Whole Genome Sequencing Guided Health Monitoring in Clinical Practice and Preventive Care

Abstract

This article examines how Whole Genome Sequencing (WGS) can be used to improve health monitoring and preventive care in clinical practice. It is meant to determine the role of genomic information in clinical practice in enhancing the detection of diseases at early stages, personalized therapy, and prevention measures. The study design was cross-sectional, where patients with chronic conditions or risk of genetic susceptibility were chosen, as well as healthy persons who acted as the baseline. The next-generation sequencing (NGS) was used as a tool for gathering genomic data, and the clinical data were made up of both medical history, lifestyle factors, and past diagnostic outcomes. The bioinformatics tools that have been used in the analysis include GATK and BCFtools to analyze the variants and combined machine learning models to correlate genetic markers with disease susceptibility. It was also found that WGS contributed to making the identification of genetic predispositions to several conditions, such as cardiovascular diseases, cancer, and neurological disorders, significantly better, to provide an opportunity to act early and tailor healthcare plans to the patient. WGS also helped to provide proactive health care by detecting hereditary indicators that had never been detected before using conventional methods. There was also the encouragement of the correct diagnosis and customized therapy in the application of WGS in clinical practice, which enhanced patient outcomes. Compared to the conventional symptom-based monitoring, the method of WGS is multidimensional and individualized and shifts healthcare towards being proactive instead of reactive. The findings highlight the possibility of using genomic data to narrow clinical practice gaps so that more effective preventive care and episodic treatment can be provided to patients, ultimately improving the overall health of patients and decreasing healthcare expenses. Future studies must aim at addressing the obstacles that include the high cost of sequencing and enhancing the integration of genomic data into routine clinical practices.