Computational Molecular Biology 2024, Vol.14, No.1, 1-8 http://bioscipublisher.com/index.php/cmb 5 Genome prediction also provides a new way to gain a deeper understanding of the mechanisms of Alzheimer's disease. In the process of studying the genes that play a key role in predictive models, researchers can explore the biological functions of these genes in the development and progression of the disease, providing clues for the discovery of new therapeutic targets. By comprehensively understanding the genetic variations associated with Alzheimer's disease, a better understanding of the pathogenesis of the disease can be achieved, providing support for the development of precision medicine. In summary, genome prediction has injected new vitality into Alzheimer's disease research and opened the door to the realization of individualized medicine. However, despite significant progress, a series of challenges still need to be faced, such as model complexity and data privacy issues. Future research needs to continuously improve genome prediction models, combining multi-source data to further improve prediction accuracy and reliability, and provide more powerful support for early intervention and treatment of Alzheimer's disease. 2.3 Case study analysis In the field of Alzheimer's disease, large-scale genomics studies provide opportunities for in-depth understanding of the genetic basis of the disease. Case study 1: Leonenko et al. (2019) developed a genome prediction model using GWAS data, focusing on Alzheimer's disease. They successfully integrated a large amount of genetic information, enabling more accurate prediction of individuals' risk of developing Alzheimer's disease. Notably, by combining genome prediction with clinical information, researchers not only improved prediction accuracy, but also provided new ways to distinguish high-risk individuals and develop early intervention plans. Case study 2: The meta-analysis study conducted by Jansen et al. (2019) has broadened our understanding of the genetic basis of Alzheimer's disease. They identified a series of new genetic variants associated with the risk of the disease, involving multiple functional pathways. This study not only provides new markers for the optimization of genome prediction models, but also deepens our understanding of the mechanism of Alzheimer's disease. Case study 3: Tan et al. (2017) introduced a new polygenic hazard score (PHS) method that is associated with amyloid and tau protein deposition in Alzheimer's disease. By focusing on these biological markers, researchers not only improved the precision of genome prediction, but also provided a new perspective for understanding the biological mechanism of Alzheimer's disease. The above studies, by integrating diverse genetic information and delving into biological markers, not only provide more comprehensive tools for individualized risk assessment, but also provide useful experiences for the development of future genome prediction models. These achievements lay a solid foundation for the prevention and treatment of Alzheimer's disease. 3 Genome Prediction and Its Association with Alzheimer's Disease 3.1 Comparison of research methods In the research on the association between genome prediction and Alzheimer's disease, different research teams have adopted different methods to reveal the relationship between genetic factors and the disease. There are certain similarities and differences among these research methods, which are mainly reflected in the following aspects: 1) Selection and weight assignment of genetic markers Different studies vary in the selection of genetic markers. Some studies focus on analyzing specific genes or gene regions, while others prefer to conduct comprehensive assessments through whole-genome approaches. Additionally, the weight assignment for different genes also varies among studies, which means that some studies may place more emphasis on the contribution of specific genes, while others may consider the role of multiple genes comprehensively.
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