Genome-wide Linkage Analyses of Quantitative and Categorical Autism Subphenotypes
Bibliographic record
Abstract
BACKGROUND: The search for susceptibility genes in autism and autism spectrum disorders (ASD) has been hindered by the possible small effects of individual genes and by genetic (locus) heterogeneity. To overcome these obstacles, one method is to use autism-related subphenotypes instead of the categorical diagnosis of autism since they may be more directly related to the underlying susceptibility loci. Another strategy is to analyze subsets of families that meet certain clinical criteria to reduce genetic heterogeneity. METHODS: In this study, using 976 multiplex families from the Autism Genome Project consortium, we performed genome-wide linkage analyses on two quantitative subphenotypes, the total scores of the reciprocal social interaction domain and the restricted, repetitive, and stereotyped patterns of behavior domain from the Autism Diagnostic Interview-Revised. We also selected subsets of ASD families based on four binary subphenotypes, delayed onset of first words, delayed onset of first phrases, verbal status, and IQ > or = 70. RESULTS: When the ASD families with IQ > or = 70 were used, a logarithm of odds (LOD) score of 4.01 was obtained on chromosome 15q13.3-q14, which was previously linked to schizophrenia. We also obtained a LOD score of 3.40 on chromosome 11p15.4-p15.3 using the ASD families with delayed onset of first phrases. No significant evidence for linkage was obtained for the two quantitative traits. CONCLUSIONS: This study demonstrates that selection of informative subphenotypes to define a homogeneous set of ASD families could be very important in detecting the susceptibility loci in autism.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".