MaveMD: A functional data resource for genomic medicine.

Area of Science:

• Genomic Medicine
• Clinical Genetics
• Bioinformatics

Background:

• Variant interpretation is a major challenge in clinical genetics, hindering precision medicine.
• Variants of Uncertain Significance (VUS) lack clear disease association, preventing clinical use.
• Barriers to using functional assay data include fragmentation, lack of standards, and calibration needs.

Purpose of the Study:

• To present MaveMD, a novel interface for the MaveDB database.
• To address challenges in clinical variant classification by integrating MAVE data.
• To facilitate the use of functional assay evidence in clinical decision-making.

Main Methods:

• Developed MaveMD, integrating MaveDB with ClinVar and ClinGen Allele Registry.
• Implemented automatic mapping of MAVE data to the human reference genome.
• Defined a new metadata model and curated 438,318 variant effect measurements from 74 MAVE datasets.

Main Results:

• MaveMD provides intuitive visualizations and structured evidence for variant classification.
• The interface simplifies the clinical translation of new MAVE data.
• The curated dataset spans 32 disease-associated genes, enhancing clinical utility.

Conclusions:

• MaveMD makes MAVE data accessible and usable for clinical variant classification.
• The tool supports effective clinical decision-making by providing calibrated functional evidence.
• MaveMD is designed to scale with future data generation, empowering clinical practice.