Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens

Improvement of the accuracy of breeding value prediction for egg production traits in Muscovy duck using low-coverage whole-genome sequence data

Low-coverage whole genome sequencing (lcWGS) is an effective low-cost genotyping technology when combined with genotype imputation approaches. It facilitates cost-effective genomic selection (GS) programs in agricultural animal populations. GS based on lcWGS data has been successfully applied to livestock such as pigs and donkeys. However, its effectiveness in poultry is poorly reported. Furthermore, due to the high linkage disequilibrium (LD) between markers and the high marker density in lcWGS data, it is necessary to explore how to effectively utilize lcWGS data for genomic prediction. Phenotypic data for egg production traits were collected from a population of 1491 Muscovy ducks, with 975 of them sequenced using low-coverage whole genomic sequencing at an average depth of ∼0.84x. In the prediction, we compared the pedigree-based best linear unbiased prediction (PBLUP) method, the genomic best linear unbiased prediction (GBLUP) method utilizing SNP marker data, and the single-step genomic best linear unbiased prediction (SSGBLUP) method, which integrates both pedigree and SNP marker information. Among the SNP-based approaches, we further extended our analysis by applying LD-based weighting of SNPs and employing a Gaussian kernel model to capture epistatic genetic effects. The result showed that the estimated heritability of egg production traits in Muscovy duck ranged from 0.071 to 0.573. Compared to the PBLUP, integrating lcWGS data and pedigree data through a single-step genetic evaluation improved the accuracy of genomic prediction for all traits in this study, with accuracy improvement ranging from 12.3 % to 43.9 % in random cross-validation. Additionally, compared to the GBLUP, the extended method of GBLUP that controls for LD heterogeneity and accounts for epistatic effects using lcWGS data showed a superior prediction performance, with accuracy improvement ranging from 0.6 %∼75.1 % in the optimal scenario. This study demonstrates that utilization of lcWGS data is a promising approach for genomic prediction of egg production traits in Muscovy duck. Our findings provide valuable strategies for optimizing genomic prediction methods using lcWGS data.

Genetic parameter estimation and genetic evaluation of important economic traits in white and yellow broilers

1. This study calculated descriptive statistics for the production traits of two broiler populations: 1) the Northeast Agricultural University broiler lines divergently selected for abdominal fat content (NEAUHLF white broilers), including fat and lean lines; and 2) the Guangxi yellow broilers. Their genetic parameters were estimated, including (co)variance components, heritability (h2) and genetic correlations (rg), using the REML method.2. Heritability estimates (h2) for NEAUHLF white broilers ranged from 0.07 to 0.61. Traits with high heritability (h2 >0.3) included body weight at 3, 5 and 7 weeks of age (BW3, BW5, BW7), carcass weight (CW), metatarsal circumference (MeC), liver weight (LW), gizzard weight (GW), spleen weight (SW) and testis weight (TeW), while in Guangxi yellow broilers, heritability estimates ranged from 0.18 to 0.76, with every trait exhibiting high heritability, except for SW (0.18).3. Positive genetic correlations for NEAUHLF were found (rg >0.3, ranging from 0.31 to 0.84) between BW7 and metatarsal length (MeL), MeC, body oblique length (BoL), chest angle (ChA), LW, GW, heart weight (HW) and SW. Genetic correlations between abdominal fat weight (AFW) and BW1, BW3, BW5, CW, MeL, keel length (KeL), BoL and LW were positive (rg >0.3, ranging from 0.31 to 0.58).4. Among the Guangxi population, BW (125 d of age) showed strong positive genetic correlations with all other traits (rg >0.3, ranging from 0.30 to 0.99), while AFW displayed strong positive genetic correlations with leg muscle weight (LeW), CW, BW and thigh diameter (TD) (rg >0.3, ranging from 0.44 to 0.51).5. It was concluded that the characteristics of the two populations were different, which means there is a need to use different strategies when performing the breeding work to improve productivity and efficiency in both broiler populations.

Genome-wide association study for body weight and feed consumption traits in Japanese quail using Bayesian approaches

The aim of this study was to perform a genome-wide association study (GWAS) based on Bayes A and Bayes B statistical methods to identify genomic loci and candidate genes associated with body weight gain, feed intake, and feed conversion ratio in Japanese quail. For this purpose, genomic data obtained from Illumina iSelect 4K quail SNP chip were utilized. After implementing various quality control steps, genotype data from a total of 875 birds for 2,015 SNP markers were used for subsequent analyses. The Bayesian analyses were performed using hibayes package in R (version 4.3.1) and Gibbs sampling algorithm. The results of the analyses showed that Bayes A accounted for 11.43, 11.65, and 11.39% of the phenotypic variance for body weight gain, feed intake, and feed conversion ratio, respectively, while the variance explained by Bayes B was 7.02, 8.61, and 6.48%, respectively. Therefore, in the current study, results obtained from Bayes A were used for further analyses. In order to perform the gene enrichment analysis and to identify the functional pathways and classes of genes that are over-represented in a large set of genes associated with each trait, all markers that accounted for more than 0.1% of the phenotypic variance for each trait were used. The results of this analysis revealed a total of 23, 38, and 14 SNP markers associated with body weight gain, feed intake, and feed conversion ratio in Japanese quail, respectively. The results of the gene enrichment analysis led to the identification of biological pathways (and candidate genes) related to lipid phosphorylation (TTC7A gene) and cell junction (FGFR4 and FLRT2 genes) associated with body weight gain, calcium signaling pathway (ADCY2 and CAMK1D genes) associated with feed intake, and glycerolipid metabolic process (LIPC gene), lipid metabolic process (ADGRF5 and ESR1 genes), and glutathione transferase (GSTK1 gene) associated with feed conversion ratio. Overall, the findings of this study can provide valuable insights into the genetic architecture of growth and feed consumption traits in Japanese quail.

Strategies to improve genomic predictions for 35 duck carcass traits in an F2 population

BACKGROUND

Carcass traits are crucial for broiler ducks, but carcass traits can only be measured postmortem. Genomic selection (GS) is an effective approach in animal breeding to improve selection and reduce costs. However, the performance of genomic prediction in duck carcass traits remains largely unknown.

RESULTS

In this study, we estimated the genetic parameters, performed GS using different models and marker densities, and compared the estimation performance between GS and conventional BLUP on 35 carcass traits in an F₂ population of ducks. Most of the cut weight traits and intestine length traits were estimated to be high and moderate heritabilities, respectively, while the heritabilities of percentage slaughter traits were dynamic. The reliability of genome prediction using GBLUP increased by an average of 0.06 compared to the conventional BLUP method. The Permutation studies revealed that 50K markers had achieved ideal prediction reliability, while 3K markers still achieved 90.7% predictive capability would further reduce the cost for duck carcass traits. The genomic relationship matrix normalized by our true variance method instead of the widely used [Formula: see text] could achieve an increase in prediction reliability in most traits. We detected most of the bayesian models had a better performance, especially for BayesN. Compared to GBLUP, BayesN can further improve the predictive reliability with an average of 0.06 for duck carcass traits.

CONCLUSION

This study demonstrates genomic selection for duck carcass traits is promising. The genomic prediction can be further improved by modifying the genomic relationship matrix using our proposed true variance method and several Bayesian models. Permutation study provides a theoretical basis for the fact that low-density arrays can be used to reduce genotype costs in duck genome selection.

The impact of genotyping strategies and statistical models on accuracy of genomic prediction for survival in pigs

BACKGROUND

Survival from birth to slaughter is an important economic trait in commercial pig productions. Increasing survival can improve both economic efficiency and animal welfare. The aim of this study is to explore the impact of genotyping strategies and statistical models on the accuracy of genomic prediction for survival in pigs during the total growing period from birth to slaughter.  RESULTS: We simulated pig populations with different direct and maternal heritabilities and used a linear mixed model, a logit model, and a probit model to predict genomic breeding values of pig survival based on data of individual survival records with binary outcomes (0, 1). The results show that in the case of only alive animals having genotype data, unbiased genomic predictions can be achieved when using variances estimated from pedigree-based model. Models using genomic information achieved up to 59.2% higher accuracy of estimated breeding value compared to pedigree-based model, dependent on genotyping scenarios. The scenario of genotyping all individuals, both dead and alive individuals, obtained the highest accuracy. When an equal number of individuals (80%) were genotyped, random sample of individuals with genotypes achieved higher accuracy than only alive individuals with genotypes. The linear model, logit model and probit model achieved similar accuracy.

CONCLUSIONS

Our conclusion is that genomic prediction of pig survival is feasible in the situation that only alive pigs have genotypes, but genomic information of dead individuals can increase accuracy of genomic prediction by 2.06% to 6.04%.

Including dominance effects in the prediction model through locus-specific weights on heterozygous genotypes can greatly improve genomic predictive abilities

The dominance effect is considered to be a key factor affecting complex traits. However, previous studies have shown that the improvement of the model, including the dominance effect, is usually less than 1%. This study proposes a novel genomic prediction method called CADM, which combines additive and dominance genetic effects through locus-specific weights on heterozygous genotypes. To the best of our knowledge, this is the first study of weighting dominance effects for genomic prediction. This method was applied to the analysis of chicken (511 birds) and pig (3534 animals) datasets. A 5-fold cross-validation method was used to evaluate the genomic predictive ability. The CADM model was compared with typical models considering additive and dominance genetic effects (ADM) and the model considering only additive genetic effects (AM). Based on the chicken data, using the CADM model, the genomic predictive abilities were improved for all three traits (body weight at 12th week, eviscerating percentage, and breast muscle percentage), and the average improvement in prediction accuracy was 27.1% compared with the AM model, while the ADM model was not better than the AM model. Based on the pig data, the CADM model increased the genomic predictive ability for all the three pig traits (trait names are masked, here designated as T1, T2, and T3), with an average increase of 26.3%, and the ADM model did not improve, or even slightly decreased, compared with the AM model. The results indicate that dominant genetic variation is one of the important sources of phenotypic variation, and the novel prediction model significantly improves the accuracy of genomic prediction.

Reliability of genomic evaluation for egg quality traits in layers

Background

Genomic evaluation, based on the use of thousands of genetic markers in addition to pedigree and phenotype information, has become the standard evaluation methodology in dairy cattle breeding programmes over the past several years. Despite the many differences between dairy cattle breeding and poultry breeding, genomic selection seems very promising for the avian sector, and studies are currently being conducted to optimize avian selection schemes. In this optimization perspective, one of the key parameters is to properly predict the accuracy of genomic evaluation in pure line layers.

Results

It was observed that genomic evaluation, whether performed on males or females, always proved more accurate than genetic evaluation. The gain was higher when phenotypic information was narrowed, and an augmentation of the size of the reference population led to an increase in accuracy prediction with regard to genomic evaluation. By taking into account the increase of selection intensity and the decrease of the generation interval induced by genomic selection, the expected annual genetic gain would be higher with ancestry-based genomic evaluation of male candidates than with genetic evaluation based on collaterals. This advantage of genomic selection over genetic selection requires more detailed further study for female candidates.

Conclusions

In conclusion, in the population studied, the genomic evaluation of egg quality traits of breeding birds at birth seems to be a promising strategy, at least for the selection of males.

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.

The impact of clustering methods for cross-validation, choice of phenotypes, and genotyping strategies on the accuracy of genomic predictions

For genomic predictors to be of use in genetic evaluation, their predicted accuracy must be a reliable indicator of their utility, and thus unbiased. The objective of this paper was to evaluate the accuracy of prediction of genomic breeding values (GBV) using different clustering strategies and response variables. Red Angus genotypes (n = 9,763) were imputed to a reference 50K panel. The influence of clustering method [k-means, k-medoids, principal component (PC) analysis on the numerator relationship matrix (A) and the identical-by-state genomic relationship matrix (G) as both data and covariance matrices, and random] and response variables [deregressed estimated breeding values (DEBV) and adjusted phenotypes] were evaluated for cross-validation. The GBV were estimated using a Bayes C model for all traits. Traits for DEBV included birth weight (BWT), marbling (MARB), rib-eye area (REA), and yearling weight (YWT). Adjusted phenotypes included BWT, YWT, and ultrasonically measured intramuscular fat percentage and REA. Prediction accuracies were estimated using the genetic correlation between GBV and associated response variable using a bivariate animal model. A simulation mimicking a cattle population, replicated 5 times, was conducted to quantify differences between true and estimated accuracies. The simulation used the same clustering methods and response variables, with the addition of 2 genotyping strategies (random and top 25% of individuals), and forward validation. The prediction accuracies were estimated similarly, and true accuracies were estimated as the correlation between the residuals of a bivariate model including true breeding value (TBV) and GBV. Using the adjusted Rand index, random clusters were clearly different from relationship-based clustering methods. In both real and simulated data, random clustering consistently led to the largest estimates of accuracy, while no method was consistently associated with more or less bias than other methods. In simulation, random genotyping led to higher estimated accuracies than selection of the top 25% of individuals. Interestingly, random genotyping seemed to overpredict true accuracy while selective genotyping tended to underpredict accuracy. When forward in time validation was used, DEBV led to less biased estimates of GBV accuracy. Results suggest the highest, least biased GBV accuracies are associated with random genotyping and DEBV.

Identification of chicken FSHR gene promoter and the correlations between polymorphisms and egg production in Chinese native hens

Egg production is an important economic trait in poultry, and it is of great significance to study the key genes and functional SNPs that affect egg laying performance. Follicle‐stimulating hormone (FSH) plays an important physiological role in the reproductive performance of humans and animals by binding to its receptor (FSHR). Studies have shown that there are many transcriptional regulatory elements in the 5′ flanking region of the FSHR gene that interact with transcription factors to regulate FSHR transcription. In this study, DNA sequencing was used to identify SNPs in the FSHR promoter sequence in both Dongxiang and Suken chickens. To detect the activity of the chicken FSHR gene promoter, we analysed the characteristics of the sequence and constructed three deletion vectors. We confirmed that the region (−18/−544) was the core promoter. Furthermore, five polymorphisms, including a 200‐bp indel at −869, C−1684T, C−1608T, G−368A and T−238A, were detected in both the Dongxiang and Suken chickens. The age at first egg (AFE) for different genotype of −869 indel in Suken chicken was significantly different (p < 0.01). For SNP C−1684T in Dongxiang chickens, the CC genotype had higher egg number at 43 weeks of age (E43) than that of the TC genotype (p < 0.05). For SNP C−1684T in Suken chickens, the TC genotype had higher AFE than that of the CC genotype (p < 0.05). For SNP C−1608T in Suken chickens, the CC genotype had higher AFE than that of the TC genotype (p < 0.05). For SNP G−368A in Suken chickens, the AG genotype had higher AFE than that of the GG genotype (p < 0.05).

Effects of genomic selection for intramuscular fat content in breast muscle in Chinese local chickens

Improvements in living standards have resulted in consumers having higher expectations for chicken meat quality. This is particularly true in Asia, where there is high consumer preference for local breeds. Nothing is presently known about the effectiveness of using genomic selection (GS) strategies in chickens to genetically improve meat quality traits that cannot be measured in living potential parents. In this study, 724 Beijing-You chickens were used as a training population; all were genotyped using Illumina 60K SNP chips, and intramuscular fat content in breast muscle (IMFbr ) was measured. Birds in the GS line were selected based on genomic estimated breeding values, IMFbr being the sole trait. Genetic progress in one generation was compared to that from conventional family-based selection, and both were evaluated against random-bred controls. Results showed that relative to the random-bred controls, IMF percentage was improved 9.62% using GS, comparable to the 10.38% improvement using family-based selection. We quantified the effectiveness of GS when applied to a meat quality trait with low heritability in chickens. We plan to introduce custom SNP chips, appropriate for native chicken breeds in China, to assist in applying GS in local breeding and accelerate genetic gain.

Relationships among mortality, performance, and disorder traits in broiler chickens: a genetic and genomic approach

Four performance-related traits [growth trait (GROW), feed efficiency trait 1 (FE1) and trait 2 (FE2), and dissection trait (DT)] and 4 categorical traits [mortality (MORT) and 3 disorder traits (DIS1, DIS2, and DIS3)] were analyzed using linear and threshold single- and multi-trait models. Field data included 186,596 records of commercial broilers from Cobb-Vantress, Inc. Average-information restricted maximum likelihood and Gibbs sampling-based methods were used to obtain estimates of the (co)variance components, heritabilities, and genetic correlations in a traditional approach using best linear unbiased prediction (BLUP). The ability to predict future breeding values (measured as realized accuracy) was checked in the last generation when traditional BLUP and single-step genomic BLUP were used. Heritability estimates for GROW, FE1, and FE2 in single- and multi-trait models were similar and moderate (0.22 to 0.26) but high for DT (0.48 to 0.50). For MORT, DIS1, and DIS2, heritabilities were 0.13, 0.24, and 0.34, respectively. Estimates from single- and multi-trait models were also very similar. However, heritability for DIS3 was higher from the single-trait threshold model than for the multi-trait linear-threshold model (0.29 vs. 0.19). Genetic correlations between growth traits and MORT were weak, except for maternal GROW, which had a moderate negative correlation (-0.50) with MORT. The genetic correlation between MORT and DIS1 was strong and positive (0.77). Feed efficiency 1, which was moderately heritable (0.25) and is highly selected for, was not genetically related to MORT of broilers and other disorders. Broiler MORT also had moderate heritability (0.13), which suggests that MORT and FE1 can be improved through selection without negatively impacting other important traits. Selection of heavier maternal GROW also may decrease offspring MORT.

Genomic dissection and prediction of feed intake and residual feed intake traits using a longitudinal model in F2 chickens

Feed efficiency traits (FETs) are important economic indicators in poultry production. Because feed intake (FI) is a time-dependent variable, longitudinal models can provide insights into the genetic basis of FET variation over time. It is expected that the application of longitudinal models as part of genome-wide association (GWA) and genomic selection (i.e. genome-wide selection (GS)) studies will lead to an increase in accuracy of selection. Thus, the objectives of this study were to evaluate the accuracy of estimated breeding values (EBVs) based on pedigree as well as high-density single nucleotide polymorphism (SNP) genotypes, and to conduct a GWA study on longitudinal FI and residual feed intake (RFI) in a total of 312 chickens with phenotype and genotype in the F2 population. The GWA and GS studies reported in this paper were conducted using β-spline random regression models for FI and RFI traits in a chicken F2 population, with FI and BW recorded for each bird weekly between 2 and 10 weeks of age. A single SNP regression approach was used on spline coefficients for weekly FI and RFI traits, with results showing that two significant SNPs for FI occur in the synuclein (SNCAIP) gene. Results also show that these regions are significantly associated with the spline coefficients (q 2) for 5- and 6-week-old birds, while GWA study results showed no SNP association with RFI in F2 chickens. Estimated breeding value predictions obtained using a pedigree-based best linear unbiased prediction (ABLUP) model were then compared with predictions based on genomic best linear unbiased prediction (GBLUP). The accuracy was measured as correlation between genomic EBV and EBV with the phenotypic value corrected for fixed effects divided by the square root of heritability. The regression of observed on predicted values was used to estimate bias of methods. Results show that prediction accuracies using GBLUP and ABLUP for the FI measured from 2nd to 10th week were between 0.06 and 0.46 and 0.03 and 0.37, respectively. These results demonstrate that genomic methods are able to increase the accuracy of predicted breeding values at later ages on the basis of both traits, and indicate that use of a longitudinal model can improve selection accuracy for the trajectory of traits in F2 chickens when compared with conventional methods.

Whole genomic prediction of growth and carcass traits in a Chinese quality chicken population

By incorporating high-density markers into breeding value prediction models, the whole genomic prediction (WGP) method can effectively accelerate genetic improvement in livestock breeding. However, the performance of WGP varies across species and populations and is affected by the underlying genetic architecture. In particular, very little is known about the performance of WGP for many chicken breeds. Here we estimate the genetic parameters and evaluate the performance of WGP for 18 growth and carcass traits in a Chinese quality chicken population. In total, 435 chickens were systematically phenotyped and genotyped using a 600K genotyping array. Two variance component estimation scenarios, 3 breeding value prediction methods, and 2 validation procedures were compared. The results showed that the heritability of these 18 traits was medium to high (ranging from 0.28 to 0.60) and that deviations existed between the heritability estimated from pedigrees and markers. Compared with conventional breeding methods, WGP could potentially increase the selection accuracy by 20% or more depending on the prediction model used, the trait under consideration, and the genetic connectedness between the training and validation individuals. Our results showed the potential of implementing genomic selection in small breeding herds.

Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens

Feed represents the major cost of chicken production. Selection for improving feed utilization is a feasible way to reduce feed cost and greenhouse gas emissions. The objectives of this study were to investigate the efficiency of genomic prediction for feed conversion ratio (FCR), residual feed intake (RFI), average daily gain (ADG) and average daily feed intake (ADFI) and to assess the impact of selection for feed efficiency traits FCR and RFI on eviscerating percentage (EP), breast muscle percentage (BMP) and leg muscle percentage (LMP) in meat-type chickens. Genomic prediction was assessed using a 4-fold cross-validation for two validation scenarios. The first scenario was a random family sampling validation (CVF), and the second scenario was a random individual sampling validation (CVR). Variance components were estimated based on the genomic relationship built with single nucleotide polymorphism markers. Genomic estimated breeding values (GEBV) were predicted using a genomic best linear unbiased prediction model. The accuracies of GEBV were evaluated in two ways: the correlation between GEBV and corrected phenotypic value divided by the square root of heritability, i.e., the correlation-based accuracy, and model-based theoretical accuracy. Breeding values were also predicted using a conventional pedigree-based best linear unbiased prediction model in order to compare accuracies of genomic and conventional predictions. The heritability estimates of FCR and RFI were 0.29 and 0.50, respectively. The heritability estimates of ADG, ADFI, EP, BMP and LMP ranged from 0.34 to 0.53. In the CVF scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR were slightly higher than those for RFI. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.360, 0.284, 0.574 and 0.520, respectively, and the model-based theoretical accuracies were 0.420, 0.414, 0.401 and 0.382, respectively. In the CVR scenario, the correlation-based accuracy and the theoretical accuracy of genomic prediction for FCR was lower than RFI, which was different from the CVF scenario. The correlation-based accuracies for FCR, RFI, ADG and ADFI were 0.449, 0.593, 0.581 and 0.627, respectively, and the model-based theoretical accuracies were 0.577, 0.629, 0.631 and 0.638, respectively. The accuracies of genomic predictions were 0.371 and 0.322 higher than the conventional pedigree-based predictions for the CVF and CVR scenarios, respectively. The genetic correlations of FCR with EP, BMP and LMP were -0.427, -0.156 and -0.338, respectively. The correlations between RFI and the three carcass traits were -0.320, -0.404 and -0.353, respectively. These results indicate that RFI and FCR have a moderate accuracy of genomic prediction. Improving RFI and FCR could be favourable for EP, BMP and LMP. Compared with FCR, which can be improved by selection for ADG in typical meat-type chicken breeding programs, selection for RFI could lead to extra improvement in feed efficiency.

Predictive performance of genomic selection methods for carcass traits in Hanwoo beef cattle: impacts of the genetic architecture

Background

Hanwoo beef is known for its marbled fat, tenderness, juiciness and characteristic flavor, as well as for its low cholesterol and high omega 3 fatty acid contents. As yet, there has been no comprehensive investigation to estimate genomic selection accuracy for carcass traits in Hanwoo cattle using dense markers. This study aimed at evaluating the accuracy of alternative statistical methods that differed in assumptions about the underlying genetic model for various carcass traits: backfat thickness (BT), carcass weight (CW), eye muscle area (EMA), and marbling score (MS).

Methods

Accuracies of direct genomic breeding values (DGV) for carcass traits were estimated by applying fivefold cross-validation to a dataset including 1183 animals and approximately 34,000 single nucleotide polymorphisms (SNPs).

Results

Accuracies of BayesC, Bayesian LASSO (BayesL) and genomic best linear unbiased prediction (GBLUP) methods were similar for BT, EMA and MS. However, for CW, DGV accuracy was 7% higher with BayesC than with BayesL and GBLUP. The increased accuracy of BayesC, compared to GBLUP and BayesL, was maintained for CW, regardless of the training sample size, but not for BT, EMA, and MS. Genome-wide association studies detected consistent large effects for SNPs on chromosomes 6 and 14 for CW.

Conclusions

The predictive performance of the models depended on the trait analyzed. For CW, the results showed a clear superiority of BayesC compared to GBLUP and BayesL. These findings indicate the importance of using a proper variable selection method for genomic selection of traits and also suggest that the genetic architecture that underlies CW differs from that of the other carcass traits analyzed. Thus, our study provides significant new insights into the carcass traits of Hanwoo cattle.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-016-0283-0) contains supplementary material, which is available to authorized users.

Comparative analysis of the GBLUP, emBayesB, and GWAS algorithms to predict genetic values in large yellow croaker (Larimichthys crocea)

Background

The advances of sequencing technology accelerate the development of theory of molecular quantitative genetics such as QTL mapping, genome-wide association study and genomic selection. This paper was designed to study genomic selection in large yellow croaker breeding. The aims of this study were: (i) to estimate heritability values of traits in large yellow croaker; (ii) to assess feasibility of genomic selection in the traits of growth rate and meat quality; (iii) to compare predictive accuracies affected by different algorithms and training sizes, and to find what training sizes could reach ideal accuracies; (iv) to compare results of GWAS with genomic prediction, and to assess feasibility of pre-selection of significant SNPs in genomic selection. 500 individuals were tested in the trait of body weight and body length, while 176 were tested in the percentage of n-3 highly unsaturated fatty acids (n-3HUFA) in muscle. GBLUP and emBayesB were used to perform genomic prediction.

Results

Genotyping-By-Sequencing method was used to construct the libraries for the NGS sequencing and find ~30,000 SNPs. Heritability estimates were 0.604, 0.586 and 0.438 for trait of body weight, body length and n-3HUFA, respectively. The predictive abilities estimated by GBLUP showed higher than that by emBayesB in traits of body weight and body length. However, the result was just the opposite in n-3HUFA. According to fit the curve of predictive accuracy, we estimated that at least 1000 individuals in training set could reach an accuracy of 0.8 in body weight and body length. GBLUP, emBayesB and GWAS could not always find significant SNPs associated with phenotypes consistently. Significant SNPs were selected by emBayesB could obtain the largest proportions to explain total additive genetic variances.

Conclusions

This research showed that genomic selection was feasible in large yellow croaker breeding. We suggest doing a test before deciding to use which algorithm in specific trait in genomic prediction. We estimated required training sizes to reach ideal predictive accuracies and assessed feasibility of pre-selection of SNPs successfully. Because of high mortality rate of fish and high cost in genomic sequencing, genomic selection may be more suitable for applying on some traits which cannot be measured on candidates directly.

Accuracy of imputation using the most common sires as reference population in layer chickens

BACKGROUND

Genotype imputation has become a standard practice in modern genetic research to increase genome coverage and improve the accuracy of genomic selection (GS) and genome-wide association studies (GWAS). We assessed accuracies of imputing 60K genotype data from lower density single nucleotide polymorphism (SNP) panels using a small set of the most common sires in a population of 2140 white layer chickens. Several factors affecting imputation accuracy were investigated, including the size of the reference population, the level of the relationship between the reference and validation populations, and minor allele frequency (MAF) of the SNP being imputed.

RESULTS

The accuracy of imputation was assessed with different scenarios using 22 and 62 carefully selected reference animals (Ref(22) and Ref(62)). Animal-specific imputation accuracy corrected for gene content was moderate on average (~ 0.80) in most scenarios and low in the 3K to 60K scenario. Maximum average accuracies were 0.90 and 0.93 for the most favourable scenario for Ref(22) and Ref(62) respectively, when SNPs were masked independent of their MAF. SNPs with low MAF were more difficult to impute, and the larger reference population considerably improved the imputation accuracy for these rare SNPs. When Ref(22) was used for imputation, the average imputation accuracy decreased by 0.04 when validation population was two instead of one generation away from the reference and increased again by 0.05 when validation was three generations away. Selecting the reference animals from the most common sires, compared with random animals from the population, considerably improved imputation accuracy for low MAF SNPs, but gave only limited improvement for other MAF classes. The allelic R(2) measure from Beagle software was found to be a good predictor of imputation reliability (correlation ~ 0.8) when the density of validation panel was very low (3K) and the MAF of the SNP and the size of the reference population were not extremely small.

CONCLUSIONS

Even with a very small number of animals in the reference population, reasonable accuracy of imputation can be achieved. Selecting a set of the most common sires, rather than selecting random animals for the reference population, improves the imputation accuracy of rare alleles, which may be a benefit when imputing with whole genome re-sequencing data.