Allele Dispersion Score: Quantifying the range of allele frequencies across populations, based on UMAP
Bibliographic record
Abstract
Abstract Genomic variation plays a crucial role in biology, serving as a base for evolution - allowing for adaptation on a species or population level. At the individual level, however, specific alleles can be implicated in diseases. To interpret genetic variants identified in an individual potentially affected with a rare genetic disease, it is fundamental to know the population frequency of each allele, ideally in an ancestry matched cohort. Equity in human genomics remains a challenge for the field, and there are not yet cohorts representing most populations. Currently, when ancestry matched cohorts are not available, pooled variant libraries are used, such as gnomAD, the Human Genome Diversity Project (HGDP) or the 1,000 Genomes Project (now known as IGSR: International Genome Sample Resource). When working with a pooled collection of variant frequencies, one of the challenges is to determine efficiently if a variant is broadly spread across populations or appears selectively in one or more populations. While this can be accomplished by reviewing tables of population frequencies, it can be advantageous to have a single score that summarizes the observed dispersion. This score would not require classifying individuals into populations, which can be complicated if it is a homogenous population, or can leave individuals excluded from all the predefined population groups. Moreover, a score would not display fine-scaled population information, which could have privacy implications and consequently be inappropriate to release. Therefore, we sought to develop a scoring method based on a Uniform Manifold Approximation and Projection (UMAP) where, for each allele, the score can range from 0 (the variant is limited to a subset of close individuals within the whole cohort) to 1 (the variant is spread among the individuals represented in the cohort). We call this score the Allele Dispersion Score (ADS). The scoring system was implemented on the IGSR dataset, and compared to the current method consisting in displaying variant frequencies for several populations in a table. The ADS correlates with the population frequencies, without requiring grouping of individuals.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".