Comparison of the Efficiency of BLUP and GBLUP in Genomic Prediction of Immune Traits in Chickens

Evaluation of selection strategies in dual-purpose and specialized breeding of indigenous chicken

This study aimed to evaluate various selection strategies for adoption in dual-purpose (ICD), meat (ICM) and layer (ICL) breeding goals in indigenous chicken breeding programs. The ICM goal aimed to improve live weight (LW12), daily gain (ADG) and egg weight (EW12) or together with feed efficiency and antibody response. For the ICL goal, age at first egg (AFE) and egg number (EN12) or together with feed efficiency and antibody response were targeted. In the ICD goal, the objective was to improve LW12, ADG, AFE and EN12 or together with feed efficiency and antibody response. Highest total index responses of US$ 49.83, US$ 65.71, and US$ 37.90 were estimated in indices targeting only production traits in the ICD, ICM and ICL goals, respectively. Highest index accuracy estimates of 0.77 and 0.70 were observed in indices that considered production and feed-related traits in the ICD and ICL goals, respectively, while in the ICM goal, the highest estimate of 0.96 was observed in an index targeting only production traits. Inbreeding levels ranged from 0.60 to 1.14% across the various indices considered in the breeding goals. Targeting only production traits in the ICD, ICM and ICL goals required the least number of generations of selection of 7.46, 5.50, and 8.52, respectively, to achieve predefined gains. Generally, a strategy targeting only production traits in a goal was the most optimal but resulted to unfavorable correlated responses in feed efficiency and antibody response. Addition of feed efficiency or/and antibody response in a goal was, however, not attractive due to the decline in total index response and accuracy and increase in inbreeding levels and number of generations of selection. Considering the feed availability and disease challenges in the tropics, choice of including feed efficiency or/and antibody response in the ICD, ICM and ICL goals should depend on targeted production system, resource availability to support breeding activities and magnitude of correlated responses on these traits when not included in the goals.

Comparison of genomic prediction accuracy using different models for egg production traits in Taiwan country chicken

In local chickens targeted for niche markets, genotyping costs are relatively high due to the small population size and diverse breeding goals. The single-step genomic best linear unbiased prediction (ssGBLUP) model, which combines pedigree and genomic information, has been introduced to increase the accuracy of genomic estimated breeding value (GEBV). Therefore, this model may be more beneficial than the genomic BLUP (GBLUP) model for genomic selection in local chickens. Additionally, the single-step genome-wide association study (ssGWAS) can be used to extend the ssGBLUP model results to animals with available phenotypic information but without genotypic data. In this study, we compared the accuracy of (G)EBVs using the pedigree-based BLUP (PBLUP), GBLUP, and ssGBLUP models. Moreover, we conducted single-SNP GWAS (SNP-GWAS), GBLUP-GWAS, and ssGWAS methods to identify genes associated with egg production traits in the NCHU-G101 chicken to understand the feasibility of using genomic selection in a small population. The average prediction accuracy of (G)EBV for egg production traits using the PBLUP, GBLUP, and ssGBLUP models is 0.536, 0.531, and 0.555, respectively. In total, 22 suggestive- and 5% Bonferroni genome-wide significant-level SNPs for total egg number (EN), average laying rate (LR), average clutch length, and total clutch number are detected using 3 GWAS methods. These SNPs are mapped onto Gallus gallus chromosomes (GGA) 4, 6, 10, 18, and 25 in NCHU-G101 chicken. Furthermore, through SNP-GWAS and ssGWAS methods, we identify 2 genes on GGA4 associated with EN and LR: ENSGALG00000023172 and PPARGC1A. In conclusion, the ssGBLUP model demonstrates superior prediction accuracy, performing on average 3.41% than the PBLUP model. The implications of our gene results may guide future selection strategies for Taiwan Country chickens. Our results highlight the applicability of the ssGBLUP model for egg production traits selection in a small population, specifically NCHU-G101 chicken in Taiwan.

Accuracy of Genomic prediction for fleece traits in Inner Mongolia Cashmere goats

The fleece traits are important economic traits of goats. With the reduction of sequencing and genotyping cost and the improvement of related technologies, genomic selection for goats has become possible. The research collect pedigree, phenotype and genotype information of 2299 Inner Mongolia Cashmere goats (IMCGs) individuals. We estimate fixed effects, and compare the estimates of variance components, heritability and genomic predictive ability of fleece traits in IMCGs when using the pedigree based Best Linear Unbiased Prediction (ABLUP), Genomic BLUP (GBLUP) or single-step GBLUP (ssGBLUP). The fleece traits considered are cashmere production (CP), cashmere diameter (CD), cashmere length (CL) and fiber length (FL). It was found that year of production, sex, herd and individual ages had highly significant effects on the four fleece traits (P < 0.01). All of these factors should be considered when the genetic parameters of fleece traits in IMCGs are evaluated. The heritabilities of FL, CL, CP and CD with ABLUP, GBLUP and ssGBLUP methods were 0.26 ~ 0.31, 0.05 ~ 0.08, 0.15 ~ 0.20 and 0.22 ~ 0.28, respectively. Therefore, it can be inferred that the genetic progress of CL is relatively slow. The predictive ability of fleece traits in IMCGs with GBLUP (56.18% to 69.06%) and ssGBLUP methods (66.82% to 73.70%) was significantly higher than that of ABLUP (36.73% to 41.25%). For the ssGBLUP method is significantly (29% ~ 33%) higher than that with ABLUP, and which is slightly (4% ~ 14%) higher than that of GBLUP. The ssGBLUP will be as an superiors method for using genomic selection of fleece traits in Inner Mongolia Cashmere goats.

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.

Mathematical Modeling and Software Tools for Breeding Value Estimation Based on Phenotypic, Pedigree and Genomic Information of Holstein Friesian Cattle in Serbia

In this paper, six univariate and two multivariate best linear unbiased prediction (BLUP) models were tested for the estimation of breeding values (BV) in Holstein Friesian cattle in Serbia. Two univariate models were formed using the numerator relationship matrix (NRM), four using the genomic relationship matrix (GRM). Multivariate models contained only an NRM. Two cases were studied, the first when only first lactations were observed, and the second when all lactations were observed using a repeatability model. A total of 6041 animals were included, and of them, 2565 had data on milk yield (MY), milk fat yield (FY), milk fat content (FC), milk protein yield (PY) and milk protein content (PC). Finally, out of those 2565 cows, 1491 were genotyped. A higher accuracy of BV was obtained when using a combination of NRM and GRM compared to NRM alone in univariate analysis, while multivariate analysis with repeated measures gave the highest accuracy with all 6041 animals. When only genotyped animals were observed, the highest accuracy of the estimated BV was calculated by the ssGBLUPp model, and the lowest by the univariate BLUP model. In conclusion, the current breeding programs in Serbia should be changed to use multivariate analysis with repeated measurements until the optimal size of the reference population, which must include genotyping data on both bulls and cows, is reached.

Heterophil/Lymphocyte Ratio Level Modulates Salmonella Resistance, Cecal Microbiota Composition and Functional Capacity in Infected Chicken

The gastrointestinal microbiota plays a vital role in ensuring the maintenance of host health through interactions with the immune system. The Heterophil/Lymphocyte (H/L) ratio reflects poultry’s robustness and immune system status. Chickens with low H/L ratio are superior to the chickens with high H/L ratio in survival, immune response, and resistance to Salmonella infection, but the underlying mechanisms remain unclear. This study aimed to identify microorganisms associated with resistance to Salmonella Enteritidis infection in chickens based on the H/L ratio. The 16S rRNA and metagenomic analysis were conducted to examine microbiome and functional capacity between the 2 groups, and Short Chain Fatty Acids (SCFAs) and histopathology were conducted to explore the potential difference between susceptible and resistant groups at 7 and 21 days post-infection (dpi). The microbiome exploration revealed that low H/L ratio chickens, compared to high H/L ratio chickens, displayed a significantly higher abundance of Proteobacteria (Escherichia coli) and Bacteroidetes (Bacteroides plebeius) at 7 and 21 dpi, respectively. Anaerostipes (r = 0.63) and Lachnoclostridium (r = 0.63) were identified as bacterial genus significantly correlated with H/L (P < 0.001). Interestingly, Bacteroides was significantly and positively correlated with bodyweight post-infection (r = 0.72), propionate (r = 0.78) and valerate (r = 0.82) contents, while Salmonella was significantly and negatively correlated with bodyweight post-infection (r = − 0.67), propionate (r = − 0.61) and valerate (r = − 0.65) contents (P < 0.001). Furthermore, the comparative analysis of the functional capacity of cecal microbiota of the chickens with high and low H/L ratio revealed that the chickens with low H/L ratio possess more enriched immune pathways, lower antibiotic resistance genes and virulence factors compared to the chickens with high H/L ratio. These results suggest that the chickens with low H/L ratio are more resistant to Salmonella Enteritidis, and it is possible that the commensal Proteobacteria and Bacteroidetes are involved in this resistance against Salmonella infection. These findings provide valuable resources for selecting and breeding disease-resistant chickens.

Investigation of the Potential of Heterophil/Lymphocyte Ratio as a Biomarker to Predict Colonization Resistance and Inflammatory Response to Salmonella enteritidis Infection in Chicken

Salmonella causes significant economic loss to the poultry industry and represents a real threat to human health. The region of difference 21 (ROD21) pathogenicity island removal is a genetic mechanism by which Salmonellaenteritidis (SE) invades the intestinal epithelium and induces systemic infection in mice. The heterophil/lymphocyte (H/L) ratio reflects the chicken’s robustness and immune system status. The H/L ratio is considered a disease resistance trait, and it could be used as a marker for selecting Salmonella resistance in live chickens. However, the association of the H/L ratio with Salmonella resistance and the inflammatory response remains to be elucidated. Moreover, the kinetics of ROD21 excision in the intestine and immune organs of chickens is unknown. Therefore, this study aimed to investigate the bacterial load, the ROD21 excision, the IL-1β, IL-8, and INF-γ blood serum concentration kinetics, and the association with the H/L ratio in chicken at 1, 3, 7, and 21 days post-SE infection. The results showed a significant correlation between the H/L ratio and the bacterial load in the ileum and caecum at 7 dpi. The ROD21 pathogenicity island absolute and relative excision in the caecum were positively correlated at 1 dpi but negatively correlated at 7 dpi with the H/L ratio. However, in the liver, we found the opposite tendency. The association of the H/L ratio with IL-1β, IL-8, and INF-γ blood serum concentrations showed that a low H/L ratio is correlated with increased IL-1β and INF-γ at 21 dpi. This study confirmed that the H/L ratio is associated with robustness and Salmonella-resistance in chicken. The methodology used in this study can separate individuals into susceptible and resistant and can help in the selection and breeding of Salmonella-resistant chickens.

ssGBLUP Method Improves the Accuracy of Breeding Value Prediction in Huacaya Alpaca

Simple Summary

Alpaca breeding takes place in the most entrenched areas of the Andes, where the conditions to implement genetic improvement programs are very difficult. Likewise, taking phenotypic records is limited in its ability to predict genetic merit accurately. For this reason, genomic information is shown as an alternative that helps to predict the genetic values of fiber traits more precisely. This study showed how genomic information increased precision by 2.623% for the fiber diameter, 6.442% for the standard deviation of the fiber diameter, and 1.471% for the percentage of medullation compared to traditional methods for predicting genetic merit, suggesting that adding genomic data in prediction models could be beneficial for alpaca breeding programs in the future.

Abstract

Improving textile characteristics is the main objective of alpaca breeding. A recently developed SNP chip for alpacas could potentially be used to implement genomic selection and accelerate genetic progress. Therefore, this study aimed to compare the increase in prediction accuracy of three important fiber traits: fiber diameter (FD), standard deviation of fiber diameter (SD), and percentage of medullation (PM) in Huacaya alpacas. The data contains a total pedigree of 12,431 animals, 24,169 records for FD and SD, and 8386 records for PM and 60,624 SNP markers for each of the 431 genotyped animals of the Pacomarca Genetic Center. Prediction accuracy of breeding values was compared between a classical BLUP and a single-step Genomic BLUP (ssGBLUP). Deregressed phenotypes were predicted. The accuracies of the genetic and genomic values were calculated using the correlation between the predicted breeding values and the deregressed values of 100 randomly selected animals from the genotyped ones. Fifty replicates were carried out. Accuracies with ssGBLUP improved by 2.623%, 6.442%, and 1.471% on average for FD, SD, and PM, respectively, compared to the BLUP method. The increase in accuracy was relevant, suggesting that adding genomic data could benefit alpaca breeding programs.