Abstract
Simple SummaryIn order to evaluate the potential application of genomic selection (GS) for Chinese indigenous cattle, we assessed the influence of combining multiple populations on the reliability of genomic predictions for 10 indigenous breeds of Chinese cattle using simulated data. We found the predictive accuracies to be low when the reference and validation populations were sampled from different breeds. When using multiple breeds for the reference population, the predictive accuracies were higher if the reference was comprised of breeds with close relationships. In addition, the accuracy increased in all scenarios when the heritability increased, and the genetic architecture of the QTL can affect genomic prediction. Our study suggested that the application of meta-populations can increase accuracy in scenarios with a reduced size of reference populations.Genomic selection (GS) has been widely considered as a valuable strategy for enhancing the rate of genetic gain in farm animals. However, the construction of a large reference population is a big challenge for small populations like indigenous cattle. In order to evaluate the potential application of GS for Chinese indigenous cattle, we assessed the influence of combining multiple populations on the reliability of genomic predictions for 10 indigenous breeds of Chinese cattle using simulated data. Also, we examined the effect of different genetic architecture on prediction accuracy. In this study, we simulated a set of genotype data by a resampling approach which can reflect the realistic linkage disequilibrium pattern for multiple populations. We found within-breed evaluations yielded the highest accuracies ranged from 0.64 to 0.68 for four different simulated genetic architectures. For scenarios using multiple breeds as reference, the predictive accuracies were higher when the reference was comprised of breeds with a close relationship, while the accuracies were low when prediction were carried out among breeds. In addition, the accuracy increased in all scenarios with the heritability increased. Our results suggested that using meta-population as reference can increase accuracy of genomic predictions for small populations. Moreover, multi-breed genomic selection was feasible for Chinese indigenous populations with genetic relationships.
Highlights
Genomic prediction has become a widely used strategy for selection of candidate animals based on the estimation of genomic estimated breeding values (GEBV) [1]
The objective of this study is to evaluate the efficiency of the potential application of multi-population genomic selection in Chinese indigenous cattle, and investigate a feasible genomic selection strategy for Chinese indigenous cattle with a small population size
The PCA result of simulated analysis was generally consistent with real data (Figure 1), and the simulated population can be divided into three groups using K-means cluster, which included group NCC (MGC, Yanhuang cattle (YHC), CDM, and Xizang cattle (XZC)), group SWC (PWC, Liangshan cattle (LSC), and ZTC), group SCHC
Summary
Genomic prediction has become a widely used strategy for selection of candidate animals based on the estimation of genomic estimated breeding values (GEBV) [1]. Many previous studies have suggested the estimated accuracy of GEBV for training populations without phenotypes mainly depends on the population size and the extent of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL s) [3,4,5]. A large reference population is difficult to construct, especially for indigenous breeds with limited population size [6,7,8]. Thomasen et al suggested a negative impact of small size reference population on the reliability of genomic prediction [9]. To avoid low estimated power from limited size of the training population, one possible resolution is to generate a large reference population by pooling multiple breeds into one meta-population
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
