Autism spectrum disorder trios from consanguineous populations are enriched for rare biallelic variants, identifying 32 new candidate genes
Bibliographic record
Abstract
Abstract Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects about 1 in 36 children in the United States, imposing enormous economic and socioemotional burden on families and communities. Genetic studies of ASD have identified de novo copy number variants (CNVs) and point mutations that contribute significantly to the genetic architecture, but the majority of these studies were conducted in populations unsuited for detecting autosomal recessive (AR) inheritance. However, several ASD studies in consanguineous populations point towards AR as an under-appreciated source of ASD variants. Methods We used whole exome sequencing to look for rare variants for ASD in 115 proband-mother-father trios from populations with high rates of consanguinity, namely Pakistan, Iran, and Saudi Arabia. Consanguinity was assessed through microarray genotyping. Results We report 84 candidate disease-predisposing single nucleotide variants and indels, with 58% biallelic, 25% autosomal dominant/ de novo , and the rest X-linked, in 39 trios. 52% of the variants were loss of function (LoF) or putative LoF (pLoF), and 47% nonsynonymous. We found an enrichment of biallelic variants, both in sixteen genes previously reported for AR ASD and/or intellectual disability (ID) and 32 previously unreported AR candidate genes (including DAGLA , ENPP6 , FAXDC2 , ILDR2 , KSR2 , PKD1L1 , SCN10A , SHH , and SLC36A1 ). We also identified eight candidate biallelic exonic loss CNVs. Conclusions The significant enrichment for biallelic variants among individuals with high F roh coefficients, compared with low F roh , either in known or candidate AR genes, confirms that genetic architecture for ASD among consanguineous populations is different to non-consanguineous populations. Assessment of consanguinity may assist in the genetic diagnostic process for 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".