Genetic testing of Behçet’s disease using next-generation sequencing to identify monogenic mimics and HLA-B*51
Bibliographic record
Abstract
OBJECTIVE: Several monogenic autoinflammatory disorders and primary immunodeficiencies can present early in life with features that may be mistaken for Behçet's disease (BD). We aimed to develop a genetic analysis workflow to identify rare monogenic BD-like diseases and establish the contribution of HLA haplotype in a cohort of patients from the UK. METHODS: Patients with clinically suspected BD were recruited from four BD specialist care centres in the UK. All participants underwent whole-exome sequencing (WES), and genetic analysis thereafter by (i) examining genes known to cause monogenic immunodeficiency, autoinflammation or vasculitis by virtual panel application; (ii) scrutiny of variants prioritized by Exomiser using Human Phenotype Ontology (HPO); (iii) identification of copy number variants using ExomeDepth; and (iv) HLA-typing using OptiType. RESULTS: Thirty-one patients were recruited: median age 15 (4-52), and median disease onset age 5 (0-20). Nine/31 (29%) patients had monogenic disease mimicking BD: five cases of Haploinsufficiency of A20 with novel TNFAIP3 variants (p.T76I, p. M112Tfs*8, p. S548Dfs*128, p. C657Vfs*14, p. E661Nfs*36); one case of ISG15 deficiency with a novel nonsense variant (ISG15: p.Q16X) and 1p36.33 microdeletion; one case of common variable immune deficiency (TNFRSF13B: p.A181E); and two cases of TNF receptor-associated periodic syndrome (TNFRSF1A: p.R92Q). Of the remaining 22 patients, eight (36%) were HLA-B*51 positive. CONCLUSION: We describe a novel genetic workflow for BD, which can efficiently detect known and potentially novel monogenic forms of BD, whilst additionally providing HLA-typing. Our results highlight the importance of genetic testing before BD diagnosis, as this has an impact on choice of therapy, prognosis and genetic counselling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".