Research Report
Progress in Genetic Research on Behavioral Traits in Livestock: Application of Whole Genome Association Analysis
Author Correspondence author
International Journal of Molecular Zoology, 2024, Vol. 14, No. 1 doi: 10.5376/ijmz.2024.14.0006
Received: 09 Jan., 2024 Accepted: 16 Feb., 2024 Published: 27 Feb., 2024
Guo M., 2024, Progress in genetic research on behavioral traits in livestock: application of whole genome association analysis, International Journal of Molecular Zoology, 14(1): 44-53 (doi: 10.5376/ijmz.2024.14.0006)
This study reviewed the application of genome-wide association analysis (Genome-wide association study, GWAS) and its progress in genetic studies of behavioral traits in domestic animals. By combing the cases of GWAS applications for behavioral traits such as stress response, social behavior, and maternal behavior in different livestock species (such as cows, pigs, sheep, poultry, etc.), this study demonstrates the contribution of GWAS in revealing the genetic basis of these complex traits, and discusses the challenges faced by GWAS research, such as the genetic heterogeneity of the complex traits, the effects of population structure and environmental factors, and the difficulty of detecting rare variants. detection difficulties. This study also emphasizes the importance of integrating genetics, epigenetics, and environmental factors in the study of behavioral traits in livestock, and explores the potential of new technologies, such as gene editing and integrated multi-omics analyses, to be applied in future studies. The study concludes with a summary of the practical applications of GWAS for livestock genetic improvement and breed selection, as well as possible improvements in livestock productivity and animal welfare. The analysis of existing studies aims to provide a reference for genetic studies of behavioral traits in livestock and to provide suggestions for future research directions and applications.
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. Genome-wide association analysis
. Livestock behavioral traits
. Genetic improvement
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. Integrated multi-omics analysis
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