Investigating shared genetic architecture between pigmentation genetics and Parkinson’s Disease
Bibliographic record
Abstract
Abstract Peripheral melanin and neuromelanin share a common biosynthetic initiation. Peripheral melanin (eumelanin and pheomelanin) is cyclically produced and degraded, while neuromelanin accumulates in dopaminergic neurons over time. Neurons containing excess neuromelanin (e.g., substantia nigra) exhibit increased degeneration in Parkinson’s patients, suggesting a potential genetic interplay between pigmentation pathways and Parkinson’s Disease (PD). We used linkage disequilibrium score regression (LDSC), polygenic risk score (PRS) analysis, Mendelian Randomization (MR), and multi-trait association analysis to examine shared genetic architecture between PD and nine pigmentation-related traits (basal cell carcinoma, brown hair, melanoma, nevi, red hair, skin colour, tanning response, vitiligo, vitamin D levels). PRS analyses identified limited shared genetic variation (max 0.15% for nevi), and MR analyses did not provide evidence of a causal relationship. Together, the ten-trait and pairwise multi-trait analyses identified 48 SNPs with suggestive pleiotropy, 31 of which were protein-coding and could be mapped to 22 different genes. Overall, while some genetic overlap exists, no definitive correlative or causal relationships were established. These results contribute to the broader understanding of the differing roles of melanin and neuromelanin, as well as potential implications in neurodegenerative diseases.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".