Advancing genomic technologies and clinical awareness accelerates discovery of disease-associated tandem repeat sequences
Bibliographic record
Abstract
Expansions of gene-specific DNA tandem repeats (TRs), first described in 1991 as a disease-causing mutation in humans, are now known to cause >60 phenotypes, not just disease, and not only in humans. TRs are a common form of genetic variation with biological consequences, observed, so far, in humans, dogs, plants, oysters, and yeast. Repeat diseases show atypical clinical features, genetic anticipation, and multiple and partially penetrant phenotypes among family members. Discovery of disease-causing repeat expansion loci accelerated through technological advances in DNA sequencing and computational analyses. Between 2019 and 2021, 17 new disease-causing TR expansions were reported, totaling 63 TR loci (>69 diseases), with a likelihood of more discoveries, and in more organisms. Recent and historical lessons reveal that properly assessed clinical presentations, coupled with genetic and biological awareness, can guide discovery of disease-causing unstable TRs. We highlight critical but underrecognized aspects of TR mutations. Repeat motifs may not be present in current reference genomes but will be in forthcoming gapless long-read references. Repeat motif size can be a single nucleotide to kilobases/unit. At a given locus, repeat motif sequence purity can vary with consequence. Pathogenic repeats can be "insertions" within nonpathogenic TRs. Expansions, contractions, and somatic length variations of TRs can have clinical/biological consequences. TR instabilities occur in humans and other organisms. TRs can be epigenetically modified and/or chromosomal fragile sites. We discuss the expanding field of disease-associated TR instabilities, highlighting prospects, clinical and genetic clues, tools, and challenges for further discoveries of disease-causing TR instabilities and understanding their biological and pathological impacts-a vista that is about to expand.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".