Genetic and Epigenetic Factors Influencing Population Variability of Allele-Specific Expression
Bibliographic record
Abstract
There is a longstanding investigation in human genetics surrounding how individuals with the same genotype display highly variable phenotypes. The impact of gene expression variability is critical for phenotypic variation. Allele-specific expression (ASE) is the preferential expression of one haplotype in an individual. While ASE is associated with several phenotypes and diseases, it remains unknown what genomic and epigenetic factors contribute to variability in ASE. This thesis explores factors contributing to ASE variation, characterized into three categories: population demographics, environment, and age. First, I revealed how population demographics, through effective population size, impact ASE at two levels: i) across populations based on evolutionary histories, and ii) across the genome through recombination and its interaction with natural selection. I demonstrated that recombination rate modulates ASE genome wide, and that loci in regions of high recombination are most efficient at adjusting ASE to protect from harmful mutations. I also demonstrated that genetic ancestry, based on population histories such as recent bottlenecks, contribute to the strength of ASE regulation. Next, I demonstrated that environmental modifiers contribute to ASE regulation, both on a geographic scale, in addition to extrinsic lifestyle factors. I determined differences between French-Canadian individuals based on their local environment, for those whose geographic location is different from their genetic ancestry. I explored medication usage as an extrinsic factor and demonstrated that certain medications are significantly associated with increases in overall levels of ASE. I also detected genes that have significantly different levels of ASE in medicated responders versus non-responders. Lastly, I demonstrated that ASE increases with age in cell types involved in adaptive immunity. I showed that healthy agers have larger increases in ASE as they age, specifically in genes involved in immune response. I also demonstrated that decreases in ASE are associated with cardiometabolic traits such as hypertension and type 2 diabetes. This thesis demonstrates genetic and epigenetic factors contributing to ASE variability, in healthy and disease contexts. My research uncovers scenarios where ASE can act as a protective mechanism, which may be playing a role in healthy aging, and opens the potential for utilizing ASE methodologies in precision medicine approaches for chronic diseases.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".