Accuracy and bias of genomic prediction with different de-regression methods

Abstract

Genomic selection has become increasingly important in the breeding of animals and plants. The response variable is an important factor, influencing the accuracy of genomic selection. The de-regressed proof (DRP) based on traditional estimated breeding value (EBV) is commonly used as response variable. In the current study, simulated data from 16th QTL-MAS Workshop and real data from Chinese Holstein cattle were used to compare accuracy and bias of genomic prediction with two methods of calculating DRP. Our results with simulated data showed that the correlation between genomic EBV and true breeding value achieved using the Jairath method (DRP_J) was superior to that achieved using the Garrick method (DRP_G) for simulated trait 1 but the reverse was true for simulated trait 3, and these two methods performed comparably for simulated trait 2. For all three simulated traits, DRP_J yielded larger bias of genomic prediction. However, DRP_J outperformed DRP_G in both accuracy and unbiasedness for four milk production traits in Chinese Holstein. In the estimation of genomic breeding value using genomic BLUP model, two methods for weighting diagonal elements of incidence matrix associated with residual error were also compared. With increasing the proportion of genetic variance unexplained by markers, the accuracy of genomic prediction was decreased and the bias was increased. Weighting by the reliability of DRP produced accuracy comparable to the evaluation where the proportion of genetic variance unexplained by markers was considered, but with smaller bias in general.

Comparing deregression methods for genomic prediction of test‐day traits in dairy cattle

“…For this reason, the GR generated more variable dEBV for all traits (which is indicated by standard deviations in Table ), suggesting that the deregression was too strong. These results corroborate with those reported by Song, Li, Zhang, Zhang, and Ding (), which also presented higher variance for dEBVs and lower generated by the GR method. Calus et al.…”

“…Garrick et al (2009) have deduced the optimal weights to be considered in R À1 based on heritability, REL EBV and the fraction of genetic variance not explained by markers. In this context, Song et al (2017) tested the GR method considering the optimal weights based on different heritabilities and proportions of genetic variance not explained by markers. However, these authors found that, given a fixed heritability, increasing the proportion of genetic variance not explained by markers decreased the accuracy of genomic prediction and increased the bias.…”

Impact of including information from bulls and their daughters in the training population of multiple‐step genomic evaluations in dairy cattle: A simulation study

“…As also reported in other studies (e.g. Song et al, ; Oliveira et al, ), high REL EBV implies in a weaker deregression (i.e. dEBVs are similar to EBVs).…”

“…However, several studies in dairy cattle have used dEBVs removing PA (e.g. Butty et al, ; Song et al, ). Among the deregression methods, JA showed more irregular increase in dEBV variability between the scenarios, which might be due to the fact that JA takes into account the genetic relationships from all related animals to estimate the dEBVs (Jansen, ).…”

“…Ostersen et al, ) or even ignored this possible double‐counting of information (e.g. Song, Li, Zhang, Zhang, & Ding, ; Oliveira et al, ). Therefore, the double‐counting that this study focuses on was not investigated (or it was not accounted for) in previous studies.…”