Computational Molecular Biology 2024, Vol.14, No.1, 1-8 http://bioscipublisher.com/index.php/cmb 1 Review and Progress Open Access Genomic Prediction and its Association with the Development of Dementia disease in the Elderly Xiaojun Li, Shuiji Zhang Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, Zhejiang, China Corresponding author: jessi.j.zhang@foxmail.com Computational Molecular Biology, 2024, Vol.14, No.1 doi: 10.5376/cmb.2024.14.0001 Received: 29 Dec., 2023 Accepted: 30 Dec., 2023 Published: 04 Jan., 2024 Copyright © 2024 Li and Zhang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Li X.J., and Zhang S.J., 2024, Genomic prediction and its association with the development of dementia disease in the elderly, Computational Molecular Biology, 14(1): 1-8 (doi: 10.5376/cmb.2024.14.0001) Abstract Dementia is a severe neurological disorder involving complex interactions between various genetic and environmental factors. This paper explores the association between genomic prediction and the development of dementia in the elderly. Through a systematic review of existing research, the study delves into genomics, the genetic basis of dementia, and the etiology related to the genome. The research further examines the methods and applications of genomic prediction, focusing on the use of polygenic risk scores and machine learning algorithms in dementia studies. Through case analyses of large-scale genomic studies, key genes associated with dementia, such as Alzheimer's disease, are revealed. Additionally, the paper thoroughly analyzes the major findings of existing research, emphasizing the filling of knowledge gaps and the provision of new insights. Finally, the paper discusses the challenges faced by genomic prediction, including methodological difficulties, challenges in data interpretation, ethical and privacy concerns, and more. Looking ahead to future research directions, the paper highlights the establishment of personalized genomic prediction models, the application of new technologies, and the potential value of genomic prediction in early diagnosis and prevention of dementia. Keywords Elderly dementia disease; Genomic prediction; Genetics; Polygenic risk scores; Machine learning algorithms Alzheimer's disease is a group of diseases mainly characterized by cognitive dysfunction, including Alzheimer's disease, vascular dementia, dancing disease, and frontotemporal dementia (Wu et al., 2021). According to statistics, Alzheimer's disease is an increasingly serious health problem among the elderly population worldwide, causing heavy burdens on patients and their families. With the trend of aging population, the incidence of Alzheimer's disease is on the rise, becoming an urgent problem to be solved in the medical field. The definition of Alzheimer's disease not only covers cognitive decline, but also includes the impact on individuals' daily living abilities. Epidemiological data of this condition show that its incidence is closely related to age and there are gender differences. According to the report of the World Health Organization, Alzheimer's disease has become a major health challenge for the elderly population worldwide (Fagundes et al., 2011). It is estimated that by 2050, the number of patients with Alzheimer's disease will exceed 200 million, posing a serious threat to the sustainability of the global health system (Nichols et al., 2022). Although in the past few decades, scientists have made significant progress in the etiology and pathophysiology of Alzheimer's disease (Simonetti et al., 2020), a radical cure has not yet been found. Therefore, more and more research is focused on understanding the genetic basis of Alzheimer's disease in order to intervene and treat it earlier. Due to the rapid development of genomics technology, genomic prediction has gradually become a popular direction in medical research. This method analyzes variations in an individual's genome to predict their risk of developing a specific disease. In the field of Alzheimer's disease, genomic prediction provides a new way to understand the role of genetic factors in the development of the disease (Oriol et al., 2019). Genomic prediction is a method based on genetic variation information to estimate individual susceptibility to a certain disease. Past studies have shown that Alzheimer's disease has a significant genetic predisposition, so using genomic prediction tools to explore its genetic basis has become the focus of scientists' attention.
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