The genetics of <scp>P</scp>arkinson's disease: Progress and therapeutic implications
Bibliographic record
Abstract
The past 15 years has witnessed tremendous progress in our understanding of the genetic basis for Parkinson's disease (PD). Notably, whereas most mutations, such as those in SNCA, PINK1, PARK2, PARK7, PLA2G6, FBXO7, and ATP13A2, are a rare cause of disease, one particular mutation in LRRK2 has been found to be common in certain populations. There has been considerable progress in finding risk loci. To date, approximately 16 such loci exist; notably, some of these overlap with the genes known to contain disease-causing mutations. The identification of risk alleles has relied mostly on the application of revolutionary technologies; likewise, second-generation sequencing methods have facilitated the identification of new mutations in PD. These methods will continue to provide novel insights into PD. The utility of genetics in therapeutics relies primarily on leveraging findings to understand the pathogenesis of PD. Much of the investigation into the biology underlying PD has used these findings to define a pathway, or pathways, to pathogenesis by trying to fit disparate genetic defects onto the same network. This work has had some success, particularly in the context of monogenic disease, and is beginning to provide clues about potential therapeutic targets. Approaches toward therapies are also being provided more directly by genetics, notably by the reduction and clearance of alpha-synuclein and inhibition of Lrrk2 kinase activity. We believe this has been an exciting, productive time for PD genetics and, furthermore, that genetics will continue to drive the etiologic understanding and etiology-based therapeutic approaches in this disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".