HLA GENOTYPES IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS IN RUSSIAN FEDERATION
Bibliographic record
Abstract
PV111 / #471 Poster Topic: AS12 - Genetics, Epigenetics, Transcriptomics Background/Purpose Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by multiorgan damage mediated by immune complexes and the autoantibodies production. Human leukocyte antigen (HLA) gene polymorphisms play an important role in the pathogenesis of SLE, however, the observed susceptibility alleles vary across ethnic groups and geographic regions.[1] The current study aims to describe the spectrum of HLA class I and HLA class II alleles in Russian patients with SLE. Methods The study was approved by the local ethics committee and included 130 patients (110 women/20 men), average age was 34.0 [26.0; 42.0] years and 235 healthy controls. All enrolled patients were diagnosed with SLE according to the 2012 SLICC classification criteria. All patients signed informed consents to be included in the study. The duration of the disease was 7.0 [4.0; 13.0] years. Eighteen (14%) patients had secondary APS. SLEDAI-2K was 6.0 [4.0; 10.0]. Clinical and laboratory characteristics are presented in Table 1. HLA-typing of HLA-A, B, C, DRB1 and DQB1 alleles from whole genome sequencing data was conducted using the HLA-HD tool with a reference panel from the IPD-IMGT/HLA database.[2] All statistical analyses were performed using Python module statsmodels. Chi-square tests were performed to evaluate the differences in HLA allele frequencies between SLE patients and healthy controls. Alpha level was set at 0.05; p -values were corrected for multiple comparisons using Benjamini-Hochberg procedure. Table 1. Clinical and laboratory characteristics of the SLE patients. Results A total of 37 HLA-A, 58 HLA-B, 37 HLA-C, 34 HLA-DRB1 and 19 HLA-DQB1 4-digit allelic groups were detected in the patients with SLE. We found 2 alleles associated with increased risk for developing SLE in the Russian population: 1) HLA-DRB1*03:01 (OR = 2.31, 95% CI = 1.47-3.62, p-value = 0.03) 2) HLA-DQB1*02:02 (OR = 15.8, 95% CI = 4.72-53.1, p-value = 0.002) According to literary data HLA-DRB1:03:01allele is a major risk factor for SLE in Europeans, in addition, it was shown that the short epitope encoded by this allele activates SLE-characteristic cellular aberrations.[3] We also noted the overrepresentation of HLA-B*13:02, HLA-DRB1*15:01, HLA-DQB1*06:02 alleles in SLE patients (Figure 1). Figure 1. Cluster analysis of patients with SLE and healthy controls Conclusions Combinations of alleles identified as a result of cluster analysis were also considered. We observed that frequency of 5-loci haplotype HLA-A*01:01 ~ HLA-B*08:01~ HLA-C*07:01 ~ HLA-DQB1*02:01~ HLA-DRB1*03:01 was significantly increased in SLE patients when compared to controls. References: [1.] Lewis MJ. Rheumatology (Oxford) 2017;56(suppl_1):i67-77. [2.] Kawaguchi S. Hum Mutat 2017;38(7): 788-97. [3.] Miglioranza Scavuzzi B. Commun Biol 2022;5(1):751.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".