Methodology for the identification of relevant loci for milk traits in dairy cattle, using machine learning algorithms.

Autor: Raschia MA; Instituto Nacional de Tecnología Agropecuaria, CICVyA-CNIA, Instituto de Genética 'Ewald A. Favret'. Hurlingham, Buenos Aires, Argentina., Ríos PJ; Universidad de Buenos Aires, Buenos Aires, Argentina.; Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Argentina., Maizon DO; Instituto Nacional de Tecnología Agropecuaria, E.E.A. Anguil. Anguil, La Pampa, Argentina.; Facultad de Agronomía, Universidad Nacional de La Pampa, Argentina., Demitrio D; Instituto Nacional de Tecnología Agropecuaria, Dirección General de Sistemas de Información, Comunicación y Procesos - Gerencia de Informática y Gestión de la Información. Buenos Aires, Argentina.; Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Argentina., Poli MA; Instituto Nacional de Tecnología Agropecuaria, CICVyA-CNIA, Instituto de Genética 'Ewald A. Favret'. Hurlingham, Buenos Aires, Argentina.; Facultad de Ciencias Agrarias y Veterinarias, Universidad del Salvador, Argentina.
Jazyk: angličtina
Zdroj: MethodsX [MethodsX] 2022 May 16; Vol. 9, pp. 101733. Date of Electronic Publication: 2022 May 16 (Print Publication: 2022).
DOI: 10.1016/j.mex.2022.101733
Abstrakt: Machine learning methods were considered efficient in identifying single nucleotide polymorphisms (SNP) underlying a trait of interest. This study aimed to construct predictive models using machine learning algorithms, to identify loci that best explain the variance in milk traits of dairy cattle. Further objectives involved validating the results by comparison with reported relevant regions and retrieving the pathways overrepresented by the genes flanking relevant SNPs. Regression models using XGBoost (XGB), LightGBM (LGB), and Random Forest (RF) algorithms were trained using estimated breeding values for milk production (EBV M ), milk fat content (EBV F ) and milk protein content (EBV P ) as phenotypes and genotypes on 40417 SNPs as predictor variables. To evaluate their efficiency, metrics for actual vs. predicted values were determined in validation folds (XGB and LGB) and out-of-bag data (RF). Less than 4500 relevant SNPs were retrieved for each trait. Among the genes flanking them, signaling and transmembrane transporter activities were overrepresented. The models trained:•Predicted breeding values for animals not included in the dataset.•Were efficient in identifying a subset of SNPs explaining phenotypic variation. The results obtained using XGB and LGB algorithms agreed with previous results. Therefore, the method proposed could be applied for future association studies on milk traits.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2022 The Authors.)
Databáze: MEDLINE
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