Relationship between immunity and growth rate in chickens is complex and multifaceted. Chicken's immune system plays a crucial role in its overall health, well-being and ability to grow and develop properly. On the other hand, indigenous chickens (ICs) are slow growing but considered to have higher immunocompetence compared to fast-growing commercial broilers. Therefore, this study aimed to evaluate immune responses to sheep red blood cell (SRBC) and phytohaemagglutinin (PHA) in slow-growing chickens, including five different Iranian ecotype ICs and a fast-growing broiler of Ross 308 (R). A total of 600 chicks, 100 from each sex and strain, were raised under identical environmental and management conditions after hatching eggs from five distinct Iranian ICs from West Azerbaijan (W), Khorasan (K), Fars (F), Isfahan (I), Mazandaran (M) and a commercial Ross 308 were collected. To investigate humoral immune responses, two SRBC injections were administered at ages 53 and 60 days, and at 67 days, the haemagglutination method was used to assess immunoglobulin Y (IgY), immunoglobulin M (IgM) and total immunoglobulin (IgT). Additionally, a commercial kit and microplate reader were used to assess the amounts of total protein and albumin/globulin. Cellular immune responses were evaluated by administering a PHA injection. Mainly SRBC responses and plasma protein levels in ICs were higher than R. The PHA responses of R and W were higher than other ICs (p < 0.05). These significant differences may confirm that the immunocompetence of ICs are higher than that in the commercial lines.

Context As evaluation of carcass components is costly and time consuming, models for prediction of broiler carcass components are useful. Aims The aim was to investigate the feasibility of machine learning methods in the prediction of carcass components from measurements on live birds during the rearing period. Methods Three machine learning methods, including regression tree, random forest and gradient-boosting trees, were applied to predict carcass yields, and benchmarked against classical linear regression. Two scenarios were defined for prediction. In the first scenario, carcass yields were predicted by live bodyweight, shank length and shank diameter features, recorded at 2, 3 and 4 weeks of age. In the second scenario, predictor features recorded at 5, 6 and 7 weeks of age were used. The two scenarios were reanalysed by including effective single-nucleotide polymorphisms associated with bodyweight, shank length and shank diameter as new predictor features. Key results The correlation coefficient between predicted and observed values for predicting weight of carcass traits ranged from 0.50 for wing to 0.59 for thigh in the first scenario, and from 0.63 for wing to 0.74 for carcass in the second scenario. These predictions for the percentage of carcass components ranged from 0.30 for wing to 0.39 for carcass and breast in the first scenario, and from 0.34 for thigh to 0.43 for carcass in the second scenario when random forest was used. Conclusions Predictive accuracy in the first scenario was lower than in the second scenario for all prediction methods. Including single-nucleotide polymorphisms as predictor features in either scenario did not increase the accuracy of the prediction. Implications In general, random forest had the best performance among machine learning methods, and classical linear regression in two scenarios, suggesting that it may be considered as an alternative to conventional linear models for prediction of carcass traits in broiler chickens.

High-density Single Nucleotide Polymorphisms (SNPs) panels are expensive, especially in developing countries. However, methods have been developed to detect critical SNPs from these panels and design low-density chips for genomic evaluation at lower cost. This study aimed to determine the efficiency of Random Forest (RF) and Gradient Boosting Machine (GBM) algorithms, and Linear Model (LM) in identification of SNPs subsets to predict Genomic Estimated Breeding Values (GEBVs) for Body Weights at 6 (BW6) and 9 (BW9) weeks in broiler chickens and compare the predicted GEBVs with those obtained by the 60K SNP panel. The data were collected on 312 F-2 chickens that genotyped with 60K Illumina SNP BeadChip. After applying quality control, the remaining 45,512 SNPs were ranked based on p-values, mean square error percentage, and relative influence, obtained by LM, RF and GBM methods, respectively. Then, subsets of top 400, 1,000, 3,000 and 5,000 SNPs, selected by each method, were employed to construct genomic relationship matrices for the prediction of GEBVs with genomic best linear unbiased prediction model. Results indicated that predicted accuracies by RF and GBM were generally higher than LM. A Subset of 1,000 SNPs selected by RF and GBM algorithms compared to the total SNPs increased accuracy from 0.38 to 0.64 and 0.66 for BW6, and from 0.42 to 0.60 and 0.66 for BW9, respectively. The findings of the present study provide that machine learning methods, especially GBM, can perform better than LM in selecting important SNPs and increasing the accuracy of genomic prediction in broiler chickens.

The aims of this investigation were to compare the accuracy and bias of prediction of Estimated Breeding Values (EBV) for Average Daily Gain (ADG) at 2-4 weeks old by employing pedigree -based BLUP and single-step Genomic BLUP (ssGBLUP) techniques. Additionally, the study aimed to identify the optimal minor allele frequencies (MAF) threshold for pre -selecting SNPs for genetic prediction. The present investigation utilized a total of 488 F2 broiler chickens, which were derived from the crossbreeding of fastgrowing Arian chickens and slow -growing native chickens from Urmia, Iran. These chickens were between 2-4 weeks old at the time of the study. Samples were genotyped using the Illumina 60K chicken Beadchip. In order to examine the impact of MAF on prediction accuracy, a total of 48,379 quality -controlled SNPs were categorized into five subgroups based on their MAF values: 0.05-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4, and 0.4-0.5. The findings substantiated the dominance of ssGBLUP over conventional BLUP techniques. The average accuracy of GP improved by 1.96, 3.87, and 2.12% using ssGBLUP compared to BLUP method for ADG at 2-4 weeks of age, respectively. Using a specific MAF bin and a subset of SNPs based on age group significantly enhanced the accuracy of genomic prediction for ADG traits. Current results highlighted that the pre -selection of SNPs based on allele frequency may provide a reasonable compromise between accuracy of results, number of independent variables to be considered and computing requirements.

The poultry industry is one of the most important agricultural subsectors, significantly contributing to protein supply and holding a unique position in terms of production and employment. To expand and boost profitability in this industry, it is important to analyze the economic factors of production, so that the factors influencing the rise in productivity of broiler production units may properly be recognized. As a result, the effective factors on the productivity of the Arian broiler sector in Iran's Kurdistan Province were investigated and prioritized in the current study. In order to evaluate the productivity of the industry, four main factors including human capital, economic, technical, and environmental variables were evaluated. The DEMATEL-ANP integrated approach was then used to determine the relative weights of the factors. The results revealed that the human capital component had the highest impact and the economic component was identified as the most influential factor among the other factors. Furthermore, the economic indicator had the highest priority, with a weight of 0.17. Of the 29 research components (sub-criteria), the "broiler farmer experience", with a weight of 0.042, exerted the greatest impact on the productivity of the province's broiler sector. The "feed cost", "day -old chicks cost", and "health care cost" ranked the second to fourth, respectively. According to the findings, more attention should be devoted to the production chain, such as input production and poultry vaccinations, in order to accomplish and also enhance productivity in the broiler industry.

Based on resource allocation theory, ignoring importance of immunity, and focus on growth and feed efficiency (FE) traits in breeding plans may lead to serious weakness in immune system performance. However, in poultry the adverse effects of selection for FE on the immune system are unclear. Therefore, an experiment was conducted to study the trade-off between FE and immunity using a total of 180 high-performing specialized male chickens from a commercial broiler line which were selected over 30 generations for growth (body weight gain, BWG) and FE (residual feed intake, RFI). Birds were reared for 42 d and 5 FE-related traits of the birds in the last week were considered including daily feed intake (DFI), feed conversion ratio (FCR), residual feed intake (RFI), residual BW gain (RG), and residual intake and gain (RIG). For all 180 chickens, immune system performance including humoral immune response, cell-mediated immunity (CMI), and the activity of lysozyme enzyme (L. activity) as innate immunity was measured. After ascending sort of each FE records, 10% of higher records (H-FE: N = 18) and 10% of lower records (L-FE: N = 18) were determined, and immunity between L-FE and H-FE groups were compared. Moreover, L-BWG and H-BWG were analyzed because BWG is one of components in the FE formula. Performance of the immune system was not statistically different for CMI in none of the studied FE groups. Moreover, high and low groups for DFI and BWG were not different regarding the immunity of the birds. Antibody titers against Newcastle disease virus (NDV) were different between low and high groups of FCR, RG, and RIG. Likewise, SRBC-derived antibodies were significantly different between RFI groups. Rather than humoral immunity, RIG had adversely effect on the innate immunity. Results of the present study showed that although RIG is a more appropriate indicator for FE, choosing for high RIG can weaken the performance of the both humoral and innate immune systems, while RFI had fewer adverse effects.

The objectives of this study were (i) to compare the accuracy and bias of estimates of breeding values for body weight (BW) at 2-7 weeks of age using pedigree-based best linear unbiased prediction (BLUP) and single-step genomic BLUP (ssGBLUP) methods, and (ii) to determine the best level of minor allele frequencies (MAFs) for pre-selection of SNPs for genomic prediction (GP). Records of 488 F2 broiler chickens obtained from crossbreeding of fast-growing Arian chickens and slow-growing Iranian native chickens at 2-7 weeks of age were used. Samples were genotyped using Illumina Chicken 60K BeadChip. To investigate the effect of MAFs on the accuracy of prediction, 48 379 quality-controlled SNPs were grouped into five subgroups with MAF bins 0.05-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4, and 0.4-0.5. Our results confirmed the superiority of ssGBLUP compared to traditional BLUP methodology. The average accuracy of GP improved by 59.03%, 220.34%, 0.46%, 5.61%, 0.45%, and 2.73% using ssGBLUP compared to BLUP for BW at 2-7 weeks of age, respectively. Depending on the age group, using a subset of SNPs with a specific MAF bin compared to all SNPs resulted in a remarkable improvement of GP accuracy for the observed traits.

Genetic improvement of body weight (BW) traits has received major consideration in the poultry industry due to their economic and environmental implications. With the rapid implementation of genomic selection (GS) in the poultry industry and a decrease in the cost of genotyping, genomic prediction (GP) is a feasible way to increase productivity. Moreover, a pre-selection of SNPs could represent a reasonable option to speed up GP. We used 312 F2 broiler chicken genotyped with 60K Illumina Beadchip to investigate the effect of reduced SNP densities on accuracy and bias of prediction using single-step genomic BLUP (ssGBLUP) for BW at 2-4 weeks of age (488 chickens). To investigate the effect of reduced SNP densities by varying minor allele frequency (MAF), SNPs were grouped into five subgroups with MAF of 0.05-0.1, 0.1-0.2, 0.2-0.3, 0.3-0.4 and 0.4-0.5. The accuracy and bias of genomic predictions from different MAF bins were compared to that using a standard array of 60k SNP genotypes and the traditional BLUP method. Our study showed that using a subset of common SNPs genotypes may increase accuracy of genomic predictions compared to using all SNPs, specifically in the studied F2 population with a limited number of genotyped/phenotyped individuals.

Ovulation rate and litter size are the main reproductive traits with high economic value in the sheep breeding industry. In this study, three Shal ewes (multiparous) and three Sangsari ewes (uniparous) at the age of 5 were used. The live weight was between 45 and 50 kg at an extremely body condition score of 3. These breeds are marked seasonal reproduction activity and are often bred in semi-closed breeding systems. Total RNAs were extracted from the ovarian tissues, and RNA sequencing was carried out. The DAVID (Database for Annotation, Visualization and Integrated Discovery) database was then used to annotate genes, and the string database and the Cytoscape software were used to investigate their interactions. Then path-act network analysis and gene-act network analysis were investigated. The results indicated that 19,932 genes were differentially expressed. The 5968 differentially expressed genes were identified in Shal ewe's ovarian tissue compared to Sangsari ewes (FDR < 0.05), of which 2921 genes were up-regulated and 3047 genes were down-regulated. Bioinformatics analysis exhibited that most of the biological processes and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways associated with significant DEGs (Differentially Expressed Genes) in the two studied breeds are associated with oocyte maturation and metabolism. MAPK signalling pathways and Ubiquitin-mediated proteolysis are the most important biological pathways associated with reproductive and fertility traits in the Shal breed. AKT3, MAPK8, MAPK9 and RELA genes are also important genes related to the fertility of multiparous sheep. Analysis of ovarian RNA-seq data identified that most of the differentially expressed genes were involved in various reproductive processes including folliculogenesis, ovulation, ovarian and embryonic development. The MAPK signalling pathway had the most interaction with other pathways, and the AKT3 gene could be a powerful candidate gene in the reproduction and fertility of Shal sheep. These results could pave the way for future efforts to address sheep prolificacy barriers.

AbstractBackgroundThe ovary has an important role in reproductive function. Animal reproduction is dominated by numerous coding genes and noncoding elements. Although long noncoding RNAs (LncRNAs) are important in biological activity, little is known about their role in the ovary and fertility.MethodsThree adult Shal ewes and three adult Sangsari ewes were used in this investigation. LncRNAs in ovarian tissue from two breeds were identified using bioinformatics analyses, and then target genes of LncRNAs were discovered. Target genes were annotated using the DAVID database, and their interactions were examined using the STRING database and Cytoscape software. The expression levels of seven LncRNAs with their target genes were assessed by real‐time PCR to confirm the RNA‐seq.ResultsAmong all the identified LncRNAs, 124 LncRNAs were detected with different expression levels between the two breeds (FDR < 0.05). According to the DAVID database, target genes were discovered to be engaged in one biological process, one cellular component, and 21 KEGG pathways (FDR < 0.05). The PES1, RPS9, EF‐1, Plectin, SURF6, CYC1, PRKACA MAPK1, ITGB2 and BRD2 genes were some of the most crucial target genes (hub genes) in the ovary.ConclusionThese results could pave the way for future efforts to address sheep prolificacy barriers.

Heat stress is a serious problem in the poultry industry. An effective tool for improving heat tolerance can be genomic selection based on single nucleotide polymorphisms. This study was performed to identify genomic regions controlling survivability to heat stress in a population of F2 chickens that accidentally experienced acute heat stress, using Illumina 60K Chicken SNP Bead Chip. After quality control in markers, 47,730 SNPs remained for genome-wide association study (GWAS). The GWAS results indicated that markers Gga_rs16111480 (p = 8.503e-08), GGaluGA354375 (p = 5.99e-07) and Gga_rs14748694 (p = 7.085e-07) located on Z chromosome showed significant association with heat stress tolerance trait. The Gga_rs16111480 marker was located inside the CEP78 gene. The marker GGaluGA354375 was located inside the LOC101752071 gene and next to the MEF2C gene. The Gga_rs14748694 marker was adjacent to LOC101752071 and MEF2C genes. Moreover, the SNP maker of Gga_rs16111480 was located on 243 kb downstream of the VPS13A gene, and the GGaluGA354375 and Gga_rs14748694 SNPs were located on 947 kb and 888 kb downstream of the ARRDC3 gene, respectively. The results of this study suggest that apart from the gene LOC101752071, which its function was unknown, each of the two MEF2C and CEP78 genes were found to be closely related to heat stress resistance in bird.

SUMMARYInconsistent results about the effects of free-range and conventional systems on economic carcase characteristics and the chemical composition of chicken meat have been reported. Free-range chicken meat has been presumably known as more nutritious and healthier than conventionally meat products so it has become highly present in the marketplace. In this study, due to an extensive systematic review plus meta-analysis, the effect of conventional and free-range rearing systems on meat quality and carcase traits has been evaluated based on thirty-nine included studies. A high level of heterogeneity was seen among studies, therefore, statistical analyses of random-effect models have been conducted to calculate summary statistics for the standardised effect size of the difference between free-range and conventional rearing systems. As a result, free-range significantly influenced abdominal fat yield, meat yellowness, protein and fat content of breast meat. However current meta-analysis showed that free-range had no significant negative effect on carcase weight, breast yield, leg yield, initial/ultimate pH, meat redness, cooking loss, drip loss, water holding capacity, and ash. In addition, two subset analyses according to sex (male, female and both sexes) and comparison type (slow-growing, fast-growing) clarified a part of sources of heterogeneity. Eventually, this study reported conclusive results that free-range significantly increased meat-related traits typically yellowness and protein of breast meat and conversely decreased abdominal fat yield and fat content of breast meat. In conclusion, free-range meat products can be characterised by different appearances due to the lower proportion of abdominal fat and yellower breast meat, also healthier and more nutritious because of lower fat concentration and higher protein content.

To comprehend the association structure between genome and complex traits, we investigated the different aspects of the novel omnigenic model for 23 traits in six trait categories in F2 cross-bred chickens from the perspectives of association signals, genomic heritability, overlapped genes, and predicted gene networks. In the current study, the high SNP-based heritability was obtained when the genetic architecture of traits was predominantly additive and non-significant markers contributed greater to explaining heritability. Moreover, we distinguished overlapping genes among apparently distinct traits indicating that traits are not inherited independently. The findings showed that the GWAS signals (core genes) were few and on average with greater beta effect sizes. Conversely, peripheral genes with non-significant effects appeared highly with co-expression connectivity in predicted gene networks. All results together allowed us to experimentally infer the highlighted role for peripheral genes in chicken’s complex traits which supports the establishment of the ‘omnigenic’ model.

Plumage color can be considered as a social signal in chickens and a breeding identification tool among breeders. The relationship between plumage color and trait groups of immunity, growth and fertility is still a controversial issue. This research aimed to determine the genome-wide additive and epistatic variants affecting plumage color variation in chickens using the chicken Illumina 60k high-density SNP array. Two scenarios of genome-wide additive association studies using all SNPs and independent SNPs were carried out. To perform epistatic association analysis, the LD pruning approach was used to reduce the complexity of the analysis. We detected seven novel significant loci using all of the SNPs in the model and 14 SNPs using the LD pruning approach associated with plumage color. Moreover, 89 significantly associated SNP-SNP interactions (P-value <10-6 ) distributed in 25 chromosomes were identified, indicating that all of the signals together putatively influence the quantitative variation of plumage color. By annotating genes relevant to top SNPs, we have distinguished 18 potential candidate genes comprising HNF4beta, CKMT1B, TBC1D22A, RPL8, CACNA2D1, FZD4, SGMS1, IRF8, OPTN, LOC420362, TRABD, OvoDA1, DAD1, USP6, RBM12B, MIR1772, MIR1709 and MIR6696 and also 89 putative gene-gene combinations responsible for plumage color variation in chickens. Furthermore, several KEGG pathways including metabolic pathway, cytokine-cytokine receptor interaction, focal adhesion, melanogenesis, glycosaminoglycan biosynthesis-keratan sulfate and sphingolipid metabolism were enriched in the gene-set analysis. The results indicated that plumage color is a highly polygenic trait which, in turn, can be affected by multiple coding genes, regulatory genes and gene-gene epistasis interactions. In addition to genes with additive effects, epistatic genes with tiny individual effect sizes but significant effects in a pair have the potential to control plumage coloration in chickens.

This study was performed to evaluate the population structure by genome-wide analysis using the Illumina 60K chicken Beadchip. One F2 population derived from the reciprocal cross between Arian fast-growing chickens and Urmia slow-growing indigenous fowls and consisting of 312 F2 birds was investigated. Quality control procedures, population clustering using the multidimensional scaling (MDS) method and allele frequency assessment in half-sib groups were applied. Then the population structure was considered, as provided by the outputs of MDS, Heatmap and neighbor-joining tree, which were consistent with structure analyses by clustering the birds into eight sub-populations of K1 to K8. The average expected heterozygosity in all half-sib families was K1 = 0.4677, K2 = 0.4635, K3 = 0.4949, K4 = 0.5235, K5 = 0.4613, K6 = 0.4273, K7 = 0.4618, and K8 = 0.4459, respectively. The heterozygosity values were found to be higher than 42% for all the eight clusters in this study, indicating a relatively high genetic variability within the groups. The fixation index (F-st) among F2 chicken clusters ranged from 0.01 to 0.29. One possible explanation for the high estimated F-st (>0.1) can be a signal of selection in the present population. Our findings can be applied in future GWASs, conservation plans and genetic improvement programs of chickens.

Heat stress, or hyperthermia, can have a serious effect on chicken performance in poultry industry in many parts of the world. Both genetics and environment play key role in the performance of a chicken and, therefore, it is important to consider both factors in addressing heat stress. On genetics level, genome-wide association studies have become a popular method for studying heat stress in recent years. A population of 202 F-2 chickens was reared for 84 days to find genes and genomic regions affecting growth traits and immune system. But, due to unexpected acute increase in temperature at day 83, 182 birds died (case) and 20 birds remained alive (control). At the age of 70 days, blood sample of all birds was collected to extract their DNA, using modified salting out method. All samples were genotyped by a 60 K Single Nucleotide Polymorphism (SNP) chip. Genome-wide association study was carried out by GCTA to identify gene and genomic regions associated with heat stress tolerance. Results indicated a close relationship between 28 SNPs, located on chromosomes 2, 3, 5, 6, 7, 12, 19, 20, and 21 and heat stress tolerance at the level of suggestive significance. Two suggestively significant markers on chromosome 5, namely, GGaluGA273356 and Gga_rs16479429, were located within and 52 Kb downstream of two genes, including MAPKBP1 and SPON1, respectively. Gene ontology analysis indicated that the resistance of chickens to acute increase of temperature might be linked to the function of MAPKBP1 and SPON1 genes and their biological pathways. These results will be useful for understanding the molecular mechanisms of SNPs and candidate genes for heat stress tolerance in chickens and provide a basis for increasing genetic resistance in breeding programs.

Genomic markers play an important role in tracing the flow of genetic causality of observable signals in animals and plants. In farm animals, the participation of male animals in the gene pool of subsequent generations are much higher than female animals and testes are the most important organs of the male reproductive system. This study was conducted to investigate simple sequence repeats (SSR) within the expressed sequence tags (ESTs) in order to classify the Bos taurus testis tissue’s genes for their relationship and specificity with related reproductive domains. A total of 48,549 publicly available EST sequences from cattle testis tissue downloaded from GenBank database, out of which, 10,237 sequences that their library made from testis tissue were extracted and specialized as the studied sequences using several searching tools and software. Across these selective sequences, 2,039 contigs, 5,097 singletons, and 153 SSRs were detected. EST-SSRs were subsequently evaluated using GenBank and categorized based on their functions in biological systems of dairy cattle. Investigation of these motifs showed that the identified EST-SSRs can be classified into 48 types that GT in dinucleotides and GCC in trinucleotides had the highest frequency. Annotation and gene ontology analysis revealed a relationship among 54 domains with the observed SSRs. Localization and characterization of such markers can help tracing the production of amino acids coded by identified repeats as shown in this study.

In order to find SNPs and genes affecting shank traits, we performed a GWAS in a chicken F2 population of eight half-sib families from five hatches derived from reciprocal crosses between an Arian fast-growing line and an Urmia indigenous slow-growing chicken. A total of 308 birds were genotyped using a 60K chicken SNP chip. Shank traits including shank length and diameter were measured weekly from birth to 12 weeks of age. A generalized linear model and a compressed mixed linear model (CMLM) were applied to achieve the significant regions. The value of the average genomic inflation factor (λ statistic) of the CMLM model (0.99) indicated that the CMLM was more effective than the generalized linear model in controlling the population structure. The genes surrounding significant SNPs and their biological functions were identified from NCBI, Ensembl and UniProt databases. The results indicated that 12 SNPs at 12 different ages passed the LD-adjusted 5% Bonferroni significant threshold. Two SNPs were significant for shank length and nine SNPs were significant for shank diameter. The significant SNPs were located near to or inside 11 candidate genes. The results showed that a number of significant SNPs in the middle ages were higher than the rest. The MXRA8 gene was related to the significant SNP at week 1 that promotes proliferation of growth plate chondrocytes. A unique SNP of Gga_rs16689511 located on chicken Z chromosome within the LOC101747628 gene was related to shank length at three different ages of birds (weeks 8, 9 and 11). The significant SNPs for shank diameter were found at weeks 4 and 7 (four and five SNPs respectively). The identifications of SNPs and genes here could contribute to a better understanding of the genetic control of shank traits in chicken.

The aim of this study was to investigate the trend of bias in genomic estimated breeding values (GEBVs) arising from selective genotyping of the candidate population in an ongoing selection scheme. The bias was calculated as the regression of true breeding values (TBVs) on GEBVs. A simulation study was performed under two scenarios with selection intensities (SI) of 0.798 and 1.755 for three traits with heritability (h2) of 0.1, 0.25 and 0.4 in 10 consecutive generations. Regression of TBVs on GEBVs was close to one for the first generation when selective genotyping was random, and it continuously receded from one as selection shifted to choose animals with high EBVs from generations 2 to 10. Biasedness became larger with increased SI and decreased h2. Further, biasedness increased over the generations but the rate of change in biasedness decreased dramatically after the second generation and became almost steady after generation 4 which may be due to Bulmer effect. The findings showed that scaling down the GEBVs, using a scale parameter, might help removing biasedness in generation 4 onwards.

The importance of ascites in the poultry industry warrants a comprehensive systematic review and in-silico modelling to explain responses seen in previous studies in this field. By identifying the genes which are effective and relevant to different indicator traits of ascites in poultry, genes were separated base on chromosomes to determine the most effective chromosome in ascites. Consequently, 12 chromosomes have been discovered as containing effective regions on ascites incidence. Meanwhile, 24 genes including MPPK2, AT1, RhoGTPase, MC4R, CDH6, NOS3, HIF-1A, OSBL6, CCDC141, BMPR2, LEPR, AGTR1, UTS2D, 5HT2B, SST, CHRD, TFRC, CDH13, ACVRL1, ARNT, ACE, ACVRL1, MEF2C, and HTR1A affect ascites according to published studies. The results show that chromosome 9, with the presence of six related genes, chromosomes 1, 2 and 7 with three related genes and Z containing two genes have the most influence on the sensitivity to the ascites syndrome, respectively.