Use of multi-trait and random regression models to identify genetic variation in tolerance to porcine reproductive and respiratory syndrome virus.

BACKGROUND: A host can adopt two response strategies to infection: resistance (reduce pathogen load) and tolerance (minimize impact of infection on performance). Both strategies may be under genetic control and could thus be targeted for genetic improvement. Although there is evidence that supports a genetic basis for resistance to porcine reproductive and respiratory syndrome (PRRS), it is not known whether pigs also differ genetically in tolerance. We determined to what extent pigs that have been shown to vary genetically in resistance to PRRS also exhibit genetic variation in tolerance. Multi-trait linear mixed models and random regression sire models were fitted to PRRS Host Genetics Consortium data from 1320 weaned pigs (offspring of 54 sires) that were experimentally infected with a virulent strain of PRRS virus to obtain genetic parameter estimates for resistance and tolerance. Resistance was defined as the inverse of within-host viral load (VL) from 0 to 21 (VL21) or 0 to 42 (VL42) days post-infection and tolerance as the slope of the reaction-norm of average daily gain (ADG21, ADG42) on VL21 or VL42. RESULTS: Multi-trait analysis of ADG associated with either low or high VL was not indicative of genetic variation in tolerance. Similarly, random regression models for ADG21 and ADG42 with a tolerance slope fitted for each sire did not result in a better fit to the data than a model without genetic variation in tolerance. However, the distribution of data around average VL suggested possible confounding between level and slope estimates of the regression lines. Augmenting the data with simulated growth rates of non-infected half-sibs (ADG0) helped resolve this statistical confounding and indicated that genetic variation in tolerance to PRRS may exist if genetic correlations between ADG0 and ADG21 or ADG42 are low to moderate. CONCLUSIONS: Evidence for genetic variation in tolerance of pigs to PRRS was weak when based on data from infected piglets only. However, simulations indicated that genetic variance in tolerance may exist and could be detected if comparable data on uninfected relatives were available. In conclusion, of the two defense strategies, genetics of tolerance is more difficult to elucidate than genetics of resistance.

Genome-wide association analysis reveals loci associated with resistance against Piscirickettsia salmonis in two Atlantic salmon (Salmo salar L.) chromosomes.

BACKGROUND: Pisciricketssia salmonis is the causal agent of Salmon Rickettsial Syndrome (SRS), which affects salmon species and causes severe economic losses. Selective breeding for disease resistance represents one approach for controlling SRS in farmed Atlantic salmon. Knowledge concerning the architecture of the resistance trait is needed before deciding on the most appropriate approach to enhance artificial selection for P. salmonis resistance in Atlantic salmon. The purpose of the study was to dissect the genetic variation in the resistance to this pathogen in Atlantic salmon. METHODS: 2,601 Atlantic salmon smolts were experimentally challenged against P. salmonis by means of intra-peritoneal injection. These smolts were the progeny of 40 sires and 118 dams from a Chilean breeding population. Mortalities were recorded daily and the experiment ended at day 40 post-inoculation. Fish were genotyped using a 50K Affymetrix® Axiom® myDesignTM Single Nucleotide Polymorphism (SNP) Genotyping Array. A Genome Wide Association Analysis was performed on data from the challenged fish. Linear regression and logistic regression models were tested. RESULTS: Genome Wide Association Analysis indicated that resistance to P. salmonis is a moderately polygenic trait. There were five SNPs in chromosomes Ssa01 and Ssa17 significantly associated with the traits analysed. The proportion of the phenotypic variance explained by each marker is small, ranging from 0.007 to 0.045. Candidate genes including interleukin receptors and fucosyltransferase have been found to be physically linked with these genetic markers and may play an important role in the differential immune response against this pathogen. CONCLUSIONS: Due to the small amount of variance explained by each significant marker we conclude that genetic resistance to this pathogen can be more efficiently improved with the implementation of genetic evaluations incorporating genotype information from a dense SNP array.

Design and development of exome capture sequencing for the domestic pig (Sus scrofa).

BACKGROUND: The domestic pig (Sus scrofa) is both an important livestock species and a model for biomedical research. Exome sequencing has accelerated identification of protein-coding variants underlying phenotypic traits in human and mouse. We aimed to develop and validate a similar resource for the pig. RESULTS: We developed probe sets to capture pig exonic sequences based upon the current Ensembl pig gene annotation supplemented with mapped expressed sequence tags (ESTs) and demonstrated proof-of-principle capture and sequencing of the pig exome in 96 pigs, encompassing 24 capture experiments. For most of the samples at least 10x sequence coverage was achieved for more than 90% of the target bases. Bioinformatic analysis of the data revealed over 236,000 high confidence predicted SNPs and over 28,000 predicted indels. CONCLUSIONS: We have achieved coverage statistics similar to those seen with commercially available human and mouse exome kits. Exome capture in pigs provides a tool to identify coding region variation associated with production traits, including loss of function mutations which may explain embryonic and neonatal losses, and to improve genomic assemblies in the vicinity of protein coding genes in the pig.

Porcine insulin receptor substrate 4 (IRS4) gene: cloning, polymorphism and association study.

Using PCR and inverse PCR techniques we obtained a 4,498 bp nucleotide sequence FN424076 encompassing the complete coding sequence of the porcine insulin receptor substrate 4 (IRS4) gene and its proximal promoter. The 1,269 amino acid porcine protein deduced from the nucleotide sequence shares 92% identity with the human IRS4 and possesses the same domains and the same number of tyrosine phosphorylation motifs as the human protein. We detected substitution FN424076:g.96C

Differences in susceptibility to Haemophilus parasuis infection in pigs.

In animal breeding programs, deoxyribonucleic acid (DNA) markers can be used to identify sires that are less susceptible to disease. These DNA markers are typically discovered in populations that display differences in susceptibility. To find those differences, it was hypothesized that sires influence their offspring responses to infection with H. parasuis. To identify differences in susceptibility, colostrum-deprived pigs derived from 6 sires were inoculated with a virulent strain of H. parasuis serovar 5. Pigs were infected at 21-d of age and euthanized 1, 2, or 3 days post-infection. Rectal temperatures, bacterial detection, clinical signs, and lesions were measured by comparing disease susceptibility in the offspring from each sire. The effect of the sire on the severity of disease in the offspring was statistically analyzed using to a 2-way ANOVA with sire and test day as fixed effects. Significant differences among sires were found for lesions, rectal temperatures from days 0-1 and 0-2 (P < 0.05) and marginal effects for clinical signs (P = 0.08). On average, the offspring of sire H94 was the most susceptible to challenge. Responses to infection were categorized to determine the clinical responses and analyzed by Chi square. Overall, 10% of all pigs infected were fully resistant to H. parasuis infection. Boar H94 didn't produce any fully resistant offspring. Differences in susceptibility to H. parasuis were observed, and the results support the hypothesis that sires influence their offspring's response to infection. Tissues from this population could be used to identify DNA markers for genetic selection of sires that produce offspring more resistant to H. parasuis infection.

Identification of Low-Confidence Regions in the Pig Reference Genome (Sscrofa10.2).

Many applications of high throughput sequencing rely on the availability of an accurate reference genome. Variant calling often produces large data sets that cannot be realistically validated and which may contain large numbers of false-positives. Errors in the reference assembly increase the number of false-positives. While resources are available to aid in the filtering of variants from human data, for other species these do not yet exist and strict filtering techniques must be employed which are more likely to exclude true-positives. This work assesses the accuracy of the pig reference genome (Sscrofa10.2) using whole genome sequencing reads from the Duroc sow whose genome the assembly was based on. Indicators of structural variation including high regional coverage, unexpected insert sizes, improper pairing and homozygous variants were used to identify low quality (LQ) regions of the assembly. Low coverage (LC) regions were also identified and analyzed separately. The LQ regions covered 13.85% of the genome, the LC regions covered 26.6% of the genome and combined (LQLC) they covered 33.07% of the genome. Over half of dbSNP variants were located in the LQLC regions. Of copy number variable regions identified in a previous study, 86.3% were located in the LQLC regions. The regions were also enriched for gene predictions from RNA-seq data with 42.98% falling in the LQLC regions. Excluding variants in the LQ, LC, or LQLC from future analyses will help reduce the number of false-positive variant calls. Researchers using WGS data should be aware that the current pig reference genome does not give an accurate representation of the copy number of alleles in the original Duroc sow's genome.

Selecting markers and evaluating coverage.

The availability of genetic markers in many species has enabled the analysis of marker-trait associations ranging from small genomic regions to genome-wide scale. An appropriate set of markers must be identified to meet the objectives of any research, using a custom discovery and selection approach or by using a commercial product. The key considerations in selecting markers are the quantity and the distribution across the genome. Though decisions about how many markers to use are often pragmatic, influenced by costs and available technology, an evaluation of the marker coverage is important in understanding how to design an effective genomic research study with reasonable expectations about the power to obtain desired results. An important parameter to evaluate coverage is linkage disequilibrium, which can be used to determine the appropriate number of markers for a particular analysis and is related to the proportion of variance that can be explained by a given marker, or power. Finally, the type of analysis used to identify marker-trait associations may depend on marker coverage as the optimal approach, from a statistical or computational standpoint, may differ with changes in marker number and distribution.

Genomic breeding value prediction using three Bayesian methods and application to reduced density marker panels.

BACKGROUND: Bayesian approaches for predicting genomic breeding values (GEBV) have been proposed that allow for different variances for individual markers resulting in a shrinkage procedure that uses prior information to coerce negligible effects towards zero. These approaches have generally assumed application to high-density genotype data on all individuals, which may not be the case in practice. In this study, three approaches were compared for their predictive power in computing GEBV when training at high SNP marker density and predicting at high or low densities: the well- known Bayes-A, a generalization of Bayes-A where scale and degrees of freedom are estimated from the data (Student-t) and a Bayesian implementation of the Lasso method. Twelve scenarios were evaluated for predicting GEBV using low-density marker subsets, including selection of SNP based on genome spacing or size of additive effect and the inclusion of unknown genotype information in the form of genotype probabilities from pedigree and genotyped ancestors. RESULTS: The GEBV accuracy (calculated as correlation between GEBV and traditional breeding values) was highest for Lasso, followed by Student-t and then Bayes-A. When comparing GEBV to true breeding values, Student-t was most accurate, though differences were small. In general the shrinkage applied by the Lasso approach was less conservative than Bayes-A or Student-t, indicating that Lasso may be more sensitive to QTL with small effects. In the reduced-density marker subsets the ranking of the methods was generally consistent. Overall, low-density, evenly-spaced SNPs did a poor job of predicting GEBV, but SNPs selected based on additive effect size yielded accuracies similar to those at high density, even when coverage was low. The inclusion of genotype probabilities to the evenly-spaced subsets showed promising increases in accuracy and may be more useful in cases where many QTL of small effect are expected. CONCLUSIONS: In this dataset the Student-t approach slightly outperformed the other methods when predicting GEBV at both high and low density, but the Lasso method may have particular advantages in situations where many small QTL are expected. When markers were selected at low density based on genome spacing, the inclusion of genotype probabilities increased GEBV accuracy which would allow a single low- density marker panel to be used across traits.

Evaluation of a genome-wide approach to multiple marker association considering different marker densities.

BACKGROUND: Genome-wide approaches to analyze single nucleotide polymorphism (SNP) data have proliferated due to the increased availability and affordability of markers, but in practice a small number of markers may be selected from sets that do not approach dense genome-wide coverage. This study focused on a genome-wide approach to identify markers useful to a breeding program using a Bayesian method to estimate effects for markers distributed across the genome at varied densities. A simulated dataset containing 4665 individual phenotypes for a quantitative trait and genotypes for 6000 SNPs spaced in 0.1 cM increments across six chromosomes was analyzed using a Bayesian approach in which effects for all single markers are simultaneously estimated. The dataset was also analyzed with marker densities reduced to 0.5, 1.0, 2.0 and 5.0 cM. Type I errors were not a major concern but replications of each analysis were performed to determine acceptance of estimated marker effects. RESULTS: The Bayesian analysis of the original dataset was able to estimate genetic values for markers in a small number of regions while shrinking other marker effects to zero. Analysis of the reduced density datasets also showed clear signals in a small number of regions where some effects appeared to be distributed across multiple markers. Replicates of the analyses provided evidence for regions with moderate and large effects. CONCLUSION: A Bayesian multiple marker approach appears to be suitable for predicting genetic values, even with reduced density datasets where large numbers of markers are not yet available for many species. These predicted genetic values can be implemented in marker assisted selection programs.

Investigation of TXNIP (thioredoxin-interacting protein) and TRX (thioredoxin) genes for growth-related traits in pigs.

It is well known that TRX and its endogenous inhibitor TXNIP help sustain the cellular reduction/oxidation balance in response to various stresses and both play a crucial role in cell proliferation and growth. Five SNPs were found in TXNIP and these allowed us to map it by linkage to SSC4. Three of the SNPs were used for association analyses in three commercial pig populations (Duroc, Hampshire, and synthetic line) with more than 1200 animals. Both the single-marker and haplotype analyses revealed significant effects of TXNIP on hot carcass weight, test daily gain, and lifetime daily gain. TRX was mapped on SSC1 but no significant associations with growth-related traits were found in the synthetic pig line in which the SNP was informative. The expression levels of TXNIP and TRX were then detected in two groups (fast growth and slow growth, respectively) with different genetic backgrounds for growth. Compared with the slow-growth group, TXNIP expression was significantly lower in the fast-growth group, whereas a marked increase in TRX expression was observed in fast-growth group. Our findings suggest that TXNIP has effects on growth-related traits in pigs and further investigations will be necessary to elucidate the underlying mechanisms involved.