Genome-wide survey of tandem repeats by nanopore sequencing shows that disease-associated repeats are more polymorphic in the general population
Bibliographic record
Abstract
BACKGROUND: Tandem repeats are highly mutable and contribute to the development of human disease by a variety of mechanisms. It is difficult to predict which tandem repeats may cause a disease. One hypothesis is that changeable tandem repeats are the source of genetic diseases, because disease-causing repeats are polymorphic in healthy individuals. However, it is not clear whether disease-causing repeats are more polymorphic than other repeats. METHODS: We performed a genome-wide survey of the millions of human tandem repeats using publicly available long read genome sequencing data from 21 humans. We measured tandem repeat copy number changes using tandem-genotypes. Length variation of known disease-associated repeats was compared to other repeat loci. RESULTS: We found that known Mendelian disease-causing or disease-associated repeats, especially CAG and 5'UTR GGC repeats, are relatively long and polymorphic in the general population. We also show that repeat lengths of two disease-causing tandem repeats, in ATXN3 and GLS, are correlated with near-by GWAS SNP genotypes. CONCLUSIONS: We provide a catalog of polymorphic tandem repeats across a variety of repeat unit lengths and sequences, from long read sequencing data. This method especially if used in genome wide association study, may indicate possible new candidates of pathogenic or biologically important tandem repeats in human genomes.
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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 | 0.001 | 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) | 0.002 | 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".