Whole-genome sequencing reveals individual and cohort level insights into chromosome 9p syndromes
Bibliographic record
Abstract
BACKGROUND: Previous genomic efforts on chromosome 9p deletion and duplication syndromes have utilized low-resolution strategies (i.e., karyotypes, chromosome microarrays). These studies have provided important initial insights into these syndromes. This current study is the first large-scale whole-genome sequencing (WGS) study of 100 individuals from families with chromosome 9p syndromes. METHODS: Through the newly formed 9P-ARCH (Advanced Research in Chromosomal Health: Genomic, Phenotypic, and Functional Aspects of 9p-Related syndromes) research network, we assembled a cohort of individuals from families with chromosome 9p syndromes. WGS was applied to 100 individuals, and other genomic technologies were applied to a subset of individuals. To prioritize genes on 9p, we utilized two independent approaches: statistical analyses of genomic data and spatial transcriptomic profiling of embryonic mouse tissue. To assess the enrichment of DNVs within genomic regions, we developed a computational tool, DiamondsDenovo ( https://github.com/TNTurnerLab/DiamondsDenovo ). RESULTS: Unlike previous low-resolution studies, we analyzed the genomic architecture of chromosome 9p syndromes, highlighting fundamental features and their commonalities and differences across individuals. A machine-learning model was developed to predict 9p deletion syndrome based on gene copy number estimates using WGS data. We identified two late-replicating regions containing most structural variant breakpoints in 9p deletion syndrome, pointing to replication-based issues as a potential cause of structural variant formation in most individuals and structural rearrangements in some individuals. Genes on 9p were prioritized based on statistical assessment of human genomic variation and through spatial transcriptomics, with 24 genes (AK3, BRD10, CD274, CDC37L1, DMRT1, DMRT2, DMRT3, DOCK8, GLIS3, JAK2, KANK1, KDM4C, PLPP6, PTPRD, PUM3, RANBP6, RCL1, RFX3, RIC1, SLC1A1, SMARCA2, UHRF2, VLDLR, and ZNG1A) identified as important for the majority (83%) of individuals with 9p deletion syndrome. Testing of the mitochondrial genome revealed excess copy number in individuals with 9p deletion syndrome. CONCLUSIONS: This study introduces the 9P-ARCH research network that is actively pursuing genomic, phenotypic, and functional aspects of 9p-related syndromes. We advanced the study of 9p-related syndromes both at the individual level and across the cohort through the largest, most comprehensive genomic analysis of 9p-related syndromes to date.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".