Whole genome sequencing in clinical practice.

Area of Science:

• Genomics
• Clinical Diagnostics
• Bioinformatics

Background:

• Whole genome sequencing (WGS) is increasingly adopted for molecular genetic diagnosis of rare diseases and identifying actionable cancer drivers.
• WGS surpasses other methods by capturing extensive genomic variation, reducing the need for sequential genetic testing.
• Despite similar lab requirements to conventional methods, WGS generates massive data, necessitating robust computational and storage infrastructure.

Purpose of the Study:

• To provide an overview of whole genome sequencing in clinical practice.
• To describe the technology and current applications of WGS.
• To highlight challenges in WGS data processing, interpretation, and clinical reporting.

Main Methods:

• Review of current literature and clinical practices regarding WGS.
• Analysis of WGS technology, data handling, and interpretation workflows.
• Examination of challenges in variant classification and clinical reporting.

Main Results:

• WGS provides a comprehensive view of genomic variation, aiding in diagnosing complex genetic disorders and pinpointing cancer-related mutations.
• Significant computational and storage resources are required for timely WGS data processing (approx. 5 million variants per analysis).
• Specialized staff and clinical collaboration are essential for interpreting WGS data and generating standard of care reports.

Conclusions:

• WGS is a powerful tool for clinical diagnostics, offering a near-complete genomic picture.
• Addressing challenges in data management, interpretation standardization, and clinical integration is crucial for WGS's broader adoption.
• Ongoing refinement of variant classification standards is necessary to fully realize the clinical utility of WGS.