International Journal of Molecular Zoology 2024, Vol.14, No.1, 44-53 http://animalscipublisher.com/index.php/ijmz 47 2.3 Challenges and solutions faced in GWAS research Although GWAS has made significant achievements in genetic research, it also faces some challenges in practical applications, mainly including: Group structure: Animal populations often have complex group structures, which may lead to false positive findings. To address this issue, researchers can use various methods such as Principal Component Analysis (PCA) or Mixed Linear Model (MLM) to correct for the influence of population structure. Multiple testing: In GWAS, thousands of SNPs are typically tested for associations with specific traits, which increases the risk of accidentally discovering significant associations. To control the false positive rate, strict statistical correction methods such as Bonferroni correction or false discovery rate (FDR) control can be used. Effect size and genetic heterogeneity: Many genetic variations related to traits have smaller effects, and the genetic basis of traits may involve the interaction of multiple genes and environmental factors. Increasing sample size, adopting more detailed phenotype classification, and integrating multi omics data are potential strategies to address this issue (Zhang et al., 2023). Overall, GWAS provides a powerful tool for revealing the genetic basis of livestock behavioral traits, but it also requires researchers to adopt appropriate strategies to overcome the challenges in analysis. By continuously optimizing methods and technologies, GWAS is expected to play a greater role in the field of animal genetics research. 3 The Application of GWAS in Genetic Research of Behavioral Traits in Livestock 3.1 Important cases of GWAS technology applied in genetic research of behavioral traits in livestock Vallee's research team conducted a GWAS study in 2016 to examine the behavioral, typological, and muscle development characteristics of Charlotte cattle. This study found a significant association between aggression, maternal care, and various types of characteristics during pregnancy, indicating that these characteristics have a complex genetic basis. The creatinine genes known to affect muscle properties have been identified to be significantly associated with muscle development (Vallee et al., 2016). Schmid and Bennewitz provided a selective review of the statistical model and experimental design of GWAS for quantifying features in livestock in 2017. They emphasized the importance of considering non additive genetics and genotype and environmental effects in GWAS data analysis (Schmid and Bennewitz, 2017). Fonseca et al conducted a systematic review of GWAS results on sperm and testicular characteristics in livestock in 2018. They used systems biology methods to identify key functional candidate genes for reproductive characteristics, highlighting the genetic mechanisms underlying these features (Fonseca et al., 2018). Freeborn et al. (2019) used GWAS, fine mapping, and multi tissue transcriptome data analysis to investigate the genetic basis of seven health characteristics in cows. This study detected significant associations and identified 20 candidate genes related to cow health, providing insights into the genetic structure of complex features and diseases (Freebern et al., 2019). 3.2 Key genes and genetic variations discovered and their biological significance GWAS research can not only identify genetic markers related to livestock behavioral traits, but also further reveal the biological mechanisms behind these markers. For example, the NR3C1 gene associated with stress response in pigs is a gene encoding glucocorticoid receptors, which play a core role in regulating stress response. Similarly, the discovery of genetic markers related to bovine social behavior suggests that social interaction behavior may be associated with specific signaling pathways and neurotransmitter systems (Figure 1). These findings provide a new perspective on the genetic basis of behavioral traits in livestock and specific molecular targets for genetic improvement.
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