A genetic basis for the variable effect of smoking/nicotine on Parkinson's disease.

Prior studies have established an inverse association between cigarette smoking and the risk of developing Parkinson's disease (PD), and currently, the disease-modifying potential of the nicotine patch is being tested in clinical trials. To identify genes that interact with the effect of smoking/nicotine, we conducted genome-wide interaction studies in humans and in Drosophila. We identified SV2C, which encodes a synaptic-vesicle protein in PD-vulnerable substantia nigra (P=1 × 10(-7) for gene-smoking interaction on PD risk), and CG14691, which is predicted to encode a synaptic-vesicle protein in Drosophila (P=2 × 10(-11) for nicotine-paraquat interaction on gene expression). SV2C is biologically plausible because nicotine enhances the release of dopamine through synaptic vesicles, and PD is caused by the depletion of dopamine. Effect of smoking on PD varied by SV2C genotype from protective to neutral to harmful (P=5 × 10(-10)). Taken together, cross-validating evidence from humans and Drosophila suggests SV2C is involved in PD pathogenesis and it might be a useful marker for pharmacogenomics studies involving nicotine.

A comprehensive analysis of deletions, multiplications, and copy number variations in PARK2.

OBJECTIVES: To perform a comprehensive population genetic study of PARK2. PARK2 mutations are associated with juvenile parkinsonism, Alzheimer disease, cancer, leprosy, and diabetes mellitus, yet ironically, there has been no comprehensive study of PARK2 in control subjects; and to resolve controversial association of PARK2 heterozygous mutations with Parkinson disease (PD) in a well-powered study. METHODS: We studied 1,686 control subjects (mean age 66.1 ± 13.1 years) and 2,091 patients with PD (mean onset age 58.3 ± 12.1 years). We tested for PARK2 deletions/multiplications/copy number variations (CNV) using semiquantitative PCR and multiplex ligation-dependent probe amplification, and validated the mutations by real-time quantitative PCR. Subjects were tested for point mutations previously. Association with PD was tested as PARK2 main effect, and in combination with known PD risk factors: SNCA, MAPT, APOE, smoking, and coffee intake. RESULTS: A total of 0.95% of control subjects and 0.86% of patients carried a heterozygous CNV mutation. CNV mutations found in 16 control subjects were all in exons 1-4, sparing exons that encode functionally critical protein domains. Thirteen patients had 2 CNV mutations, 5 had 1 CNV and 1 point mutation, and 18 had 1 CNV mutation. Mutations found in patients spanned exons 2-9. In whites, having 1 CNV was not associated with increased risk (odds ratio 1.05, p = 0.89) or earlier onset of PD (64.7 ± 8.6 heterozygous vs 58.5 ± 11.8 normal). CONCLUSIONS: This comprehensive population genetic study in control subjects fills the void for a PARK2 reference dataset. There is no compelling evidence for association of heterozygous PARK2 mutations, by themselves or in combination with known risk factors, with PD.

Bivariate random effects meta-analysis of ROC curves.

Meta-analysis of receiver operating characteristic (ROC)-curve data is often done with fixed-effects models, which suffer many shortcomings. Some random-effects models have been proposed to execute a meta-analysis of ROC-curve data, but these models are not often used in practice. Straightforward modeling techniques for multivariate random-effects meta-analysis of ROC-curve data are needed. The 1st aim of this article is to present a practical method that addresses the drawbacks of the fixed-effects summary ROC (SROC) method of Littenberg and Moses. Sensitivities and specificities are analyzed simultaneously using a bivariate random-effects model. The 2nd aim is to show that other SROC curves can also be derived from the bivariate model through different characterizations of the estimated bivariate normal distribution. Thereby the authors show that the bivariate random-effects approach not only extends the SROC approach but also provides a unifying framework for other approaches. The authors bring the statistical meta-analysis of ROC-curve data back into a framework of relatively standard multivariate meta-analysis with random effects. The analyses were carried out using the software package SAS (Proc NLMIXED).