VikNGS: A C++ Variant Integration Kit for Next Generation Sequencing association analysis
Bibliographic record
Abstract
Abstract Motivation Integration of next generation sequencing data (NGS) across different research studies can improve the power of genetic association testing by increasing sample size and can obviate the need for sequencing controls. Unfortunately, if differential genotype uncertainty across studies is not accounted for, combining data sets can also produce spurious association results. The robust variance score statistic (RVS) for genetic association of rare and common variants has been shown to effectively adjust for bias caused by the differences in read depth in case-control genetic association studies when the two groups were sequenced using different experimental designs. To enable consortium research, the aggregation of several data sets for genetic association analysis of quantitative and binary traits with covariate adjustment is required, and we developed the Variant Integration Kit for NGS (VikNGS) that expands the functionality of RVS (vRVS) for this purpose. Results VikNGS is a fast and computationally efficient cross-platform software package that provides an implementation for vRVS, as well as conventional rare and common variant genotype-based association analysis approaches. The package includes a graphical user interface that contains power simulation functionality and data visualization tools. Availability and Implementation The VikNGS package can be downloaded at http://www.tcag.ca/tools/index.html Documentation can be found at https://VikNGSdocs.readthedocs.io/en/latest/ Contact lisa.strug@sickkids.ca Supplementary information Supplementary data are available at Bioinformatics online.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.116 | 0.074 |
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".