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Updated: Jun 24, 2026
A Strategy to Identify de Novo Mutations in Common Disorders such as Autism and Schizophrenia
Published on: June 15, 2011
Identification of the minimal combination of clinical features in probands for efficient mutation detection in the
Chantal Stheneur1, Gwenaëlle Collod-Béroud, Laurence Faivre
1AP-HP, Hôpital Ambroise Paré, Service de Pédiatrie, Boulogne, France. chantal.stheneur@apr.aphp.fr
Abstract:
Mutations identified in the fibrillin-1 (FBN1) gene have been associated with Marfan syndrome (MFS). Molecular analysis of the gene is classically performed in probands with MFS to offer diagnosis for at-risk relatives and in children highly suspected of MFS. However, FBN1 gene mutations are found in an ill-defined group of diseases termed 'type I fibrillinopathies', which are associated with an increased risk of aortic dilatation and dissection. Thus, there is growing awareness of the need to identify these non-MFS probands, for which FBN1 gene screening should be performed. To answer this need we compiled the molecular data obtained from the screening of the FBN1 gene in 586 probands, which had been addressed to our laboratory for molecular diagnosis. In this group, the efficacy of FBN1 gene screening was high in classical MFS probands (72.5%,), low (58%) in those referred for incomplete MFS and only slight (14.3%) for patients referred as possible MFS. Using recursive partitioning, we found that the best predictor of the identification of a mutation in the FBN1 gene was the presence of features in at least three organ systems, combining one major, and various minor criteria. We also show that our original recommendation of two systems involved with at least one with major criterion represents the minimal criteria because in probands not meeting these criteria, the yield of mutation identification drastically falls. This recommendation should help clinicians and biologists in identifying probands with a high probability of carrying a FBN1 gene mutation, and thus optimize biological resources.
Insights
Fibrillin-1 (FBN1) gene mutations are linked to Marfan syndrome (MFS) and other fibrillinopathies. Identifying key clinical features improves the diagnostic yield of FBN1 gene screening in at-risk individuals.
Area of Science:
- Genetics
- Cardiovascular Medicine
- Molecular Biology
Background:
- Mutations in the fibrillin-1 (FBN1) gene are the primary cause of Marfan syndrome (MFS).
- FBN1 gene mutations also underlie a spectrum of related disorders, termed type I fibrillinopathies, associated with significant cardiovascular risks like aortic dilatation and dissection.
- There is a clinical need to identify individuals with these non-MFS fibrillinopathies who may benefit from FBN1 gene screening.
Purpose of the Study:
- To evaluate the efficacy of FBN1 gene screening across different clinical presentations.
- To identify predictive criteria for optimizing FBN1 gene mutation detection in probands.
- To refine diagnostic guidelines for FBN1-related disorders.
Main Methods:
- Molecular data from 586 probands undergoing FBN1 gene screening were analyzed.
- Recursive partitioning was employed to identify predictors of mutation detection.
- Diagnostic yields were compared based on clinical referral categories (classical MFS, incomplete MFS, possible MFS).
Main Results:
- FBN1 gene screening yielded mutations in 72.5% of classical MFS cases, 58% of incomplete MFS, and 14.3% of possible MFS cases.
- The strongest predictor for identifying an FBN1 mutation was the presence of features across at least three organ systems, including one major criterion.
- Minimal criteria of two involved systems with at least one major criterion were confirmed as essential for a significantly reduced mutation detection rate.
Conclusions:
- Clinical features involving at least three organ systems, with one major criterion, best predict FBN1 gene mutations.
- The established minimal criteria (two systems, one major criterion) are crucial for efficient FBN1 gene screening.
- These findings aid clinicians in selecting appropriate candidates for FBN1 gene testing, optimizing resource allocation.
