Difficulties in diagnosing Marfan syndrome using current FBN1 databases
Kristian A Groth1,2, Mette Gaustadnes2, Kasper Thorsen2
1Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark.
Summary
Genetic variant databases often misinterpret Marfan syndrome (MFS) classifications. Reliable MFS diagnosis requires expert review of genetic data, not just database information, especially with increasing genetic testing.
Area of Science:
- Genetics
- Medical Diagnostics
- Bioinformatics
Background:
- Marfan syndrome (MFS) diagnosis relies heavily on FBN1 mutation testing.
- Advancements in genetic sequencing increase the volume of genetic variants requiring interpretation.
- Databases are crucial for evaluating disease-causing effects of genetic variants.
Purpose of the Study:
- To evaluate genetic variants in four major databases against MFS diagnostic criteria.
- To compare database classifications with literature-based evidence for MFS-causing variants.
- To assess the reliability of current variant databases for MFS diagnosis.
Main Methods:
- Assessed 23 common variants from ESP6500, classified as MFS-causing in HGMD.
- Evaluated variant data in HGMD, UMD-FBN1, ClinVar, and UniProt.
- Compared database classifications with MFS diagnostic criteria and literature.
Main Results:
- None of the 23 assessed variants showed clear association with MFS.
- Discrepancies were found between database classifications and actual evidence for MFS causality.
- Existing database classifications for these variants were found to be inaccurate.
Conclusions:
- Current genetic variant databases are unreliable for diagnosing MFS due to misinterpretations.
- Accurate MFS diagnosis necessitates expert evaluation of variant data and literature.
- The increasing volume of genetic test results exacerbates the challenge of accurate variant interpretation.
