Sex chromosome aneuploidy impacts on human gene expression and regulation: a systematic review
Bibliographic record
Abstract
BACKGROUND: Sex chromosome aneuploidies (SCAs) are collectively common genetic disorders that impact diverse body systems. The molecular mechanisms by which an extra or missing sex chromosome increases clinical risk are not fully understood, but they likely involve imbalances in expression and regulation of dosage-sensitive genes. There has been a recent surge in transcriptomic and epigenomic studies on genomic effects of SCAs - making it an opportune time to: (i) map existing knowledge of SCA impacts on gene expression and regulation; (ii) resolve consensus findings on SCA dosage-sensitive genes; and (iii) define gaps and high priority areas for future research. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (protocol registered on the international Prospective Register of Systematic Reviews PROSPERO CRD42024473984), we searched nine databases and screened the titles and abstracts of 2421 records, thoroughly evaluated 161 full-text articles, and identified 57 eligible studies for abstraction of methodological features and results. RESULTS: Our review spans 18 years of research and encompasses samples from 930 individuals with SCAs and 2192 euploidic controls. The recent acceleration in publication rates outstrips that for biomedical research as a whole. Studies have only recently started to diversify away from the most studied SCAs (47,XXY and 45,X karyotypes), tissue types (blood-derived, gonadal) and measurement methods (transcriptomic analysis by RNAseq). We identify a core set of dosage-sensitive genes that are recurrently impacted by SCAs across multiple tissues. These genes concentrate in 3 protein-protein interaction networks that are predominantly enriched for chromatin remodelling, and represent candidate drivers of downstream phenotypes. CONCLUSIONS: This systematic review of SCA impacts on the human genome helps to target the future research efforts that are now needed to (i) address existing knowledge gaps by diversifying the karyotypes, tissues and genomic features analyzed, and (ii) test the causal role for recurrently dysregulated genes. Meeting these goals would provide a molecular foundation to drive both basic and clinical understanding of sex chromosome influences on human phenotypic variation.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".