A comparative study of runs of homozygosity islands common among 12 Australian beef cattle breeds
Abstract Cattle breeds exhibit phenotypic and genomic differences shaped by artificial and natural selection. Runs of homozygosity (ROH) analyses detect contiguous homozygous regions on chromosomes that arise as genomic diversity decreases over time. These regions may reflect shared ancestry within a population, selection pressures, or demographic processes. This study aimed to identify ROH profiles, common ROH islands, and their associated genes affected by them in twelve Australian beef cattle breeds (Alexandria, Angus, Brahman, Brangus, Charolais, Droughtmaster, Hereford, Kynuna, Limousin, Santa Gertrudis, Shorthorn, and Speckle Park). The dataset included 463,877 animals from the BREEDPLAN evaluation system, with marker densities ranging from 33K to 100K across breeds. An inverse relationship was observed between effective population size (Ne) and genome-wide ROH profiles, with populations exhibiting smaller Ne values generally accumulating more and longer ROH segments. The Speckle Park showed the highest amount of ROH and small Ne (121), while the Droughtmaster showed the lowest amount of ROH and high Ne (300), indicating their evolutionary backgrounds during the breeding program. Common ROH islands were identified on chromosomes 1, 3, 5, 6, 7, 8, 13, 14, 24, and 26. Common ROH islands on chromosomes 3, 8, and 13 primarily reflected founder contributions from Angus, Brahman, and Shorthorn to their admixed populations. In contrast, common ROH islands on chromosomes 1, 5, 6, 7, 14, 24, and 26 among breeds suggest convergent selection. Identified candidate genes within common ROH islands were enriched in biological processes related to adaptability, reproductive, and production traits, suggesting the effects of artificial and natural selection on economically important traits in Australian beef breeds. These results emphasize the importance of demographic history and selection pressures when interpreting ROH patterns and highlight specific genomic regions that can inform targeted breeding programs, monitor genetic diversity at the genomic segment level, and optimize genotyping array design.