Abstract 9929: Novel Mutations Identified by Whole Exome Sequencing in Families With Congenital Heart Disease
Bibliographic record
Abstract
Congenital heart disease (CHD) is a complex disorder with multifactorial etiology and high heritability. We used whole exome sequencing to identify novel variants associated with familial CHD. Methods: CHD patients were enrolled in a prospective biobank registry (n=2800). Whole exome sequencing was performed in 12 affected members with LV outflow tract defects and 7 non-affected members from 5 families using Illumina Hi-Seq 2000, and Agilent SureSelect Human All Exon v.2 Kit for sequence capture. Results: Sequence alignment to the reference human genome (GRCh37/hg19) identified over 48K single nucleotide variants (SNVs) and indels (average 820 novel coding variants per individual). SNVs were called using SOAP tools and SIFT, Polyphen and Mutation Taster algorithms were used to predict pathogenicity. Affected and unaffected members were compared using the dominant model to identify novel CHD-associated variants. We identified 216 novel SNVs that segregated within affected members (not seen in unaffected members). These included 82 potentially pathogenic variants on 69 genes expressed in the heart and/or vasculature. Of these, 51 were inherited and 31 were de novo. Novel inherited variants in two genes on the Eph (ephrin) family receptor interacting proteins segregated within 4 affected members from 2 families. Eph receptors form the largest subfamily of receptor tyrosine kinases that are components of cell adhesion, migration, and signalling pathways and are involved in organ development. Additional inherited and de novo variants are being further characterized to determine if they are related to cardiac developmental pathways. Conclusions: Next-generation sequencing can detect novel variants on susceptibility genes that identify new pathways involved in CHD causation. Replication in additional CHD families and candidate gene resequencing will help validate the association of observed variants with 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".