New Sequencing technologies help revealing unexpected mutations in Autosomal Dominant Hypercholesterolemia
Bibliographic record
Abstract
Autosomal dominant hypercholesterolemia (ADH) is characterized by elevated LDL-C levels leading to coronary heart disease. Four genes are implicated in ADH: LDLR, APOB, PCSK9 and APOE. Our aim was to identify new mutations in known genes, or in new genes implicated in ADH. Thirteen French families with ADH were recruited and studied by exome sequencing after exclusion, in their probands, of mutations in the LDLR, PCSK9 and APOE genes and fragments of exons 26 and 29 of APOB gene. We identified in one family a p.Arg50Gln mutation in the APOB gene, which occurs in a region not usually associated with ADH. Segregation and in-silico analysis suggested that this mutation is disease causing in the family. We identified in another family with the p.Ala3396Thr mutation of APOB, one patient with a severe phenotype carrying also a mutation in PCSK9: p.Arg96Cys. This is the first compound heterozygote reported with a mutation in APOB and PCSK9. Functional studies proved that the p.Arg96Cys mutation leads to increased LDL receptor degradation. This work shows that Next-Generation Sequencing (exome, genome or targeted sequencing) are powerful tools to find new mutations and identify compound heterozygotes, which will lead to better diagnosis and treatment of ADH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".