Topic: Innovations in Genomic Testing for Chronic Disease

Genomic testing has revolutionized the diagnosis and management of chronic diseases by enabling earlier detection and personalized treatments. Advances from targeted gene panels to whole-genome sequencing have deepened our understanding of complex conditions like diabetes, cardiovascular, neurodegenerative, and autoimmune diseases, driving precision medicine tailored to individual genetics. However, challenges remain, including clinical variability, ethical concerns, unequal access to testing, and integrating genomics into practice. Emerging tools such as multi-omics, machine learning, and liquid biopsies are further transforming genomic testing, opening new avenues for early intervention and disease monitoring.

This Special Issue seeks to assemble cutting-edge research and comprehensive reviews focusing on the latest innovations in genomic testing for chronic diseases. We welcome contributions from the fields of medical genetics, genomics, molecular diagnostics, clinical research, bioinformatics, and health policy.

Topics of interest include, but are not limited to:

● Novel genomic and multi-omics technologies for chronic disease diagnosis;

● Integration of genetic testing into clinical workflows and personalized treatment plans;

● Advances in liquid biopsy and non-invasive genomic testing;

● Machine learning and AI applications in genetic data interpretation;

● Ethical, legal, and social implications (ELSI) of genomic testing in chronic diseases;

● Assessment and clinical applications of polygenic risk scores for chronic diseases.

We invite original research articles, reviews, and expert perspectives that highlight both current advancements and future directions in this rapidly evolving domain. Through this Special Issue, we aim to promote interdisciplinary collaboration and accelerate the translation of genomic testing innovations into improved care for patients living with chronic diseases.

Genomic testing, chronic disease, genomic medicine, precision health, molecular diagnostics, multi-omics, machine learning, ethical implications, personalized medicine, disease monitoring