The study led by Chessari, Reich, Criscione, and colleagues, soon to be published in BMC Genomics, compares the effectiveness of SNP array technology and imputed genomic data in analyzing population structure and runs of homozygosity (ROH) hotspots in horse breeds. SNP arrays provide high-resolution genetic variation data, while imputed data, derived through statistical inference, can fill gaps where direct genotype information is limited. Their research highlights how combining these methodologies enhances understanding of genetic diversity, ancestral lineages, and inbreeding effects, which are critical for sustainable breeding and conservation efforts.

By analyzing a diverse dataset of horse breeds, the study reveals that SNP arrays offer precise accuracy in many contexts, but imputed data can complement these findings, especially in regions with sparse SNP coverage. The identification of ROH hotspots informs on genetic health and potential risks from reduced diversity. The authors propose standardized protocols for integrating SNP and imputed data, providing valuable guidance for researchers and breeders. Ultimately, this work advances equine genetics by offering practical tools to optimize breeding strategies, preserve genetic distinctiveness, and support the long-term viability of horse populations worldwide.