A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research

Abstract

Improvements in next-generation sequencing (NGS) technology and computer systems have enabled personalized therapies based on genomic information. Recently, health management strategies using genomics and big data have been developed for application in medicine and public health science. In this review, I first discuss the development of a genomic information management system (GIMS) to maintain a highly detailed health record and detect diseases by collecting the genomic information of one individual over time. Maintaining a health record and detecting abnormal genomic states are important; thus, the development of a GIMS is necessary. Based on the current research status, open public data, and databases, I discuss the possibility of a GIMS for clinical use. I also discuss how the analysis of genomic information as big data can be applied for clinical and research purposes. Tremendous volumes of genomic information are being generated, and the development of methods for the collection, cleansing, storing, indexing, and serving must progress under legal regulation. Genetic information is a type of personal information and is covered under privacy protection; here, I examine the regulations on the use of genetic information in different countries. This review provides useful insights for scientists and clinicians who wish to use genomic information for healthy aging and personalized medicine.

Keywords: aging; genomic information; health management; healthy aging; personalized medicine.

Figure 1

Overview of the genomic-information-based health management system. (A) The five processes required for the utilization of clinical and genomic information. (B) Clinical and genomic information should provide evidence to enable diagnosis or prognosis and to predict healthy status in normal individuals. (C) Along with clinical and genomic information, big data from various sources can be used for clinical decision-making through appropriate storage and indexing. (D) The final goal is to suggest an appropriate strategy for health management in healthy conditions and to provide customized treatment in diseased conditions, based on all data obtained for each individual. AI, artificial intelligence; CDM, common data model; CDW, clinical data warehouse; DB, database; info, information; QC, quality control.

Figure 2

Changes in gene expression and splicing patterns following changes in DNA methylation by aging or disease progression explained via a car model. (A) When CpG sites located in the upstream region of the transcription start site are hypermethylated, gene expression decreases. When the CTCF binding site becomes hypermethylated, exon skipping occurs. In this epigenetic pattern change model, as gene hypermethylation increases, the number of transcripts and skipping phenomenon of exon 2b decrease. (B) Humans age and undergo changes in DNA methylation patterns over time. Factors that accelerate or reduce aging have been discovered. (C) A car model proposing the use of the genomic information management system presented in this review as an ideal system for delaying aging. (D) Maintaining detrimental lifestyle habits, irregular and inaccurate health screenings, and improper restrictions by the government on the use of genomic information can accelerate aging.

Figure 3

Integrated strategies of the clinical decision support system (CDSS). Clinical data consist of patient information, drug prescription, data from health check-ups, and medical images. These data should be cleaned and indexed in a format commonly used in the field. In addition, as external data, scientific papers and clinical guidelines can be deposited in storage or cloud computing systems using web crawler or text mining tools. Machine learning can be used as an algorithm for CDSS; the languages mainly used are Python and R. Machine learning aims to visualize this appropriately and provide patient-specific clinical insights to clinicians. Using the CDSS developed as the model, researchers can obtain clues to the development of a genomic information management system (GIMS).

Figure 4

Example of an ideal genomic information management system (GIMS). (A) Many public omics data are deposited in NCBI GEO, NCBI SRA, and TCGA. These can be used as reference data for analyzing omics data to be produced in the future and can help discover clinical insights or evidence. (B) When using omics data, a compromise must be found between the disclosure of information for public benefit and maintaining the privacy and security of patients and participants. (C) In the GIMS, individual genomic, clinical, laboratory data, and questionnaire-based diet and exercise information can be classified into risk, borderline, and healthy groups using machine learning. This can be generalized and used as a health management system and can facilitate the assessment of the need for treatment intervention, as well as aid the provision of lifestyle-related suggestions for maintaining a healthy state.