find-tfbs: a tool to identify functional non-coding variants associated with complex human traits using open chromatin maps and phased whole-genome sequences
Bibliographic record
Abstract
Abstract Motivation Whole-genome DNA sequencing (WGS) enables the discovery of non-coding variants, but tools are lacking to prioritize the subset that functionally impacts human phenotypes. DNA sequence variants that disrupt or create transcription factor binding sites (TFBS) can modulate gene expression. find-tfbs efficiently scans phased WGS in large cohorts to identify and count TFBSs in regulatory sequences. This information can then be used in association testing to find putatively functional non-coding variants associated with complex human diseases or traits. Results We applied find-tfbs to discover functional non-coding variants associated with hematological traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) WGS dataset (N max =44,709). We identified >2000 associations at P <1×10 −9 , implicating specific blood cell-types, transcription factors and causal genes. The vast majority of these associations are captured by variants identified in large genome-wide association studies (GWAS) for blood-cell traits. find-tfbs is computationally efficient and robust, allowing for the rapid identification of non-coding variants associated with multiple human phenotypes in very large sample size. Availability https://github.com/Helkafen/find-tfbs and https://github.com/Helkafen/find-tfbs-demo Contacts sebastian.meric.de.bellefon@umontreal.ca and guillaume.lettre@umontreal.ca Supplementary information Supplementary data are available.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.023 |
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".