Bibliographic record
Abstract
PURPOSE OF REVIEW: Expression quantitative trait loci (eQTL) mapping studies are the next most important step in genomics to identify susceptibility genes and molecular pathways involved in human diseases following the completion of genome-wide association studies (GWAS). This article reviews the emerging concepts in genetics of gene expression and the empirical value of eQTL mapping to refine GWAS asthma susceptibility loci. RECENT FINDINGS: eQTL mapping studies were paramount to reveal the cis and trans control of gene expression, the cell type and tissue specificity of eQTLs, and the pleiotropic nature of eQTL single nucleotide polymorphisms. A small number of eQTL studies were recently performed in tissues and cell types that are relevant for asthma and are used to interpret the biology underpinning GWAS loci including the most robust asthma susceptibility locus on 17q21. SUMMARY: The full potential of eQTL mapping studies is just starting to be revealed. Imminent progress is expected owing to the accelerating advances in sequencing technologies to map genetic variants and transcriptomes as well as the development of bioinformatics and computational algorithms to exploit integrative genomic approaches. A short-term challenge in the field of asthma is the creation of well powered eQTL datasets testing gene expression and other molecular phenotypes in disease-relevant tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".