Effect of marker-data editing on the accuracy of genomic prediction

Genomic selection is a method to predict breeding values using genome-wide single-nucleotide polymorphism (SNP) markers. High-quality marker data are necessary for genomic selection. The aim of this study was to investigate the effect of marker-editing criteria on the accuracy of genomic predictions in the Nordic Holstein and Jersey populations. Data included 4429 Holstein and 1071 Jersey bulls. In total, 48222 SNP for Holstein and 44305 SNP for Jersey were polymorphic. The SNP data were edited based on (i) minor allele frequencies (MAF) with thresholds of no limit, 0.001, 0.01, 0.02, 0.05 and 0.10, (ii) deviations from HardyWeinberg proportions (HWP) with thresholds of no limit, chi-squared p-values of 0.001, 0.02, 0.05 and 0.10, and (iii) GenCall (GC) scores with thresholds of 0.15, 0.55, 0.60, 0.65 and 0.70. The marker data sets edited with different criteria were used for genomic prediction of protein yield, fertility and mastitis using a Bayesian variable selection and a GBLUP model. De-regressed EBV were used as response variables. The result showed little difference between prediction accuracies based on marker data sets edited with MAF and deviation from HWP. However, accuracy decreased with more stringent thresholds of GC score. According to the results of this study, it would be appropriate to edit data with restriction of MAF being between 0.01 and 0.02, a p-value of deviation from HWP being 0.05, and keeping all individual SNP genotypes having a GC score over 0.15.