Accuracy of Genomic Selection for Important Economic Traits of Cashmere and Meat Goats Assessed by Simulation Study

Abstract

Genomic selection in plants and animals has become a standard tool for breeding because of the advantages of high accuracy and short generation intervals. Implementation of this technology is hindered by the high cost of genotyping and other factors. The aim of this study was to determine an optional marker density panel and reference population size for using genomic selection of goats, with speculation on the number of QTLs that affect the important economic traits of goats. In addition, the effect of buck population size in the reference population on the accuracy of genomic estimated breeding value (GEBV) was discussed. Based on the previous genetic evaluation results of Inner Mongolia White Cashmere Goats, live body weight (LBW, h2 = 0.11) and fiber diameter (FD, h2 = 0.34) were chosen to perform genomic selection in this study. Reasonable genome parameters and generation transmission processes were set, and phenotypic and genotype data of the two traits were simulated. Then, different sizes of the reference population and validation population were selected from progeny. The GEBVs were obtained by six methods, including GBLUP (Genomic Best Linear Unbiased Prediction), ssGBLUP (Single Step Genomic Best Linear Unbiased Prediction), BayesA, BayesB, Bayesian ridge regression, and Bayesian LASSO. The correlation coefficient between the predicted and realized phenotypes from simulation was calculated and used as a measure of the accuracy of GEBV in each trait. The results showed that the medium marker density Panel (45 K) could be used for genomic selection in goats, which can ensure the accuracy of the GEBV. The reference population size of 1,500 can achieve greater genetic progress in genomic selection for fiber diameter and live body weight in goats by comparing with the population size below this level. The accuracy of the GEBV for live body weight and fiber diameter was better when the number of QTLs was 100 and 50, respectively. Additionally, the accuracy of GEBV was discovered to be good when the buck population size was up to 200. Meanwhile, the accuracy of the GEBV for medium heritability traits (FDs) was found to be higher than the accuracy of the GEBV for low heritability traits (LBWs). These findings will provide theoretical guidance for genomic selection in goats by using real data.

Progress and Application of Genomic Selection in Sheep and Goat

“…The results found that the accuracy of GS in each model increased by 0.1-0.2 compared with that of their parents, and conventional ssGBLUP had the highest accuracy in this population. Yan (Yan et al, 2022) conducted a simulated study of GS on body weight and wool fiber diameter in Inner Mongolia Cashmere goat, and GBLUP, ssGBLUP, Bayes A, Bayes B, Bayesian R and Bayesian LASSO methods were used to calculate the GEBV, respectively. The results showed that the most suitable model needs to be selected in combination with the current conditions, and the most suitable model that enable GEBV with the highest accuracy.…”

“…The prediction accuracy of the six traits ranged from 0.28 to 0.60. Yan (Yan et al, 2022) simulated GS study on wool fiber diameter of Inner Mongolia Cashmere goat, and found that GS was more accurate than conventional selection methods. Those studies on GS of wool traits have shown that the accuracy of GS is higher than that of traditional selection methods, and the accuracy of GBLUP in GS wool traits of sheep and goat is higher than that of Bayes A.…”

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

“…Generally, the larger the reference population size, the richer the genotype data and phenotype information, and the higher the accuracy of GEBV obtained ( 15 ). Takeda et al compared the estimated breeding values for five carcass traits of Japanese black cattle under different reference population sizes ( 16 ).…”

An effective hyper-parameter can increase the prediction accuracy in a single-step genetic evaluation

“…The accuracy of genomic prediction is important, which depends on several factors such as marker density, linkage disequilibrium (LD) between the quantitative trait loci (QTLs) and markers, the sample size of reference, the heritability of the trait, the number of QTLs, and the distribution of QTL effects. The prediction accuracy is also determined by the method used ( Yan et al, 2022 ).…”