High diagnostic rate of trio exome sequencing in consanguineous families with neurogenetic diseases
Bibliographic record
Abstract
Consanguineous marriages have a prevalence rate of 24% in Turkey. These carry an increased risk of autosomal recessive genetic conditions, leading to severe disability or premature death, with a significant health and economic burden. A definitive molecular diagnosis could not be achieved in these children previously, as infrastructures and access to sophisticated diagnostic options were limited. We studied the cause of neurogenetic disease in 246 children from 190 consanguineous families recruited in three Turkish hospitals between 2016 and 2020. All patients underwent deep phenotyping and trio whole exome sequencing, and data were integrated in advanced international bioinformatics platforms. We detected causative variants in 119 known disease genes in 72% of families. Due to overlapping phenotypes 52% of the confirmed genetic diagnoses would have been missed on targeted diagnostic gene panels. Likely pathogenic variants in 27 novel genes in 14% of the families increased the diagnostic yield to 86%. Eighty-two per cent of causative variants (141/172) were homozygous, 11 of which were detected in genes previously only associated with autosomal dominant inheritance. Eight families carried two pathogenic variants in different disease genes. De novo (9.3%), X-linked recessive (5.2%) and compound heterozygous (3.5%) variants were less frequent compared to non-consanguineous populations. This cohort provided a unique opportunity to better understand the genetic characteristics of neurogenetic diseases in a consanguineous population. Contrary to what may be expected, causative variants were often not on the longest run of homozygosity and the diagnostic yield was lower in families with the highest degree of consanguinity, due to the high number of homozygous variants in these patients. Pathway analysis highlighted that protein synthesis/degradation defects and metabolic diseases are the most common pathways underlying paediatric neurogenetic disease. In our cohort 164 families (86%) received a diagnosis, enabling prevention of transmission and targeted treatments in 24 patients (10%). We generated an important body of genomic data with lasting impacts on the health and wellbeing of consanguineous families and economic benefit for the healthcare system in Turkey and elsewhere. We demonstrate that an untargeted next generation sequencing approach is far superior to a more targeted gene panel approach, and can be performed without specialized bioinformatics knowledge by clinicians using established pipelines in populations with high rates of consanguinity.
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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 | 0.000 | 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".