Combining human tissue and iPSC-derived cardiomyocyte eQTL datasets to understand noncoding genetic variants: boosting the cardiogenetics toolbox
Bibliographic record
Abstract
Advancements in next-generation sequencing and genome-wide association studies (GWAS) have revealed hundreds of loci associated with various cardiovascular diseases, highlighting the important role genetic variants play in disease pathogenesis and identifying potential therapeutics. Notably, most GWAS variants are located in noncoding genomic regions, which do not directly affect protein function. Instead, these variants are often found in genomic regions containing regulatory elements, such as promoters, enhancers, and silencers. Consequently, they regulate gene expression levels and the cell-type specificity of transcripts via modulation of transcription factor binding and chromatin accessibility [ 1 ]. Unlike variants in coding regions, where the pathogenic effect of the variant could be predicted by changes in amino acid sequence, understanding the impact of noncoding variants requires comprehensive transcriptomic and epigenomic investigations, rendering the process more challenging and costly. Additionally, the pathogenicity of noncoding variants is more difficult to interpret clinically due to our limited understanding and the scarcity of noncoding variant risk prediction tools.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".