Evaluation of genome based estimated breeding values for meat quality in a berkshire population using high density single nucleotide polymorphism chips

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.

Using genomic selection to improve the accuracy of genomic prediction for multi-populations in pigs

The size of the reference group is among the most critical determinants of genomic estimated breeding values (GEBVs) accuracy. However, small- and medium-sized pig farms often need help accumulating adequate reference data, posing significant challenges to breeding programs. To solve this problem, exploring the potential benefits of combining reference groups of different sizes is necessary to improve GEBV accuracy. The primary objective of this investigation was to assess a more effective statistical model for combined multi-populations and its potential to enhance the accuracy of GEBVs for small and medium populations. Three populations were simulated using the QMSim software, each consisting of different sizes (300, 600, and 1 500, respectively). To assess the impact of heritability on the accuracy of GEBVs, four different levels of heritability (0.05, 0.15, 0.35, and 0.5) were simulated. Simultaneously, to investigate the impact of kinship on multi-populations, the study created four distinct scenarios for the three sizes of populations. These scenarios included: (1) the three groups are all independent, (2) the large group and the small group with a familial connection (n = 1 800), a middle group (n = 600) acting independently with no kinship, (3) the large group with a familial connection to the middle group (n = 2 100) but no connection to the small group (n = 300), and (4) the small group with a familial connection to the middle group (n = 900), while the large group (n = 1 500) acted independently with no kinship. This study evaluates and compares the accuracy of predicting breeding values using four different methods, including genomic best linear unbiased prediction (GBLUP), single-stepGBLUP (ssGBLUP), and two Bayesian models (Bayes A and Bayes B), with varying sizes of reference groups. In each scenario, three different prediction strategies were compared: (1) Merging all three different sizes of populations for predicting, (2) predicting each independent population separately, and (3) the other two populations predict the population. Our findings reveal that combining populations enhances the Bayesian models, with Bayes B yielding the highest accuracy. In independent populations, the best linear unbiased prediction (BLUP) models demonstrated the highest accuracy. However, in cases where populations were related and the heritability was high, the Bayes B model exhibited the highest overall accuracy (slightly higher than BLUP models) in the independent population. Our results underscore the importance of considering population combinations when using genetic models to predict breeding values, particularly for pig farmers with limited resources.

Assessment of genomic breeding values and their accuracies for carcass traits in Jeju Black cattle using whole-genome SNP chip panels

The objective of the present study was to evaluate the breeding value and accuracy of genomic estimated breeding values (GEBVs) of carcass traits in Jeju Black cattle (JBC) using Hanwoo steers and JBC as a reference population using the single-trait animal model. Our research included genotype and phenotype information on 19,154 Hanwoo steers with 1097 JBC acting as the reference population. Likewise, the test population consisted of 418 genotyped JBC individuals with no phenotypic records for those carcass traits. For estimating the accuracy of GEBV, we divided the entire population into three groups. Hanwoo and JBC make up the first group; Hanwoo and JBC, who has both the genotype and phenotypic records, are referred to as the reference (training) population, and JBC, who lacks phenotypic information is referred to as the test (validation) population. The second group consists of the JBC (without phenotype) as the test population and Hanwoo as a reference population with phenotype and genotypic data. The only JBCs in the third group are those who have genotypic and phenotypic data on them as a reference population but no phenotypic data on them as a test population. The single-trait animal model was used in all three groups for statistical purposes. The reference populations estimated heritabilities for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) as 0.30, 0.26, 0.26, and 0.34 for the Hanwoo steer and 0.42, 0.27, 0.26, and 0.48 for JBC. The average accuracy for carcass traits in Group 1 was 0.80 for the Hanwoo and JBC reference population compared with 0.73 for the JBC test population. Although the average accuracy for carcass traits in Group 2 was 0.80, it was 0.80 for the Hanwoo reference population and only 0.56 for the JBC test population. The average accuracy for the JBC reference and test populations was 0.68 and 0.50, respectively, when they were included in the accuracy comparison without the Hanwoo reference population. Groups 1 and 2 used Hanwoo as reference population, which led to a better average accuracy; however, Group 3 only used the JBC reference and test population, which led to a lower average accuracy. This might be due to the fact that Group 3 used a smaller reference size than the group that came before it and that the genetic makeup of the Hanwoo and JBC breeds differed. The GEBV accuracy for MS was higher than that of other traits across all three analysis groups, followed by CWT, EMA, and BF, which may be partially explained by the MS traits' higher heritability. This study suggests that in order to achieve more accuracy, a large reference population particular to a breed should be established. Therefore, to increase the accuracy of GEBV prediction and the genetic benefit from genomic selection in JBC, individual reference breeds, and large populations are required.

Association Analysis between Genetic Variants of elovl5a and elovl5b and Poly-Unsaturated Fatty Acids in Common Carp (Cyprinus carpio)

Simple Summary

PUFAs have an essential impact on human health, but their availability constitutes a critical bottleneck in food production. Although fish is the traditional source of PUFAs, it is limited by the stagnation of fisheries. Many studies aim to increase the PUFA products of fish. Genetic markers are efficient in aquaculture breeding. Fatty acid desaturase 2 (fads2) and elongase 5 (elovl5) are the rate-limiting enzymes in the synthesis of PUFAs. The allo-tetraploid common carp is able to biosynthesize endogenous PUFAs. However, selective breeding common carp with high PUFA contents was hindered due to a lack of effective molecular markers. For future breeding common carp capable of producing endogenous PUFAs more effectively, we previously identified the polymorphisms in the coding regions of two duplicated fads2, fads2a and fads2b. However, the polymorphisms in the duplicated elovl5, elovl5a and elovl5b, were not detected. This study screened the genetic variants in the coding regions of elovl5a and elovl5b. Moreover, the joint effects of multiple coding SNPs in fads2b and elovl5b, two major genes regulating the PUFA biosynthesis, were evidenced with the increased explained percentages of the PUFA contents. These polymorphisms in these two genes were used to evaluate the breeding values of PUFAs. These SNPs would be potential markers for future selection to improve the PUFA contents in common carp.

Abstract

The allo-tetraploid common carp, one widely cultured food fish, is able to produce poly-unsaturated fatty acids (PUFAs). The genetic markers on the PUFA contents for breeding was limited. The polymorphisms in elovl5a and elovl5b, the rate-limiting enzymes in the PUFA biosynthesis, have not been investigated yet. Herein, we identified one coding SNP (cSNP) in elovl5a associated with the content of one PUFA and two cSNPs in elovl5b with the contents of eight PUFAs. The heterozygous genotypes in these three loci were associated with higher contents than the homozygotes. Together with previously identified two associated cSNPs in fads2b, we found the joint effect of these four cSNPs in fads2b and elovl5b on the PUFA contents with the increased explained percentages of PUFA contents. The genotype combinations of more heterozygotes were associated with higher PUFA contents than the other combinations. Using ten genomic selection programs with all cSNPs in fads2b and elovl5b, we obtained the high and positive correlations between the phenotypes and the estimated breeding values of eight PUFAs. These results suggested that elovl5b might be the major gene corresponding to common carp PUFA contents compared with elovl5a. The cSNP combinations in fads2b and elovl5b and the optimal genomic selection program will be used in the future selection breeding to improve the PUFA contents of common carp.

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.

Genetic parameter estimation and gene association analyses for meat quality traits in open-air free-range Iberian pigs

Meat quality of Iberian pigs is defined by the combination of their genetic characteristics and the particular production system. To carry out a genetic analysis of the main meat quality traits, we estimated their heritabilities, genetic correlations and the association effects of 32 selected SNPs of 12 candidate genes. A total of ten traits were measured in longissimus dorsi samples from 1,199 Iberian pigs fattened in the traditional free-range system: water holding capacity (thawing, cooking and centrifuge force water losses), instrumental colour (lightness L*, redness a* and yellowness b*), myoglobin content, shear force on cooked meat, and shear force and maximum compression force on dry-cured loin. Estimated heritability values were low to moderate (0.01 to 0.43) being the lowest for L* and the highest for cooking loss. Strong genetic correlations between water holding capacity traits (0.93 to 0.96) and between myoglobin content and a* (0.94) were observed. The association analyses revealed 19 SNPs significantly associated with different traits. Consistent and strong effects were observed between PRKAG3 SNPs (rs319678464G > C and rs330427832C > T), MYH3_rs81437544T > C, CASP3_rs319658214G > T and CTSL_rs332171512A > G and water losses. Also for CAPN1_rs81358667G > A and CASP3_rs319658214G > T and shear force. The SNPs mapping on PRKAG3 showed the highest effects on Minolta colour traits. Genotyping of these SNPs could be useful for the selection of Iberian young boars with similar estimated breeding values for productive traits.

Development of a low-density panel for genomic selection of pigs in Russia

Genomic selection is routinely used worldwide in agricultural breeding. However, in Russia, it is still not used to its full potential partially due to high genotyping costs. The use of genotypes imputed from the low-density chips (LD-chip) provides a valuable opportunity for reducing the genotyping costs. Pork production in Russia is based on the conventional 3-tier pyramid involving 3 breeds; therefore, the best option would be the development of a single LD-chip that could be used for all of them. Here, we for the first time have analyzed genomic variability in 3 breeds of Russian pigs, namely, Landrace, Duroc, and Large White and generated the LD-chip that can be used in pig breeding with the negligible loss in genotyping quality. We have demonstrated that out of the 3 methods commonly used for LD-chip construction, the block method shows the best results. The imputation quality depends strongly on the presence of close ancestors in the reference population. We have demonstrated that for the animals with both parents genotyped using high-density panels high-quality genotypes (allelic discordance rate < 0.05) could be obtained using a 300 single nucleotide polymorphism (SNP) chip, while in the absence of genotyped ancestors at least 2,000 SNP markers are required. We have shown that imputation quality varies between chromosomes, and it is lower near the chromosome ends and drops with the increase in minor allele frequency. Imputation quality of the individual SNPs correlated well across breeds. Using the same LD-chip, we were able to obtain comparable imputation quality in all 3 breeds, so it may be suggested that a single chip could be used for all of them. Our findings also suggest that the presence of markers with extremely low imputation quality is likely to be explained by wrong mapping of the markers to the chromosomal positions.

Comparison of genomic predictions for carcass and reproduction traits in Berkshire, Duroc and Yorkshire populations in Korea

Objective

A genome-based best linear unbiased prediction (GBLUP) method was applied to evaluate accuracies of genomic estimated breeding value (GEBV) of carcass and reproductive traits in Berkshire, Duroc and Yorkshire populations in Korean swine breeding farms.

Methods

The data comprised a total of 1,870, 696, and 1,723 genotyped pigs belonging to Berkshire, Duroc and Yorkshire breeds, respectively. Reference populations for carcass traits consisted of 888 Berkshire, 466 Duroc, and 1,208 Yorkshire pigs, and those for reproductive traits comprised 210, 154, and 890 dams for the respective breeds. The carcass traits analyzed were backfat thickness (BFT) and carcass weight (CWT), and the reproductive traits were total number born (TNB) and number born alive (NBA). For each trait, GEBV accuracies were evaluated with a GEBV BLUP model and realized GEBVs.

Results

The accuracies under the GBLUP model for BFT and CWT ranged from 0.33–0.72 and 0.33–0.63, respectively. For NBA and TNB, the model accuracies ranged 0.32 to 0.54 and 0.39 to 0.56, respectively. The realized accuracy estimates for BFT and CWT ranged 0.30 to 0.46 and 0.09 to 0.27, respectively, and 0.50 to 0.70 and 0.70 to 0.87 for NBA and TNB, respectively. For the carcass traits, the GEBV accuracies under the GBLUP model were higher than the realized GEBV accuracies across the breed populations, while for reproductive traits the realized accuracies were higher than the model based GEBV accuracies.

Conclusion

The genomic prediction accuracy increased with reference population size and heritability of the trait. The GEBV accuracies were also influenced by GEBV estimation method, such that careful selection of animals based on the estimated GEBVs is needed. GEBV accuracy will increase with a larger sized reference population, which would be more beneficial for traits with low heritability such as reproductive traits.

Identification of a Bromodomain-containing Protein 2 (BRD2) Gene Polymorphic Variant and Its Effects on Pork Quality Traits in Berkshire Pigs

Bromodomain-containing protein 2 (BRD2) is a nuclear serine/threonine kinase involved in transcriptional regulation. We investigated the expression and association of the BRD2 gene as a candidate gene for meat quality traits in Berkshire pigs. BRD2 mRNA was expressed at relatively high levels in muscle tissue. Statistical analysis revealed that the c.1709G>C polymorphism of the BRD2 gene was significantly associated with carcass weight, meat color (a*, redness), protein content, cooking loss, water-holding capacity, carcass temperatures 4, 12 and 24 h postmortem, and the 24 h postmortem pH in 384 Berkshire pigs. Therefore, this polymorphism in the porcine BRD2 gene may be used as a candidate genetic marker to improve meat quality traits in pigs.

The rs196952262 Polymorphism of the AGPAT5 Gene is Associated with Meat Quality in Berkshire Pigs

High-quality meat is of great economic importance to the pig industry. The 1-acylglycerol-3-phosphate-O-acyltransferase 5 (AGPAT5) enzyme converts lysophosphatidic acid to phosphatidic acid in the mitochondrial membrane. In this study, we found that the porcine AGPAT5 gene was highly expressed in muscle tissue, influencing meat characteristics, and we also identified a non-synonymous single-nucleotide polymorphism (nsSNP) (rs196952262, c.673 A>G) in the gene, associated with a change of isoleucine 225 to valine. The presence of this nsSNP was significantly associated with meat color (lightness), lower cooking loss, and lower carcass temperatures 1, 4, and 12 h after slaughter (items T1, T4, and T12 on the recognized quality scale, respectively), and tended to increase backfat thickness and the water-holding capacity. These results suggest that nsSNP (c.673A>G) of the AGPAT5 gene is a potential genetic marker of high meat quality in pigs.