Methodology for the identification of relevant loci for milk traits in dairy cattle, using machine learning algorithms.
MethodsX
; 9: 101733, 2022.
Article
em En
| MEDLINE
| ID: mdl-35637693
ABSTRACT
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 (EBVM), milk fat content (EBVF) and milk protein content (EBVP) 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 trainedâ¢Predicted breeding values for animals not included in the dataset.â¢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.
Dairy cattle; EBVF, estimated breeding values for milk fat content; EBVM, estimated breeding values for milk production; EBVP, estimated breeding values for milk protein content; Estimated breeding values; FDR, false discovery rate; GWAS, genome-wide association study; HxJ, Holstein x Jersey; LGB, LightGBM; LightGBM; MAE, mean absolute error; ML, machine learning; MSE, mean squared error; Milk fat content; Milk production; Milk protein content; RF, Random Forest; RMSE, root mean square error; Random forest; SNP, single nucleotide polymorphism; Single nucleotide polymorphisms; XGB, XGBoost; XGBoost
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
MethodsX
Ano de publicação:
2022
Tipo de documento:
Article