Supplementary Data s11

Supplementary data

Meta-analysis dataset

For the meta-analysis, data were obtained either from dbGaP1, 2 or directly from the investigator who performed the GWAS.3-5 Only a subset of the NIA Phase II dataset of 1745 cases and 4047 controls have been deposited into the public domain; these include the 260 PD cases and 263 controls also included in NIA Phase I as well as an additional 671 PD cases and 535 controls. All datasets employed standard UK Brain Bank criteria6 for the diagnosis of PD, with a modification to allow the inclusion of cases that had a family history of PD.

An Illumina genotyping array (250, 300, 370, 550, 610, 1M, Omni1) was used by all studies. Prior to performing imputation, SNPs were excluded if: 1) there was ambiguity to strand (A/T or C/G); 2) mapped improperly to build 36.1; 3) minor allele frequency (MAF) < 0.01; 4) SNP call rate < 98%; 5) violation of Hardy Weinberg Equilibrium (HWE) in controls at p< 10-5; 6) haplotype-based test for non-random missing genotype data (i.e. minimum mishap test7) at p< 10-5; 7) significant differences in the SNP genotype rate between cases and controls at p< 10-4; 8) significant differences in the SNP genotype rate between males and females at p< 10-4; 9) significant differences in the SNP minor allele frequency between males and females at p< 10-7; 10) significant homogeneity test (Fisher’s exact test) across all control populations at p< 10-3 (n=506). Imputation was then performed for all autosomes using MACH 1.0.8 Each dataset was imputed separately. In each case, the subset of 500 samples (250 cases and 250 controls) with the highest call rates were used to perform the initial model parameter calculation using the --greedy setting and 100 iterations. These parameter files were then used to perform the imputation on all other individuals in the dataset. Prior to meta-analysis, results were filtered for imputation efficiency and any study with a MACH-derived Rsq < 0.30 did not contribute a result for that SNP to the meta-analysis.

Reference

1. Fung HC, Scholz S, Matarin M, et al. Genome-wide genotyping in Parkinson's disease and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol 2006;5:911-6.

2. Simon-Sanchez J, Schulte C, Bras JM, et al. Genome-wide association study reveals genetic risk underlying Parkinson's disease. Nat Genet 2009;41:1308-12.

3. Pankratz N, Wilk JB, Latourelle JC, et al. Genomewide association study for susceptibility genes contributing to familial Parkinson disease. Hum Genet 2009;124:593-605.

4. Edwards TL, Scott WK, Almonte C, et al. Genome-wide association study confirms SNPs in SNCA and the MAPT region as common risk factors for Parkinson disease. Ann Hum Genet 2010;74:97-109.

5. Hamza TH, Zabetian CP, Tenesa A, et al. Common genetic variation in the HLA region is associated with late-onset sporadic Parkinson's disease. Nat Genet 2010;42:781-5.

6. Hughes AJ, Daniel SE, Kilford L, Lees AJ. Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 1992;55:181-4.

7. Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-75.

8. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010;34:816-34.

Supplementary Table 1. Genes and SNPs in the pathway.

1. PROTEIN DOMAIN SPECIFIC BINDING