Classical Human Leukocyte Antigen Alleles and C4 Haplotypes Are Not Significantly Associated With Depression
Bibliographic record
Abstract
BACKGROUND: The prevalence of depression is higher in individuals with autoimmune diseases, but the mechanisms underlying the observed comorbidities are unknown. Shared genetic etiology is a plausible explanation for the\noverlap, and in this study we tested whether genetic variation in the major histocompatibility complex (MHC), which is\nassociated with risk for autoimmune diseases, is also associated with risk for depression.\nMETHODS: We fine-mapped the classical MHC (chr6: 29.6–33.1 Mb), imputing 216 human leukocyte antigen (HLA)\nalleles and 4 complement component 4 (C4) haplotypes in studies from the Psychiatric Genomics Consortium Major\nDepressive Disorder Working Group and the UK Biobank. The total sample size was 45,149 depression cases and\n86,698 controls. We tested for association between depression status and imputed MHC variants, applying both a\nregion-wide significance threshold (3.9 3 1026\n) and a candidate threshold (1.6 3 1024\n).\nRESULTS: No HLA alleles or C4 haplotypes were associated with depression at the region-wide threshold. HLAB*08:01 was associated with modest protection for depression at the candidate threshold for testing in HLA\ngenes in the meta-analysis (odds ratio = 0.98, 95% confidence interval = 0.97–0.99).\nCONCLUSIONS: We found no evidence that an increased risk for depression was conferred by HLA alleles, which\nplay a major role in the genetic susceptibility to autoimmune diseases, or C4 haplotypes, which are strongly associated with schizophrenia. These results suggest that any HLA or C4 variants associated with depression either are\nrare or have very modest effect sizes.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".