Advances in genetic sequencing and genomics in the detection and analyses of genetic variants in neurological disorders: A review
Bibliographic record
Abstract
With recent advances in genetics and genomic sequencing, it has become possible to screen for genetic variants and polymorphisms in the human genome that contribute to heritable and familial forms of neurological diseases. With an increasing proportion of the population aged 55 years and older, there will be an increased incidence of neurological disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), which will place an increasing burden on our healthcare system and an increasing need for resources and expertise to treat and manage these diseases. This review will highlight recent advances in genetic sequencing and genomics that have allowed for improved detection and diagnosis of AD, PD and Multiple Sclerosis (MS). Alzheimer’s disease is the leading cause of dementia in seniors and is characterized by cognitive decline, memory loss and impairment in the formation of new memories. Parkinson’s disease is an extrapyramidal movement disorder characterized by resting tremor, muscular rigidity, bradykinesia, hypokinesia and postural instability. Multiple sclerosis is a chronic inflammatory and demyelinating disease of the CNS characterized by motor symptoms, cognitive impairments, fatigue, muscle weakness and autonomic dysfunction and is the most common neurological disorder in young adults. Finally, this article will address some considerations when integrating genetic sequencing and testing into the current diagnosis and management of neurological diseases.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".