Estimating inbreeding using dense marker panels and pedigree information
Avainsanat:
genomic inbreeding, genomic relationship matrix, single nucleotide polymorphismAbstrakti
The aim of this simulation study was to compare the accuracy and bias of different inbreeding (F) estimators exploiting dense panels of diallelic markers and pedigree information. All genotype simulations were started by generating an ancestral population at mutation-drift equilibrium considering an effective size of 1000 and a mutation rate (µ) of 5.10-4. Two types of subpopulation were derived from the ancestral population for 10 discrete generations. They differed by the level of selection applied both on males and females: no selection or a structure close to a breeding program with selection of the best 40 males and 500 females on EBV with accuracy of 0.85 and 0.71, respectively, on a trait with heritability of 0.3. Marker panels were made up of 36 000 biallelic markers (18 per cM) and were available for animals in the last 4 generations. Pedigrees were recorded on the last 8 generations. For each scenario, 30 replicates were carried out. Analysed estimators were the correlation (VR1) and regression (VR3) estimators described to build the genomic relationship matrix by VanRaden in 2008. Other estimators included the weighted corrected similarity (WCS) estimator published by Ritland in 1996 and a modified WCS estimator accounting for pedigree information (WPCS). Pedigree-based inbreeding (PED) was also estimated using exhaustive pedigree information. Inbreeding estimates were correlated and regressed to the true simulated genomic F values to assess the precision and bias of estimators, respectively. Main results show that use of dense marker information improves the estimation of F, whatever the scenario. The accuracy of F estimates and the bias were increased in presence of selection, except for PED. Across scenarios, VR3, WCS and WPCS were the most correlated with true F values. In the situation where pedigree was exhaustive, VR3 performed as well as WCS and WPCS but had a larger variability over replicates. Although less biased on average, VR1 was less accurate than other estimators especially when allele frequencies were not properly defined. Accounting for pedigree information into WCS did not increase its estimation accuracy and did not reduce bias in the tested scenarios. Finally, error in estimating inbreeding trends over time in selected populations was greater for some marker-based estimators (VR3, VR1) than PED estimator. WCS and WPCS rendered the most accurate estimations of inbreeding trends. Thus, results indicate that WCS, which can be also used with multiallelic markers, is a promising estimator both to build the genomic relationship matrix for genomic evaluations and to better assess genetic diversity in selected populations.
