Immunoseq: the identification of functionally relevant variants through targeted capture and sequencing of active regulatory regions in human immune cells
Bibliographic record
Abstract
BACKGROUND: The observation that the genetic variants identified in genome-wide association studies (GWAS) frequently lie in non-coding regions of the genome that contain cis-regulatory elements suggests that altered gene expression underlies the development of many complex traits. In order to efficiently make a comprehensive assessment of the impact of non-coding genetic variation in immune related diseases we emulated the whole-exome sequencing paradigm and developed a custom capture panel for the known DNase I hypersensitive site (DHS) in immune cells - "Immunoseq". RESULTS: We performed Immunoseq in 30 healthy individuals where we had existing transcriptome data from T cells. We identified a large number of novel non-coding variants in these samples. Relying on allele specific expression measurements, we also showed that our selected capture regions are enriched for functional variants that have an impact on differential allelic gene expression. The results from a replication set with 180 samples confirmed our observations. CONCLUSIONS: We show that Immunoseq is a powerful approach to detect novel rare variants in regulatory regions. We also demonstrate that these novel variants have a potential functional role in immune cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".