Targeted next-generation sequencing in monogenic dyslipidemias
Bibliographic record
Abstract
PURPOSE OF REVIEW: To evaluate the potential clinical translation of high-throughput next-generation sequencing (NGS) methods in diagnosis and management of dyslipidemia. RECENT FINDINGS: Recent NGS experiments indicate that most causative genes for monogenic dyslipidemias are already known. Thus, monogenic dyslipidemias can now be diagnosed using targeted NGS. Targeting of dyslipidemia genes can be achieved by either: designing custom reagents for a dyslipidemia-specific NGS panel; or performing genome-wide NGS and focusing on genes of interest. Advantages of the former approach are lower cost and limited potential to detect incidental pathogenic variants unrelated to dyslipidemia. However, the latter approach is more flexible because masking criteria can be altered as knowledge advances, with no need for re-design of reagents or follow-up sequencing runs. Also, the cost of genome-wide analysis is decreasing and ethical concerns can likely be mitigated. DNA-based diagnosis is already part of the clinical diagnostic algorithms for familial hypercholesterolemia. Furthermore, DNA-based diagnosis is supplanting traditional biochemical methods to diagnose chylomicronemia caused by deficiency of lipoprotein lipase or its co-factors. SUMMARY: The increasing availability and decreasing cost of clinical NGS for dyslipidemia means that its potential benefits can now be evaluated on a larger scale.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".