Genome-wide assessment identifies novel runs of homozygosity linked to Parkinson’s disease etiology across diverse ancestral populations
Bibliographic record
Abstract
Objective: We conducted the first large-scale, multi-ancestral investigation of Parkinson's disease (PD) to examine the impact of genome-wide homozygosity on disease risk and age at onset. Using genotyping, imputed, and whole-genome sequencing (WGS) data from 16,599 PD cases and 13,585 controls across nine ancestral populations from the Global Parkinson's Genetics Program, we aimed to identify novel regions of homozygosity contributing to PD heritability. Methods: ). ROHs were intersected with known PD, pallido-pyramidal syndrome, and atypical parkinsonism gene regions and risk loci to assess pleomorphic or pleiotropic contributions. Homozygosity mapping identified ROH overlaps in families, consanguineous individuals, and early-onset PD (EOPD) cases. Results: were observed between cases and controls across multiple ancestral groups, persisting after excluding known PD-associated recessive genes. Our analysis revealed distinct patterns of ROH enrichment associated with age at onset, suggesting recessive genetic modifiers of PD across diverse ancestral backgrounds. Homozygosity mapping identified 672 case-exclusive ROH pools, 21 prioritized variants, and 1,300 ROHs enriched in cases. Finally, 167 ROHs in consanguineous individuals and EOPD overlapped known PD and risk loci. Interpretation: Our findings suggest that ROH regions contribute to PD heritability in a global context, with a portion attributed to recessive allelic architecture. We developed an open-science framework for unbiased homozygosity mapping. Future studies should use larger, diverse cohorts and WGS data to uncover rare recessive variants linked to PD susceptibility.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".