Bibliographic record
Abstract
Two major challenges facing the genetics of Autism Spectrum Disorders (ASD) are the large and growing number of candidate risk genes and gene variants of unknown functional significance. The goals of this dissertation were to combine emerging methods in CRISPR-Cas9 genome engineering with machine vision phenomics to gain insight into the functions of ASD risk genes and the functional impact of specific variants. I developed a pipeline to discover the functions of ASD risk genes by obtaining strains of the genetic model organism Caenorhabditis elegans with inactivating mutations in each gene and observed the phenotypic consequences using machine vision. I quantified 26 phenotypes spanning morphology, locomotion, tactile sensitivity, and learning in >27,000 animals representing 135 genotypes (98 strains with mutations in different genes and 37 strains with additional alleles of a subset of these genes), allowing us to identify disruptions in habituation (a neural circuit’s plastic ability to decrease responding to repeated sensory stimuli) as a common impairment. I then clustered genes by similarity in phenomic profiles and used epistasis analysis to discover parallel networks centered on CHD8•chd-7 and NLGN3•nlg-1 that underlie mechanosensory hyper-responsivity and impaired habituation learning. Next, I demonstrated how this database can facilitate experiments that determine the functional consequences of missense variants and whether phenotypic alterations are reversible. Further, I developed a broadly applicable CRSIRP-Cas9 genome editing strategy to replace C. elegans genes with human genes that allows for in vivo analysis of human genetic variation with unprecedented precision. Finally, I contributed to the development of a multi-model system pipeline for high-confidence assessment of missense variants in the ASD risk gene PTEN. This work charts the phenotypic landscape of ASD-associated genes, offers in vivo variant functional assays, and potential therapeutic targets 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 | 0.000 | 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) | 0.002 | 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".