Clinical genetic testing in Parkinson’s disease: meta-analysis of diagnostic yield across sequencing technologies and global regions
Bibliographic record
Abstract
Abstract Background Parkinson’s disease (PD) is a genetically heterogeneous neurodegenerative disorder. While monogenic forms are well-characterized, the diagnostic utility of next-generation sequencing (NGS) in PD remains unclear. This meta-analysis aimed to evaluate the diagnostic yield of NGS technologies, including whole exome sequencing (WES), targeted gene panels (TGP), and multiplex ligation-dependent probe amplification (MLPA), in identifying pathogenic/likely pathogenic (P/LP) variants in PD and to explore global disparities in access to genetic testing. Methods A systematic search was conducted across PubMed, Scopus, and Google Scholar using relevant MeSH terms and keywords. Studies published in English up to April 2024 were considered. Clinical studies using NGS or MLPA to assess genetic variants in PD patients were included. Data was extracted from 13 eligible studies involving 4,712 individuals with familial, sporadic, early-onset parkinson’s disease (EOPD), or late-onset parkinson’s disease (LOPD). Quality assessment was performed using the Newcastle–Ottawa Scale (NOS). Random-effects meta-analysis was applied to estimate diagnostic yields, assess heterogeneity (I²), and evaluate publication bias using Egger’s test. Results The overall diagnostic yield was 12% (95% confidence interval (CI, 0.07–0.19). Yields varied by genomic technology: 21% for WES, 9% for TGP, and 5% for MLPA. Familial PD had a higher diagnostic yield (19%) than sporadic PD (5%), and EOPD showed higher yield (13%) compared to LOPD (11%). Frequently detected pathogenic/likely pathogenic variants occurred in GBA (39%), PRKN (28%), and LRRK2 (16%) genes. Notable heterogeneity was observed across studies (I² = 97.9%). Global analysis showed unequal access to genetic testing, with higher adoption in high inequality-adjusted human development index (IHDI) countries. Conclusions Genomic technologies, especially WES, provide meaningful diagnostic insights in PD, particularly in familial and early-onset cases. However, significant disparities exist in global adoption, highlighting a need for broader access and standardized protocols. These findings underscore the importance of integrating genetic screening into routine clinical practice and expanding research efforts in underrepresented populations to advance precision medicine in PD.
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 | 0.045 | 0.085 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.065 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".