Assessment of genomic prediction accuracy using different selection and evaluation approaches in a simulated Korean beef cattle population

Impacts of reference population size and methods on the accuracy of genomic prediction for fleece traits in Inner Mongolia Cashmere Goats

Introduction

Inner Mongolia Cashmere Goats (IMCGs) are famous for its cashmere quality and it's a unique genetic resource in China. Therefore, it is necessary to use genomic selection to improve the accuracy of selection for fleece traits in Inner Mongolia cashmere goats. The aim of this study was to determine the effect of methods (GBLUP, BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) and the reference population size on accuracy of genomic selection in IMCGs.

Methods

This study fully utilizes the pedigree and phenotype records of fleece traits in 2255 individuals, genotype of 50794 SNPs after quality control, and environmental data to perform genomic selection of fleece traits. Then GBLUP and Bayes series methods (BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) were used to perform estimates of genetic parameter and genomic breeding value. And the accuracy of genomic estimated breeding value (GEBV) is evaluated using the five-fold cross validation method. And the analysis of variance and multiple comparison methods were used to determine the best method for genomic selection in fleece traits of IMCGs. Further the different reference population sizes (500, 1000, 1500, and 2000) was set. Then the best method was applied to estimate genome breeding values, and evaluate the impact of reference population sizes on the accuracy of genome selection for fleece traits in IMCGs.

Results

It was found that the genomic prediction accuracy for each fleece trait in IMCGs by GBLUP method is highest, and it is significantly higher than that obtained by Bayesian method. The accuracy of breeding value estimation is 58.52% -68.49%. Also, it was found that the size of the reference population has a significant impact on the accuracy of genome prediction of fleece traits. When the reference population size is 2000, the accuracy of genomic prediction for each fleece trait is significantly higher than other levels, with accuracy of 55.47% -67.87%. This provides a theoretical basis for design a reasonable genome selection plan for Inner Mongolia cashmere goats in the later stag.

Improving Breeding Value Reliability with Genomic Data in Breeding Groups of Charolais

The aim of this study was to assess the impact of incorporating genomic data using the single-step genomic best linear unbiased prediction (ssGBLUP) method compared to the best linear unbiased prediction (BLUP) method on the reliability of breeding values for age at first calving, calving interval, and productive longevity at 78 months in Charolais cattle. The study included 48,590 purebred Charolais individuals classified into four subgroups based on genotyping and performance records. The results showed that considering genotypes significantly improved genomic estimated breeding values (GEBV) reliability across all categories except nongenotyped individuals. For young genotyped individuals, the increase in reliability was up to 27% for both sexes. The highest average reliability was achieved for genotyped proven bulls and cows with performance records, and the inclusion of genomic data further improved the reliability by up to 22% and 21% for cows and bulls, respectively. The gain in reliability was observed mainly during the first three calvings, and then the differences decreased. The imported individuals showed lower estimated breeding values (EBV) and GEBV reliabilities than the domestic population, probably due to the weak genetic connection with the domestic population. However, when the progeny of imported heifers were sired by domestic bulls, the reliability increased by up to 24%. For nongenotyped individuals, only a slight increase in reliability was observed; however, the number of genotyped individuals in the population was still relatively small.

Selective genotyping to implement genomic selection in beef cattle breeding

Genomic selection (GS) plays an essential role in livestock genetic improvement programs. In dairy cattle, the method is already a recognized tool to estimate the breeding values of young animals and reduce generation intervals. Due to the different breeding structures of beef cattle, the implementation of GS is still a challenge and has been adopted to a much lesser extent than dairy cattle. This study aimed to evaluate genotyping strategies in terms of prediction accuracy as the first step in the implementation of GS in beef while some restrictions were assumed for the availability of phenotypic and genomic information. For this purpose, a multi-breed population of beef cattle was simulated by imitating the practical system of beef cattle genetic evaluation. Four genotyping scenarios were compared to traditional pedigree-based evaluation. Results showed an improvement in prediction accuracy, albeit a limited number of animals being genotyped (i.e., 3% of total animals in genetic evaluation). The comparison of genotyping scenarios revealed that selective genotyping should be on animals from both ancestral and younger generations. In addition, as genetic evaluation in practice covers traits that are expressed in either sex, it is recommended that genotyping covers animals from both sexes.

Estimation of breeding value and accuracy using pedigree and genotype of Hanwoo cows (Korean cattle)

The genetic improvement of Hanwoo is dependent on the estimated breeding value (EBV) of pedigree-based Korean proven bull's number, and the genetic evaluation for cows is difficult due to insufficient pedigree and test records. Genomic selection involves utilizing the individual's genotype to estimate the breeding value (BV) and is determined to be an appropriate evaluation method for cows who lack test information. This study used pedigree and genotype to estimate and analyse BV and accuracy of Hanwoo cows in the Gyeongnam area using pedigree best linear unbiased prediction (PBLUP) and genomic best linear unbiased prediction (GBLUP). The test group acquired pedigree and genotype of 919 Hanwoo cows in the Gyeongnam area. The traits used for analysis were carcass weight (CWT), eye muscle areas (EMA), backfat thickness (BFT) and marbling score (MS). PBLUP used Reference group 1 containing the pedigree and phenotype of 919 Hanwoo cows and 545,483 heads to construct the numeric relationship matrix and estimated the EBV and accuracy. GBLUP used Reference group 2 containing the genotype and phenotype of 919 Hanwoo cows and 17,226 heads to construct the genomic relationship matrix and estimated the genomic EBV (GEBV) and accuracy. In the order of CWT, EMA, BFT and MS, the accuracy of PBLUP was 0.488, 0.480, 0.482 and 0.486 while the accuracy of GBLUP was higher with 0.779, 0.758, 0.766 and 0.791. And for 104 cows without relationship coefficient on pedigree to the reference group, the accuracy as PBLUP was estimated to be 0, but for GBLUP, it was possible to estimate the accuracy for all individuals. If GBLUP is applied to cows raised in general farms, the genetic evaluation can be performed even on animals without pedigree and high-accuracy estimation, enabling selection of excellent cows. Accordingly, by securing the genetic diversity of cows, it is expected to increase the profitability of farms by decreasing the inbreeding rate and increasing efficiency of elite calf production.

Optimal population size to detect quantitative trait locus in Korean native chicken: a simulation study

Objective

A genomic region associated with a particular phenotype is called quantitative trait loci (QTL). To detect the optimal F₂ population size associated with QTLs in native chicken, we performed a simulation study on F₂ population derived from crosses between two different breeds.

Methods

A total of 15 males and 150 females were randomly selected from the last generation of each F₁ population which was composed of different breed to create two different F₂ populations. The progenies produced from these selected individuals were simulated for six more generations. Their marker genotypes were simulated with a density of 50K at three different heritability levels for the traits such as 0.1, 0.3, and 0.5. Our study compared 100, 500, 1,000 reference population (RP) groups to each other with three different heritability levels. And a total of 35 QTLs were used, and their locations were randomly created.

Results

With a RP size of 100, no QTL was detected to satisfy Bonferroni value at three different heritability levels. In a RP size of 500, two QTLs were detected when the heritability was 0.5. With a RP size of 1,000, 0.1 heritability was detected only one QTL, and 0.5 heritability detected five QTLs. To sum up, RP size and heritability play a key role in detecting QTLs in a QTL study. The larger RP size and greater heritability value, the higher the probability of detection of QTLs.

Conclusion

Our study suggests that the use of a large RP and heritability can improve QTL detection in an F₂ chicken population.