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

Genetics and Genomic Regions Affecting Response to Newcastle Disease Virus Infection under Heat Stress in Layer Chickens.

Newcastle disease virus (NDV) is a highly contagious avian pathogen that poses a tremendous threat to poultry producers in endemic zones due to its epidemic potential. To investigate host genetic resistance to NDV while under the effects of heat stress, a genome-wide association study (GWAS) was performed on Hy-Line Brown layer chickens that were challenged with NDV while under high ambient temperature to identify regions associated with host viral titer, circulating anti-NDV antibody titer, and body weight change. A single nucleotide polymorphism (SNP) on chromosome 1 was associated with viral titer at two days post-infection (dpi), while 30 SNPs spanning a quantitative trait loci (QTL) on chromosome 24 were associated with viral titer at 6 dpi. Immune related genes, such as CAMK1d and CCDC3 on chromosome 1, associated with viral titer at 2 dpi, and TIRAP, ETS1, and KIRREL3, associated with viral titer at 6 dpi, were located in two QTL regions for viral titer that were identified in this study. This study identified genomic regions and candidate genes that are associated with response to NDV during heat stress in Hy-Line Brown layer chickens. Regions identified for viral titer on chromosome 1 and 24, at 2 and 6 dpi, respectively, included several genes that have key roles in regulating the immune response

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

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. -------------------------------------------------------------------------------

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

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

Molecular breeding techniques to improve egg quality

Since the release of the chicken genome sequence in 2004 (Hillier et al., 2004), the tool box available for genetic improvement of the multiple traits that define egg quality has improved tremendously. That initial sequence has been revised and improved several times with Gallus_gallus-4.0 now available in multiple publicly accessible websites. An improved Gallus_gallus-5.0 version is soon to be released. Genome sequence comparisons between mammalian and avian species have allowed for the identification and subsequent annotation of genes, and initiated the understanding of the functions of the genes and the interdependent relationships between the thousands of genes that comprise multicellular organisms\u27. The recent sequencing of multiple breeds of chickens, both research and commercially utilized lines, revealed the presence of millions of genetic variants and resulted in the development of a commercially available chip that can simultaneously query 600000 single DNA base sequence variants (Kranis et al., 2013). Numerous methodologies are now available that allow for detection of gene expression in specific tissues or relative gene expression between different treatments or individuals (Rapaport et al., 2013). To aid in the analysis of the large amounts of genetic information that is generated, various computer programmes have been developed that allow for the integration and eventual application of this information into a breeding programme (Wolc et al., 2016)