How Biorepositories Can Help Your Research

1. How Biorepositories Can Help Your Research Tatiana Foroud, Ph.D. Department of Medical and Molecular Genetics

2. Overview • Sample collection for genetic studies • Typical DNA applications for genetic studies • Results from several ongoing studies

3. Collecting Samples for Genetic Studies • Often the overlooked item in a protocol • Several large national studies that failed to obtain biological samples for DNA extraction • Many options now available for obtaining DNA • Critical that appropriate consent is obtained for future studies and potential sample sharing

4. DNA Extraction Buccal brushes Saliva Blood draw Guthrie card

5. Use of DNA for Studies • DNA must be of high quality • Amount of DNA required varies depending on the type of application • Sample storage must be secure and retrieval accurate

6. DNA Quality Assessment • A biorepository can provide assurances of DNA identity and quality • SNP fingerprint to ensure the correct sample is being provided • Relationships among samples • Gender validation • Future quality/integrity of sample • Future needs? • Race/ethnicity assessment through SNP genotyping

7. Clinical Data Set • Development of a minimal clinical dataset can be very useful • Ensure collect critical variables for future analyses (outcome, complications, severity) using standard instruments, measures, etc • Relevant comorbidities to provide future broader uses of data?

8. Common DNA Studies • Analyze single nucleotide polymorphisms (SNPs) • Limited scale or genome wide • Analyze DNA sequence to detect novel variation • Gene dosage abnormalities (deletions/ duplications) • Limited scale or genome wide

9. SNP Assay • Technology available on campus for SNP genotyping • Suited for moderate to large scale projects, working in 384-well format, but can accommodate smaller projects

10. ADH1C ADH1A ADH4 ADH5 ADH7 ADH6 ADH1B 74.9 13.8 15.2 43.4 61.4 34.6 365 kb 107 SNPs analyzed Alcohol dependence and the ADH Gene cluster Adapted from Howard Edenberg

11. ADH Gene Cluster Modified from Edenberg et al, 2006

12. COGA: Collaborative Study on the Genetics of Alcoholism Co-Principal Investigators: B. Porjesz, V. Hesselbrock, H. Edenberg, L. Bierut Nine centers where data collection, analysis and storage take place: Univ. of Connecticut V. Hesselbrock Indiana University H. Edenberg, J. Nurnberger, PM Conneally, T. Foroud University of Iowa S. Kuperman, R. Crowe SUNY HSC @ Brooklyn B. Porjesz Washington University L. Bierut, A. Goate, J. Rice Univ. of Calif. (UCSD) M. Schuckit Howard University R. Taylor Rutgers University J. Tischfield Southwest Foundation L. Almasy NIAAA Staff Collaborator: M. Guo This national collaborative study is supported by the NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). In memory of Henri Begleiter and Theodore Reich, Principal and Co-Principal Investigators of COGA since its inception. We are indebted to their leadership in the establishment and nurturing of COGA, and acknowledge with great admiration their seminal scientific contributions to the field.

13. Sequence Variation • Provides ability to obtain long sequence reads, exceptional accuracy and high throughput Bloomington: Genome Sequencer FLX (GS FLX) Purdue: SOLiD Analyzer

14. National Cell Repository for Alzheimer Disease NIA funded resource to make DNA and associated clinical material available to qualified researchers U24 AG021886

15. Basics of NCRAD • Samples from over 6,000 individuals are currently available • Establish lymphoblastoid cell lines (LCL) for a continuous source of DNA • Obtain brain tissue to confirm the AD diagnosis • Can also be used for additional studies • Detailed clinical and family history information is available from all sampled subjects

16. Accomplishments of NCRAD • Samples distributed to > 80 investigators • >25,000 DNA samples have been distributed to researchers • >1500 LCL have been distributed • DNA has been genotyped at central NIH laboratories and found to be of high quality • > 120 publications generated from these samples

17. Genome Wide Association Studies

18. Genome Wide Association Studies DNA inherited in blocks, so not all 10 million SNPs have to be tested Genotype several hundred thousand SNPs Controls Cases Compare frequency of SNP alleles in two groups Compare frequency of SNP genotypes in two groups

19. What does a SNP Chip look like?

20. Copy Number Variation Increased number of copies in some samples Data from 270 HapMap Samples on Chromosome 11

21. Genome Wide Association Study in Familial Parkinson Disease R01 NS037167

22. Sample Demographics

23. Overview of Data Verification

24. Cryptic Relatives • Despite criteria of each study to allow participation in only one genetic study, still identified related samples • Siblings • Parent-offspring

25. Overview of Data Verification

26. DNA Source • Whole genome amplified DNA • Overall lower genotype call rates • Typically between 90-95% • Also were able to identify samples from lymphoblastoid cell lines • Mosaics (i.e. partial loss of X chromosome)

27. Overview of Data Verification

28. PROGENI, GenePD, CEPH Utah CEPH families are all in the main cluster African American Additional Hispanics

29. Samples Removed from Analysis

30. GWAS Results Pankratz & Wilk et al., 2008

31. Acknowledgements- PROGENI Indiana University Nathan Pankratz, Ph.D. Bernardino Ghetti, M.D. Cheryl Halter, M.S., CCRC Claire Wegel, M.S. Cincinnati Children’s Hosp. William C. Nichols, Ph.D. Michael Pauciulo Veronika E. Elsaesser Diane K. Marek Univ. of Tennessee Ronald Pfeiffer, M.D. University of Rochester Alice Rudolph, Ph.D. PSG Investigators & Coordinators Supported by NS37167

32. How Biorepositories Can Help Your Research • Provide expertise regarding sample acquisition • Provide high quality DNA for genetic studies • Identify core resources to advance genetic hypotheses