Application of Bayesian genomic prediction methods to genome-wide association analyses.

BACKGROUND: Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses. RESULTS: By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance. CONCLUSIONS: Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.

Predictions of the accuracy of genomic prediction: connecting R2, selection index theory, and Fisher information.

BACKGROUND: Deterministic predictions of the accuracy of genomic estimated breeding values (GEBV) when combining information sources have been developed based on selection index theory (SIT) and on Fisher information (FI). These two approaches have resulted in slightly different results when considering the combination of pedigree and genomic information. Here, we clarify this apparent contradiction, both for the combination of pedigree and genomic information and for the combination of subpopulations into a joint reference population. RESULTS: First, we show that existing expressions for the squared accuracy of GEBV can be understood as a proportion of the variance explained. Next, we show that the apparent discrepancy that has been observed between accuracies based on SIT vs. FI originated from two sources. First, the FI referred to the genetic component that is captured by the marker genotypes, rather than the full genetic component. Second, the common SIT-based derivations did not account for the increase in the accuracy of GEBV due to a reduction of the residual variance when combining information sources. The SIT and FI approaches are equivalent when these sources are accounted for. CONCLUSIONS: The squared accuracy of GEBV can be understood as a proportion of the variance explained. The SIT and FI approaches for combining information for GEBV are equivalent and provide identical accuracies when the underlying assumptions are equivalent.

Quality of breeding value predictions from longitudinal analyses, with application to residual feed intake in pigs.

BACKGROUND: An important goal in animal breeding is to improve longitudinal traits. The objective of this study was to explore for longitudinal residual feed intake (RFI) data, which estimated breeding value (EBV), or combination of EBV, to use in a breeding program. Linear combinations of EBV (summarized breeding values, SBV) or phenotypes (summarized phenotypes) derived from the eigenvectors of the genetic covariance matrix over time were considered, and the linear regression method (LR method) was used to facilitate the evaluation of their prediction accuracy. RESULTS: Weekly feed intake, average daily gain, metabolic body weight, and backfat thickness measured on 2435 growing French Large White pigs over a 10-week period were analysed using a random regression model. In this population, the 544 dams of the phenotyped animals were genotyped. These dams did not have own phenotypes. The quality of the predictions of SBV and breeding values from summarized phenotypes of these females was evaluated. On average, predictions of SBV at the time of selection were unbiased, slightly over-dispersed and less accurate than those obtained with additional phenotypic information. The use of genomic information did not improve the quality of predictions. The use of summarized instead of longitudinal phenotypes resulted in predictions of breeding values of similar quality. CONCLUSIONS: For practical selection on longitudinal data, the results obtained with this specific design suggest that the use of summarized phenotypes could facilitate routine genetic evaluation of longitudinal traits.

Comparison of linkage disequilibrium estimated from genotypes versus haplotypes for crossbred populations.

BACKGROUND: Linkage disequilibrium (LD) is commonly measured based on the squared coefficient of correlation [Formula: see text] between the alleles at two loci that are carried by haplotypes. LD can also be estimated as the [Formula: see text] between unphased genotype dosage at two loci when the allele frequencies and inbreeding coefficients at both loci are identical for the parental lines. Here, we investigated whether [Formula: see text] for a crossbred population (F1) can be estimated using genotype data. The parental lines of the crossbred (F1) can be purebred or crossbred. METHODS: We approached this by first showing that inbreeding coefficients for an F1 crossbred population are negative, and typically differ in size between loci. Then, we proved that the expected [Formula: see text] computed from unphased genotype data is expected to be identical to the [Formula: see text] computed from haplotype data for an F1 crossbred population, regardless of the inbreeding coefficients at the two loci. Finally, we investigated the bias and precision of the [Formula: see text] estimated using unphased genotype versus haplotype data in stochastic simulation. RESULTS: Our findings show that estimates of [Formula: see text] based on haplotype and unphased genotype data are both unbiased for different combinations of allele frequencies, sample sizes (900, 1800, and 2700), and levels of LD. In general, for any allele frequency combination and [Formula: see text] value scenarios considered, and for both methods to estimate [Formula: see text], the precision of the estimates increased, and the bias of the estimates decreased as sample size increased, indicating that both estimators are consistent. For a given scenario, the [Formula: see text] estimates using haplotype data were more precise and less biased using haplotype data than using unphased genotype data. As sample size increased, the difference in precision and biasedness between the [Formula: see text] estimates using haplotype data and unphased genotype data decreased. CONCLUSIONS: Our theoretical derivations showed that estimates of LD between loci based on unphased genotypes and haplotypes in F1 crossbreds have identical expectations. Based on our simulation results, we conclude that the LD for an F1 crossbred population can be accurately estimated from unphased genotype data. The results also apply for other crosses (F2, F3, Fn, BC1, BC2, and BCn), as long as (selected) individuals from the two parental lines mate randomly.

Genetic analysis of disease resilience of wean-to-finish pigs under a natural disease challenge model using reaction norms.

BACKGROUND: Disease resilience is the ability to maintain performance across environments with different disease challenge loads (CL). A reaction norm describes the phenotypes that a genotype can produce across a range of environments and can be implemented using random regression models. The objectives of this study were to: (1) develop measures of CL using growth rate and clinical disease data recorded under a natural polymicrobial disease challenge model; and (2) quantify genetic variation in disease resilience using reaction norm models. METHODS: Different CL were derived from contemporary group effect estimates for average daily gain (ADG) and clinical disease phenotypes, including medical treatment rate (TRT), mortality rate, and subjective health scores. Resulting CL were then used as environmental covariates in reaction norm analyses of ADG and TRT in the challenge nursery and finisher, and compared using model loglikelihoods and estimates of genetic variance associated with CL. Linear and cubic spline reaction norm models were compared based on goodness-of-fit and with multi-variate analyses, for which phenotypes were separated into three traits based on low, medium, or high CL. RESULTS: Based on model likelihoods and estimates of genetic variance explained by the reaction norm, the best CL for ADG in the nursery was based on early ADG in the finisher, while the CL derived from clinical disease traits across the nursery and finisher was best for ADG in the finisher and for TRT in the nursery and across the nursery and finisher. With increasing CL, estimates of heritability for nursery and finisher ADG initially decreased, then increased, while estimates for TRT generally increased with CL. Genetic correlations for ADG and TRT were low between high versus low CL, but high for close CL. Linear reaction norm models fitted the data significantly better than the standard genetic model without genetic slopes, while the cubic spline model fitted the data significantly better than the linear reaction norm model for most traits. Reaction norm models also fitted the data better than multi-variate models. CONCLUSIONS: Reaction norm models identified genotype-by-environment interactions related to disease CL. Results can be used to select more resilient animals across different levels of CL, high-performance animals at a given CL, or a combination of these.

Response of three local chicken ecotypes of Ghana to lentogenic and velogenic Newcastle disease virus challenge.

This study was carried out to assess the response of three Ghanaian local chicken ecotypes to LaSota (lentogenic) and virulent field strains of Newcastle disease virus (NDV). Local chickens sampled from the Interior Savannah (IS), Forest (FO) and Coastal Savannah (CS) agro-ecological zones were bred and their offspring were challenged with LaSota NDV at 4 weeks of age. The LaSota challenge was replicated four times with different chicken groups. A total of 1438 chicks comprising 509 Coastal Savannah, 518 Forest and 411 Interior Savannah ecotypes were used. Pre- and post-challenge anti-NDV antibody titre levels were determined via ELISA assays. A second trial was conducted by introducing sick birds infected with virulent NDV to a flock of immunologically naïve chickens at 4 weeks old. Body weights were measured pre- and post-infection. Sex of the chickens was determined using a molecular method. In both trials, there was no significant difference among ecotypes in body weight and growth rate. In the LaSota trial, anti-NDV antibody titre did not differ by ecotype or sex. However, there was a positive linear relationship between body weight and antibody titre. In the velogenic NDV trial, survivability and lesion scores were similar among the three ecotypes. This study confirms that a relatively high dose of LaSota (NDV) challenge has no undesirable effect on Ghanaian local chicken ecotypes. All three Ghanaian local chicken ecotypes were susceptible to velogenic NDV challenge. Resistance to NDV by Ghanaian local chickens appears to be determined more by the individual's genetic makeup than by their ecotype.

Investigating the genetic architecture of disease resilience in pigs by genome-wide association studies of complete blood count traits collected from a natural disease challenge model.

BACKGROUND: Genetic improvement for disease resilience is anticipated to be a practical method to improve efficiency and profitability of the pig industry, as resilient pigs maintain a relatively undepressed level of performance in the face of infection. However, multiple biological functions are known to be involved in disease resilience and this complexity means that the genetic architecture of disease resilience remains largely unknown. Here, we conducted genome-wide association studies (GWAS) of 465,910 autosomal SNPs for complete blood count (CBC) traits that are important in an animal's disease response. The aim was to identify the genetic control of disease resilience. RESULTS: Univariate and multivariate single-step GWAS were performed on 15 CBC traits measured from the blood samples of 2743 crossbred (Landrace × Yorkshire) barrows drawn at 2-weeks before, and at 2 and 6-weeks after exposure to a polymicrobial infectious challenge. Overall, at a genome-wise false discovery rate of 0.05, five genomic regions located on Sus scrofa chromosome (SSC) 2, SSC4, SSC9, SSC10, and SSC12, were significantly associated with white blood cell traits in response to the polymicrobial challenge, and nine genomic regions on multiple chromosomes (SSC1, SSC4, SSC5, SSC6, SSC8, SSC9, SSC11, SSC12, SSC17) were significantly associated with red blood cell and platelet traits collected before and after exposure to the challenge. By functional enrichment analyses using Ingenuity Pathway Analysis (IPA) and literature review of previous CBC studies, candidate genes located nearby significant single-nucleotide polymorphisms were found to be involved in immune response, hematopoiesis, red blood cell morphology, and platelet aggregation. CONCLUSIONS: This study helps to improve our understanding of the genetic basis of CBC traits collected before and after exposure to a polymicrobial infectious challenge and provides a step forward to improve disease resilience.

Quantitative analysis of the blood transcriptome of young healthy pigs and its relationship with subsequent disease resilience.

BACKGROUND: Disease resilience, which is the ability of an animal to maintain performance under disease, is important for pigs in commercial herds, where they are exposed to various pathogens. Our objective was to investigate population-level gene expression profiles in the blood of 912 healthy F1 barrows at ~ 27 days of age for associations with performance and health before and after their exposure to a natural polymicrobial disease challenge at ~ 43 days of age. RESULTS: Most significant (q < 0.20) associations of the level of expression of individual genes in blood of young healthy pigs were identified for concurrent growth rate and subjective health scores prior to the challenge, and for mortality, a combined mortality-treatment trait, and feed conversion rate after the challenge. Gene set enrichment analyses revealed three groups of gene ontology biological process terms that were related to disease resilience: 1) immune and stress response-related terms were enriched among genes whose increased expression was unfavorably associated with both pre- and post-challenge traits, 2) heme-related terms were enriched among genes that had favorable associations with both pre- and post-challenge traits, and 3) terms related to protein localization and viral gene expression were enriched among genes that were associated with reduced performance and health traits after but not before the challenge. CONCLUSIONS: Gene expression profiles in blood from young healthy piglets provide insight into their performance when exposed to disease and other stressors. The expression of genes involved in stress response, heme metabolism, and baseline expression of host genes related to virus propagation were found to be associated with host response to disease.

Multiple trait breeding programs with genotype-by-environment interactions based on reaction norms, with application to genetic improvement of disease resilience.

BACKGROUND: Genotype-by-environment interactions for a trait can be modeled using multiple-trait, i.e. character-state, models, that consider the phenotype as a different trait in each environment, or using reaction norm models based on a functional relationship, usually linear, between phenotype and a quantitative measure of the quality of the environment. The equivalence between character-state and reaction norm models has been demonstrated for a single trait. The objectives of this study were to extend the equivalence of the reaction norm and character-state models to a multiple-trait setting and to both genetic and environmental effects, and to illustrate the application of this equivalence to the design and optimization of breeding programs for disease resilience. METHODS: Equivalencies between reaction norm and character-state models for multiple-trait phenotypes were derived at the genetic and environmental levels, which demonstrates how multiple-trait reaction norm parameters can be derived from multiple-trait character state parameters. Methods were applied to optimize selection for a multiple-trait breeding goal in a target environment based on phenotypes collected in a healthy and disease-challenged environment, and to optimize the environment in which disease-challenge phenotypes should be collected. RESULTS AND CONCLUSIONS: The equivalence between multiple-trait reaction norm and multiple-trait character-state parameters allow genetic improvement for a multiple-trait breeding goal in a target environment to be optimized without recording phenotypes and estimating parameters for the target environment.

Predicting the accuracy of genomic predictions.

BACKGROUND: Mathematical models are needed for the design of breeding programs using genomic prediction. While deterministic models for selection on pedigree-based estimates of breeding values (PEBV) are available, these have not been fully developed for genomic selection, with a key missing component being the accuracy of genomic EBV (GEBV) of selection candidates. Here, a deterministic method was developed to predict this accuracy within a closed breeding population based on the accuracy of GEBV and PEBV in the reference population and the distance of selection candidates from their closest ancestors in the reference population. METHODS: The accuracy of GEBV was modeled as a combination of the accuracy of PEBV and of EBV based on genomic relationships deviated from pedigree (DEBV). Loss of the accuracy of DEBV from the reference to the target population was modeled based on the effective number of independent chromosome segments in the reference population (Me). Measures of Me derived from the inverse of the variance of relationships and from the accuracies of GEBV and PEBV in the reference population, derived using either a Fisher information or a selection index approach, were compared by simulation. RESULTS: Using simulation, both the Fisher and the selection index approach correctly predicted accuracy in the target population over time, both with and without selection. The index approach, however, resulted in estimates of Me that were less affected by heritability, reference size, and selection, and which are, therefore, more appropriate as a population parameter. The variance of relationships underpredicted Me and was greatly affected by selection. A leave-one-out cross-validation approach was proposed to estimate required accuracies of EBV in the reference population. Aspects of the methods were validated using real data. CONCLUSIONS: A deterministic method was developed to predict the accuracy of GEBV in selection candidates in a closed breeding population. The population parameter Me that is required for these predictions can be derived from an available reference data set, and applied to other reference data sets and traits for that population. This method can be used to evaluate the benefit of genomic prediction and to optimize genomic selection breeding programs.

Vector space algebra for scaling and centering relationship matrices under non-Hardy-Weinberg equilibrium conditions.

BACKGROUND: Scales are linear combinations of variables with coefficients that add up to zero and have a similar meaning to "contrast" in the analysis of variance. Scales are necessary in order to incorporate genomic information into relationship matrices for genomic selection. Statistical and biological parameterizations using scales under different assumptions have been proposed to construct alternative genomic relationship matrices. Except for the natural and orthogonal interactions approach (NOIA) method, current methods to construct relationship matrices assume Hardy-Weinberg equilibrium (HWE). The objective of this paper is to apply vector algebra to center and scale relationship matrices under non-HWE conditions, including orthogonalization by the Gram-Schmidt process. THEORY AND METHODS: Vector space algebra provides an evaluation of current orthogonality between additive and dominance vectors of additive and dominance scales for each marker. Three alternative methods to center and scale additive and dominance relationship matrices based on the Gram-Schmidt process (GSP-A, GSP-D, and GSP-N) are proposed. GSP-A removes additive-dominance co-variation by first fitting the additive and then the dominance scales. GSP-D fits scales in the opposite order. We show that GSP-A is algebraically the same as the NOIA model. GSP-N orthonormalizes the additive and dominance scales that result from GSP-A. An example with genotype information on 32,645 single nucleotide polymorphisms from 903 Large-White × Landrace crossbred pigs is used to construct existing and newly proposed additive and dominance relationship matrices. RESULTS: An exact test for departures from HWE showed that a majority of loci were not in HWE in crossbred pigs. All methods, except the one that assumes HWE, performed well to attain an average of diagonal elements equal to one and an average of off diagonal elements equal to zero. Variance component estimation for a recorded quantitative phenotype showed that orthogonal methods (NOIA, GSP-A, GSP-N) can adjust for the additive-dominance co-variation when estimating the additive genetic variance, whereas GSP-D does it when estimating dominance components. However, different methods to orthogonalize relationship matrices resulted in different proportions of additive and dominance components of variance. CONCLUSIONS: Vector space methodology can be applied to measure orthogonality between vectors of additive and dominance scales and to construct alternative orthogonal models such as GSP-A, GSP-D and an orthonormal model such as GSP-N. Under non-HWE conditions, GSP-A is algebraically the same as the previously developed NOIA model.

Heritability of perching behavior and its genetic relationship with incidence of floor eggs in Rhode Island Red chickens.

BACKGROUND: As cage-free production systems become increasingly popular, behavioral traits such as nesting behavior and temperament have become more important. The objective of this study was to estimate heritabilities for frequency of perching and proportion of floor eggs and their genetic correlation in two Rhode Island Red lines. RESULTS: The percent of hens observed perching tended to increase and the proportion of eggs laid on the floor tended to decrease as the test progressed. This suggests the ability of hens to learn to use nests and perches. Under the bivariate repeatability model, estimates of heritability in the two lines were 0.22 ± 0.04 and 0.07 ± 0.05 for the percent of hens perching, and 0.52 ± 0.05 and 0.45 ± 0.05 for the percent of floor eggs. Estimates of the genetic correlation between perching and floor eggs were - 0.26 ± 0.14 and - 0.19 ± 0.27 for the two lines, suggesting that, genetically, there was some tendency for hens that better use perches to also use nests; but the phenotypic correlation was close to zero. Random regression models indicated the presence of a genetic component for learning ability. CONCLUSIONS: In conclusion, perching and tendency to lay floor eggs were shown to be a learned behavior, which stresses the importance of proper management and training of pullets and young hens. A significant genetic component was found, confirming the possibility to improve nesting behavior for cage-free systems through genetic selection.

Further host-genomic characterization of total antibody response to PRRSV vaccination and its relationship with reproductive performance in commercial sows: genome-wide haplotype and zygosity analyses.

BACKGROUND: The possibility of using antibody response (S/P ratio) to PRRSV vaccination measured in crossbred commercial gilts as a genetic indicator for reproductive performance in vaccinated crossbred sows has motivated further studies of the genomic basis of this trait. In this study, we investigated the association of haplotypes and runs of homozygosity (ROH) and heterozygosity (ROHet) with S/P ratio and their impact on reproductive performance. RESULTS: There was no association (P-value ≥ 0.18) of S/P ratio with the percentage of ROH or ROHet, or with the percentage of heterozygosity across the whole genome or in the major histocompatibility complex (MHC) region. However, specific ROH and ROHet regions were significantly associated (P-value ≤ 0.01) with S/P ratio on chromosomes 1, 4, 5, 7, 10, 11, 13, and 17 but not (P-value ≥ 0.10) with reproductive performance. With the haplotype-based genome-wide association study (GWAS), additional genomic regions associated with S/P ratio were identified on chromosomes 4, 7, and 9. These regions harbor immune-related genes, such as SLA-DOB, TAP2, TAPBP, TMIGD3, and ADORA. Four haplotypes at the identified region on chromosome 7 were also associated with multiple reproductive traits. A haplotype significantly associated with S/P ratio that is located in the MHC region may be in stronger linkage disequilibrium (LD) with the quantitative trait loci (QTL) than the previously identified single nucleotide polymorphism (SNP) (H3GA0020505) given the larger estimate of genetic variance explained by the haplotype than by the SNP. CONCLUSIONS: Specific ROH and ROHet regions were significantly associated with S/P ratio. The haplotype-based GWAS identified novel QTL for S/P ratio on chromosomes 4, 7, and 9 and confirmed the presence of at least one QTL in the MHC region. The chromosome 7 region was also associated with reproductive performance. These results narrow the search for causal genes in this region and suggest SLA-DOB and TAP2 as potential candidate genes associated with S/P ratio on chromosome 7. These results provide additional opportunities for marker-assisted selection and genomic selection for S/P ratio as genetic indicator for litter size in commercial pig populations.

Gene expression in tonsils in swine following infection with porcine reproductive and respiratory syndrome virus.

BACKGROUND: Porcine reproductive and respiratory syndrome (PRRS) is a threat to pig production worldwide. Our objective was to understand mechanisms of persistence of PRRS virus (PRRSV) in tonsil. Transcriptome data from tonsil samples collected at 42 days post infection (dpi) were generated by RNA-seq and NanoString on 51 pigs that were selected to contrast the two PRRSV isolates used, NVSL and KS06, high and low tonsil viral level at 42 dpi, and the favorable and unfavorable genotypes at a genetic marker (WUR) for the putative PRRSV resistance gene GBP5. RESULTS: The number of differentially expressed genes (DEGs) differed markedly between models with and without accounting for cell-type enrichments (CE) in the samples that were predicted from the RNA-seq data. This indicates that differences in cell composition in tissues that consist of multiple cell types, such as tonsil, can have a large impact on observed differences in gene expression. Based on both the NanoString and the RNA-seq data, KS06-infected pigs showed greater activation, or less inhibition, of immune response in tonsils at 42 dpi than NVSL-infected pigs, with and without accounting for CE. This suggests that the NVSL virus may be better than the KS06 virus at evading host immune response and persists in tonsils by weakening, or preventing, host immune responses. Pigs with high viral levels showed larger CE of immune cells than low viral level pigs, potentially to trigger stronger immune responses. Presence of high tonsil virus was associated with a stronger immune response, especially innate immune response through interferon signaling, but these differences were not significant when accounting for CE. Genotype at WUR was associated with different effects on immune response in tonsils of pigs during the persistence stage, depending on viral isolate and tonsil viral level. CONCLUSIONS: Results of this study provide insights into the effects of PRRSV isolate, tonsil viral level, and WUR genotype on host immune response and into potential mechanisms of PRRSV persistence in tonsils that could be targeted to improve strategies to reduce viral rebreaks. Finally, to understand transcriptome responses in tissues that consist of multiple cell types, it is important to consider differences in cell composition.

Cross-validation of best linear unbiased predictions of breeding values using an efficient leave-one-out strategy.

Empirical estimates of the accuracy of estimates of breeding values (EBV) can be obtained by cross-validation. Leave-one-out cross-validation (LOOCV) is an extreme case of k-fold cross-validation. Efficient strategies for LOOCV of predictions of phenotypes have been developed for a simple model with an overall mean and random marker or animal genetic effects. The objective here was to develop and evaluate an efficient LOOCV method for prediction of breeding values and other random effects under a general mixed linear model with multiple random effects. Conventional LOOCV of EBV requires inverting an (n-1)×(n-1) covariance matrix for each of n (= number of observations) data sets. Our efficient LOOCV obtains the required inverses from the inverse of the covariance matrix for all n observations. The efficient method can be applied to complex models with multiple fixed and random effects, but requires fixed effects to be treated as random, with large variances. An alternative is to precorrect observations using estimates of fixed effects obtained from the complete data, but this can lead to biases. The efficient LOOCV method was compared to conventional LOOCV of predictions of breeding values in terms of computational demands and accuracy. For a data set with 3,205 observations and a model with multiple random and fixed effects, the efficient LOOCV method was 962 times faster than the conventional LOOCV with precorrection for fixed effects based on each training data set but resulted in identical EBV. A computationally efficient LOOCV for prediction of breeding values for single- and multiple-trait mixed models with multiple fixed and random effects was successfully developed. The method enables cross-validation of predictions of breeding values and of any linear combination of random and/or fixed effects, along with leave-one-out precorrection of validation phenotypes.

The genetic basis of natural antibody titers of young healthy pigs and relationships with disease resilience.

BACKGROUND: Disease resilience is the ability to maintain performance under pathogen exposure but is difficult to select for because breeding populations are raised under high health. Selection for resilience requires a trait that is heritable, easy to measure on healthy animals, and genetically correlated with resilience. Natural antibodies (NAb) are important parts of the innate immune system and are found to be heritable and associated with disease susceptibility in dairy cattle and poultry. Our objective was to investigate NAb and total IgG in blood of healthy, young pigs as potential indicator traits for disease resilience. RESULTS: Data were from Yorkshire x Landrace pigs, with IgG and IgM NAb (four antigens) and total IgG measured by ELISA in blood plasma collected ~ 1 week after weaning, prior to their exposure to a natural polymicrobial challenge. Heritability estimates were lower for IgG NAb (0.12 to 0.24, + 0.05) and for total IgG (0.19 + 0.05) than for IgM NAb (0.33 to 0.53, + 0.07) but maternal effects were larger for IgG NAb (0.41 to 0.52, + 0.03) and for total IgG (0.19 + 0.05) than for IgM NAb (0.00 to 0.10, + 0.04). Phenotypically, IgM NAb titers were moderately correlated with each other (average 0.60), as were IgG NAb titers (average 0.42), but correlations between IgM and IgG NAb titers were weak (average 0.09). Phenotypic correlations of total IgG were moderate with NAb IgG (average 0.46) but weak with NAb IgM (average 0.01). Estimates of genetic correlations among NAb showed similar patterns but with small SE, with estimates averaging 0.76 among IgG NAb, 0.63 among IgM NAb, 0.17 between IgG and IgM NAb, 0.64 between total IgG and IgG NAb, and 0.13 between total IgG and IgM NAb. Phenotypically, pigs that survived had slightly higher levels of NAb and total IgG than pigs that died. Genetically, higher levels of NAb tended to be associated with greater disease resilience based on lower mortality and fewer parenteral antibiotic treatments. Genome-wide association analyses for NAb titers identified several genomic regions, with several candidate genes for immune response. CONCLUSIONS: Levels of NAb in blood of healthy young piglets are heritable and potential genetic indicators of resilience to polymicrobial disease.

Investigating the relationship between vaginal microbiota and host genetics and their impact on immune response and farrowing traits in commercial gilts.

Our objectives were to evaluate the interaction between host genetics and vaginal microbiota and their relationships with antibody (Ab) response to porcine reproductive and respiratory syndrome virus (PRRSV) vaccination and farrowing performance in commercial gilts. The farrowing performance traits were number born alive, number weaning (NW), total number born, number born dead, stillborn, mummies and preweaning mortality (PWM). The vaginal microbiota was collected on days 4 (D4) and 52 (D52) after vaccination for PRRSV. Blood samples were collected on D52 for Ab measurement. Actinobacteria, Bacterioidetes, Firmicutes, Proteobacteria and Tenericutes were the most abundant Phyla identified in the vaginal microbiota. Heritability ranged from ~0 to 0.60 (Fusobacterium) on D4 and from ~0 to 0.63 (Terrisporobacter) on D52, with 43 operational taxonomic units (OTUs) presenting moderate to high heritability. One major QTL on chromosome 12 was identified for 5 OTUs (Clostridiales, Acinetobacter, Ruminococcaceae, Campylobacter and Anaerococcus), among other 19 QTL. The microbiability for Ab response to PRRSV vaccination was low for both days (<0.07). For farrowing performance, microbiability varied from <0.001 to 0.15 (NW on D4). For NW and PWM, the microbiability was greater than the heritability estimates. Actinobacillus, Streptococcus, Campylobacter, Anaerococcus, Mollicutes, Peptostreptococcus, Treponema and Fusobacterium showed different abundance between low and high Ab responders. Finally, canonical discriminant analyses revealed that vaginal microbiota was able to classify gilts in high and low Ab responders to PRRSV vaccination with a misclassification rate of <0.02. Although the microbiota explained limited variation in Ab response and farrowing performance traits, there is still potential to explore the use of vaginal microbiota to explain variation in traits such as NW and PWM. In addition, these results revealed that there is a partial control of host genetic over vaginal microbiota, suggesting a possibility for genetic selection on the vaginal microbiota.

The effects of a globin blocker on the resolution of 3'mRNA sequencing data in porcine blood.

BACKGROUND: Gene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of globin mRNA in porcine blood. These limitations can be overcome by the use of QuantSeq 3'mRNA sequencing (QuantSeq) combined with a method to deplete or block the processing of globin mRNA prior to or during library construction. Here, we validated the effectiveness of QuantSeq using a novel specific globin blocker (GB) that is included in the library preparation step of QuantSeq. RESULTS: In data set 1, four concentrations of the GB were applied to RNA samples from two pigs. The GB significantly reduced the proportion of globin reads compared to non-GB (NGB) samples (P = 0.005) and increased the number of detectable non-globin genes. The highest evaluated concentration (C1) of the GB resulted in the largest reduction of globin reads compared to the NGB (from 56.4 to 10.1%). The second highest concentration C2, which showed very similar globin depletion rates (12%) as C1 but a better correlation of the expression of non-globin genes between NGB and GB (r = 0.98), allowed the expression of an additional 1295 non-globin genes to be detected, although 40 genes that were detected in the NGB sample (at a low level) were not present in the GB library. Concentration C2 was applied in the rest of the study. In data set 2, the distribution of the percentage of globin reads for NGB (n = 184) and GB (n = 189) samples clearly showed the effects of the GB on reducing globin reads, in particular for HBB, similar to results from data set 1. Data set 3 (n = 84) revealed that the proportion of globin reads that remained in GB samples was significantly and positively correlated with the reticulocyte count in the original blood sample (P < 0.001). CONCLUSIONS: The effect of the GB on reducing the proportion of globin reads in porcine blood QuantSeq was demonstrated in three data sets. In addition to increasing the efficiency of sequencing non-globin mRNA, the GB for QuantSeq has an advantage that it does not require an additional step prior to or during library creation. Therefore, the GB is a useful tool in the quantification of whole gene expression profiles in porcine blood.

Acute systemic inflammatory response to lipopolysaccharide stimulation in pigs divergently selected for residual feed intake.

BACKGROUND: It is unclear whether improving feed efficiency by selection for low residual feed intake (RFI) compromises pigs' immunocompetence. Here, we aimed at investigating whether pig lines divergently selected for RFI had different inflammatory responses to lipopolysaccharide (LPS) exposure, regarding to clinical presentations and transcriptomic changes in peripheral blood cells. RESULTS: LPS injection induced acute systemic inflammation in both the low-RFI and high-RFI line (n = 8 per line). At 4 h post injection (hpi), the low-RFI line had a significantly lower (p = 0.0075) mean rectal temperature compared to the high-RFI line. However, no significant differences in complete blood count or levels of several plasma cytokines were detected between the two lines. Profiling blood transcriptomes at 0, 2, 6, and 24 hpi by RNA-sequencing revealed that LPS induced dramatic transcriptional changes, with 6296 genes differentially expressed at at least one time point post injection relative to baseline in at least one line (n = 4 per line) (|log2(fold change)| ≥ log2(1.2); q < 0.05). Furthermore, applying the same cutoffs, we detected 334 genes differentially expressed between the two lines at at least one time point, including 33 genes differentially expressed between the two lines at baseline. But no significant line-by-time interaction effects were detected. Genes involved in protein translation, defense response, immune response, and signaling were enriched in different co-expression clusters of genes responsive to LPS stimulation. The two lines were largely similar in their peripheral blood transcriptomic responses to LPS stimulation at the pathway level, although the low-RFI line had a slightly lower level of inflammatory response than the high-RFI line from 2 to 6 hpi and a slightly higher level of inflammatory response than the high-RFI line at 24 hpi. CONCLUSIONS: The pig lines divergently selected for RFI had a largely similar response to LPS stimulation. However, the low-RFI line had a relatively lower-level, but longer-lasting, inflammatory response compared to the high-RFI line. Our results suggest selection for feed efficient pigs does not significantly compromise a pig's acute systemic inflammatory response to LPS, although slight differences in intensity and duration may occur.

Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis.

BACKGROUND: Population stratification and cryptic relationships have been the main sources of excessive false-positives and false-negatives in population-based association studies. Many methods have been developed to model these confounding factors and minimize their impact on the results of genome-wide association studies. In most of these methods, a two-stage approach is applied where: (1) methods are used to determine if there is a population structure in the sample dataset and (2) the effects of population structure are corrected either by modeling it or by running a separate analysis within each sub-population. The objective of this study was to evaluate the impact of population structure on the accuracy and power of genome-wide association studies using a Bayesian multiple regression method. METHODS: We conducted a genome-wide association study in a stochastically simulated admixed population. The genome was composed of six chromosomes, each with 1000 markers. Fifteen segregating quantitative trait loci contributed to the genetic variation of a quantitative trait with heritability of 0.30. The impact of genetic relationships and breed composition (BC) on three analysis methods were evaluated: single marker simple regression (SMR), single marker mixed linear model (MLM) and Bayesian multiple-regression analysis (BMR). Each method was fitted with and without BC. Accuracy, power, false-positive rate and the positive predictive value of each method were calculated and used for comparison. RESULTS: SMR and BMR, both without BC, were ranked as the worst and the best performing approaches, respectively. Our results showed that, while explicit modeling of genetic relationships and BC is essential for models SMR and MLM, BMR can disregard them and yet result in a higher power without compromising its false-positive rate. CONCLUSIONS: This study showed that the Bayesian multiple-regression analysis is robust to population structure and to relationships among study subjects and performs better than a single marker mixed linear model approach.