1. HuGENet Network of Networks Workshop: GEO-PD Consortium Demetrius M. Maraganore, MD Professor of Neurology Mayo Clinic College of Medicine Rochester, MN

2. Edmond J. Safra Global Genetics Consortia Michael J. Fox Foundation ($1.2 million initiative) Five grants awarded Tatiana ForoudCollaborative studies of a chromosome 5 PD susceptibility gene Demetrius MaraganoreCollaborative pooled analysis of the SNCA REP1variant and PD Haydeh PayamiGene-environment interaction in PD: predicting the onset, prognosis, and response to treatment Clemens ScherzerGene expression in PD Lorene NelsonGenetic and environmental factors in PD http://www.michaeljfox.org/news/article.php?id=114

3. Handling non-participation Be inclusive Invitation of all correspondence authors of published genetic association studies for a targeted gene and disease to participate in a collaborative pooled analysis Invitation of additional investigators to participate (e.g., correspondence authors of published genetic association studies for other genes and the same disease) Recognize participants Shared leadership (core PIs and co-PIs, Global Site PIs and co-Is) Authorships (multiple authors per site) Subcontracts Foster collegiality Annual meeting of the consortium Cope Metaanalysis of published data, including non-participating sites secondary analyses

4. Other scientific issues Comparison subjects Siblings, unrelated controls, or both Considerations on population stratification Case-only studies Correlation of genotypes to age at onset, or to prognostic outcomes (modifier genes) Gene interactions Gene-environment interactions Likely to require prospective study design Globally informative SNPs Haplotype tagging, LD mapping in diverse populations

5. Data flow Participant requirements N 100 cases, 100 controls Minimal dataset study characteristics clinical characteristics genotypes Sample sharing n = 20 DNAs (200 ng each) Willingness to share de-identified individual level data supplemental data online Transfer of minimal dataset to statistical core Formatted Excel spreadsheet Data archived in SAS database Checks for missing data, errors query sheets to investigators

6. Standardization of phenotypes and genotypes Standardization of phenotypes (formatted Excel spreadsheets) Study characteristics sources of cases: community or clinic sources of controls: community or hospital, blood bank, spouses diagnostic criteria (references) Individual level data cases and controls: source, age at study, gender, ethnicity, genotypes cases only: age at onset, family history (1 1st degree relative) Standardization of genotypes (DNAs for re-genotyping) List of 20 lab ids, genotypes sent to statistical core heterozygosity checks 20 DNAs (200 ng each) sent to laboratory core re-genotyping blinded to original allele calling List of new genotypes sent to statistical core tests of reliability (if < 90% reliability, the study is excluded) post-coding of all genotypes (with laboratory core as reference) genotyping reports to contributing sites (reliability, HWE, post-coded genotypes, cleaned datasets)

7. Other standardization issues Exclusion of studies Failure to provide minimal datasets, DNAs by deadlines Genotyping reliability < 90% Lack of HWE in controls Statistical considerations Tests for heterogeneity, HWE Unadjusted analyses (missing data) Adjusted analyses (confounders) study, age at study, gender Stratified analyses (genetic heterogeneity) ethnicity age at study gender family history