FounderRare: A Novel Statistical Package to Identify Rare Variants in Complex Diseases
Bibliographic record
Abstract
Abstract FounderRare is a statistical package designed for the identification of rare variants in complex diseases. It leverages population genealogies, including those shaped by founder effects and identity-by-descent (IBD) segments, distinguishing it from other tools. This paper serves as a theoretical guide to the FounderRare package, illustrating its operations and capabilities. The package implements an IBD-based approach that computes the number of copies, among affected individuals, of the shared haplotype within genomic regions. The genome is partitioned into regions, within which clusters of affected individuals sharing specific IBD segments are identified. Statistical tests, denoted as S msg and S all , are included to evaluate the enrichment of IBD sharing among affected individuals. These tests rely on simulations of the null distribution and are designed to identify causal regions in the absence of control samples. FounderRare is optimized for cohorts comprising several thousand individuals—sample sizes typically required to achieve sufficient statistical power in rare variant analyses. By utilizing genotype array data, this tool enables cost-effective analysis at scale for researchers investigating complex diseases. It aids in pinpointing genomic regions likely to harbor rare variants, thereby contributing to a deeper understanding of the underlying genetic structure. FounderRare, R package, rare variants, complex diseases, population genealogy, identical-by-descent (IBD) segments
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.017 |
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".