RT Journal Article
SR Electronic
T1 Predicting Dog Phenotypes from Genotypes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.04.13.488108
DO 10.1101/2022.04.13.488108
A1 Emily R. Bartusiak
A1 Míriam Barrabés
A1 Aigerim Rymbekova
A1 Julia Gimbernat-Mayol
A1 Cayetana López
A1 Lorenzo Barberis
A1 Daniel Mas Montserrat
A1 Xavier Giró-i-Nieto
A1 Alexander G. Ioannidis
YR 2022
UL http://biorxiv.org/content/early/2022/04/14/2022.04.13.488108.1.abstract
AB 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 StatementThe authors have declared no competing interest.