Genome sequencing data analysis for rare disease gene discovery.

Area of Science:

• Genomics
• Medical Genetics
• Bioinformatics

Background:

• Rare diseases affect 300 million globally, with most having genetic origins.
• Patients often face lengthy diagnostic odysseys due to diagnostic challenges.
• Genomic advances offer diagnostic potential but are limited by data analysis complexities.

Purpose of the Study:

• To review current methodological developments and tools for rare disease genetic variant discovery.
• To recommend data interpretation methods for effective variant prioritization.
• To aid in understanding the genetic basis of rare diseases.

Main Methods:

• Review of the latest methodological developments and tools for variant interpretation.
• Categorization of resources based on the variant interpretation workflow (processing, calling, annotation, filtration, prioritization).
• Focus on trio- or family-based genome data analysis for individual cases.

Main Results:

• A comprehensive overview of available tools and datasets for variant prioritization is presented.
• The review categorizes resources according to the variant interpretation workflow.
• Emphasis is placed on annotation and prioritization steps for variant discovery.

Conclusions:

• Effective rare disease diagnosis requires understanding diverse tools and datasets for variant prioritization.
• A combination of tools and iterative approaches is recommended for elucidating causative variants.
• This review provides a guide to navigate the complexities of rare disease genetic analysis.