Abstract
Introduction Genetic factors and environmental exposures, including pesticides, contribute to the risk of Parkinson's disease (PD). There have been few studies of gene and pesticide exposure interactions in PD, and all of the prior studies used a candidate gene approach. Methods We performed the first genome-wide gene-environment interaction analysis of pesticide exposure and risk of Parkinson's disease. Analyses were performed using data on >700,000 single nucleotide polymorphisms (SNPs) in 364 discordant sibling pairs. In addition to testing for SNP-pesticide interaction effects, we also performed exploratory analyses of gene-pesticide interactions at the gene level. Results None of the gene-environment interaction results were significant after genome-wide correction for multiple testing (α = 1.5E-07 for SNP-level tests; α = 2.1E-06 for gene-level tests). Top results in the SNP-level tests provided suggestive evidence (P < 5.0E-06) that the effect of pesticide exposure on PD risk may be modified by SNPs in the ERCC6L2 gene (P = 2.4E-06), which was also supported by suggestive evidence in the gene-level analysis (P = 4.7E-05). None of the candidate genes assessed in prior studies of gene-pesticide interactions reached statistical support in this genome-wide screen. Conclusion Although no significant interactions were identified, several of the genes with suggestive evidence of gene-environment interaction effects have biological plausibility for PD risk. Further investigation of the role of those genes in PD risk, particularly in the context of pesticide exposure, in large and carefully recruited samples is warranted.
| Original language | English |
|---|---|
| Journal | Parkinsonism and Related Disorders |
| Volume | 32 |
| Pages (from-to) | 25-30 |
| Number of pages | 6 |
| ISSN | 1353-8020 |
| DOIs | |
| Publication status | Published - 01.11.2016 |
Funding
DMM has received philanthropic support from the Auxiliary of NorthShore University HealthSystem; serves on the editorial board of Parkinsonism and Related Disorders; is an author on 2 pending patents: (1) method to treat Parkinson's disease and (2) method to predict Parkinson's disease; and receives research support from the Agency for Health Care Research and Quality (1R01HS024057-01). RF spouse has received philanthropic support from the Auxiliary of NorthShore University HealthSystem; serves on the editorial board of Parkinsonism and Related Disorders; is an author on 2 pending patents: (1) method to treat Parkinson's disease and (2) method to predict Parkinson's disease; and receives research support from the Agency for Health Care Research and Quality (1R01HS024057-01). This work was supported by funding from the National Institutes of Health grant 2R01ES10751 to DMM, and a Mayo Clinic Division of Biomedical Statistics and Informatics Meritorious Award to JMB. The authors wish to thank Vimal Patel, PhD, Medical & Scientific Writer at NorthShore Neurological Institute for assistance with the preparation of this manuscript. Appendix A