Statistical Modelling of Population-Level Exonic Variant Frequency Data with an Emphasis on Rare Variants
Bibliographic record
Abstract
Introduction & Objective: Rare variants with allele frequency smaller than 1% are postulated to be associated with disease susceptibility. Since allele frequencies vary globally, the use of population control data that does not match the study population can produce bias. The research question is to identify factors that explain variation in allele frequency across populations. The secondary question is to evaluate the potential bias in using population as control data when studying variants. We use data from gnomAD (Genome Aggregation Database) to answer these questions. Methods: We apply each of three model formulations: Linear, Logistic, and Poisson to explain how the frequency or count of variants depends on population subgroup/ancestry, functional annotation, sex, and disease status. We also evaluate interactions between population subgroups and functional annotation. Results: For very rare variants (allele frequency < 0.1%), likelihood ratio testing (LRT) provides evidence that allele frequencies vary with functional annotation and population in all three model formulations. By LRT, interactions of population and functional annotation are significant in the Logistic model and the Poisson model. The goodness-of-fit statistics show a better fit in the linear model compared to low frequency variants. Conclusion: We observe that population & functional annotation affect variant frequencies, and conclude that detection of differences across populations and annotations is model scale-dependent, especially for different degrees of rareness. Therefore, statisticians need to carefully consider the potential for bias when using gnomAD as control data. Moreover, gnomAD is a great resource for studies dealing with rare variants.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".