Ethnicity- and sex-specific genome wide association study on Parkinson’s disease
Bibliographic record
Abstract
Most previous genome-wide association studies (GWASs) on Parkinson's disease (PD) focus on the European population. There are several sex-specific clinical differences in PD, but little is known about its genetic background. We aimed to perform an ethnicity-specific and sex-specific GWAS on PD in the Korean population. A total of 1050 PD patients and 5000 controls were included. For primary analysis, we performed a GWAS using a logistic additive model adjusted for age and sex. The same statistical models were applied to sex-specific analyses. Genotyping was performed using a customized microarray chip optimized for the Korean population. Nine single nucleotide polymorphisms (SNPs) including four in the SNCA locus and three from the PARK16 locus were associated with PD in Koreans. The rs34778348 in the LRRK2 locus showed a strong association, though failed to pass cluster quality control. There were no notable genome-wide significant markers near the MAPT or GBA1 loci. In the female-only analysis, rs34778348 in LRRK2 and the four other SNPs in the SNCA showed a strong association with PD. In the male-only analysis, no SNP surpassed the genome-wide significance threshold under Bonferroni correction; however, the most significant signal was rs708726 in the PARK16 locus. This ethnicity- and sex-specific GWAS on PD implicate the pan-ethnic effect of SNCA, the universal but East-Asian inclined effect of PARK16, the East Asian-specific role of LRRK2 G2385R variants, and the possible disproportionate effect of SNCA and PARK16 between sexes for PD susceptibility. These findings suggest the different genetic contributions to sporadic PD in terms of ethnicity and sex.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".