Integrating Whole Genome and Transcriptome Sequencing to Characterize the Genetic Architecture of Isoform Variation and its Implications for Health and Disease
Bibliographic record
Abstract
ABSTRACT We created a comprehensive whole blood splice variation quantitative trait locus (sQTL) resource by analyzing isoform expression ratio (isoform-to-gene) in Framingham Heart Study (FHS) participants (discovery: n=2,622; validation: n=1,094) with whole genome (WGS) and transcriptome sequencing (RNA-seq) data. External replication was conducted using WGS and RNA-seq from the Jackson Heart Study (JHS, n=1,020). We identified over 3.5 million cis -sQTL-isoform pairs ( p <5e-8), comprising 1,176,624 cis -sQTL variants and 10,883 isoform transcripts from 4,971 sGenes, with significant change in isoform-to-gene ratio due to allelic variation. We validated 61% of these pairs in the FHS validation sample ( p <1e-4). External validation ( p <1e-4) in JHS for the top 10,000 and 100,000 most significant cis -sQTL-isoform pairs was 88% and 69%, respectively, while overall pairs validated at 23%. For 20% of cis -sQTLs in the FHS discovery sample, allelic variation did not significantly correlate with overall gene expression. sQTLs are enriched in splice donor and acceptor sites, as well as in GWAS SNPs, methylation QTLs, and protein QTLs. We detailed several sentinel cis -sQTLs influencing alternative splicing, with potential causal effects on cardiovascular disease risk. Notably, rs12898397 (T>C) affects splicing of ULK3 , lowering levels of the full-length transcript ENST00000440863.7 and increasing levels of the truncated transcript ENST00000569437.5, encoding proteins of different lengths. Mendelian randomization analysis demonstrated that a lower ratio of the full-length isoform is causally associated with lower diastolic blood pressure and reduced lymphocyte percentages. This sQTL resource provides valuable insights into how transcriptomic variation may influence health outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".