---

Four nonlinear models including Logistic, Gompertz-Laird, Richards, and von Bertalanffy were compared to achieve the best prediction of growth parameters describing the growth curve in a crossbred chicken population. Growth data (weekly body weights of chicken from birth to 84 days of age) were collected on 303 birds (174 females and 129 males) of F₂ cross of the Arian line broiler chicken (Line B) and Urmia native chicken. Some statistical criteria such as Akaike Information Criterion (AIC), Corrected Akaike Information Criterion for small sample sizes (AICc), and Bayesian Information Criterion (BIC) were used to find the best model. The results showed that the estimated values of the initial weight (W₀) and final Weight (W_f) in male were significantly (P< 0.01) higher than the female birds in all models. The average estimated initial weight calculated by Gompertz-Laird (0.038 kg) was closer to the average observed initial weight (0.044 kg). Regardless of sex of the birds, the calculated age (t_i) and Weight (W_i) at the inflection point were relatively the same in Gompertz-Laird, Richards and von Bertalanffy models, indicating that the growth patterns described by these models are similar. Meanwhile, the different t_i and W_i values between the sexes in the four models revealed the different growth pattern in males and females. The goodness of fit indices (R² and adjusted R²) were higher than 0.97 in all models, indicating that these models could appropriately be fitted on the growth data. However, based on the AIC, AICc, and BIC criteria, Gompertz-Laird model showed better performance, therefore, it was chosen as the best model to analyze the growth pattern in crossbred of .

---

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 F₂ 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 MAPKBP1and 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.

---

Alternative splicing, alternative transcript start site, and alternative transcript polyadenylation site are the main factors resulting in diversity of the transcripts of a gene. The main objectives of this study were to analyze the alternation process in breeds of sheep and goat, and to identify its role in differentiation of breeds of a species. RNA-seq data were prepared from ovarian tissue of two breeds of Shal and Sangsari sheep and two breeds of Tibetan and Jintang black goats. Reads were aligned to the reference genome and significant genes with respect to differential exon usage were identified. The statistical comparison revealed that 8,104 genes were significantly different in exon usage between the sheep breeds and 173 genes differed between the goat breeds. Out of the 121,861 studied exons, only 22.7% were preserved during future generations between the breeds, of which 99.3% did not display any alternatives. The high protection was probably due to the lack of involvement of the exons in alternative process. The genes with differential exon usage in goat had a higher percentage of alternatives than those in sheep. The interracial analysis showed that alternative splicing was the most influential type of alternatives in the breeds of sheep and goats. It seems that the conservation process of the exons is related to the contribution of these exons in alterative process in both sheep and goat breeds. The significant PI3K-Akt and alternative splicing pathways play a role in cell growth, development of ovaries, and mRNAs splicing.

---

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 fast-growing 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.

---

Fatty Liver Hemorrhagic Syndrome (FLHS) is common in poultry. Long non-coding RNAs (lncRNAs) regulate gene expression in a variety of ways at epigenetic, chromatin remodeling, transcriptional, and translational levels. Chicken liver produces lipoproteins and most of the precursors to egg yolk with the help of RNA such as MicroRNAs (miRNAs) and lncRNAs. In order to analyze lncRNAs in liver, RNA-seq data of six samples were downloaded from National Center for Biotechnology Information (NCBI) (3 birds with fatty livers from the paternal group and 3 control birds).Then, using the DESeq2 package, the difference in expression of lncRNAs in the samples was analyzed. Functional enrichment analysis was established by STRING and the PPI network visualized by Cytoscape. Annotation of the data was carried out by DAVID 6.8. The biological pathways were searched in Kyoto Encyclopedia of Genes and Genomes (KEGG). The results of the analysis of Differentially Expressed Genes (DEGs) showed that there were 24356 annotated genes. Also, 101 lncRNAs were found. Gene Ontology (GO) term enrichment analysis suggested that DEGs significantly enriched in metallocarboxypeptidase activity, protein ubiquitination, etc. KEGG pathway analysis showed that DEGs related with biosynthesis of antibiotics and biosynthesis of amino acids (P< 0.05). Examination of gene loci revealed that the expression process of GCGR, PDK3 and PCK1 genes was in line with the expression of neighboring lncRNAs. Examination of this number of lncRNAs along with their target genes can help in selecting laying hen lines with less chance of developing fatty livers.

---

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 F₂ 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.