs to ∼200k SNPs. In doing so, we explore the impact of using a very limited number of SNPs for prediction. Our experiments demonstrate that these phenotypes in dogs can be predicted with as few as 0.5% of randomly selected SNPs (i.e., 992 SNPs) and that dog breeds can be classified with 50% balanced accuracy with as few as 0.02% SNPs (i.e., 40 SNPs).

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* The first author conducted this work during an internship at Stanford University.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted April 14, 2022.
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Predicting Dog Phenotypes from Genotypes
Emily R. Bartusiak, Míriam Barrabés, Aigerim Rymbekova, Julia Gimbernat-Mayol, Cayetana López, Lorenzo Barberis, Daniel Mas Montserrat, Xavier Giró-i-Nieto, Alexander G. Ioannidis
bioRxiv 2022.04.13.488108; doi: https://doi.org/10.1101/2022.04.13.488108
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Predicting Dog Phenotypes from Genotypes
Emily R. Bartusiak, Míriam Barrabés, Aigerim Rymbekova, Julia Gimbernat-Mayol, Cayetana López, Lorenzo Barberis, Daniel Mas Montserrat, Xavier Giró-i-Nieto, Alexander G. Ioannidis
bioRxiv 2022.04.13.488108; doi: https://doi.org/10.1101/2022.04.13.488108

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