Hyper-IgM syndrome: genes and variants

Genes linked to Hyper-IgM syndrome

• CD40LG: CD40 ligand

  It is displayed by activated CD4 T cells and engages CD40 on B cells and antigen-presenting cells to drive class switching and effective adaptive immunity. Loss-of-function variants cause X-linked hyper-IgM syndrome, with impaired immunoglobulin class switching and opportunistic infections.

  19 disease-causing and 61 uncertain variants in CD40LG are linked to Hyper-IgM syndrome.

• AICDA: Single-stranded DNA cytosine deaminase

  It initiates somatic hypermutation and class-switch recombination in activated B cells by deaminating cytosines in immunoglobulin genes. Biallelic loss-of-function variants cause hyper-IgM syndrome type 2, with impaired antibody diversification and recurrent infections.

  16 disease-causing and 85 uncertain variants in AICDA are linked to Hyper-IgM syndrome.

• CD40: Tumor necrosis factor receptor superfamily member 5

  Its engagement on B cells and antigen-presenting cells promotes antibody class switching, germinal-center responses, and broader adaptive immune activation. Loss-of-function variants can cause hyper-IgM immunodeficiency, while excessive signaling contributes to autoimmunity and inflammation.

  2 disease-causing and 4 uncertain variants in CD40 are linked to Hyper-IgM syndrome.

• UNG: Uracil-DNA glycosylase

  It removes uracil from DNA and initiates base-excision repair, protecting the genome from cytosine deamination and misincorporated uracil. Biallelic loss-of-function variants can cause hyper-IgM syndrome through defective antibody class-switch recombination.

  1 disease-causing and 81 uncertain variants in UNG are linked to Hyper-IgM syndrome.

Where Hyper-IgM syndrome variants cluster

• CD40LG THD (positions 122–261): 17 of 19 disease-causing changes, 1.7× more than its size predicts.
• AICDA Required for interaction with RNF126 (positions 88–116): 5 of 16 disease-causing changes, 2.1× more than its size predicts.

Known disease-causing variants in Hyper-IgM syndrome

Uncertain variants in Hyper-IgM syndrome that look disease-causing

Which prediction tools work for Hyper-IgM syndrome

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

• PolyPhen-2: 98 out of 100 (learned from overlapping clinical labels, so this is optimistic)
• CADD: 98 out of 100
• CATVariant: 97 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)
• phyloP: 89 out of 100
• SIFT: 88 out of 100
• MetaLR: 79 out of 100 (learned from overlapping clinical labels, so this is optimistic)

Diseases related to Hyper-IgM syndrome

• Inflammatory bowel disease, also linked to CD40

Frequently asked questions

Which genes are linked to Hyper-IgM syndrome?

In CATVariant, Hyper-IgM syndrome is linked to 4 analyzed proteins: CD40LG (CD40 ligand), AICDA (Single-stranded DNA cytosine deaminase), CD40 (Tumor necrosis factor receptor superfamily member 5) and UNG (Uracil-DNA glycosylase).

How many genetic variants are linked to Hyper-IgM syndrome?

343 variants: 38 are classified as disease-causing (pathogenic or likely pathogenic) in ClinVar and 231 are of uncertain significance or have conflicting reports.

Which uncertain variants in Hyper-IgM syndrome look disease-causing?

3 uncertain variants reach the likely-pathogenic range of the ACMG/AMP points scale on computable evidence, for example CD40LG F229S, CD40LG Y172D and CD40LG A123V. These are leads for expert review, not diagnoses.

Which variant effect predictor works best for Hyper-IgM syndrome?

Among tools not trained on clinical labels, CADD separates this disease's known disease-causing variants from harmless ones best (AUROC 0.98, based on 15 disease-causing and 20 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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