Meta-analysis of exome array data identifies six novel genetic loci for lung function
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> Over 90 regions of the genome have been associated with lung function to date, many of which have also been implicated in chronic obstructive pulmonary disease. </ns4:p> <ns4:p> <ns4:bold>Methods:</ns4:bold> We carried out meta-analyses of exome array data and three lung function measures: forced expiratory volume in one second (FEV <ns4:sub>1</ns4:sub> ), forced vital capacity (FVC) and the ratio of FEV <ns4:sub>1</ns4:sub> to FVC (FEV <ns4:sub>1</ns4:sub> /FVC). These analyses by the SpiroMeta and CHARGE consortia included 60,749 individuals of European ancestry from 23 studies, and 7,721 individuals of African Ancestry from 5 studies in the discovery stage, with follow-up in up to 111,556 independent individuals. </ns4:p> <ns4:p> <ns4:bold>Results:</ns4:bold> We identified significant (P<2·8x10 <ns4:sup>-7</ns4:sup> ) associations with six SNPs: a nonsynonymous variant in <ns4:italic>RPAP1</ns4:italic> , which is predicted to be damaging, three intronic SNPs ( <ns4:italic>SEC24C, CASC17</ns4:italic> and <ns4:italic>UQCC1</ns4:italic> ) and two intergenic SNPs near to <ns4:italic>LY86</ns4:italic> and <ns4:italic>FGF10.</ns4:italic> Expression quantitative trait loci analyses found evidence for regulation of gene expression at three signals and implicated several genes, including <ns4:italic>TYRO3</ns4:italic> and <ns4:italic>PLAU</ns4:italic> . </ns4:p> <ns4:p> <ns4:bold>Conclusions:</ns4:bold> Further interrogation of these loci could provide greater understanding of the determinants of lung function and pulmonary disease. </ns4:p>
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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 | 0.022 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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; both teacher heads agree on what is shown here.
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".