Computational Molecular Biology 2024, Vol.14, No.1, 1-8 http://bioscipublisher.com/index.php/cmb 4 Variations in the BIN1 (Bridging Integrator 1) gene are strongly associated with the risk of developing Alzheimer's disease (Gao et al., 2021). The BIN1 protein is involved in the regulation of membrane morphology, and its abnormalities can lead to amyloid deposition and neuronal damage. Some variations in the SORL1 (Sortilin-Related Receptor 1) gene have been found to be associated with an increased genetic risk of Alzheimer's disease. The SORL1 protein plays a role in amyloid clearance and trafficking, and its abnormalities can lead to abnormal amyloid aggregation. These genes are involved in multiple biological processes, and their mutations or variations can affect the pathogenesis of Alzheimer's disease through various pathways, including abnormal amyloid deposition, neuronal damage, and inflammatory responses. These findings provide a more comprehensive understanding of the pathogenesis of Alzheimer's disease and provide clues for exploring potential therapeutic targets. 1.4 The impact of the interaction between multiple genes and the environment The development of Alzheimer's disease is not only influenced by single genes, but also by the complex interactions between multiple genes and the environment. The introduction of polygenic risk scores (PRS) allows researchers to comprehensively consider the contribution of multiple genetic variants to disease risk. Additionally, environmental factors such as lifestyle, education level, psychosocial factors, etc. have also been found to be associated with the risk of Alzheimer's disease, and their interactions with genes further increase the complexity of the research. Researchers are working hard to reveal the interactions between these multiple factors to more comprehensively and accurately assess individual risk of Alzheimer's disease. Understanding these complex genetic and environmental interactions is crucial for developing prevention strategies and personalized treatment plans. 2 Methods and Applications of Genome Prediction 2.1 Basic principles of genome prediction Genome prediction is a method that predicts an individual's susceptibility to a certain disease by analyzing the genetic variations in their genome. The basic principle of this method is to establish a model that associates known genetic variations associated with the disease with disease risk, and then use this model to analyze the individual's genome data and estimate their likelihood of developing the disease. In genome prediction, commonly used methods include Polygenic Risk Scores (PRS) and machine learning algorithms (Lambert et al., 2019). PRS calculates a score by summing the risk weights of multiple genetic variants in an individual's genome, reflecting their overall genetic risk for a certain disease. Machine learning algorithms learn the genetic characteristics of the disease from a large amount of genome data, and then predict the risk for new individuals. 2.2 The application of genome prediction in Alzheimer's disease research As a cutting-edge technology, genome prediction has already shown great potential in Alzheimer's disease research. Its main areas of application include risk assessment, early diagnosis, and a deeper understanding of the genetics of Alzheimer's disease. By analyzing individual genome data, researchers can calculate the individual's likelihood of developing the disease, This enables more accurate personalized risk assessment. This is important for identifying high-risk populations, optimizing resource allocation, and developing personalized prevention strategies. For example, some researchers have successfully identified high-risk groups for Alzheimer's disease by constructing Polygenic Risk Scores (PRS) (Clark et al., 2022), providing strong support for personalized health management. By analyzing genome data, researchers can identify genetic markers associated with early lesions, providing the opportunity to identify patients before symptoms appear. This is crucial for early intervention, delaying disease progression, and improving treatment outcomes. Some studies have shown that combining genome prediction models with clinical symptoms can more accurately predict individuals' risk of developing the disease (Oriol et al., 2019), providing a new direction for early intervention and treatment.
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