Genomic selection for the improvement of antibody response to Newcastle disease and avian influenza virus in chickens

Genetic and non-genetic factors influencing KLH binding natural antibodies and specific antibody response to Newcastle disease in Kenyan chicken populations

This study aimed at investigating the influence of genetic and non-genetic factors on immune traits to inform on possibilities of genetic improvement of disease resistance traits in local chicken of Kenya. Immune traits such as natural and specific antibodies are considered suitable indicators of an individual's health status and consequently, used as indicator traits of disease resistance. In this study, natural antibodies binding to Keyhole Limpet Hemocyanin (KLH-NAbs) was used to measure general disease resistance. Specific antibodies binding to Newcastle disease virus (NDV-IgG) post vaccination was used to measure specific disease resistance. Titers of KLH-NAbs isotypes (KLH-IgM, KLH-IgG and KLH-IgA) and NDV-IgG were measured in 1,540 chickens of different ages ranging from 12 to 56 weeks. A general linear model was fitted to determine the effect of sex, generation, population type, phylogenetic cluster, line, genotype and age on the antibody traits. A multivariate animal mixed model was fitted to estimate heritability and genetic correlations among the antibody traits. The model constituted of non-genetic factors found to have a significant influence on the antibody traits as fixed effects, and animal and residual effects as random variables. Overall mean (±SE) concentration levels for KLH-IgM, KLH-IgG, KLH-IgA and NDV-IgG were 10.33 ± 0.04, 9.08 ± 0.02, 6.00 ± 0.02 and 10.12 ± 0.03, respectively. Sex, generation and age (linear covariate) significantly (p < 0.05) influenced variation across all the antibody traits. Genotype effects (p < 0.05) were present in all antibody traits, apart from KLH-IgA. Interaction between generation and line was significant (p < 0.05) in KLH-IgM and NDV-IgG while nesting phylogenetic cluster within population significantly (p < 0.05) influenced all antibody traits, apart from KLH-IgA. Heritability estimates for KLH-IgM, KLH-IgG, KLH-IgA and NDV-IgG were 0.28 ± 0.08, 0.14 ± 0.06, 0.07 ± 0.04 and 0.31 ± 0.06, respectively. There were positive genetic correlations (0.40-0.61) among the KLH-NAbs while negative genetic correlations (-0.26 to -0.98) were observed between the KLH-NAbs and NDV-IgG. Results from this study indicate that non-genetic effects due to biological and environmental factors influence natural and specific antibodies and should be accounted for to reduce bias and improve accuracy when evaluating the traits. Subsequently, the moderate heritability estimates in KLH-IgM and NDV-IgG suggest selection possibilities for genetic improvement of general and specific immunity, respectively, and consequently disease resistance. However, the negative correlations between KLH-NAbs and NDV-IgG indicate the need to consider a suitable approach that can optimally combine both traits in a multiple trait selection strategies.

Heritability Estimate for Antibody Response to Vaccination and Survival to a Newcastle Disease Infection of Native chicken in a Low-Input Production System

The Newcastle disease virus (NDV) is the deadliest chicken pathogen in low-input village poultry, and selecting for NDV resistance has been recommended as a sustainable strategy in backyard poultry production systems. However, selecting for disease resistance needs precision data from either a big population sample size or on many generations with good pedigree records for effective prediction of heritability (h²) and breeding values of the foundation stock. Such conditions are almost impossible to meet in low-input backyard production systems. This study aimed at proposing a realistic method for estimating the heritability of the immune response to vaccination and survival of NDV infection in village poultry production to inform a breeding strategy for ND resistance in Cameroon. A 1 and 3% selection intensity of cocks and hens for higher antibody (ab) response (ABR) to vaccination followed by progeny selection of chickens who survived an experimental NDV infection was conducted from an initial population of 1,702 chickens. The selection induced an increase of 1012.47units/ml (p<0.01) of the NDV antibody of the progeny as well as an effective survival rate (ESR) increase of 11.75%. Three methods were used to estimate the heritability (h2) of NDV antibody response to vaccination. h2 was low irrespective of the method with estimates of 0.2227, 0.2442, and 0.2839 for the breeder’s equation method, the graphical method, and the full-sib/half-sib nested design, respectively. The mortality rate of infected chickens was high (86%). The antibody response to selection was not influenced by sex and genetic type even though the opposite was observed (p<0.05) for the ESR to NDV infection with naked neck chickens recording an ESR of 14% against 2.25% for the normal feather type. A very low heritability (0.0891) was observed for the survival against NDV infection. We confirm the evidence of disease resistance and the effect of selection for antibody response to vaccination on the improvement of the survival against NDV disease. Although the full sib/half sib nested design is more appropriate in case of availability of pedigree information, the direct methods are still useful in case of unavailability of full pedigree information. It is recommended that gene expression analysis should be prioritized for disease-resistance assessment and selection of native breeds of poultry.

Genetic and phenotypic parameters for Aleutian disease tests and their correlations with pelt quality, reproductive performance, packed-cell volume, and harvest length in mink

Aleutian disease (AD), caused by the Aleutian mink disease virus (AMDV), is a major health concern that results in global economic losses to the mink industry. The unsatisfactory outcome of the culling strategy, immunoprophylaxis, and medical treatment in controlling AD have urged mink farmers to select AD resilient mink based on several detection tests, including enzyme-linked immunosorbent assay (ELISA), counterimmunoelectrophoresis (CIEP), and iodine agglutination test (IAT). However, the genetic analysis of these AD tests and their correlations with pelt quality, reproductive performance, packed-cell volume (PCV), and harvest length (HL) have not been investigated. In this study, data on 5,824 mink were used to estimate the genetic and phenotypic parameters of four AD tests, including two systems of ELISA, CIEP, and IAT, and their genetic and phenotypic correlations with two pelt quality, five female reproductive performance, PCV, and HL traits. Significances (P < 0.05) of fixed effects (sex, year, dam age, and color type), covariates (age at harvest and blood sampling), and random effects (additive genetic, permanent environmental, and maternal effects) were determined under univariate models using ASReml 4.1 software. The genetic and phenotypic parameters for all traits were estimated under bivariate models using ASReml 4.1 software. Estimated heritabilities (±SE) were 0.39 ± 0.06, 0.61 ± 0.07, 0.11 ± 0.07, and 0.26 ± 0.05 for AMDV antigen-based ELISA (ELISA-G), AMDV capsid protein-based ELISA, CIEP, and IAT, respectively. The ELISA-G also showed a moderate repeatability (0.58 ± 0.04) and had significant negative genetic correlations (±SE) with reproductive performance traits (from -0.41 ± 0.16 to -0.49 ± 0.12), PCV (-0.53 ± 0.09), and HL (-0.45 ± 0.16). These results indicated that ELISA-G had the potential to be applied as an indicator trait for genetic selection of AD resilient mink in AD endemic ranches and therefore help mink farmers to reduce the adverse effects caused by AD.

Genomic Analysis of IgG Antibody Response to Common Pathogens in Commercial Sows in Health-Challenged Herds

Losses due to infectious diseases are one of the main factors affecting productivity in the swine industry, motivating the investigation of disease resilience-related traits for genetic selection. However, these traits are not expected to be expressed in the nucleus herds, where selection is performed. One alternative is to use information from the commercial level to identify and select nucleus animals genetically superior for coping with pathogen challenges. In this study, we analyzed the genetic basis of antibody (Ab) response to common infectious pathogens in health-challenged commercial swine herds as potential indicator traits for disease resilience, including Ab response to influenza A virus of swine (IAV), Mycoplasma hyopneumoniae (MH), porcine circovirus (PCV2), and Actinobacillus pleuropneumoniae (APP; different serotypes). Ab response was measured in blood at entry into gilt rearing, post-acclimation (∼40 days after entering the commercial herd), and parities 1 and 2. Heritability estimates for Ab response to IAV, MH, and PCV2 ranged from 0 to 0.76. Ab response to APP ranged from 0 to 0.40. The genetic correlation (r _G ) of Ab response to IAV with MH, PCV2, PRRSV, and APP_mean (average Ab responses for all serotypes of APP) were positive (>0.29) at entry. APP_mean was negatively correlated with PCV2 and MH at entry and parity 2 but positively correlated with MH at post-acclimation and parity 1. Genomic regions associated with Ab response to different APP serotypes were identified on 13 chromosomes. The region on chromosome 14 (2 Mb) was associated with several serotypes of APP, explaining up to 4.3% of the genetic variance of Ab to APP7 at entry. In general, genomic prediction accuracies for Ab response were low to moderate, except average Ab response to all infectious pathogens evaluated. These results suggest that genetic selection of Ab response in commercial sows is possible, but with variable success depending on the trait and the time-point of collection. Future work is needed to determine genetic correlations of Ab response with disease resilience, reproductive performance, and other production traits.

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

: Poultry diseases pose a large threat to poultry production. Selection to improve immune traits is a feasible way to prevent and control avian diseases. The objective of this study was to investigate the efficiency of estimation of genetic parameters for antibody response to avian influenza virus (Ab-AIV), antibody response to Newcastle disease virus (Ab-NDV), sheep red blood cell antibody titer (SRBC), the ratio of heterophils to lymphocytes (H/L), immunoglobulin G (IgG), the spleen immune index (SII), thymus immune index (TII), thymus weight at 100 d (TW) and the spleen weight at 100 d (SW) in Beijing oil chickens, by using the best linear unbiased prediction (BLUP) method and genomic best linear unbiased prediction (GBLUP) method. The phenotypic data used in the two methods were the same and were from 519 individuals. With the BLUP model, Ab-AIV, Ab-NDV, SRBC, H/L, IgG, TII, and TW had low heritability ranging from 0.000 to 0.281, whereas SII and SW had high heritability of 0.631 and 0.573. With the GBLUP model, all individuals were genotyped with Illumina 60K SNP chips, and Ab-AIV, Ab-NDV, SRBC, H/L and IgG had low heritability ranging from 0.000 to 0.266, whereas SII, TII, TW and SW had moderate heritability ranging from 0.300 to 0.472. We compared the prediction accuracy obtained from BLUP and GBLUP through 50 time 5-fold cross-validation (CV), and the results indicated that BLUP provided a slightly higher accuracy of prediction than GBLUP in this population.

Advantages and limitations of multiple-trait genomic prediction for Fusarium head blight severity in hybrid wheat (Triticum aestivum L.)

Predictabilities for wheat hybrids less related to the estimation set were improved by shifting from single- to multiple-trait genomic prediction of Fusarium head blight severity. Breeding for improved Fusarium head blight resistance (FHBr) of wheat is a very laborious and expensive task. FHBr complexity is mainly due to its highly polygenic nature and because FHB severity (FHBs) is greatly influenced by the environment. Associated traits plant height and heading date may provide additional information related to FHBr, but this is ignored in single-trait genomic prediction (STGP). The aim of our study was to explore the benefits in predictabilities of multiple-trait genomic prediction (MTGP) over STGP of target trait FHBs in a population of 1604 wheat hybrids using information on 17,372 single nucleotide polymorphism markers along with indicator traits plant height and heading date. The additive inheritance of FHBs allowed accurate hybrid performance predictions using information on general combining abilities or average performance of both parents without the need of markers. Information on molecular markers and indicator trait(s) improved FHBs predictabilities for hybrids less related to the estimation set. Indicator traits must be observed on the predicted individuals to benefit from MTGP. Magnitudes of genetic and phenotypic correlations along with improvements in predictabilities made plant height a better indicator trait for FHBs than heading date. Thus, MTGP having only plant height as indicator trait already maximized FHBs predictabilities. Provided a good indicator trait was available, MTGP could reduce the impacts of genotype environment [Formula: see text] interaction on STGP for hybrids less related to the estimation set.

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.

Genetic and genomic basis of antibody response to porcine reproductive and respiratory syndrome (PRRS) in gilts and sows

Background

Our recent research showed that antibody response to porcine reproductive and respiratory syndrome (PRRS), measured as sample-to-positive (S/P) ratio, is highly heritable and has a high genetic correlation with reproductive performance during a PRRS outbreak. Two major quantitative trait loci (QTL) on Sus scrofa chromosome 7 (SSC7; QTL_MHC and QTL₁₃₀) accounted for ~40 % of the genetic variance for S/P. Objectives of this study were to estimate genetic parameters for PRRS S/P in gilts during acclimation, identify regions associated with S/P, and evaluate the accuracy of genomic prediction of S/P across populations with different prevalences of PRRS and using different single nucleotide polymorphism (SNP) sets.

Methods

Phenotypes and high-density SNP genotypes of female pigs from two datasets were used. The outbreak dataset included 607 animals from one multiplier herd, whereas the gilt acclimation (GA) dataset included data on 2364 replacement gilts from seven breeding companies placed on health-challenged farms. Genomic prediction was evaluated using GA for training and validation, and using GA for training and outbreak for validation. Predictions were based on SNPs across the genome (SNP_All), SNPs in one (SNP_MHC and SNP₁₃₀) or both (SNP_SSC7) QTL, or SNPs outside the QTL (SNP_Rest).

Results

Heritability of S/P in the GA dataset increased with the proportion of PRRS-positive animals in the herd (from 0.28 to 0.47). Genomic prediction accuracies ranged from low to moderate. Average accuracies were highest when using only the 269 SNPs in both QTL regions (SNP_SSC7, with accuracies of 0.39 and 0.31 for outbreak and GA validation datasets, respectively. Average accuracies for SNP_ALL, SNP_MHC, SNP₁₃₀, and SNP_Rest were, respectively, 0.26, 0.39, 0.21, and 0.05 for the outbreak, and 0.28, 0.25, 0.22, and 0.12, for the GA validation datasets.

Conclusions

Moderate genomic prediction accuracies can be obtained for PRRS antibody response using SNPs located within two major QTL on SSC7, while the rest of the genome showed limited predictive ability. Results were obtained using data from multiple genetic sources and farms, which further strengthens these findings. Further research is needed to validate the use of S/P ratio as an indicator trait for reproductive performance during PRRS outbreaks.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-016-0230-0) contains supplementary material, which is available to authorized users.

Multiple-trait- and selection indices-genomic predictions for grain yield and protein content in rye for feeding purposes

KEY MESSAGE

Exploiting the benefits from multiple-trait genomic selection for protein content prediction relying on additional grain yield information within training sets is a realistic genomic selection approach in rye breeding.

ABSTRACT

Multiple-trait genomic selection (MTGS) was specially designed to benefit from the information of genetically correlated indicator traits in order to improve genomic prediction accuracies. Two segregating F3:4 rye testcross populations genotyped using diversity array technology markers and evaluated for grain yield (GY) and protein content (PC) were considered. The aims of our study were to explore the benefits of MTGS over single-trait genomic selection (STGS) for GY and PC prediction and to apply GS to predict different selection indices (SIs) for GY and PC improvement. Our results using a two-trait model (2TGS) empirically confirm that the ideal scenario to exploit the benefits of MTGS would be when the predictions of a relatively low heritable target trait with scarce phenotypic records are supported by an intensively phenotyped genetically correlated indicator trait which has higher heritability. This ideal scenario is expected for PC in practice. According to our GS implementation, MTGS can be performed in order to achieve more cycles of selection by unit of time. If the aim is to exclusively improve the prediction accuracy of a scarcely phenotyped trait, 2TGS will be a more accurate approach than a three-trait model which incorporates an additional correlated indicator trait. In general for balanced phenotypic information, we recommend to perform GS considering SIs as single traits, this method being a simple, direct and efficient way of prediction.

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.