Structural variants: changing the landscape of chromosomes and design of disease studies
Bibliographic record
Abstract
The near completeness of human chromosome sequences is facilitating accurate characterization and assessment of all classes of genomic variation. Particularly, using the DNA reference sequence as a guide, genome scanning technologies, such as microarray-based comparative genomic hybridization (array CGH) and genome-wide single nucleotide polymorphism (SNP) platforms, have now enabled the detection of a previously unrecognized degree of larger-sized (non-SNP) variability in all genomes. This heterogeneity can include copy number variations (CNVs), inversions, insertions, deletions and other complex rearrangements, most of which are not detected by standard cytogenetics or DNA sequencing. Although these genomic alterations (collectively termed structural variants or polymorphisms) have been described previously, mainly through locus-specific studies, they are now known to be more global in occurrence. Moreover, as just one example, CNVs can contain entire genes and their number can correlate with the level of gene expression. It is also plausible that structural variants may commonly influence nearby genes through chromosomal positional or domain effects. Here, we discuss what is known of the prevalence of structural variants in the human genome and how they might influence phenotype, including the continuum of etiologic events underlying monogenic to complex diseases. Particularly, we highlight the newest studies and some classic examples of how structural variants might have adverse genetic consequences. We also discuss why analysis of structural variants should become a vital step in any genetic study going forward. All these progresses have set the stage for a golden era of combined microscopic and sub-microscopic (cytogenomic)-based research of chromosomes leading to a more complete understanding of the human genome.
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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 | 0.100 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".