Heat stress is becoming a major problem because it limits growth in poultry production, especially in tropical areas. The development of genetic lines of Thai native chickens (TNC) which can tolerate the tropical climate with the least compromise on growth performance is therefore necessary. This research aims to analyze the appropriate growth curve function and to estimate the effect of heat stress on the genetic absolute growth rate (AGR) in TNC and Thai synthetic chickens (TSC). The data comprised 35,355 records for body weight from hatching to slaughtering weight of 7241 TNC and 10,220 records of 2022 TSC. The best-fitting growth curve was investigated from three nonlinear regression models (von Bertalanffy, Gompertz, and logistic) and used to analyze the individual AGR. In addition, a repeatability test-day model on the temperature-humidity index (THI) function was used to estimate the genetic parameters for heat stress. The Gompertz function produced the lowest mean squared error (MSE) and Akaike information criterion (AIC) and highest the pseudo-coefficient of determination (Pseudo-R2) in both chicken breeds. The growth rates in TSC were higher than TNC; the growth rates of males were greater than females, but the age at inflection point in females was lower than in males in both chicken breeds. The THI threshold started at 76. The heritability of the AGR was 0.23 and 0.18 in TNC and TSC, respectively. The additive variance and permanent environmental variance of the heat stress effect increased sharply after the THI of 76. The growth rate decreased more severely in TSC than TNC. In conclusion, the Gompertz function can be applied with the THI to evaluate genetic performance for heat tolerance and increase growth performance in slow-growing chicken.
Toll-like receptors (TLRs) are transmembrane proteins important for directing immune responses. Their primary role is to recognize pathogens based on single-nucleotide polymorphism (SNP) characteristics. TLR2 is categorized as a pattern recognition receptor (PRR) that is important for the recognition of pathogens. Nucleotide variation in the coding region determines the conformation of the TLR protein, affecting its protein domain efficiency. This study aimed to identify SNPs in the coding region of TLR2 to enhance available genetic tools for improving health and production in swamp buffalo. A total of 50 buffaloes were randomly sampled from the northeastern part of Thailand for genomic DNA extraction and sequencing. Nucleotide sequences were aligned and compared with cattle and river buffalo based on the database. The results showed, there were 29 SNP locations in swamp buffalo and 14 different locations in both cattle and buffaloes. Haplotype analysis revealed that 27 haplotypes occurred. Swamp buffalo were identified from 13 SNPs based on biallelic analysis, which found eight synonymous and five nonsynonymous SNPs. Nucleotide diversity (π) was 0.16, indicating genetic diversity. Genetic diversity (haplotype diversity; HD) was high at 0.99 ± 0.04. This indicates a high probability that the two sample haplotypes are different. The π and HD values are important indicators of the genetic diversity of the swamp buffalo population. In summary, the Thai swamp buffalo population detected a polymorphism of the coding region of the TRL2 gene. Therefore, further, in-depth study of the relationship between these genes in the immune system and disease resistance should be recommended.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.