Abstract #165
Section: Breeding and Genetics (orals)
Session: Breeding and Genetics II: Methodologies, Inbreeding and Breeding Strategies
Format: Oral
Day/Time: Monday 2:00 PM–2:15 PM
Location: Room 301 B
Session: Breeding and Genetics II: Methodologies, Inbreeding and Breeding Strategies
Format: Oral
Day/Time: Monday 2:00 PM–2:15 PM
Location: Room 301 B
# 165
Managing population diversity through genomic optimal contribution selection.
Christian Maltecca*1, Gebreyohans Gebregiwergis3, Jeremy T. Howard4, Christine F. Baes2, Francesco Tiezzi1, 1North Carolina State University, Raleigh, NC, 2University of Guelph, Guelph, ON, Canada, 3Norwegian University of Life Sciences, Ås, Norway, 4University of Nebraska-Lincoln, Lincoln, NE.
Key Words: optimal contribution, genomic selection, runs of homozygosity (ROH)
Managing population diversity through genomic optimal contribution selection.
Christian Maltecca*1, Gebreyohans Gebregiwergis3, Jeremy T. Howard4, Christine F. Baes2, Francesco Tiezzi1, 1North Carolina State University, Raleigh, NC, 2University of Guelph, Guelph, ON, Canada, 3Norwegian University of Life Sciences, Ås, Norway, 4University of Nebraska-Lincoln, Lincoln, NE.
Managing population diversity has been traditionally accomplished through pedigree information and various systems aimed at constraining the accumulation of inbreeding in mating populations. Optimal contribution selection (OCS) has been a popular method to guarantee long-term gains without compromising variability. Little information is available on the impact of using genomic information in conjunction with OCS. In this research, we investigated the use of alternative metrics of ancestry in OCS in simulated scenarios using genomic information. One production trait and one fitness trait were generated with the GenoDiver software following a typical dairy population structure. For the production trait, a polygenic trait (h2 0.5, 1000 QTL) was simulated. For the fitness trait, partial dominance was simulated with varying proportions of lethal and sub-lethal fitness trait loci (FTL) ranging from 0 to 5% of the total FTL number. OCS was simulated for 30 generations. At each generation, genomic information was used to obtain breeding values of individuals, while different relationship measures (pedigree, genomic, and ROH (10 MB and 20Mb) were employed for optimal contribution. Selection was performed only on the production trait. Genetic progress for both production and fitness in all scenarios was measured at generation 30. Diversity measures analyzed included homozygosity, lethal equivalents, fitness, segregating (sub-)lethals, QTL and FTL lost. All methods were compared with a baseline of no OCS. In all cases, OCS maintained greater genomic variability in respect to the baseline. ROH-based methods achieved larger genetic gains compared with other methods. Pedigree-based methods maintained the largest variability with the lowest genetic gain. Genomic-based OCS was best at constraining homozygosity at lethal loci, while ROH-based methods were more effective for constraining sublethal loci. Both ROH- and genomic-based OCS were effective, with the ROH resulting in a good compromise between short-term genetic response and long-term fitness management.
Key Words: optimal contribution, genomic selection, runs of homozygosity (ROH)
