Genome-wide genetic ancestry measurements to predict lung function in European populations
Bibliographic record
Abstract
To the Editor: A number of models have been proposed to predict spirometric lung function using age, sex, height and self-reported ethnicity [1, 2]. These models are particularly important to derive per cent predicted lung function and establish the severity of lung diseases such as chronic obstructive pulmonary disease (COPD) [3]. Compared to self-reported race and/or ethnicity, genetic data can provide more accurate and objective measurements of ancestry and has the potential to alleviate some of the problems related to the lack of consensus on the definition of race and ethnicity worldwide [4]. A recent report suggested that genetically determined ancestry improves predicted lung-function measurements in African Americans [5]. Here, we test whether genetic ancestry derived from genome-wide genotyping data is useful to predict lung function in a diverse European population. The population used in this study is part of a lung expression quantitative trait loci (eQTL) mapping study [6, 7]. Briefly, research participants were recruited from three academic sites: Laval University (Quebec, QC, Canada), University of British Columbia (Vancouver, BC, Canada), and Groningen University (Groningen, the Netherlands), henceforth referred to as Laval, UBC, and Groningen, respectively. All subjects were genotyped for ∼1.2 million single nucleotide polymorphisms (SNPs) using the Illumina Human1M-Duo BeadChip (Illumina, Inc., San Diego, CA, USA). Subjects from Laval and UBC provided written informed consent and the study was approved by the ethics committees of the respective study sites. At Groningen, the study protocol was consistent with the research code of the University Medical Center Groningen and Dutch national ethical and professional guidelines (“Code of conduct; Dutch federation of biomedical scientific societies”; http://www.federa.org). Prior to the analysis, standard genotyping quality controls and exclusion of patients with disorders that affected lung function (other than COPD) were …
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".