Application of Exome Sequencing to Mendelian Disorders and the Emergence of Personalised Medicine
Bibliographic record
Abstract
Abstract Since the introduction of next‐generation sequencing, the field of genomic medicine has expanded rapidly. Whole‐exome sequencing (WES) can sequence thousands of functional genes at a time and has become the tool of choice for the discovery of causative genetic variants, especially for Mendelian diseases. This approach also expands the knowledge of novel mutations of established genes linked to a particular disease, and helps to uncover the complex interplay between modifier variants that contribute to a disease phenotype. For these reasons, WES can perform diagnoses that would be difficult to accomplish with traditional methods, particularly for diseases that exhibit a broad genetic or phenotypic heterogeneity. Together, these trends already have begun to influence the quality of patient care and appear to fulfil the promise of personal medicine. The article summarises recent literature that highlights these trends, and discusses the limitations and ethical considerations surrounding this new technology. Key Concepts: Next‐generation sequencing is driving the emergence of personalised medicine. Superiority of WES as a diagnostic tool over traditional genetic methods is apparent, especially for diseases that are genetically and phenotypically heterogenous. WES utility is a rapidly expanding field of genetic aetiology. The underlying genetic complexity of monogenic diseases might have been underappreciated. The clinical manifestation of monogenic diseases is driven by a complex interplay of a multitude of genetic modifiers. The incidentalome remains an important ethical issue, but important steps forward have been taken with the work of Dr. Berg and collaborators commencing in 2011. The rapidly expanding field of genomics faces important technical limitations: storage space and analysis capability. Important ethical questions need to be addressed with regard to the use of NGS technology; this includes rights to data access and issues regarding follow‐up data reanalysis.
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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 | 0.039 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".