Decoding variants of uncertain significance in systemic autoinflammatory diseases

Area of Science:

• Immunology
• Genetics
• Computational Biology

Background:

• Genetic testing has transformed autoinflammatory disease diagnosis, identifying numerous genetic variants.
• A significant number of identified variants are classified as variants of uncertain significance (VUS), hindering diagnosis and treatment.
• This VUS challenge applies to both monogenic and genetically complex autoinflammatory disorders.

Purpose of the Study:

• To review the impact of genetic testing on autoinflammatory disease diagnosis.
• To discuss the challenges posed by variants of uncertain significance (VUS).
• To explore the role of computational tools and functional assays in variant classification and interpretation.

Main Methods:

• Review of advances in protein structure prediction, machine learning, and artificial intelligence for variant classification.
• Discussion of functional screening approaches, including multiplexed functional assays and deep mutational scanning.
• Analysis of large-scale variant datasets, focusing on genes like NLRP3, MEFV, and ADA2.

Main Results:

• Computational tools offer powerful frameworks for variant classification, but require validation through functional assays.
• Functional screening and large-scale analyses have assessed numerous variants, generating valuable datasets for improved interpretation.
• Significant progress has been made in predicting the effects of missense VUS.

Conclusions:

• Advances in computational and functional methods enhance the potential for accurate VUS effect prediction in autoinflammatory diseases.
• Challenges remain, particularly in understanding the impact of non-coding VUS.
• Improved variant interpretation through these methods will refine genetic diagnosis and therapeutic strategies.