HuGENet Network of Networks Workshop:GEO-PD Consortium

Demetrius M. Maraganore, MD

Professor of Neurology

Mayo Clinic College of Medicine

Rochester, MN

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

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

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

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

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)

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