Cost-effective genomic prediction for fertility traits: A comparison of machine learning and conventional models using low-coverage sequencing in Holstein heifers
Zhuangbiao Zhang, Ao Wang, Qiyan Wang, S.Q. Gao, L.L. Wang, Honghong Hu, Hojjat Asadollahpour Nanaei, Ali Mujtaba Shah, G.L. Liu, K. Zhu, X.Z. Lv, R. Li, Yu Yan Jiang
This study compares machine learning and conventional models for genomic prediction of fertility traits in Holstein heifers using low-coverage sequencing data, finding that KRR_GRM improves accuracy over traditional methods when using only genomic data.
animal · Apr 24, 2026Peer-reviewedGenomics