Exploring the common genetic architecture of autism spectrum disorder using a novel multi-polygenic risk score approach
Bibliographic record
Abstract
ABSTRACT Compared to disorders of similar heritability and contribution of common variants, few genome-wide significant loci have been implicated in autism spectrum disorder (ASD). This undermines the use of polygenic risk scores (PRSs) to investigate the common genetic architecture of ASD. Deconstructing PRS-ASD into its related traits via “developmental deconstruction” could reveal the underlying genetic liabilities of ASD. Using the data of >24k individuals with ASD and >28k of their unaffected family members from the SSC, SPARK, and MSSNG cohorts, we computed the PRSs for ASD and 11 genetically-related traits. We applied an unsupervised learning approach to the ASD-related PRSs to derive “multi-PRSs” that captured their variability in orthogonal dimensions. We found that multi-PRSs captured a similar proportion of genetic risk for ASD in cases versus intrafamilial controls (OR multi-PRS =1.10, R 2 =0.501%), compared to PRS-ASD itself (OR PRS-ASD =1.16, R 2 =0.619%). While multi-PRS dimensions conferred risk for ASD, they had “mirroring” effects on developmental phenotypes among cases with ASD. We posit that this phenomenon may partially account for the paucity of genome-wide significant loci and the clinical heterogeneity of ASD. This approach can serve as a proxy for PRS-ASD in cases where non-overlapping and well-powered GWAS summary statistics are difficult to obtain, or accounting for heterogeneity in a single dimension is preferable. This approach may also capture the overall liability for a condition ( i.e .: genetic “P-factor”). Altogether, we present a novel approach to studying the role of inherited, additive, and non-specific genetic risk factors in ASD.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".