Collaborative Approach for Genetic Studies in Parkinson's Disease Research

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 Foroud Collaborative studies of a chromosome 5 PD susceptibility gene • Demetrius Maraganore Collaborative pooled analysis of the SNCA REP1variant and PD • Haydeh Payami Gene-environment interaction in PD: predicting the onset, prognosis, and response to treatment • Clemens Scherzer Gene expression in PD • Lorene Nelson Genetic 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