Epistatic interactions of multiple single nucleotide polymorphisms (SNPs) are now believed to affect individual susceptibility to common diseases. The detection of such interactions, however, is a challenging task in large scale association studies. Ant colony optimization (ACO) algorithms have been shown to be useful in detecting epistatic interactions.

Heat stress (HS) is one of the most important stressors in chickens, and its adverse effects are primarily caused by disturbing the redox homeostasis. An increase in electron leakage from the mitochondrial electron transport chain is the major source of free radical production under HS, which triggers other enzymatic systems to generate more radicals. As a defense mechanism, cells have enzymatic and non-enzymatic antioxidant systems that work cooperatively against free radicals. The generation of free radicals, particularly the reactive oxygen species (ROS) and reactive nitrogen species (RNS), under HS condition outweighs the cellular antioxidant capacity, resulting in oxidative damage to macromolecules, including lipids, carbohydrates, proteins, and DNA. Understanding these detrimental oxidative processes and protective defense mechanisms is important in developing mitigation strategies against HS. This review summarizes the current understanding of major oxidative and antioxidant systems and their molecular mechanisms in generating or neutralizing the ROS/RNS. Importantly, this review explores the potential mechanisms that lead to the development of oxidative stress in heat-stressed chickens, highlighting their unique behavioral and physiological responses against thermal stress. Further, we summarize the major findings associated with these oxidative and antioxidant mechanisms in chickens.

The availability of high-density (HD) marker panels, genome wide variants and sequence data creates an unprecedented opportunity to dissect the genetic basis of complex traits, enhance genomic selection (GS) and identify causal variants of disease. The disproportional increase in the number of parameters in the genetic association model compared to the number of phenotypes has led to further deterioration in statistical power and an increase in co-linearity and false positive rates. At best, HD panels do not significantly improve GS accuracy and, at worst, reduce accuracy. This is true for both regression and variance component approaches. To remedy this situation, some form of single nucleotide polymorphisms (SNP) filtering or external information is needed. Current methods for prioritizing SNP markers (i.e. BayesB, BayesCπ) are sensitive to the increased co-linearity in HD panels which could limit their performance. In this study, the usefulness of FST, a measure of allele frequency variation among populations, as an external source of information in GS was evaluated. A simulation was carried out for a trait with heritability of 0.4. Data was divided into three subpopulations based on phenotype distribution (bottom 5%, middle 90%, top 5%). Marker data were simulated to mimic a 770 K and 1.5 million SNP marker panel. A ten-chromosome genome with 200 K and 400 K SNPs was simulated. Several scenarios with varying distributions for the quantitative trait loci (QTL) effects were simulated. Using all 200 K markers and no filtering, the accuracy of genomic prediction was 0.77. When marker effects were simulated from a gamma distribution, SNPs pre-selected based on the 99.5, 99.0 and 97.5% quantile of the FST score distribution resulted in an accuracy of 0.725, 0.797, and 0.853, respectively. Similar results were observed under other simulation scenarios. Clearly, the accuracy obtained using all SNPs can be easily achieved using only 0.5 to 1% of all markers. These results indicate that SNP filtering using already available external information could increase the accuracy of GS. This is especially important as next-generation sequencing technology becomes more affordable and accessible to human, animal and plant applications.

This study aimed at assessing inbreeding and its effect on growth and fertility traits using the longtime closed line 1 Hereford cattle population. Inbreeding was estimated based on pedigree (F_PED) and genomic information. For the latter, three estimates were derived based on the diagonal elements of the genomic relationship matrix using estimated (F_GRM) or fixed (F_GRM0.5) minor allele frequencies or runs of homozygosity (ROH) (F_ROH). A pedigree containing 10,186 animals was used to calculate F_PED. Genomic inbreeding was evaluated using 785 animals genotyped for 30,810 SNP. Traits analyzed were birth weight (BWT), weaning weight (WWT), yearling weight (YWT), ADG, and age at first calving (AFC). The number of ROH per animal ranged between 6 and 119 segments with an average of 83. The shortest and longest segments were 1.36 and 64.86 Mb long, respectively, reflecting both ancient and recent inbreeding occurring in the last 30 to 40 generations. The average inbreeding was 29.2%, 16.1%, 30.2%, and 22.9% for F_PED, F_GRM, F_GRM0.5, and F_ROH, respectively. F_ROH provided the highest correlations with F_PED (r = 0.66). Across paternal half-sib families, with minimal variation in F_PED, there were substantial variations in their genomic inbreeding. Inbreeding depression analyses showed that a 1% increase in an animal’s F_PED resulted in a decrease of 1.20 kg, 2.03 kg, and 0.004 kg/d in WWT, YWT, and ADG, respectively. Maternal inbreeding showed significantly negative effects on progeny growth performance. AFC increased by 1.4 and 0.8 d for each 1% increase in F_PED of the cow and her dam, respectively. Using genomic inbreeding, similar impact on growth traits was observed although the magnitude of the effect varied between methods. Across all genomic measures, WWT, YWT, and ADG decreased by 0.21 to 0.53 kg, 0.46 to 1.13 kg, and 0.002 to 0.006 kg/d for each 1% increase in genomic inbreeding, respectively. Four chromosomes (9, 12, 17, and 27) were identified to have a significant association between their homozygosity (F_ROH-CHR) and growth traits. Variability in genomic inbreeding could be useful when deciding between full and half-sib selection candidates. Despite the high level of inbreeding in this study, its negative impact on growth performance was not as severe as expected, which may be attributed to the purging of the deleterious alleles due to natural or artificial selection over time.

A multiple trait linear-threshold model was used to analyze data for BW, residual feed intake, breast meat yield (BMY), conformation score (CS), area (AR), tibial dyschondroplasia, valgus, varus, and rotated tibia. Leg soundness traits were considered as binary responses. At the liability scale, the model included the fixed effects of flock-week of hatch, and sex of the bird and the genetic additive effect, and the error terms as random. The random maternal effect was included in the model only for BW. A full Bayesian implementation of the model was straightforward even though large number of traits and missing records were present. As expected, binary traits have the lowest heritability. Heritability ranged from 0.12 for tibial dyschondroplasia to 0.44 for BMY. Genetic correlations between BW and conformation traits were moderate to high. Residual feed intake was negatively correlated with BW (-0.15), AR (-0.13), BMY (-0.04), and CS (-0.12). Genetic correlation between leg soundness traits were generally low and negative with the exception of the correlation between valgus and varus (-0.70) and between varus and rotated tibia (-0.39). Genetic correlations between BW, BMY, CS, and AR with leg soundness traits were in general negative and low in magnitude. Thus, selecting for improved leg soundness will have minimal effect on BW and carcass traits. Furthermore, genetic improvement in residual feed intake will result in improvements in carcass traits. Simultaneous genetic improvement in leg soundness and innovative husbandry practices should improve broiler welfare without significant adverse effects on production efficiency.

The purpose of this study was to compare methods for handling censored days to calving records in beef cattle data, and verify results of an earlier simulation study. Data were records from natural service matings of 33,176 first-calf females in Australian Angus herds. Three methods for handling censored records were evaluated. Censored records (records on noncalving females) were assigned penalty values on a within-contemporary group basis under the first method (DCPEN). Under the second method (DCSIM), censored records were drawn from their respective predictive truncated normal distributions, whereas censored records were deleted under the third method (DCMISS). Data were analyzed using a mixed linear model that included the fixed effects of contemporary group and sex of calf, linear and quadratic covariates for age at mating, and random effects of animal and residual error. A Bayesian approach via Gibbs sampling was used to estimate variance components and predict breeding values. Posterior means (PM) (SD) of additive genetic variance for DCPEN, DCSIM, and DCMISS were 22.6d² (4.2d²), 26.1d² (3.6d²), and 13.5d² (2.9d²), respectively. The PM (SD) of residual variance for DCPEN, DCSIM, and DCMISS were 431.4d² (5.0d²), 371.4d² (4.5d²), and 262.2d² (3.4d²), respectively. The PM (SD) of heritability for DCPEN, DCSIM, and DCMISS were 0.05 (0.01), 0.07 (0.01), and 0.05 (0.01), respectively. Simulating trait records for noncalving females resulted in similar heritability to the penalty method but lower residual variance. Pearson correlations between posterior means of animal effects for sires with more than 20 daughters with records were 0.99 between DCPEN and DCSIM, 0.77 between DCPEN and DCMISS, and 0.81 between DCSIM and DCMISS. Of the 424 sires ranked in the top 10% and bottom 10% of sires in DCPEN, 91% and 89%, respectively, were also ranked in the top 10% and bottom 10% in DCSIM. Little difference was observed between DCPEN and DCSIM for correlations between posterior means of animal effects for sires, indicating that no major reranking of sires would be expected. This finding suggests little difference between these two censored data handling techniques for use in genetic evaluation of days to calving.