A cystic fibrosis lung disease modifier locus harbors tandem repeats associated with gene expression
Bibliographic record
Abstract
Abstract Variable number of tandem repeats (VNTRs) are major source of genetic variation in human. However due to their repetitive nature and large size, it is challenging to genotype them by short-read sequencing. Therefore, there is limited understanding of how they contribute to complex traits such as cystic fibrosis (CF) lung function. Genome-wide association study (GWAS) of CF lung disease identified two independent signals near SLC9A3 displaying a high density of VNTRs and CpG islands. Here, we used long-read (PacBio) phased sequence (N=58) to identify the boundaries and lengths of 49 common (frequency >2%) VNTRs in the region. Subsequently, associations of the VNTRs with gene expression were investigated in CF nasal epithelia using RNA sequencing (N=46). Two VNTRs tagged by the two GWAS signals and overlapping CpG islands were independently associated with SLC9A3 expression in CF nasal epithelia. The two VNTRs together explained 24% of SLC9A3 gene expression variation. One of them was also associated with TPPP expression. We then showed that the VNTR lengths can be estimated with good accuracy in short-read sequence in a subset of individuals with data on both long (PacBio) and short-read (10X Genomics) technologies (N=52). VNTR lengths were then estimated in the Genotype-Tissue Expression project (GTEx) and their association with gene expression was investigated. Both VNTRs were associated with SLC9A3 expression in multiple non-CF GTEx tissues including lung. The results confirm that VNTRs can explain substantial variation in gene expression and be responsible for GWAS signals, and highlight the critical role of long-read sequencing.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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 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".