Integrated rare variant-based risk gene prioritization in disease case-control sequencing studies
Bibliographic record
Abstract
Rare variants of major effect play an important role in human complex diseases and can be discovered by sequencing-based genome-wide association studies.Here, we introduce an integrated approach that combines the rare variant association test with gene network and phenotype information to identify risk genes implicated by rare variants for human complex diseases.Our data integration method follows a 'discovery-driven' strategy without relying on prior knowledge about the disease and thus maintains the unbiased character of genome-wide association studies.Simulations reveal that our method can outperform a widely-used rare variant association test method by 2 to 3 times.In a case study of a small disease cohort, we uncovered putative risk genes and the corresponding rare variants that may act as genetic modifiers of congenital heart disease in 22q11.2deletion syndrome patients.These variants were missed by a conventional approach that relied on the rare variant association test alone. Author summaryCase-control sequencing studies are a promising design to uncover risk genes of human complex diseases implicated by rare variants.The recent development of different types of rare variant association tests has improved the statistical power to identify disease genes that harbor risk rare variants.However, none of the recent sequencing-based genomewide association studies identified robust disease association of rare variants or genes based on them.Due to limited sample sizes that can be feasibly achieved in real applications, current rare variant association tests can only generate marginal association signals for most risk genes.Here we proposed an integrated method that combined association signals with orthogonal biological evidence to uncover risk genes in sequencing studies.Designed to address the lack-of-power issue, our method was shown to effectively uncover risk genes with marginal association signals in data simulation.Indeed, in a real application demonstrated in our case study our method disclosed important risk genes of congenital heart disease in 22q11.2deletion syndrome that were missed by the previous study.
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".