Animal Molecular Breeding 2024, Vol.14, No.1, 54-61 http://animalscipublisher.com/index.php/amb 59 Figure 2 Manhattan plots of body weight traits for four fine-wool sheep breeds (Lu et al., 2020) Note: The gray horizontal lines in the Manhattan plots indicate the suggestive significance (10−6) thresholds. A: birth weight; B: weaning weight; C: yearling weight; D: adult weight 5 Challenges of Genome-Wide Association Analysis in Optimization of Mutton Production Traits Optimization of mutton production traits is an important topic in animal husbandry, and genome-wide association analysis is considered an effective method. However, in practice, it faces many challenges, including data acquisition and processing, quality control of genotype data, and selection and optimization of statistical models. 5.1 Difficulties in data collection and processing The optimization of mutton production traits requires a large amount of data support, including information on body shape, growth rate, muscle tissue structure, etc. However, there are many difficulties in the actual data collection process. First, sheep herds are widely distributed and numerous, making it difficult to collect and standardize data. Differences in breeding conditions and management levels in different regions and environments will also challenge the consistency of data, posing difficulties for the effective use of data. Human interference may occur during the collection process, such as inaccurate data recording, sampling errors, etc., which further increases the complexity of data processing. 5.2 Challenges in genotype data quality control In genome-wide association analysis, the quality of genotype data directly affects the accuracy and reliability of the analysis. However, the quality of genotype data is affected by many factors, including sample source, DNA extraction method, sequencing technology, etc. These factors may lead to problems such as missing values and incorrect genotype assignments in the data, thus affecting the credibility of the results of association analysis. Therefore, how to effectively control the quality of genotype data has become a major challenge in genome-wide association analysis. In addition, for large-scale sample data, efficient quality control methods are also particularly important to ensure data quality and the credibility of analysis results. 5.3 Selection and optimization of statistical models In genome-wide association analysis, choosing an appropriate statistical model is critical to the accuracy and interpretability of the results. However, traditional statistical models may not be able to adequately explain the complex relationships in mutton production traits because these traits are affected by polygenic genetic and environmental factors. Therefore, it is necessary to design and optimize statistical models suitable for mutton production traits to improve the effect and accuracy of correlation analysis. With the continuous development of computer technology, the introduction of new methods such as machine learning provides new ideas and methods for the optimization of statistical models (Sun and Zhao, 2020). However, this also brings more challenges to the
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