Harnessing New Genomics Technologies to Assess Environmental Risk Factors That May Cause Heritable Genetic Disease
Bibliographic record
Abstract
It is becoming increasingly apparent that heritable mutations play a major role in developmental disorders, yet we still know little about the role of environmental agents in the etiology of heritable mutations.This is because germ cell mutations are rare and difficult to detect.In this thesis, novel methods were developed to address data gaps in our understanding of mutation induction in sperm and offspring.To test the hypothesis that mutagens induce germline mutations that are inherited in the offspring, MutaMouse males were exposed to 100 mg/kg/day of benzo[a]pyrene (BaP), a known mutagen and common environmental pollutant, and mated to produce offspring.First, sperm from exposed males were analyzed for microsatellite mutations using a single-molecule amplification approach, and results confirmed that BaP induces microsatellite mutations.Second, the MutaMouse model, which uses a mutation reporting transgene, was adapted to facilitate mutation detection in sperm.Pairing this method with next-generation sequencing (NGS) revealed that BaP mutation spectrum in sperm differs from somatic tissues.Third, offspring of BaP-exposed males were screened for the induction of large copy number variations (CNVs).The quantity of CNVs in the BaP group was not significantly greater than controls; however, the types of CNVs were different, suggesting that BaP preferentially induces higher numbers of duplications.Fourth, genomes of the offspring were examined using NGS to detect all de novo mutations that were transmitted.Offspring of BaP-treated sires showed a 2-fold increase in genomewide mutations compared to controls.Induced mutations observed in BaP-treated mice were consistent with the expected BaP mutation spectrum.Lastly, using data from cigarette smoke exposure studies, a common route of BaP exposure, the global impact of iii smoking on genetic disease burden (1.4 million aneuploidies, 2-8 million mutations) was estimated conservatively at 86 billion dollars per generation for intellectual disease alone.Overall, this work furthers our understanding of heritable mutagenesis and allows for predictions to be made on the genetic disease burden of human exposures.Furthermore, these developments will be useful for future analyses of germ cell mutagens, and will be important for making recommendations on future assessments of mutagenic hazards to the germline.I am grateful for the 5 amazing years I have enjoyed as a PhD student at Health Canada and Carleton University.I would have never imagined how much my life would change when I headed to the Environmental Mutagenesis and Genomics Society meeting in Montreal in 2011.There, I spoke with Dr. Carole Yauk about a potential PhD and just over 5 years later here is my finished thesis!It has been a wonderful journey and I have many people I need to thank.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".