Genomic characterisation of Varzese: an iconic dual-purpose autochthonous cattle breed

The Varzese cattle breed is an autochthonous breed native to Lombardy and is mainly reared in mountainous areas of the ‘Appennino Lombardo’. This breed has experienced a significant population decline due to competition from advancements in agricultural mechanisation and higher-yielding cosmopolitan breeds. By the 1990s, the breed had reached critically low numbers, but efforts to preserve biodiversity and the recognition of its high-quality meat and milk products have led to its revival. This study aimed to assess the population structure and detect selection signatures of the Varzese breed (n = 82), comparing it with two closely related Italian autochthonous breeds: Reggiana (n = 196) and Modenese (n = 92). Using high-density single-nucleotide polymorphism (SNP) chip data, we conducted population structure analyses, including Multidimensional Scaling, genetic admixture analysis and Linkage disequilibrium decay. Selection signatures were identified through various methods, including Runs of Homozygosity (ROH), Fixation Index (FST), Integrated Haplotype Score (iHS), and Cross-Population Extended Haplotype Homozygosity (XP-EHH). Our findings reveal a close genetic relationship between Varzese and Reggiana breeds, with significant selection sweeps on chromosome 3. Annotation of these selected regions identified genes associated with key traits for dairy and beef production, highlighting opportunities for genetic improvement and conservation of the Varzese breed. This study provides a comprehensive genomic characterisation that can inform future breeding and conservation strategies for this endangered cattle breed. This research was funded by the PSRN (Programma di Sviluppo Rurale Nazionale) 2014–2020 and 2020–2023, measure 10.2, Dual Breeding and Dual Breeding 2 projects (co-funded by the European Agricultural Fund for Rural Development of the European Union and by the Italian Ministry of Agriculture, Food and Forestry – MiPAAF).