Genetic findings of children with congenital heart diseases using chromosome microarray and trio-based whole exome sequencing
Bibliographic record
Abstract
Abstract Background Congenital heart disease (CHD) is the most common type of birth defects. Genetic factors are the important contributor to the etiology of CHD. However, the underlying genetic causes in most individuals remain unclear. Methods 101 individuals with CHD and their unaffected parents were included in this study. Chromosome microarray analysis (CMA) as a first-tier clinical diagnostic tool was applied for all affected individuals, followed by trio-based whole exome sequencing (WES). The function of the genes involved in the genetic variants in the cohort was analyzed. Results We detected aneuploidies in 2 individuals (trisomy 21 and monosomy X), other pathogenic/likely pathogenic copy number variants (CNVs) in 20 individuals, and pathogenic /likely pathogenic SNVs/InDels in 9 individuals. The combined genetic diagnostic yield was 30.7%, including 21.8% with chromosomal abnormalities and 8.9% with sequence-level variants. Nineteen CNVs in 19 individuals were associated with 14 recurrent chromosomal microdeletion/microduplication syndromes, the most common being 22q11.2 deletion syndrome. Pathogenic/likely pathogenic sequence-level variants were identified in nine genes, including GATA6, FLNA, KANSL1, HNRNPK, TRAF7, KAT6A, PKD1L1, RIT1, and SMAD6. The function of the genes involved in the CHD relevant CNVs and SNVs was analyzed indicating enriched genes are mainly associated with development of multiple organs, not only heart, but also brain and endocrine system. Conclusions CMA is a first-tier clinical diagnostic test to define the underlying genomic architecture of CHD. Trio-based WES increases the diagnostic yield, and should be part of the diagnostic algorithm. Our study expands the genes interaction networks for genetic study of CHD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".