Assessing population structure and genetic diversity in U.S. Suffolk sheep to define a framework for genomic selection

Long-term sustainability of breeds depends on having sufficient genetic diversity for adaptability to change, whether driven by climatic conditions or by priorities in breeding programs. Genetic diversity in Suffolk sheep in the U.S. was evaluated in four ways: 1) using genetic relationships from pedigree data [(n=64,310 animals recorded in the U.S. National Sheep Improvement Program (NSIP)]; 2) using molecular data (n=304 Suffolk genotyped with the OvineHD BeadChip); 3) comparing Australian (n=109) and Irish (n=55) Suffolk sheep to those in the U.S. using molecular data; and 4) assessing genetic relationships (connectedness) among active Suffolk flocks (n=18) in NSIP. By characterizing genetic diversity, a goal was to define the structure of a reference population for use for genomic selection strategies in this breed. Pedigree-based mean inbreeding level for the most recent year of available data was 5.5%. Ten animals defined 22.8% of the current gene pool. The effective population size (Ne) ranged from 27.5 to 244.2 based on pedigree and was 79.5 based on molecular data. Expected (HE) and observed (HO) heterozygosity were 0.317 and 0.306, respectively. Model-based population structure included 7 subpopulations. From Principal Component Analysis, countries separated into distinct populations. Within the U.S. population, flocks formed genetically disconnected clusters. A decline in genetic diversity over time was observed from both pedigree and genomic-based derived measures with evidence of population substructure as measured by FST. Using these measures of genetic diversity, a framework for establishing a genomic reference population in U.S. Suffolk sheep engaged in NSIP was proposed.Funding provided by: Agricultural Research ServiceCrossref Funder Registry ID: http://dx.doi.org/10.13039/100007917Award Number: Funding provided by: American Sheep Industry AssociationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100017466Award Number