Unexpected allelic heterogeneity and spectrum of mutations in Fowler syndrome revealed by next-generation exome sequencing
Bibliographic record
Abstract
Protein coding genes constitute approximately 1% of the human genome but harbor 85% of the mutations with large effects on disease-related traits. Therefore, efficient strategies for selectively sequencing complete coding regions (i.e., "whole exome") have the potential to contribute our understanding of human diseases. We used a method for whole-exome sequencing coupling Agilent whole-exome capture to the Illumina DNA-sequencing platform, and investigated two unrelated fetuses from nonconsanguineous families with Fowler Syndrome (FS), a stereotyped phenotype lethal disease. We report novel germline mutations in feline leukemia virus subgroup C cellular-receptor-family member 2, FLVCR2, which has recently been shown to cause FS. Using this technology, we identified three types of genetic abnormalities: point-mutations, insertions-deletions, and intronic splice-site changes (first pathogenic report using this technology), in the fetuses who both were compound heterozygotes for the disease. Although revealing a high level of allelic heterogeneity and mutational spectrum in FS, this study further illustrates the successful application of whole-exome sequencing to uncover genetic defects in rare Mendelian disorders. Of importance, we show that we can identify genes underlying rare, monogenic and recessive diseases using a limited number of patients (n=2), in the absence of shared genetic heritage and in the presence of allelic heterogeneity.
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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 | 0.001 | 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) | 0.001 | 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".