Abstract
The Katahdin hair breed gained popularity in the United States as low input and prolific, with a propensity to exhibit parasite resistance. With the introduction of genomically enhanced estimated breeding values (GEBV) to the Katahdin genetic evaluation, defining the diversity present in the breed is pertinent. Utilizing pedigree records (n = 92,030) from 1984 to 2019 from the National Sheep Improvement Program, our objectives were to (i) estimate the completeness and quality of the pedigree, (ii) calculate diversity statistics for the whole pedigree and relevant reference subpopulations and (iii) assess the impact of current diversity on genomic selection. Reference 1 was Katahdins born from 2017 to 2019 (n = 23,494), while reference 2 was a subset with at least three generations of Katahdin ancestry (n = 9327). The completeness of the whole pedigree, and the pedigrees of reference 1 and reference 2, were above 50% through the fourth, fifth and seventh generation of ancestors, respectively. Effective population size (Ne) averaged 111 animals with a range from 42.2 to 451.0. The average generation interval was 2.9 years for the whole pedigree and reference 1, and 2.8 years for reference 2. The mean individual inbreeding and average relatedness coefficients were 1.62% and 0.91%, 1.74% and 0.90% and 2.94% and 1.46% for the whole pedigree, reference 1, and reference 2, respectively. There were over 300 effective founders in the whole pedigree and reference 1, with 169 in reference 2. Effective number of ancestors were over 150 for the whole pedigree and reference 1, while there were 67 for reference 2. Prediction accuracies increased as the reference population grew from 1k to 7.5k and plateaued at 15k animals. Given the large number of founders and ancestors contributing to the base genetic variation in the breed, the Ne is sufficient to maintain diversity while achieving progress with selection. Stable low rates of inbreeding and relatedness suggest that incorporating genetic conservation in breeding decisions is currently not of high priority. Current Ne suggests that with limited genotyping, high levels of accuracy for genomic prediction can be achieved. However, intense selection on GEBV may cause loss of genetic diversity long term.