27 Machine Learning Algorithms Based on Haplotype Libraries for Classification of Stillbirth Susceptibility in Holstein Cows. (8th October 2021)

• Record Type: Journal Article
• Authors: Fonseca, Pablo A S; Tornatore, Massimo; Cánovas, Angela
• Is Part Of: Journal of animal science. Volume 99(2021)Supplement 3
• Page Start: 15
• Page End: 16
• Publication Date: 2021-10-08
• Subjects: stillbirth -- machine learning -- haplotypes; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/skab235.025
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: Reduced fertility is one of the main causes of economic losses in dairy farms. The cost of a stillbirth is estimated in US$ 938 per case in Holstein herds. Machine learning (ML) is gaining popularity in the livestock sector as a mean to identify hidden patterns and due to its potential to address dimensionality problems. Here we investigate the application of ML algorithms for the prediction of cows with higher stillbirth susceptibility in two scenarios: cows with >25% and >33.33% of stillbirths among birth records. These thresholds correspond to percentiles 75 (still_75) and 90 (still_90), respectively. A total of 10, 570 cows and 50, 541 birth records were collected to perform a haplotype-based genome-wide association study. Five-hundred significant pseudo single nucleotide polymorphisms (pseudo-SNPs) (False-Discovery Rate< 0.05) were used as input features of ML-based predictions to determine if the cow is in the top-75 and top-90 percentiles. Table 1 shows the classification performance of the investigated ML and linear models. The ML models outperformed linear models for both thresholds. In general, still_75 showed higher F1 values compared to still_90, suggesting a lower misclassification ratio when a less stringent threshold is used. We observe that accuracy of the models in our study is higher when compared to ML-based prediction accuracies in other breeds, e.g. compared to the accuracies of 0.46 and 0.67 that were achieved using SNPs for body weight in Brahman and fertility traits in Nellore, respectively. Xgboost algorithm shows the highest balanced accuracy (BA; 0.625), F1-score (0.588) and area under the curve (AUC; 0.688), suggesting that xgboost can achieve the highest predictive performance and the lowest difference in misclassification ratio between classes. The ML applied over haplotype libraries is an interesting approach for the detection of animals with higher susceptibility to stillbirths due to highest predictive accuracy and relatively lower misclassification ratio.