Resequencing analysis of five Mendelian genes and the top genes from genome-wide association studies in Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Most sequencing studies in Parkinson's disease (PD) have focused on either a particular gene, primarily in familial and early onset PD samples, or on screening single variants in sporadic PD cases. To date, there is no systematic study that sequences the most common PD causing genes with Mendelian inheritance [α-synuclein (SNCA), leucine-rich repeat kinase 2 (LRRK2), PARKIN, PTEN-induced putative kinase 1 (PINK1) and DJ-1 (Daisuke-Junko-1)] and susceptibility genes [glucocerebrosidase beta acid (GBA) and microtubule-associated protein tau (MAPT)] identified through genome-wide association studies (GWAS) in a European-American case-control sample (n=815). RESULTS: Disease-causing variants in the SNCA, LRRK2 and PARK2 genes were found in 2% of PD patients. The LRRK2, p.G2019S mutation was found in 0.6 % of sporadic PD and 4.8 % of familial PD cases. Gene-based analysis suggests that additional variants in the LRRK2 gene also contribute to PD risk. The SNCA duplication was found in 0.8 % of familial PD patients. Novel variants were found in 0.8% of PD cases and 0.6 % of controls. Heterozygous Gaucher disease-causing mutations in the GBA gene were found in 7.1 % of PD patients. Here, we established that the GBA variant (p.T408M) is associated with PD risk and age at onset. Additionally, gene-based and single-variant analyses demostrated that GBA gene variants (p.L483P, p.R83C, p.N409S, p.H294Q and p.E365K) increase PD risk. CONCLUSIONS: Our data suggest that the impact of additional untested coding variants in the GBA and LRRK2 genes is higher than previously estimated. Our data also provide compelling evidence of the existence of additional untested variants in the primary Mendelian and PD GWAS genes that contribute to the genetic etiology of sporadic PD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".