Implementation of CCEGA

1. Implementation of CCEGA Kirk C. Wilhelmsen Department of Genetics and Neurology

2. Background and Assumptions • The era of massively parallel genetic analysis has begun • Hypothesis generated analysis needs to be complimented by exploratory hypothesis generating research • Cross disciplinary expertise is needed • Most studies have not designed with the intent of trying to share information between projects or with best practices

3. Barriers to Efficient Data Use • Information Tower of Bable • ELSI/IRB limitations of data sharing • Culture of autonomy • Redundant development • e.g. Proprietary data formats for analysis • Best practices not always used

4. Genetic Studies Design Ascertainment Sample Collection Phenotype Collection Laboratory Processing Genotype Laboratory Phenotype collection Primary & Exploratory Analysis Result Visualization Hypothesis Generation Informatics ELSI

5. Clinical Laboratory Analysis • Typically three independent groups • Many groups work on different projects • Each group has independent informatics effort In Practice

6. Most projects independent of others • Groups have different strengths • Redundancy of development efforts Clinical Clinical Clinical Laboratory Laboratory Laboratory Analysis Analysis Analysis Clinical Clinical Clinical Laboratory Laboratory Laboratory Analysis Analysis Analysis Clinical Laboratory Clinical Clinical Clinical Analysis Laboratory Laboratory Clinical Laboratory Analysis Analysis Clinical Laboratory Analysis Clinical Laboratory Analysis Clinical Laboratory Analysis Laboratory Clinical Analysis Analysis Clinical Laboratory Clinical Laboratory Analysis Laboratory Analysis Analysis

7. Variable compliance HIPPA/ELSI issues • e.g. Use of PHI in labs • e.g. Sharing non-paternity data with clinic • Independent data management • identifiers for subjects Clinical Laboratory Analysis Issues:Clinic to Lab

8. Clinical Laboratory Analysis Issues:Lab to Analysis • Independent data management • Data version control • Redundancy

9. Clinical Laboratory Analysis Issues:Clinic to Analysis • Variable compliance HIPPA/ELSI issues • Unmonitored use of PHI • Independent data management • Data version control • Redundancy

10. Informatics Clinical Laboratory Analysis

11. Clinical Informatics Laboratory Analysis • Advantage • Reduced redundancy and increased efficiency • Reusable infrastructure • Encourage best practices • Facilitate data sharing • Increase cooperation • Change culture • Cross training • Facilitate HIPPA/IRB compliance • Facilitate exploratory genetic analysis • Disadvantages • Loss of autonomy • Conformance with standards • Potential for forced data sharing

12. Goals • Develop the preliminary data to apply for a P50 to create the CCEGA • Deliverables • Develop a protocol for prospective using ongoing studies as examples to define best practices • Develop a prototype informatics infrastructure • Demonstrate the utility of data mining with established project • Facilitate use of best practices for existing projects • Develop an environment for cross training each other and trainees

13. Working Groups • ELSI • Exploratory Analysis • Association • Proteomics and Transcriptional Profiling • Case Control • Family • Informatics • Integration

14. Goals For Working Groups • Have fun and keep having fun • Enrich each others work through collaboration and education • Develop pilot projects • Educate other groups • Workshops • colloquia • Educate Trainees • Establish tutorials • Train through participation in projects Get Preliminary Data for P50