Hojjat Asadollahpour Nanaei

Cost-effective genomic prediction for fertility traits: A comparison of machine learning and conventional models using low-coverage sequencing in Holstein heifers

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.

The first sheep graph-based pan-genome reveals the spectrum of structural variations and their effects on tail phenotypes

The paper constructs a graph-based sheep pan-genome to identify structural variations and their association with tail phenotypes in domestic and wild sheep.