Genetic Selection Research Articles

Sexual dimorphism in the environmental sensitivity of weaning weight in beef cattle

This study investigates the environmental sensitivity and sexual dimorphism in the genetic parameters of weaning weight (WW) in Nelore beef cattle. Five reaction norms models were employed to analyze the 148,131 WW records, considering different definitions of the environmental descriptor (ED). (Co)variance components and genetic parameters were separately estimated for males and females. Results reveal substantial sexual dimorphism in various parameters. Significant divergence across sexes was observed for environmental sensitivity, with males showing an increased response to favorable environments while females genetically responded better to more challenging environments. Including sex in defining the ED can inflate the estimated breeding values of sires with a higher number of male progeny records, highlighting the importance of choosing a reliable ED in analyses of traits influenced by sexual dimorphism and genotype-by-environment interaction (G × E). The observed sexual dimorphism and environmental sensitivity underscore the complexity of genetic evaluation of WW in beef cattle populations, with implications for selection strategies. The findings suggest that a sex-separated trait analysis considering G × E effects is most appropriate for genetic evaluations of WW. However, a simpler single-trait model excluding sex from the ED definition yielded a higher coincidence of selected sires with the sex-separated analysis. In conclusion, understanding sexual dimorphism and environmental sensitivity is crucial for genetic evaluations and selection decisions in beef cattle breeding programs, emphasizing the need for careful model selection and ED definition.

An Overview of Artificial Insemination: A Journey from Past to Present

Artificial insemination (AI) has a rich history spanning centuries, beginning with early experiments in plant and animal reproduction. This article traces the development of AI from its earliest recorded instances in 1322 A.D. to modern-day advancements. Notable pioneers such as Spallanzani, Hunter, and Ivanoff contributed significantly to the evolution of AI techniques. The introduction of AI across various animal species, including cattle, horses, sheep, goats, pigs, and poultry, revolutionized breeding practices worldwide. Key milestones include the development of semen extenders, the invention of artificial vaginas for semen collection, and the advent of cryopreservation techniques. Moreover, advancements in sperm quality assessment and estrous synchronization further enhanced AI efficiency. The introduction of sexed semen technology has provided a means to control offspring gender, offering new avenues for genetic selection. Overall, AI has played a crucial role in improving livestock genetics, disease control, and reproductive efficiency, making significant contributions to agricultural productivity and sustainability.

Genetic algorithm feature selection resilient to increasing amounts of data imputation

This paper investigates the robustness of a genetic algorithm (GA) in feature selection across a dataset with increasing imputed missing values. Feature selection can be beneficial in predictive modeling to reduce computational costs and potentially improve performance. Beyond these benefits, it also enables a clearer understanding of the algorithm's decision-making processes. In the context of real-world datasets that can contain missing values, feature selection becomes more challenging.A robust feature selection algorithm should be able to identify the key features despite missing data values. We investigate the effectiveness of this approach against two other feature selection algorithms on a dataset with increasingly imputed values to determine whether it can sustain good performance with only the selected features.Our results reveal that compared to the other two methods, the features selected by GA resulted in better classification performance across different imputation rates and methods.

Assessing selection potential for Pacific oyster (Crassostrea gigas) to a North American OsHV-1 μvar: Comparing two experimental assay methods

Ostreid Herpesvirus - 1 is a global threat to Pacific oyster aquaculture as catastrophic farm losses have occurred where OsHV-1 microvariants (μvars) become endemic. Genetic selection has proven effective for increasing survival to OsHV-1 exposure in multiple Pacific oyster breeding programs, but a novel μvar detected in San Diego Bay California has the potential to severely impact commercial aquaculture along the North American Pacific coast as no OsHV-1 μvar selected population is available commercially. Using a Molluscan Broodstock Program population descending from imports originating in the Miyagi prefecture (Japan), we evaluated the potential for genetic selection to improve survival against the San Diego Bay OsHV-1 μvar using a laboratory trial. Two experimental methods were evaluated, a common garden method where all 15 full-sibling families were pooled, and a petri dish method, where each family was confounded within replicate. No striking differences were found between experimental methods, and both experiments displayed high heritability (0.69 and 0.64) of the tested trait, a broad range of estimated breeding values, and a moderate prediction accuracy for the survival trait. A significant genotype-by-environment interaction occurred, highlighting variability in laboratory-based disease challenges, but the two experiments yielded similar results with minor to moderate re-ranking of mean family survival. Overall, the Miyagi population of Pacific oysters curated by the Molluscan Broodstock Program is a promising population to use for artificial selection to increase survival against San Diego Bay OsHV-1 μvar in attempts to obviate farm losses if the virus expands its currently isolated range.

Genetic background of walking ability and its relationship with leg defects, mortality, and performance traits in turkeys (Meleagris gallopavo)

This study aimed to explore the genetic basis of walking ability and potentially related performance traits in turkey purebred populations. Phenotypic, pedigree, and genomic datasets from 2 turkey lines hatched between 2010 and 2023 were included in the study. Walking ability data, defined based on a scoring system ranging from 1 (worst) to 6 (best), were collected on 192,019 animals of a female line and 235,461 animals of a male line. Genomic information was obtained for 46,427 turkeys (22,302 from a female line and 24,125 from a male line) using a 65K single nucleotide polymorphism (SNP) panel. Variance components and heritability for walking ability were estimated. Furthermore, genetic and phenotypic correlations among walking ability, mortality and disorders, and performance traits were calculated. A genome-wide association study (GWAS) was also conducted to identify SNPs associated with walking ability. Walking ability is moderately heritable (0.23 ± 0.01) in both turkey lines. The genetic correlations between walking ability and the other evaluated traits ranged from -0.02 to -0.78, with leg defects exhibiting the strongest negative correlation with walking ability. In the female line, 31 SNPs were associated with walking ability and overlapped with 116 genes. These positional genes are linked to 6 gene ontology (GO) terms. Notably, genes such as CSRP2, DDX1, RHBDL1, SEZ6L, and CTSK are involved in growth, development, locomotion, and bone disorders. GO terms, including fibronectin binding (GO:0001968), peptide cross-linking (GO:0018149), and catabolic process (GO:0009057), are directly linked with mobility. In the male line, 66 markers associated with walking ability were identified and overlapped with 281 genes. These genes are linked to 12 GO terms. Genes such as RB1CC1, TNNI1, MSTN, FN1, SIK3, PADI2, ERBB4, B3GNT2, and BACE1 are associated with cell growth, myostatin development, and disorders. GO terms in the male line are predominantly related to lipid metabolism. In conclusion, walking ability is moderately heritable in both populations. Furthermore, walking ability can be enhanced through targeted genetic selection, emphasizing its relevance to both animal welfare and productivity.

Exploring the genetic control of sweat gland characteristics in beef cattle for enhanced heat tolerance

BackgroundThermal stress in subtropical regions is a major limiting factor in beef cattle production systems with around $369 million being lost annually due to reduced performance. Heat stress causes numerous physiological and behavioral disturbances including reduced feed intake and decreased production levels. Cattle utilize various physiological mechanisms such as sweating to regulate internal heat. Variation in these traits can help identify genetic variants that control sweat gland properties and subsequently allow for genetic selection of cattle with greater thermotolerance.MethodsThis study used 2,401 Brangus cattle from two commercial ranches in Florida. Precise phenotypes that contribute to an animal’s ability to manage heat stress were calculated from skin biopsies and included sweat gland area, sweat gland depth, and sweat gland length. All animals were genotyped with the Bovine GGP F250K, and BLUPF90 software was used to estimate genetic parameters and for Genome Wide Association Study.ResultsSweat gland phenotypes heritability ranged from 0.17 to 0.42 indicating a moderate amount of the phenotypic variation is due to genetics, allowing producers the ability to select for favorable sweat gland properties. A weighted single-step GWAS using sliding 10 kb windows identified multiple quantitative trait loci (QTLs) explaining a significant amount of genetic variation. QTLs located on BTA7 and BTA12 explained over 1.0% of genetic variance and overlap the ADGRV1 and CCDC168 genes, respectively. The variants identified in this study are implicated in processes related to immune function and cellular proliferation which could be relevant to heat management. Breed of Origin Alleles (BOA) were predicted using local ancestry in admixed populations (LAMP-LD), allowing for identification of markers’ origin from either Brahman or Angus ancestry. A BOA GWAS was performed to identify regions inherited from particular ancestral breeds that might have a significant impact on sweat gland phenotypes.ConclusionsThe results of the BOA GWAS indicate that both Brahman and Angus alleles contribute positively to sweat gland traits, as evidenced by favorable marker effects observed from both genetic backgrounds. Understanding and utilizing genetic traits that confer better heat tolerance is a proactive approach to managing the impacts of climate change on livestock farming.

Estimation of genetic parameters for maintenance energy requirements and residual feed intake in Nellore cattle.

We estimated heritabilities and genetic and phenotypic correlation estimates for maintenance energy requirements (NEmR), residual feed intake (RFI), growth, carcass and reproductive indicator traits, using data from 41 feed efficiency trials in Brazil, comprising 4381 males and females. Continuous traits were analysed using a linear animal model and threshold traits were analysed using a threshold animal model. The heritability estimates were low for RFI (0.190) and NEmR (0.193); other heritabilities were mainly moderate (growth and carcass traits) or high (sexual precocity traits). The genetic correlation of RFI with NEmR was high (0.701). The genetic correlations of NEmR were low with carcass and reproductive traits, and moderate with growth traits. Thus, selection to improve weaning weight and female sexual precocity indicator traits would not affect maintenance energy requirement. Genetic selection to reduce maintenance energy requirements is feasible and would also reduce DMI and RFI. Selection to improve RFI can be used to identify animals with lower maintenance energy requirements. Long-term selection to reduce RFI and NEmR would have favourable effects on yearling weight, carcass muscle indicator traits and female sexual precocity. Genetic (co)variance component estimates for NEmR, in conjunction with economic values of selection criteria, may be used to develop novel approaches for genetic selection to improve efficiency of beef production.

Lung adenocarcinomas without driver genes converge to common adaptive strategies through diverse genetic, epigenetic, and niche construction evolutionary pathways

Somatic evolution selects cancer cell phenotypes that maximize survival and proliferation in dynamic environments. Although cancer cells are molecularly heterogeneous, we hypothesized convergent adaptive strategies to common host selection forces can be inferred from patterns of epigenetic and genetic evolutionary selection in similar tumors. We systematically investigated gene mutations and expression changes in lung adenocarcinomas with no common driver genes (n = 313). Although 13,461 genes were mutated in at least one sample, only 376 non-synonymous mutations evidenced positive evolutionary selection with conservation of 224 genes, while 1736 and 2430 genes exhibited ≥ two-fold increased and ≥ 50% decreased expression, respectively. Mutations under positive selection are more frequent in genes with significantly altered expression suggesting they often “hardwire” pre-existing epigenetically driven adaptations. Conserved genes averaged 16-fold higher expression in normal lung tissue compared to those with selected mutations demonstrating pathways necessary for both normal cell function and optimal cancer cell fitness. The convergent LUAD phenotype exhibits loss of differentiated functions and cell–cell interactions governing tissue organization. Conservation with increased expression is found in genes associated with cell cycle, DNA repair, p53 pathway, epigenetic modifiers, and glucose metabolism. No canonical driver gene pathways exhibit strong positive selection, but extensive down-regulation of membrane ion channels suggests decreased transmembrane potential may generate persistent proliferative signals. NCD LUADs perform niche construction generating a stiff, immunosuppressive microenvironment through selection of specific collagens and proteases. NCD LUADs evolve to a convergent phenotype through a network of interconnected genetic, epigenetic, and ecological pathways.

39 Using genetics and technology to improve swine robustness

Abstract Abstract Pig robustness has been an increasing concern in the global swine industry over the last 10 yr. This trend of increased mortality across all phases of production continues to worsen over time despite greater focus on research, management, and other production practices to control it. During this time, dramatic improvements in genetic progress have been realized through the selection of economically important traits. Historically, swine genetic companies have relied on data from their purebred animals reared in high-health and well-managed environments which has proven difficult to provide meaningful insight on variation in robustness traits. More recently, leading genetic companies have invested in large commercial test herds where crossbred pigs and breeding animals are individually identified with pedigree and genomic information. These pigs are reared under challenging and varying commercial conditions and mortality, removals, and reproductive efficiency data are gathered and used to generate crossbred breeding values. This type of data increases the accuracy of genetic estimates as well as creates new traits aimed at improving robustness and ease of management. Further investments have been made in precision livestock farming technologies such as computer vision which is quickly being developed due to the low cost of cameras and the ability to use edge computing to process images in real-time or to transfer images and data to the cloud. These developments are particularly compelling in the genetic improvement space when they can be paired with individual animal identification enabling novel traits to be measured in large populations more consistently and at a reduced cost compared with previous labor-intensive or cost-prohibitive methods. Early examples of this are the use of cameras to score feet and leg soundness in replacement gilts as lameness and associated feet and leg injuries are still one of the largest contributors to sow culls and mortality. This new digital feet and leg soundness scoring method has initially resulted in dramatically greater accuracy and heritability compared with conventional subjective human scoring methods. Other camera-based algorithms have been developed to track individual pigs and assess their daily behaviors and interactions. These developments are key to collecting new phenotypes for the selection of positive behavior traits for genetic selection compared with what was previously possible with human observer or scan sampling methods. Gene editing is another promising technology that has been used in human health and plant breeding but is now being widely researched in livestock. This technology has been used to create the first PRRS-resistant pigs to address one of the most costly diseases in the industry. New sources of data and technologies are key to creating and capturing existing and novel phenotypes on large populations of pigs in commercial environments to aid in the genetic improvement of non-conventional traits.

Feed intake in housed dairy cows: validation of a three-dimensional camera−based feed intake measurement system

Measuring feed intake accurately is crucial to determine feed efficiency and for genetic selection. A system using three-dimensional (3D) cameras and deep learning algorithms can measure the volume of feed intake in dairy cows, but for now, the system has not been validated for feed intake expressed as weight of feed. The aim of this study was to validate the weight of feed intake predicted from the 3D cameras with the actual measured weight. It was hypothesised that diet−specific coefficients are necessary for predicting changes in weight, that the relationship between weight and volume is curvilinear throughout the day, and that manually pushing the feed affects this relationship. Twenty-four lactating Danish Holstein cows were used in a cross-over design with four dietary treatments, 2 × 2 factorial arranged with either grass-clover silage or maize silage as silage factor, and barley or dried beet pulp as concentrate factor. Cows were adapted to the diets for 11 d, and for 3 d to tie-stall housing before camera measurements. Six cameras were used for recording, each mounted over an individual feeding platform equipped with a weight scale. When building the predictive models, four cameras were used for training, and the remaining two for testing the prediction of the models. The most accurate predictions were found for the average feed intake over a period when using the starting density of the feed pile, which resulted in the lowest errors, 6% when expressed as RMSE and 5% expressed as mean absolute error. A model including curvilinear effects of feed volume and the impact of manual feed pushing was used on a dataset including daily time points. When cross-validating, the inclusion of a curvilinear effect and a feed push effect did not improve the accuracy of the model for neither the feed pile nor the feed removed by the cow between consecutive time points. In conclusion, measuring daily feed intake from this 3D camera system in the present experimental setup could be accomplished with an acceptable error (below 8%), but the system should be improved for individual meal intake measurements if these measures were to be implemented.

A comprehensive integration of factors affecting vitamin B12 concentration in milk of Holstein cows: genetic variability, milk productivity, animal characteristics, and feeding management

Daily vitamin B12 (VB12) requirements of humans can naturally be fulfilled by animal product consumption, especially products from ruminants because of bacteria dwelling in their rumen. Indeed, only bacteria can synthesize this vitamin. Milk is hence an excellent source of VB12. This cross-sectional study was undertaken to unravel factors, such as genetic variation, diet and cow characteristics, and milk production, explaining the large variation in milk B12 concentration among cows by using an integrative approach. Milk samples from 2 consecutive milkings were collected from 3,533 Canadian Holstein cows (1,239 first, 932 s, and 1,362 third and more lactations) located in 99 herds with various feeding management. For genetic variation analysis purpose, pedigrees were traced back for 3 complete generations for each sire and dam. A total of 10,021 identities were used in the subsequent genetic analyses. Milk VB12 averaged 4.2 ng/mL with a range between 0.7 and 9.0 ng/mL. Dietary fiber (NDF from forage, dietary NDF, ADF, and lignin) increased and dietary components related to energy (non-fiber carbohydrate, starch, net energy of lactation, and percentage of concentrate) decreased VB12 in milk. Milk VB12 varied with days in milk, with a similar pattern as milk fat and protein concentration lactation curves. Milk VB12 increased as age at calving increased. When disregarding the herd variance, heritability value was 0.37, meaning that milk VB12 can be modified by genetic selection. The final model including factors related to the diet, animal characteristics and milk productivity, and genetic variation explained 79% (pseudo-R2) of the milk VB12 variation. When excluding the random effect of the cow, i.e., excluding the animal and genetic relationships, the pseudo-R2 dropped to 43%, reinforcing the importance of genetic variation in explaining milk VB12 variation. To our knowledge, the present study is the most comprehensive evaluation of factors affecting milk VB12 variation including the greatest number of cows from various lactation stages.

Genetic diversity, heritability, and estimated breeding values for growth of domesticated Asian seabass Latescalcarifer from Thailand

To establish a new breeding program, genetic diversity of five domesticated populations and two commercial stocks of Asian seabass (Lates calcarifer) were examined. Moderate levels of genetic diversity based on microsatellite polymorphism were found. The number of alleles per locus (NA) was 3.429–11.571. Observed (Ho) and expected (He) heterozygosities were 0.416–0.772 and 0.423–0.805. FST and genetic heterogeneity analysis revealed significant genetic differences of most pairwise comparisons (P < 0.001). Genetic distance between populations was 0.000–0.703. Phylogenetic analysis divided examined populations to two genetic stocks; A: DOF-Chachoengsao 2, DOF-Songkhla, and DOF-Trang and B: Commercial Farm 1 (Phuket) and Commercial Farm 2 (Chachengsao), DOF-Chachoengsao 1 and DOF-Krabi. The base population (G0, 51 families) was established from crosses between these founder populations. Heritability and estimated breeding values (EBVs) for growth in G0 families were evaluated at 519 day post hatch (dph, mean body weight = 616.64 ± 266.80 g, N = 1655). The heritability (h2) for growth of L. calcarifer in this study was 0.386 ± 0.020. The additive genetic coefficient of variation was 28% indicating high potential of genetic improvement for growth in this population. The distribution of EBVs showed high variation both between and within families. Fish exhibiting high EBVs (SATREPS-DOF-G0-hEBVs) clustered with a phylogenetic clade A while the SATREPS-DOF-G0-lEBVs group was phylogenetically allocated to clade B. Genetic selection with +1SD criteria is expected to achieve genetic gain of 28.5% per generation. The information on genetic diversity and EBVs estimates allows efficient implementation of our ongoing breeding program of L. calcarifer.

High-throughput and cost-effective genotyping by low-coverage whole genome sequencing with genotype imputation in Pacific oyster, Crassostrea gigas

In order to improve the application of genomic resources in genomic selection breeding of aquaculture species, it is imperative to develop high throughput genotyping methods that are suitable for aquatic species with highly heterozygous and complex genomes. This study aimed to address the issue of accurate genotype imputation for lcWGS (low coverage whole genome sequencing) without reference panel in aquatic species. We performed lcWGS with an average depth of 2.8× on 360 individuals of Crassostrea gigas, then performed genotype imputation through reference-free method to achieve genome-wide genotyping. The results demonstrated that STITCH showed outstanding performance in conducting genotype imputation without reference panel, with R2 of 0.860 ± 0.055 and GC of 0.936 ± 0.022. The comparison of accuracy among different populations indicated that the selected breeding population with higher level of linkage disequilibrium within the population showed significant improvement in imputation accuracy compared to the wild population (P < 0.01). The diminishing marginal utility in accuracy beyond 300 samples suggested that hundreds of individuals were adequate for accurate genotyping by lcWGS. The imputed genotypes showed high accuracy in identifying genetic relationships and potential application in selection breeding. This study provides valuable information toward application of high-throughput and cost-effective genotyping in accelerating genetic analysis and genomic selection of economic traits in aquaculture species.

A study of jamming resource allocation based on a hyperheuristic framework

There are massive data and rapidly changing battlefield situations in modern electronic warfare, which is a challenge to jamming resource allocation. It is difficult for the existing optimization algorithms to balance optimization capability and calculation speed at the same time. Above all, this study designs a fuzzy multiattribute evaluation model based on behavioral characteristics. The model comprehensively integrates the characteristics of signal behavior, motion behavior, and external intelligence and can effectively deal with unknown threat signals. Then, this study proposes an improved genetic selection electronic warfare operator (EWO) hyperheuristic (GAEWHH) algorithm. As an emergent optimization algorithm, the HH framework has not previously been applied to the problem of jamming resource allocation. This is a two-level algorithm framework that can isolate problem domains. The high level uses an improved genetic algorithm to search the heuristic space, and four EWOs based on the problem domain are designed for the low level to search the solution space. Combining different EWOs can change the population diversity, evolution direction, and algorithm complexity of the GAEWHH algorithm, which improves the algorithm performance to meet battlefield situation requirements. The experiment shows that for large-scale problems, the GAEWHH algorithm is better than the mainstream evolutionary algorithm in terms of optimization capability and better than Google OR-Tools in terms of calculation speed. In this way, the GAEWHH algorithm achieves a balance between optimization capability and calculation speed.

Ecological tradeoffs lead to complex evolutionary trajectories and sustained diversity on dynamic fitness landscapes

The course and outcome of evolution are critically determined by the fitness landscape, which maps genotype to fitness. Most theory has considered static fitness landscapes or fitness landscapes that fluctuate according to abiotic environmental changes. In the presence of biotic interactions between coexisting genotypes, the fitness landscape becomes dynamic and frequency-dependent.Here, we introduce a fitness landscape model that incorporates ecological interactions between individuals in a population. In the model, fitness is determined by individuals competing for resources according to a set of traits they possess. An individual’s genotype determines the trait values through a Rough Mount Fuji fitness landscape model, allowing for tunable epistasis (i.e. non-additive gene interaction) and trait correlations (i.e. whether there are tradeoffs or synergies in the ability to use resources). Focusing on the effects of epistasis and trait correlations, we quantify the resulting eco-evolutionary dynamics under simulated Wright–Fisher dynamics (i.e. including genetic drift, mutation, and selection under the assumption of a constant population size) on the dynamics fitness landscape in comparison with a similar, static, fitness landscape model without ecological interactions.Whereas the non-ecological model ultimately leads to the maintenance of one main genotype in the population, evolution in the ecological model can lead to the long-term coexistence of several genotypes at intermediate frequencies across much of the parameter range. Including ecological interactions increases steady-state diversity whenever the trait correlations are not too strong. However, strong epistasis can hinder coexistence, and additive genotype–phenotype maps yield the highest haplotype diversity at the steady state. Interestingly, we frequently observe long-term coexistence also in the absence of induced trade-offs in the ability to consume resources.In summary, our simulation study presents a new dynamic fitness landscape model that highlights the complex eco-evolutionary consequences of a (finite) genotype–phenotype-fitness map in the presence of biotic interactions.

Exploration of Genome-Wide Recombination Rate Variation Patterns at Different Scales in Pigs.

Meiotic recombination is a prevalent process in eukaryotic sexual reproduction organisms that plays key roles in genetic diversity, breed selection, and species evolution. However, the recombination events differ across breeds and even within breeds. In this study, we initially computed large-scale population recombination rates of both sexes using approximately 52 K SNP genotypes in a total of 3279 pigs from four different Chinese and Western breeds. We then constructed a high-resolution historical recombination map using approximately 16 million SNPs from a sample of unrelated individuals. Comparative analysis of porcine recombination events from different breeds and at different resolutions revealed the following observations: Firstly, the 1Mb-scale pig recombination maps of the same sex are moderately conserved among different breeds, with the similarity of recombination events between Western pigs and Chinese indigenous pigs being lower than within their respective groups. Secondly, we identified 3861 recombination hotspots in the genome and observed medium- to high-level correlation between historical recombination rates (0.542~0.683) and estimates of meiotic recombination rates. Third, we observed that recombination hotspots are significantly far from the transcription start sites of pig genes, and the silico-predicted PRDM9 zinc finger domain DNA recognition motif is significantly enriched in the regions of recombination hotspots compared to recombination coldspots, highlighting the potential role of PRDM9 in regulating recombination hotspots in pigs. Our study analyzed the variation patterns of the pig recombination map at broad and fine scales, providing a valuable reference for genomic selection breeding and laying a crucial foundation for further understanding the molecular mechanisms of pig genome recombination.

Tibia properties in broilers raised on their own commercial diets with different growth rates and sex during a 10-week rearing period.

Genetic selection applied to broilers results in fast growth and an increase in meat yield. However, this situation causes welfare problems in broilers. The aim of this study is to determine the weekly changes in the tibia characteristics in broilers raised on their own commercial diets. In the study, 168 (84 female and 84 male) slow-growing (Hubbard-Isa Red JA) and 168 (84 female and 84 male) fast-growing (Ross-308) day-old broiler chicks were used. Six broilers from each genotype and sex group were weighed weekly and slaughtered to determine the tibia properties during the 10-week fattening period. Fast-growing broilers had higher tibia weight and longer length, diaphysis diameter and medullary canal diameter than those of slow-growing broilers at the same age. In fast-growing genotypes, the cortical index was low only in the 2nd week, and Robusticity and Seedor indices were observed to be better throughout the whole fattening. The ash content of the total tibia in the fast-growing broilers was higher in all of the examined weeks except the 4th week and the 9th week of fattening than that in the slow-growing broilers. Although the amount of ash per unit body weight in the 1st week of fattening was higher in fast-growing broilers, this situation reversed after the 4th week. The level of all minerals examined in total tibia weight is high in fast-growing broilers, and they differed according to the fattening period. When comparing tibia characteristics of two different genotypes fed their own commercial diets, the tibia structure was found to be stronger in fast-growing broilers compared to other genotypes at the same age, but slow-growing broilers were more prominent in terms of body weight. It was observed that the mineral density was higher in male broilers, except in the 1st week.

Weighted gene co-expression network analysis identifies important modules and hub genes involved in the regulation of breast muscle yield in broilers.

Increasing breast meat production is one of the primary goals of the broiler industry. Over the past few decades, tremendous progress has been made in genetic selection and the identification of candidate genes for improving the breast muscle mass. However, the molecular network contributing to muscle production traits in chickens still needs to be further illuminated. A total of 150 1-day-old male 817 broilers were reared in a floor litter system. At the market age of 50 d, eighteen healthy 817 broilers were slaughtered and the left pectoralis major muscle sample from each bird was collected for RNA-seq sequencing. The birds were then plucked and eviscerated and the whole breast muscle was removed and weighed. Breast muscle yield was calculated as the ratio of the breast muscle weight to the eviscerated weight. To identify the co-expression networks and hub genes contributing to breast muscle yield in chickens, we performed weighted gene co-expression network analysis (WGCNA) based on the 18 transcriptome datasets of pectoralis major muscle from eighteen 817 broilers. The WGCNA analysis classified all co-expressed genes in the pectoral muscle of 817 broilers into 44 modules. Among these modules, the turquoise and skyblue3 modules were found to be most significantly positively (r = 0.78, p = 1e-04) and negatively (r = -0.57, p = 0.01) associated with breast meat yield, respectively. Further analysis identified several hub genes (e.g., DLX3, SH3RF2, TPM1, CAV3, MYF6, and CFL2) that involved in muscle structure and muscle development were identified as potential regulators of breast meat production. The present study has advanced our understanding of the molecular regulatory networks contributing to muscle growth and breast muscle production and will contribute to the molecular breeding of chickens in the future.

Investigation of post-immunization immune response gene expression kinetics in lymphoid tissues of White Leghorn and Indian native chicken

Understanding the basal expression kinetics of genes related to innate immunity aids effective genetic selection for immune response and how host genetics behaves with tolerance to Newcastle disease (ND) is obscure. It was hence aimed to investigate post-immunization mRNA expression kinetics of some immune response genes in White Leghorn, Aseel and Kadaknath chicken. Day-old chicks were vaccinated with ND RDF-1 strain with booster dose at 28-days of age at the experimental layer farm of ICAR-Central Avian Research Institute, Izatnagar (India) and lymphoid tissues were collected in RNAlater at 42-days of age. Total RNA was extracted and first strand cDNA was synthesized. Relative quantification of mRNA expression of IL1-β, IFN-γ, iNOS and TLR15 gene was assessed by qRT-PCR. The mRNA expressions significantly varied among the genes in spleen and thymus, and moderate expression of IFN-γ in all chicken genotypes suggested good protection against Newcastle disease virus. IL1-β-mRNA expressed at the highest level (P˂ 0.05) in spleen followed by thymus and bursa, whereas IFN-γ and iNOS expressed highly (P˂ 0.05) in thymus followed by spleen and bursa. In case of TLR15, the expression was significantly the highest in spleen followed by bursa and thymus. Kadaknath had the highest (P˂ 0.05) expression of IL1-β in thymus and that of iNOS in spleen, whereas Aseel showed the least (P > 0.05) expression than Kadaknath and White Leghorn. The study revealed the existence of wide variation in basal expression levels of immune response genes among different lymphoid tissues and lack of discernible differences in the expression between the chicken genotypes.

Analysis of carcass traits, meat quality, amino acid and fatty acid profiles between different duck lines

To investigate the effect of genetic selection on meat quality in ducks, twenty of each fast growth ducks (LCA) and slow growth ducks (LCC) selected from F6 generation of Cherry Valley ducks (♂) x Liancheng white ducks (♀) were analyzed for carcass characteristics, meat quality (physicochemical and textural characteristics), amino acid and fatty acid profiles at 7 wk. Results showed that live body weight, slaughter weight, eviscerated yield and abdominal fat percentage of LCA were significantly higher than those in LCC ducks (P < 0.01). Moreover, the average area and diameter of myofiber were larger in LCA than LCC ducks (P < 0.01). The breast and thigh muscles of LCA exhibited significantly lower water holding capacity and thermal loss compared with LCC ducks (P < 0.01). In addition, the content of nonessential amino acids (Glu, Asp, and Arg) in breast muscles and Asp, Ser, Thr, and Met in thigh muscles was higher in LCC than LCA ducks (P < 0.05). The proportion of polyunsaturated fatty acids (PUFA) in breast muscles of LCC was higher than LCA ducks (P < 0.05). However, the content of saturated fatty acids (SFA) in breast and thigh muscles of LCA was higher compared with LCC ducks (P < 0.05). The proportion of monounsaturated fatty acids (MUFA) in thigh muscles was significantly higher in LCC compared with LCA ducks (P < 0.01). Finally, multiple traits were evaluated by applying principal component analysis (PCA) and the results indicated that PUFA and SFA in breast muscles of LCA played important roles in meat quality, followed by Warner-Bratzler shear force (WBSF) and MUFA. However, water holding capacity (WHC) had a dominant effect in meat quality of thigh muscles in both LCA and LCC ducks.