Genetic variance components of the growth curve for Isfahan indigenous chicken

BACKGROUND

Being able to model a growth curve using three or four non-linear functional parameters could help explain the growth phenomenon in a precise way and would allow the comparison of an animal's development rate, optimize management and feeding strategies and guide animal production strategies.

OBJECTIVE

The goal of this study was to estimate the genetic parameters of growth traits of Isfahan indigenous chicken in Iran and to determine the best non-linear model describing the growth curve.

METHODS

The prediction of additive genetic parameters was performed using the REML method by WOMBAT. Direct heritability of the studied traits and genetic correlations between them were obtained. The Logistic, Gompertz, von Bertalanffy, Brody, Negative exponential, Weibull, Janoschek and Bridges models were compared based on the coefficient of determination (R2 ), mean square error (MSE) and akaike information criterion.

RESULTS

The Gompertz model was identified as the best model for describing the growth curve for Isfahan native chicken. The heritability of maturity weights (A), initial weight (B) and maturity rate (K) parameters were 0.223 ± 0.002, 0.016 ± 0.005 and 0.087 ± 0.001, respectively.

CONCLUSION

This study shows that Isfahan indigenous chicken has the genetic potential for improving growth and reproduction based on their desirable heritabilities and correlations using appropriate models.

Genome-wide association study on abdomen depth, head width, hip width, and withers height in native cattle of Guilan (Bos indicus)

Native breeds in any country are a national capital, and their preservation is of great importance. Native Cattle of Guilan (NCG) is one of the few pure native breeds in Iran and the West Asia region. During the last decade, NCG population has decreased by more than 40%. This study aimed to identify significant single nucleotide polymorphisms (SNPs) and candidate genes associated with meat production traits in NCG using a genome-wide association study (GWAS). The blood and hair samples were collected from 72 NCG individuals and genotyped using the Illumina Bovine SNP50 chip. The results of the genomic scan showed that several SNPs were associated with abdominal depth, head width, hip width, and withers height in NCG. Several candidate genes were identified, including multiple epidermal growth factor-like domains 11 (MEGF11), Methionine Sulfoxide Reductase A (MSRA), chondroitin sulfate synthase 3 (CHSY3), Cyclin-Dependent Kinase 7 (CDK7), and Parkin (PRKN) genes, which are involved in muscle growth, meat tenderness, differentiation of fat cells, fat metabolism, and adipogenesis. These genes can contribute to meat quantity and quality in NCG. This study provided valuable insights into the genetics of NCG and the identification of effective genes associated with meat production traits. The results of this study could be used for the preservation and sustainable use of this breed of native cattle, as an important genetic resource in Iran.

Interaction networks and pathway analysis of genetic resistance to gastrointestinal nematodes in sheep

Gastrointestinal nematode (GINs) infections are one of the causative agents of health and economic issues in sheep production systems worldwide. Considerable genetic variations in resistance or susceptibility in different sheep breeds are documented, but published results are conflicting. Recent advances obtained by high-throughput technologies such as commercial SNP chips, whole-genome sequencing, or whole transcriptome profiling provide new insights into breeding for host resistance or nematode control at the genetic levels. This study aimed to identify potential biomarkers associated with the resistance to ovine GINs through a network analysis approach. Comprehensive gene and protein interaction networks were reconstructed for candidate genes involved in the most related immune pathways associated with resistance to ovine GINs using data mining from literature. Generally, 30 genes including CD53, CHIA, RELN, HRH1, EPS15, LRP8, ATP2B1, IL4, IL5, IL13, IL2RA, IL23R, TNFα, IFNγ, TBX21, SH3RF1, HERC2, PTPN1, BIN1, HERC5, C3AR1, NOS2, STAT5B, STAT4, CCL1, CCL8, VIL1, CXCR1, CXCR2, and CXCR4 located on chromosomes 1, 2, 3, 4, 5, 6, 11, 13, 19, and 20 have been found as containing effective regions with the most related pathways to nematode infections. The results obtained by network analysis showed two functional modules, belonging to the interleukins family (IL4, IL5, IL13, IL23R, and IL2RA) and chemokine receptors or ligands family (CXCR1, CXCR2, CXCR4, CCL1, and CCL8). Interleukins are a group of cytokines that are expressed by white blood cells with a major role in the immune system. Chemokines are also a family of chemoattractant cytokines which play a vital role in cell migration that influence the immune system by a process known as chemotaxis. The results provide useful information for the functional annotation of candidate genes related to parasite resistance and add new information towards a consensus on quantitative trait loci (QTLs) related to the incidence of nematode infections.

Genetic analysis of growth curve in Moghani Sheep using Bayesian and restricted maximum likelihood

This study was conducted to predict the genetic (co)variance components of growth curve parameters of Moghani sheep breed using the following information: birth weight (N = 7278), 3-mo-old weight (N = 5881), 6-mo-old weight (N = 5013), 9-mo-old weigh (N = 2819], and 12-mo-old weight (N = 2883). The growth parameters (A: maturity weight, B: growth rate, and K: maturity rate) were calculated using Gompertz, Logistic, Brody, and Von Bertalanffy nonlinear models via NLIN procedure of SAS software. The aforementioned models were compared using Akaike information criterion, root mean square error, adjusted co-efficient of determination. Also, both Bayesian (using MTGSAM) and RMEL (using WOMBAT) paradigms were adapted to predict the genetic (co)variance components of growth parameters (A, B, K) due to the best fitted growth models. It was turned out that Von Bertalanffy best fitted to the data in this study. The year of birth and lamb gender had a significant effect on maturity rate (P < 0.01). Also it turned out that within the growth parameter, with increasing (co)variance matrix complexity, the Bayesian paradigm fitted well to the data than the restricted maximum likelihood (REML) one. However, for simple animal model and across all growth parameters, REML outperformed Bayesian. In this way, the h2a predicted (0.15 ± 0.05), (0.11±.05), and (0.04 ± 0.03) for A, B, and K parameters, respectively. Practically, in terms of breeding plan, we could see that genetic improvement of growth parameters in this study is not a tractable strategy to follow up and improvement of the management and environment should be thoroughly considered. In terms of paradigm comparison, REML's bias correction bears up an advantageous approach as far as we are concerned with small sample size. To this end, REML predictions are fairly accurate but the mode of posterior distributions could be overestimated. Finally, the differences between REML and Bayesian estimates were found for all parameter data in this study. We conclude that simulation studies are necessary in order to trade off these parading in the complex random effects scenarios of genetic individual model.

Estimation of genetic parameters for production, reproduction, and growth curve of Fars indigenous chicken

Iranian native chicken, including Fars indigenous chicken, is an important genetic resource due to its adaptation to stressful environmental conditions, good endurance and resistance to disease. The aim of this research was to determine the genetic infrastructure of Fars indigenous chicken using several nonlinear functions. The dataset included body weight at hatch (BW1), body weight at the 8th week (BW8), body weight at the 12th week (BW12), weight at sexual maturity (WSM), age at sexual maturity (ASM), number of eggs in the first 12 weeks of laying period (EN), egg weight at the first day of laying (EW1), average egg weight at the 28thday of laying (EW28), and average egg weight at weeks 28, 30, and 32 of the laying period (AEW). Growth models were fitted using the NLIN procedure and WOMBAT software was used to predict variance components for the best fit model parameters. Results suggested three-parameter models, for example, Gompertz, fitted better to the data than others. The maturity weight (A), initial weight (B), and maturity rate (K) parameters in the Gompertz model were 1996.8 ± 6.63, 4.11 ± 0.03, and 0.021 ± 0.0001, respectively. The heritability of A, B, and K parameters were 0.03, 0.05, and 0.12, respectively.

RNA-Seq Bayesian Network Exploration of Immune System in Bovine

Background:

The stress is one of main factors effects on production system. Several factors (both genetic and environmental elements) regulate immune response to stress.

Objectives:

In order to determine the major immune system regulatory genes underlying stress responses, a learning Bayesian network approach for those regulatory genes was applied to RNA-Seq data from a bovine leukocyte model system.

Material and Methods:

The transcriptome dataset GSE37447 was used from GEO and a Bayesian network on differentially expressed genes was learned to investigate the gene regulatory network.

Results:

Applying the method produced a strongly interconnected network with four genes (TERF2IP, PDCD10, DDX10 and CENPE) acting as nodes, suggesting these genes may be important in the transcriptome regulation program of stress response. Of these genes TERF2IP has been shown previously to regulate gene expression, act as a regulator of the nuclear factor-kappa B (NF-κB) signalling, and to activate expression of NF-κB target genes; PDCD10 encodes a conserved protein associated with cell apoptosis; DDX10 encodes a DEAD box protein and is believed to be associated with cellular growth and division; and CENPE involves unstable spindle microtubule capture at kinetochores. Together these genes are involved in DNA damage of apoptosis, RNA splicing, DNA repairing, and regulating cell division in the bovine genome. The topology of the learned Bayesian gene network indicated that the genes had a minimal interrelationship with each other. This type of structure, using the publically available computational tool, was also observed on human orthologous genes of the differentially expressed genes.

Conclusions:

Overall, the results might be used in transcriptomic-assisted selection and design of new drug targets to treat stress-related problems in bovines.

Fat harvesting site is an important determinant of proliferation and pluripotency of adipose-derived stem cells

To define the optimal fat harvest site and detect any potential differences in adipose-derived stem cells (ASCs) proliferation properties in camels, aspirates from the abdomen and hump sites were compared. Obtained results revealed that ASCs from both abdomen and hump exhibited spindle-shaped and fibroblast-like morphology with hump-derived ASCs being smaller in size and narrower in overall appearance than abdominal ASCs. Abdominal ASCs required a greater time for proliferation than the hump-derived cells. These results were further confirmed with a tetrazolium-based colorimetric assay (MTT) which showed a greater cell proliferation rate for hump ASCs than for the abdomen. Under inductive conditions, ASCs from both abdominal and hump fat deposits maintained their lineage differentiation potential into adipogenic, chondrogenic, and osteogenic lineages during subsequent passages without any qualitative difference. However, expression of alkaline phosphatase was higher in osteogenic differentiated cells from the hump compared with those of the abdomen. Moreover, the increase in calcium content in hump-derived stem cells was higher than that in abdominal-derived stem cells. In conclusion, our findings revealed that ASCs can be obtained from different anatomical locations, although ASCs from the hump fat region may be the ideal stem cell sources for use in cell-based therapies.