# Presentation: 585759

## Replication and Collaboration

- Three models of collaboration
- Put data out there (SHARe)
- Organize consortium prospectively (CARE)
- Encourage collaboration (STAMPEED), but need to provide infrastructure
- Facilitating rapid replication
- Lab infrastructure, queuing
- Availability and type of samples
- Whom to type
- Agreement to do very rapidly
- Established resource

## Challenges of Attempting Replication in Different Populations or Settings

- Beware of blaming lack of association on differences in exposures or populations
- Issues in replication can raise intriguing scientific questions
- Note lack of association of FTO in DGI diabetes scan due to controlling for obesity
- Differences in population genetics can influence ability to confirm finding, especially with alleles at very high frequency
- SLC30A8 in African sample due to 97% allele frequency
- 8q24 rs6983267 risk allele for prostate cancer is 96% in African-Americans

## Cross-Study Analyses

- Imputation for cross-platform comparisons essentially solved problem but need to do actual genotyping in lab before reporting
- How to temper over-blown expectations of GWA studies?
- Importance of reliable, standardized phenotyping– can come out of this meeting?
- Changes over time
- Major issue 50 years from now
- Addition of comorbidity data to clinical studies?
- Need to plan in advance for in silico comparisons, to ensure compatibility of formats, genotypes, phenotypes

## Follow-Up Studies

- High-throughput sequencing available now, still have need for better methods of targeted sequencing
- When is the right time to develop animal models for effect on phenotype
- Animal models have limits– Huntington’s CAG repeat doesn’t reproduce phenotype in mouse
- Establishing causality for small effect size loci is challenging
- Methods for detecting non-SNP variation

## Follow-Up Studies: Three Types of Sequencing

- Common allele driving your association, may be SNP in LD to those typed and MAF > 3-4%: sequence through area of LD in modest number ( n ~ 96)
- May be other haplotypes not in LD affecting function: look in functional unit (gene) outside LD block, still for common SNPs (n ~ 100)
- What if multiple rare alleles affecting function: targeted sequencing in areas not tolerant of variation (n ~ 1,000), esp extreme phenotypes

## Data Sharing

- Earlier you can start on formulating data for sharing the better
- dbGaP happy to help you, starting with those willing to share
- Technically easier to put data together from outset rather than trying to connect post facto
- Versioning of datasets will occur
- Culture shift regarding impact on careers– positive aspects in NIA Parkinsons study
- De-identifying tough especially with image data
- Challenges of linking samples in repositories to datasets – should we pursue

## Consent/Approval

- Problems with proprietary consents
- Framingham experience with proposed privatization
- Why consents are limited to one condition: IRBs and community concerns
- Consents specifying that results not to be provided may not be “liberating”
- Value of intermediate step of Technical Advisory Group review in GAIN
- How to deal with time limited consent in sharing

## Consent/Approval (2)

- Every individual in one and only one consent category for dbGaP– need meticulous records
- No tribal consultation policy estab by NIH
- Value of working with patient organizations
- Community involvement has to be ongoing process, not one-time thing
- Biggest problem is getting controls
- What if there are conflicting consents in one ppt?
- Cultural sensitivities in minorities and foreign settings, religious concerns?
- Acceptability of narrower consents?

## Unanswered Questions that May Need Further Investigation

- Utility of convenient freezer controls?
- Exploring the role for weighting for functional or known SNPs
- Can we develop an “alpha spending function” similar to sequential analyses of clinical trials, for GWA?
- What is the value of heritability in justifying GWAS and complex diseases?
- How should public health importance of trait weigh in decisions on replication?

## Unanswered Questions that May Need Further Investigation (2)

- Importance and weighting of joint analysis versus any single study?
- How many scans of a given well-studied complex disease do we need?
- Reverse direction of function to association– MDM2 related to TP53 and cancer but not associated?
- What do complex consent forms do to participation rates, especially for controls?

## Things We at NIH Can Do

- Making association results available, esp negative ones
- Need repository of negative studies– importance of denominator of studies
- Provide infrastructure funding for collaborations and consortia– critical for young investigators
- Give priority to funding studies that collaborate or are part of consortia
- How do we deal with ‘hostile’ takeovers when academic collaborations are challenged by commercial interests?

## Things We at NIH Can Do (2)

- Supplement for reconsent, set timetables
- Support strategic genotyping
- Make sure DNA and samples collected in large-scale studies and trials
- Pre-publication analysis in dbGaP such as imputation, phenotype harmonization
- Can set up for rapid response– set aside repository samples waiting for replication
- Need for standardization of phenotypes for cross-study analyses

## Things We at NIH Can Do (3)

- Stimulate better methods of targeted sequencing
- Extensively sequence HapMap samples to identify common SNPs– avoid redundant  sequencing attempts
- Prospectively set up data collection process to be suitable for dbGaP
- Need to capture experience of data sharing
- Provide straw person consent elements– general use, sharing, genetics, future use, not discarded

## Things that Can Come Out of This Meeting: Action Items

- Catalog GWA studies going on or planned
- Better (or at least standardized) phenotyping...
- Shift playing field to protect young investigators

## Many Thanks to...

- Stephen Chanock, NCI
- Kat Gwinn, NINDS
- Thomas Lehner, NIMH
- Chris O’Donnell, NHLBI
- Jim Ostell, NLM

- Maggie Bartlett
- Catherine Bennet
- Mia Diggs
- Eva Kakoza
- Mukul Nerurkar
- Steve Robinson
- Larry Thompson
- Susan Vasquez

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