Exploring Genetic Variants in Ellis-Van Creveld Syndrome: Insights from a Consanguineous Family
Bibliographic record
Abstract
Background: Ellis-Van Creveld Syndrome (EVC) is a rare genetic disorder characterized by skeletal abnormalities and developmental anomalies, presenting significant challenges in diagnosis and understanding its genetic underpinnings. Objective: This study aimed to comprehensively investigate the clinical and molecular aspects of EVC within a consanguineous family from Pakistan, identifying the genetic variants involved. Methods: A detailed clinical assessment was conducted on all family members, documenting phenotypic features such as polydactyly, syndactyly, dental abnormalities, and short stature. Pedigrees were constructed following established guidelines to depict familial relationships and inheritance patterns. Venous blood samples were collected for genomic DNA extraction using the Phenol-Chloroform Method. Microsatellite marker analysis was performed for linkage mapping, guided by the UCSC Genome Browser and Rutgers Combined Linkage-Physical Map. Mutation screening of the EVC and EVC2 genes was conducted using Sanger sequencing, with primers designed targeting exon-intron boundaries and coding exons. PCR amplification and sequencing were performed according to standard protocols. Data analysis was carried out using SPSS version 25. Results: Clinical assessment revealed classical EVC phenotypes, including bilateral postaxial polydactyly in 80% of affected individuals and dental abnormalities in 70%. Microsatellite marker analysis identified linkage to the EVC/EVC2 locus on chromosome 4p16.2. However, Sanger sequencing of the 21 coding exons of EVC and the 22 exons of EVC2 did not detect any pathogenic mutations. Haplotype analysis confirmed the segregation of specific markers with affected individuals, suggesting the involvement of additional genetic factors. Conclusion: The study's findings challenge the traditional understanding of EVC, highlighting the necessity for advanced genomic techniques such as whole-genome sequencing to fully elucidate its genetic contributors. These insights underscore the heterogeneous nature of EVC and emphasize the importance of comprehensive genetic screening for accurate diagnosis and personalized management of this rare genetic disorder.
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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 | 0.001 | 0.001 |
| 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".