Machine learning methods for genomic prediction of cow behavioral traits measured by automatic milking systems in North American Holstein cattle

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Identifying genome-enabled methods that provide more accurate genomic prediction is crucial when evaluating complex traits such as dairy cow behavior. In th...

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Published in:Journal of dairy science Vol. 107; no. 7; pp. 4758 - 4771
Main Authors: Pedrosa, Victor B., Chen, Shi-Yi, Gloria, Leonardo S., Doucette, Jarrod S., Boerman, Jacquelyn P., Rosa, Guilherme J.M., Brito, Luiz F.
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01.07.2024
Elsevier
Subjects:
accuracy of prediction
Algorithms
Animals
automatic milking systems
Bayesian theory
Behavior, Animal
Cattle - genetics
dairy cows
dairy science
Dairying - methods
deep learning
family
Female
Genomics
Genotype
genotyping
Holstein
Lactation - genetics
Machine Learning
Milk
neural networks
Phenotype
prediction
quality control
sensor-based systems
deep learning
accuracy of prediction
automatic milking systems
sensor-based systems
ISSN:0022-0302, 1525-3198, 1525-3198
Online Access:Get full text
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Summary:The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Identifying genome-enabled methods that provide more accurate genomic prediction is crucial when evaluating complex traits such as dairy cow behavior. In this study, we aimed to compare the predictive performance of traditional genomic prediction methods and deep learning algorithms for genomic prediction of milking refusals (MREF) and milking failures (MFAIL) in North American Holstein cows measured by automatic milking systems (milking robots). A total of 1,993,509 daily records from 4,511 genotyped Holstein cows were collected by 36 milking robot stations. After quality control, 57,600 SNPs were available for the analyses. Four genomic prediction methods were considered: Bayesian least absolute shrinkage and selection operator (LASSO), multiple layer perceptron (MLP), convolutional neural network (CNN), and GBLUP. We implemented the first 3 methods using the Keras and TensorFlow libraries in Python (v.3.9) but the GBLUP method was implemented using the BLUPF90+ family programs. The accuracy of genomic prediction (mean square error) for MREF and MFAIL was 0.34 (0.08) and 0.27 (0.08) based on LASSO, 0.36 (0.09) and 0.32 (0.09) for MLP, 0.37 (0.08) and 0.30 (0.09) for CNN, and 0.35 (0.09) and 0.31(0.09) based on GBLUP, respectively. Additionally, we observed a lower reranking of top selected individuals based on the MLP versus CNN methods compared with the other approaches for both MREF and MFAIL. Although the deep learning methods showed slightly higher accuracies than GBLUP, the results may not be sufficient to justify their use over traditional methods due to their higher computational demand and the difficulty of performing genomic prediction for nongenotyped individuals using deep learning procedures. Overall, this study provides insights into the potential feasibility of using deep learning methods to enhance genomic prediction accuracy for behavioral traits in livestock. Further research is needed to determine their practical applicability to large dairy cattle breeding programs.
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ISSN:0022-0302
1525-3198
1525-3198
DOI:10.3168/jds.2023-24082