Understanding the population structure of North American patients with cystic fibrosis
Bibliographic record
Abstract
It is generally presumed that the cystic fibrosis (CF) population is relatively homogeneous, and predominantly of European origin. The complex ethnic make-up observed in the CF patients collected by the North American CF Modifier Gene Consortium has brought this assumption into question, and suggested the potential for population substructure in the three CF study samples collected from North America. It is well appreciated that population substructure can result in spurious genetic associations. To understand the ethnic composition of the North American CF population, and to assess the need for population structure adjustment in genetic association studies with North American CF patients, genome-wide single-nucleotide polymorphisms on 3076 unrelated North American CF patients were used to perform population structure analyses. We compared self-reported ethnicity to genotype-inferred ancestry, and also examined whether geographic distribution and cystic fibrosis transmembrane regulator (CFTR) mutation type could explain the population structure observed. Although largely Caucasian, our analyses identified a considerable number of CF patients with admixed African-Caucasian, Mexican-Caucasian and Indian-Caucasian ancestries. Population substructure was present and comparable across the three studies of the consortium. Neither geographic distribution nor CFTR mutation type explained the population structure. Given the ethnic diversity of the North American CF population, it is essential to carefully detect, estimate and adjust for population substructure to guard against potential spurious findings in CF genetic association studies. Other Mendelian diseases that are presumed to predominantly affect single ethnic groups may also benefit from careful analysis of population structure.
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 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 | 0.000 | 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 | 0.001 |
| 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".