Validation and Further Characterization of a Major Quantitative Trait Locus Associated with Host Response to Experimental Infection with Porcine Reproductive and Respiratory Syndrome Virus

Infectious diseases are costly to the swine industry; porcine reproductive and respiratory syndrome (PRRS) is the most devastating. In earlier work, a quantitative trait locus associated with resistance/susceptibility to PRRS virus was identified on Sus scrofa chromosome 4 using approximately 560 experimentally infected animals from a commercial cross. The favorable genotype was associated with decreased virus load and increased weight gain (WG). The objective here was to validate and further characterize the association of the chromosome 4 region with PRRS resistance using data from two unrelated commercial crossbred populations. The validation populations consisted of two trials each of approximately 200 pigs sourced from different breeding companies that were infected with PRRS virus and followed for 42 days post-infection. Across all five trials, heritability estimates were 0.39 and 0.34 for viral load (VL; area under the curve of log-transformed viremia from 0 to 21 days post-infection) and WG to 42 days post-infection respectively. Effect estimates of SNP WUR10000125 in the chromosome 4 region were in the same directions and of similar magnitudes in the two new trials as had been observed in the first three trials. Across all five trials, the 1-Mb region on chromosome 4 explained 15 percent of genetic variance for VL and 11 percent for WG. The effect of the favorable minor allele at SNP WUR10000125 was dominant. Ordered genotypes for SNP WUR10000125 showed that the effect was present irrespective of whether the favorable allele was paternally or maternally inherited. These results demonstrate that selection for host response to PRRS virus infection could reduce the economic impact of PRRS

Estimation of genetic variation in residual variance in female and male broiler chickens

In breeding programs, robustness of animals and uniformity of end product can be improved by exploiting genetic variation in residual variance. Residual variance can be defined as environmental variance after accounting for all identifiable effects. The aims of this study were to estimate genetic variance in residual variance of body weight, and to estimate genetic correlations between body weight itself and its residual variance and between female and male residual variance for broilers. The data sets comprised 26 972 female and 24 407 male body weight records. Variance components were estimated with ASREML. Estimates of the heritability of residual variance were in the range 0.029 (s.e.50.003) to 0.047 (s.e.50.004). The genetic coefficients of variation were high, between 0.35 and 0.57. Heritabilities were higher in females than in males. Accounting for heterogeneous residual variance increased the heritabilities for body weight as well. Genetic correlations between body weight and its residual variance were 20.41 (s.e.50.032) and 20.45 (s.e.50.040), respectively, in females and males. The genetic correlation between female and male residual variance was 0.11 (s.e.50.089), indicating that female and male residual variance are different traits. Results indicate good opportunities to simultaneously increase the mean and improve uniformity of body weight of broilers by selection

Prediction Accuracy of Pedigree and Genomic Estimated Breeding Values over Generations in Layer Chickens

This study investigated the accuracy of estimated breeding values (EBV) over different training generations in layer chickens using pedigree and marker-based models. On average, the accuracy of EBV based on markers was higher than that based on pedigree. The accuracy of all methods increased with an increase in the number of generations in training data, but slightly dropped or remained even after including training generations far apart from validation

Evaluation of Egg Production in Layers Using Random Regression Models

The objectives of this study were to estimate genetic parameters for egg production over the age trajectory in three commercial layer breeding lines, which represent different biotypes for egg production, and to validate the use of breeding values for slope as a measure of persistency to be used in the selection program. Egg production data of over 26,000 layers per line from six consecutive generations were analyzed. Daily records were cumulated into biweekly periods. Data were analyzed with a random regression model with linear polynomials on period for random additive genetic and permanent environmental effects. In all lines, a nonzero genetic variance for mean and slope and a positive genetic correlation between mean and slope were estimated. Breeding values for slope well reflected the shape of the egg production curve and can be used to select for persistency of egg production. The model proposed in this study appealing for implementation in large and multiple populations under commercial conditions by breeding companies or other breeding organizations

Egg Shell Quality Assessment–Do We Need Multiple Records?

The objective of this study was to estimate repeatability within and between ages for dynamic stiffnessin two lines of layer chickens in order to verify if multiple records are necessary to adequately describe a bird’s genetic merit for egg shell quality.Repeatability was low across ages to moderate within age,which suggests that for accurate evaluation eggs should be collected at different stages of laying cycle,with additional benefit from analyzing more than one egg within age

Comparison of analyses of the QTLMAS XIV common dataset. I: genomic selection

Background - For the XIV QTLMAS workshop, a dataset for traits with complex genetic architecture has been simulated and released for analyses by participants. One of the tasks was to estimate direct genomic values for individuals without phenotypes. The aim of this paper was to compare results of different approaches used by the participants to calculate direct genomic values for quantitative trait (QT) and binary trait (BT). Results - Participants applied 26 approaches for QT and 15 approaches for BT. Accuracy for QT was between 0.26 and 0.89 for males and between 0.31 and 0.89 for females, and for BT ranged from 0.27 to 0.85. For QT, percentage of lost response to selection varied from 8% to 83%, whereas for BT the loss was between 15% and 71%. Conclusions - Bayesian model averaging methods predicted breeding values slightly better than GBLUP in a simulated data set. The methods utilizing genomic information performed better than traditional pedigree based BLUP analyses. Bivariate analyses was slightly advantageous over single trait for the same method. None of the methods estimated the non-additivity of QTL affecting the QT, which may be one of the constrains in accuracy observed in real data. -------------------------------------------------------------------------------

Juvenile IGF-I: An Early Bio-marker for Feed Efficiency in Pigs

At Iowa State University, purebred Yorkshire pigs have been divergently selected for increased and decreased feed efficiency based on residual feed intake for ten generations. In this study, juvenile IGF-I serum concentrations were measured in these divergently selected lines, with the goal of validating juvenile IGF-I as an early blood bio-marker to help select young piglets for later feed efficiency performance. Previous findings (Bunter et al., 2002, 2005, 2010) and this validation study support that lower juvenile IGF-I concentration in piglets is genetically correlated with increased grow-finish feed efficiency. IGF-I concentration is a moderately heritable trait that is more cost and time effective to measure than feed intake and feed efficiency. These characteristics make IGF-I a useful bio-marker for feed efficiency in swine

Genetic Basis of Resistance to Avian Influenza

Two high pathogenic avian influenza (HPAI) outbreaks haveseverelyaffected the poultry industryon the American continentwithin the last four years; a 2012 H7N3 outbreak in Mexicoand a 2015 H5N2 outbreak in the US. Blood samples were collectedfrom survivors of each outbreak plus age and genetics matched non-affected controls. As surviving birds could contain natural genetic mutation(s) that make them resistant to HPAI,the goal of the present study was to identify genomic regions associated with resistance to HPAIand to determinewhether resistance regionsare the same fordifferent virus strains.Four genomic regions were identified for the H5N2 outbreak and fivedifferent regions were identified for the H7N3 outbreak. The apparentdifferent genomic regions of resistance for different virus strains is achallengefor the poultry industry, as it requires amore diversified strategyfor improving resistance toAI