Effects of ROH-based versus GRM-based future inbreeding penalties on genetic gain, variance components, and inbreeding depression in dairy cattle

Managing inbreeding while maintaining genetic gain is a critical challenge in dairy cattle breeding programs.Current North American genetic evaluations adjust breeding values for expected future inbreeding using genomic relationship matrix (GRM) approaches, but runs of homozygosity (ROH)-based methods may offer advantages for detecting recent autozygosity.This study compared ROH-based versus GRM-based inbreeding penalties across 20 generations of simulated genomic selection.Using AlphaSimR, we simulated dairy cattle populations under 3 genetic scenarios: additive only, low non-additive effects ( 2 D/ 2 A = 0.10, inbreeding depression = 0.30G per 10% F), and high non-additive effects ( 2 D/ 2 A = 0.50, inbreeding depression = 1.20G per 10% F).Each penalty method was tested at 3 intensities (0.5 , 1 , 2 the inbreeding depression parameter) with 10 replicates per scenario-treatment combination.GRM-based penalties achieved lower inbreeding accumulation than ROH-based penalties, with differences of 0.13 to 0.22 in genomic F at matched penalty intensities.However, this enhanced inbreeding control was associated with reduced genetic gain, with GRM-based selection achieving 0.6 to 1.7 genetic standard deviations less gain than ROH-based selection.GRM-based selection retained more additive variance (66-107%) compared with ROHbased selection (63-74%), with values exceeding 100% indicating partial recovery of variance depleted during burn-in.Dominance variance retention was also greater for GRM-based selection (105-114% vs. 93-98%), reflecting maintained heterozygosity.Interestingly, GRMbased selection accumulated more inbreeding depression than ROH-based approaches despite lower inbreeding levels, potentially reflecting more effective purging under the stronger directional selection maintained by ROH-based methods.Results illustrate trade-offs between inbreeding control and genetic gain, with optimal strategy depending on breeding program objectives and trait genetic architecture.