Risk homozygous haplotype regions for autism identifies population-specific ten genes for numerous pathways
Bibliographic record
Abstract
Abstract Background Recessive homozygous haplotype (rHH) mapping is a reliable tool for identifying recessive genes by detecting homozygous segments of identical haplotype structures. These are shared at a higher frequency amongst probands compared to parental controls. Finding out such rHH blocks in autism subjects can help in deciphering the disorder etiology. Objectives The study aims to detect rHH segments of identical haplotype structure shared at a higher frequency in autism subjects than controls to identify recessive genes responsible for autism manifestation. Methods In the present study, 426 unrelated autism genotyped probands with 232 parents (116 trios) were obtained from Gene Expression Omnibus (GEO) Database. Homozygosity mapping analyses have been performed on the samples using standardized algorithms using the Affymetrix GeneChip® 500K SNP Nsp and Sty mapping arrays datasets. Results A total of 38 homozygous haplotype blocks were revealed across sample datasets. Upon downstream analysis, 10 autism genes were identified based on selected autism candidate genes criteria. Further, expressive Quantitative Trait Loci (QTL) analysis of SNPs revealed various binding sites for regulatory proteins BX3, FOS, BACH1, MYC, JUND, MAFK, POU2F2, RBBP5, RUNX3, and SMARCA4 impairing essential autism genes CEP290, KITLG, CHD8, and INS2. Pathways and processes such as adherens junction, dipeptidase activity, and platelet-derived growth factor—vital to autism manifestation were identified with varied protein-protein clustered interactions. Conclusion These findings bring various population clusters with significant rHH genes. It is suggestive of the existence of common but population-specific risk alleles in related autism subjects.
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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 | 0.001 |
| 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 | 0.000 |
| 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".