Association of<i>MICA</i>and HLA‐B alleles with leprosy in two endemic populations in Brazil
Bibliographic record
Abstract
Abstract Leprosy is a prevalent disease in Brazil, which ranks as the country with the second highest number of cases in the world. The disease manifests in a spectrum of forms, and genetic differences in the host can help to elucidate the immunopathogenesis. For a better understanding ofMICAassociation with leprosy, we performed a case–control and a family‐based study in two endemic populations in Brazil.MICAandHLA‐Balleles were evaluated in 409 leprosy patients and in 419 healthy contacts by PCR‐SSOP‐Luminex‐based technology. In the familial study, analysis of 46 families was completed by direct sequencing of all exons and 3′/5′untranslated regions, using the Ilumina MiSeq platform. All data were collected between 2006 and 2009. Statistical analysis was performed using the Chi‐square or Fisher's exact test together with a multivariate analysis. Family‐based association was assessed by transmission disequilibrium test (TDT) software FBAT 2.0.4. We found associations between the haplotypeMICA*002‐HLA‐B*35with leprosy in both the per se and the multibacillary (MB) forms when compared to healthy contacts. TheMICAallele*008was associated with the clinical forms of paucibacillary (PB). Additionally,MICA*029was associated with the clinical forms of MB. The association ofMICA*029allele (MICA‐A4 variant) with the susceptibility to the MB form suggests this variant for the transmembrane domain of the MICA molecule may be a risk factor for leprosy. TwoMICAand nineHLA‐Bvariants were found associated with leprosy per se in the Colônia do Prata population. Linkage disequilibrium analysis revealed perfect linkage disequilibrium (LD) betweenHLA‐Bmarkers rs2596498 and rs2507992, and high LD (R2 = .92) between these and the marker rs2442718. This familial study demonstrates thatMICAassociation signals are not independent from those observed forHLA‐B. Our findings contribute the knowledge pool of the immunogenetics of Hansen's disease and reveals a new association of theMICA*029allele.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".