Cystic fibrosis: genes and variants

Genes linked to Cystic fibrosis

• CFTR: Cystic fibrosis transmembrane conductance regulator

  An epithelial chloride channel and regulator of salt and water movement across cell surfaces. Its activity helps keep airway, intestinal, and other epithelial fluids balanced, while CFTR disruption causes cystic fibrosis and related disorders.

  155 disease-causing and 1,433 uncertain variants in CFTR are linked to Cystic fibrosis.

Weakly linked (only a few uncertain records): PIK3CG, HFE and TSC2.

Where Cystic fibrosis variants cluster

• CFTR Extracellular (positions 99–122): 13 of 155 disease-causing changes, 5.2× more than its size predicts.
• CFTR ABC transporter 1 (positions 423–646): 43 of 155 disease-causing changes, 1.8× more than its size predicts.
• CFTR Cytoplasmic (positions 1035–1095): 13 of 155 disease-causing changes, 2.0× more than its size predicts.
• CFTR Cytoplasmic (positions 147–195): 11 of 155 disease-causing changes, 2.1× more than its size predicts.
• CFTR Cytoplasmic (positions 1–77): 15 of 155 disease-causing changes, 1.9× more than its size predicts.

Known disease-causing variants in Cystic fibrosis

Showing 60 of 155.

Uncertain variants in Cystic fibrosis that look disease-causing

Which prediction tools work for Cystic fibrosis

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

• MutPred2: 97 out of 100 (learned from overlapping clinical labels, so this is optimistic)
• MetaLR: 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)
• EVE: 92 out of 100
• CATVariant: 90 out of 100 (learned from overlapping clinical labels, so this is optimistic)
• AlphaMissense: 87 out of 100
• PolyPhen-2: 83 out of 100 (learned from overlapping clinical labels, so this is optimistic)
• CADD: 83 out of 100
• SIFT: 80 out of 100
• phyloP: 75 out of 100

Same protein, different disease

• Bronchiectasis with or without elevated sweat chloride 1 is also caused by CFTR variants; they fall in the same places as the Cystic fibrosis variants (18 disease-causing).
• Congenital bilateral aplasia of vas deferens from CFTR mutation is also caused by CFTR variants; they fall in the same places as the Cystic fibrosis variants (10 disease-causing).
• Hereditary pancreatitis is also caused by CFTR variants; they fall in the same places as the Cystic fibrosis variants (7 disease-causing).

Diseases related to Cystic fibrosis

• Bronchiectasis with or without elevated sweat chloride 1, also linked to CFTR
• Congenital bilateral aplasia of vas deferens from CFTR mutation, also linked to CFTR
• Hereditary pancreatitis, also linked to CFTR
• Ivacaftor response - Efficacy, also linked to CFTR

Frequently asked questions

Which genes are linked to Cystic fibrosis?

In CATVariant, Cystic fibrosis is linked to 1 analyzed protein: CFTR (Cystic fibrosis transmembrane conductance regulator).

How many genetic variants are linked to Cystic fibrosis?

1,712 variants: 155 are classified as disease-causing (pathogenic or likely pathogenic) in ClinVar and 1,436 are of uncertain significance or have conflicting reports.

Which uncertain variants in Cystic fibrosis look disease-causing?

23 uncertain variants reach the likely-pathogenic range of the ACMG/AMP points scale on computable evidence, for example CFTR Q552K, CFTR D979A, CFTR Y161C, CFTR Y109N and CFTR P1021S. These are leads for expert review, not diagnoses.

Which variant effect predictor works best for Cystic fibrosis?

Among tools not trained on clinical labels, EVE separates this disease's known disease-causing variants from harmless ones best (AUROC 0.92, based on 70 disease-causing and 18 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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