Replication Test for Association of the IL-1 Receptor Antagonist Gene, IL1RN, with Attention-Deficit/Hyperactivity Disorder
Bibliographic record
Abstract
Attention-deficit/hyperactivity disorder (ADHD) has a strong genetic basis, and aberrant brain dopaminergic and noradrenergic activity is implicated in its etiology. Interleukin-1 (IL-1), its antagonist, IL-1Ra, and IL-1 receptors are all present in the brain, and IL-1 has been shown to influence both dopaminergic and noradrenergic function. Recently, Segman et al. [1] tested the IL-1Ra gene, IL1RN, as a candidate for involvement in ADHD. Using the transmission/disequilibrium test (TDT) to examine 77 nuclear ADHD families for the inheritance of alleles of an intronic 86-bp VNTR polymorphism, they found significant evidence for biased transmission of the 4-repeat allele (p=0.04) and non-transmission of the 2-repeat allele (p=0.03). Here, we sought to replicate this in an independent sample of families. In contrast to the previous findings, our analysis of 178 ADHD families showed no evidence for biased transmission of these alleles (p=0.81 and p=1.00, respectively). Our lack of evidence for association of this IL1RN polymorphism with ADHD, based on a much larger sample of families, suggests that the original finding may have been a spurious (i.e. false-positive) result. These findings highlight the need for further investigations of this marker, in additional independent ADHD samples, in the future.
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".