Identification of deleterious and regulatory genomic variations in known asthma loci
Bibliographic record
Abstract
BACKGROUND: Candidate gene and genome-wide association studies have identified hundreds of asthma risk loci. The majority of associated variants, however, are not known to have any biological function and are believed to represent markers rather than true causative mutations. We hypothesized that many of these associated markers are in linkage disequilibrium (LD) with the elusive causative variants. METHODS: We compiled a comprehensive list of 449 asthma-associated variants previously reported in candidate gene and genome-wide association studies. Next, we identified all sequence variants located within the 305 unique genes using whole-genome sequencing data from the 1000 Genomes Project. Then, we calculated the LD between known asthma variants and the sequence variants within each gene. LD variants identified were then annotated to determine those that are potentially deleterious and/or functional (i.e. coding or regulatory effects on the encoded transcript or protein). RESULTS: > 0.6) with known asthma variants. Annotations of these LD variants revealed that several have potentially deleterious effects including frameshift, alternate splice site, stop-lost, and missense. Moreover, 24 of the LD variants have been reported to regulate gene expression as expression quantitative trait loci (eQTLs). CONCLUSIONS: This study is proof of concept that many of the genetic loci previously associated with complex diseases such as asthma are not causative but represent markers of disease, which are in LD with the elusive causative variants. We hereby report a number of potentially deleterious and regulatory variants that are in LD with the reported asthma loci. These reported LD variants could account for the original association signals with asthma and represent the true causative mutations at these loci.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".