FGFR2-related craniosynostosis: genes and variants

Genes linked to FGFR2-related craniosynostosis

• FGFR2: Fibroblast growth factor receptor 2

  Its fibroblast-growth-factor signaling regulates proliferation, differentiation, and developmental patterning across multiple tissues. Germline activating variants cause several craniosynostosis syndromes, while somatic mutations, amplification, or fusions can drive cancer.

  54 disease-causing and 179 uncertain variants in FGFR2 are linked to FGFR2-related craniosynostosis.

Where FGFR2-related craniosynostosis variants cluster

• FGFR2 Ig-like C2-type 3 (positions 256–358): 34 of 54 disease-causing changes, 5.0× more than its size predicts.

Known disease-causing variants in FGFR2-related craniosynostosis

Which prediction tools work for FGFR2-related craniosynostosis

How often each tool ranks a disease-causing variant above a harmless one (AUROC × 100).

• CATVariant: 94 out of 100 (learned from overlapping clinical labels, so this is optimistic)
• REVEL: 93 out of 100 (learned from overlapping clinical labels, so this is optimistic)
• SIFT: 91 out of 100
• PolyPhen-2: 91 out of 100 (learned from overlapping clinical labels, so this is optimistic)
• CADD: 85 out of 100
• phyloP: 84 out of 100

Same protein, different disease

• Crouzon syndrome is also caused by FGFR2 variants; they fall in the same places as the FGFR2-related craniosynostosis variants (22 disease-causing).
• Acrocephalosyndactyly type I is also caused by FGFR2 variants; they fall in the same places as the FGFR2-related craniosynostosis variants (6 disease-causing).
• Antley-Bixler syndrome without genital anomalies or disordered steroidogenesis is also caused by FGFR2 variants; they fall in the same places as the FGFR2-related craniosynostosis variants (5 disease-causing).
• Levy-Hollister syndrome is also caused by FGFR2 variants; they fall mostly in different places as the FGFR2-related craniosynostosis variants (5 disease-causing).
• Beare-Stevenson cutis gyrata syndrome is also caused by FGFR2 variants; they fall mostly in different places as the FGFR2-related craniosynostosis variants (3 disease-causing).

Diseases related to FGFR2-related craniosynostosis

• Pfeiffer syndrome, also linked to FGFR2
• Crouzon syndrome, also linked to FGFR2
• Colorectal cancer, also linked to FGFR2
• Gastric cancer, also linked to FGFR2
• Bilateral sensorineural hearing impairment, also linked to FGFR2
• Jackson-Weiss syndrome, also linked to FGFR2
• Antley-Bixler syndrome without genital anomalies or disordered steroidogenesis, also linked to FGFR2
• Common craniosynostosis syndromes, also linked to FGFR2
• Acrocephalosyndactyly type I, also linked to FGFR2
• Levy-Hollister syndrome, also linked to FGFR2
• Saethre-Chotzen syndrome, also linked to FGFR2
• Beare-Stevenson cutis gyrata syndrome, also linked to FGFR2

Frequently asked questions

Which genes are linked to FGFR2-related craniosynostosis?

In CATVariant, FGFR2-related craniosynostosis is linked to 1 analyzed protein: FGFR2 (Fibroblast growth factor receptor 2).

How many genetic variants are linked to FGFR2-related craniosynostosis?

242 variants: 54 are classified as disease-causing (pathogenic or likely pathogenic) in ClinVar and 179 are of uncertain significance or have conflicting reports.

Which uncertain variants in FGFR2-related craniosynostosis look disease-causing?

None of the uncertain variants currently reaches the likely-pathogenic range on computable evidence alone.

Which variant effect predictor works best for FGFR2-related craniosynostosis?

Among tools not trained on clinical labels, SIFT separates this disease's known disease-causing variants from harmless ones best (AUROC 0.91, based on 41 disease-causing and 12 harmless variants).

About this data

Variant–disease links come from ClinVar, Open Targets and UniProt, pooled from the latest public CATVariant analysis of each human protein. Evidence scores use the ACMG/AMP Bayesian points scale with computable criteria only (position among known disease variants, rarity in gnomAD, calibrated predictors, deep mutational scanning); there is no family or patient data, so they prioritise variants for expert review and never classify them.

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