Associations of HLA alleles with specific language impairment
Bibliographic record
Abstract
BACKGROUND: Human leukocyte antigen (HLA) loci have been implicated in several neurodevelopmental disorders in which language is affected. However, to date, no studies have investigated the possible involvement of HLA loci in specific language impairment (SLI), a disorder that is defined primarily upon unexpected language impairment. We report association analyses of single-nucleotide polymorphisms (SNPs) and HLA types in a cohort of individuals affected by language impairment. METHODS: We perform quantitative association analyses of three linguistic measures and case-control association analyses using both SNP data and imputed HLA types. RESULTS: Quantitative association analyses of imputed HLA types suggested a role for the HLA-A locus in susceptibility to SLI. HLA-A A1 was associated with a measure of short-term memory (P = 0.004) and A3 with expressive language ability (P = 0.006). Parent-of-origin effects were found between HLA-B B8 and HLA-DQA1*0501 and receptive language. These alleles have a negative correlation with receptive language ability when inherited from the mother (P = 0.021, P = 0.034, respectively) but are positively correlated with the same trait when paternally inherited (P = 0.013, P = 0.029, respectively). Finally, case control analyses using imputed HLA types indicated that the DR10 allele of HLA-DRB1 was more frequent in individuals with SLI than population controls (P = 0.004, relative risk = 2.575), as has been reported for individuals with attention deficit hyperactivity disorder (ADHD). CONCLUSION: These preliminary data provide an intriguing link to those described by previous studies of other neurodevelopmental disorders and suggest a possible role for HLA loci in language disorders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".