# Presentation: 755685

- Andrew Singleton, Chief of the Molecular Genetics Section; Acting Chief Laboratory of Neurogenetics, National Institute on Aging singleta@mail.nih.gov

## Rewards and Challenges:

- I.		Persuading collaborators of the pros of open 	data sharing
- II.	Ensuring sample collection and consent allows 	data posting and sharing
- III.	Striving to use standardized phenotype (and 	genotype) measures that can be applied 	across past, present and future studies
- IV.	Deciding what, where and how to post
- V.		Continued support of the data and 	reasonable responses to queries

## Data sharing

- This is my data.....it took my time, effort and resources to generate – why should I share the spoils
- My post-docs and junior investigators need these data for their careers
- My future funding is dependent on the findings from this dataset – why should I jeopardize this?
- Others will not analyze the data appropriately

## Pros of data sharing

- There is simply too much for one group to do
- Spreads the analytical burden
- Billions of genetic data points x thousands of phenotypes
- ‘Good will’ – the open access plan encourages collaboration
- Generally - no single investigator can prove association unequivocally

## Persuading collaborators of the pros:

***Wang et al., NatRevGen, 2005***

- Size matters

## Persuading collaborators of the pros:

- Follow-up size matters

## Persuading collaborators of the pros:

- Size matters

## Emerging technologies

- Sharing promotes collaboration – critical for survival in the coming era
- EMERGING TECHNOLOGIES
- GW assay of epigenetic variation
- Digital expression
- GW assay of allelic expression
- High density resequencing
- GW sequencing

## Rewards and Challenges:

- I.		Persuading collaborators of the pros of open 	data sharing
- II.	Ensuring sample collection and consent allows 	data posting and sharing
- III.	Striving to use standardized phenotype (and 	genotype) measures that can be applied 	across past, present and future studies
- IV.	Deciding what, where and how to post
- V.		Continued support of the data and 	reasonable responses to queries

## Sample collection and consent

- Covered well already – however, to reinforce:
- Intellectual property
- Tiered consents
- Important to seek counsel on what is a real barrier and what is an artificial barrier
- Funding decisions likely to be highly influenced by ability to share

## Rewards and Challenges:

- I.		Persuading collaborators of the pros of open 	data sharing
- II.	Ensuring sample collection and consent allows 	data posting and sharing
- III.	Striving to use standardized phenotype (and 	genotype) measures that can be applied 	across past, present and future studies
- IV.	Deciding what, where and how to post
- V.		Continued support of the data and 	reasonable responses to queries

## Standardized genotype

- Different platforms; (affymetrix and illumina)
- Different chips and different versions
- Different calling algorithms
- Ensuring measures of genotyping quality across labs

- Seek advice from an experienced user who has made all the mistakes

## Standardized genotype

- Different platforms; (affymetrix and illumina)
- Different chips and different versions
- Different calling algorithms
- Ensuring measures of genotyping quality across labs

- Release of raw unfiltered data helps resolve these problems

## Standardized phenotype

- Maximising impact of data (phenotypes!) – digital nature of data means pooling is achievable
- Requires that phenotypic measures be directly compatible
- Association is likely to be complex, so reducing complexity is a must
- Decide on a measure and stick to it

## Rewards and Challenges:

- I.		Persuading collaborators of the pros of open 	data sharing
- II.	Ensuring sample collection and consent allows 	data posting and sharing
- III.	Striving to use standardized phenotype (and 	genotype) measures that can be applied 	across past, present and future studies
- IV.	Deciding what, where and how to post
- V.		Continued support of the data and 	reasonable responses to queries

## What, when, where and how	

- P-values and OR
- Genotype frequencies
- Individual genotypes (ie per sample)
- Calling metrics
- Raw image data

- What level of phenotypic data?

## What, when, where and how	

- Embargoed release
- On publication
- X months following data production
- Immediately on data production

## Where and how

- Depositing at a central database
- Depositing on an institutionally supported web-site or distributing in an *ad hoc* manner

## Where and how

- Depositing at a central database

## Where and how

- Depositing at a central database

- The main time investment is a one time deposit of data

- Infrastructure burden removed

- Some data-cleaning and manipulation
-  phenotypic data
-  Genotypic data – imputation, clustering
- Data tied in to NCBI databases

## Continued support

- Depositing on a local web-site or distributing in an *ad hoc* manner –
- Requires significant time and resources to distribute the data
- Being prepared to answer questions
- Being asked to re-analyze

- Recommend release to dbGAP; and only supplementary release locally if there is substantial infrastructure in place

## Our experience

- In the last 18 months generation of ~3 billion genotypes using Illumina platform
- Neurological disease (case-control)
- Diverse populations to catalog variation
- Epidemiological cohorts with focus on aging phenotypes

##  Generation and analysis of genome wide SNP genotype data in PD, ALS, stroke, AD and controls 
 Six postdocs generated these data; all have one first author paper and five “joint 1st author” papers

-  Generation and analysis of genome wide SNP genotype data in PD, ALS, stroke, AD and controls
-  Six postdocs generated these data; all have one first author paper and five “joint 1st author” papers

- Our experience

**Notes:**
In a genotyping effort that was jointly sponsored by NIA and NINDS we performed a large scale genetic association study in Parkinson’s disease. We generated more than 200 million genotypes in roughly 600 individuals with the aim of identifying genetic risk factors for this debilitating disease. Not only did these data allow us to look for common changes in the sequence of the genome that may play a role in the disease but also this technique allowed us to observe regions of the genome that are deleted or present as extra copies in each individual.

##  Paradigm – NOT to define all risk loci
 Searching for high risk loci – (none found)
 Generate GWSNP data in publicly available DNA 	samples
 Generating data that can be mined and augmented by 	other interested researchers

-  Paradigm – NOT to define all risk loci
-  Searching for high risk loci – (none found)
-  Generate GWSNP data in publicly available DNA 	samples
-  Generating data that can be mined and augmented by 	other interested researchers

- Our experience

**Notes:**
In a genotyping effort that was jointly sponsored by NIA and NINDS we performed a large scale genetic association study in Parkinson’s disease. We generated more than 200 million genotypes in roughly 600 individuals with the aim of identifying genetic risk factors for this debilitating disease. Not only did these data allow us to look for common changes in the sequence of the genome that may play a role in the disease but also this technique allowed us to observe regions of the genome that are deleted or present as extra copies in each individual.

## >600,000,000 genotypes from PD, ALS stroke and control cohorts posted publicly 

 downloaded by >500 unique visitors

- >600,000,000 genotypes from PD, ALS stroke and control cohorts posted publicly
-  downloaded by >500 unique visitors

- _http://ccr.coriell.org/ninds/_

- _http://www.ncbi.nlm.nih.gov/sites/entrez?db=gap_

- Two manuscripts published; five in press/preparation by ‘downloaders’

**Notes:**
The raw genotypes generated as a part of this effort have been released into the public domain and as such this release of genotype data was the largest outside of the International Haplotype Map project. It is our belief that given the resources and effort required to generate these data, they should be available to other researchers. Because the control population genotyped was screened for absence of other neurological disorders, the genotype information from this group can be used as a control for other diseases. Not only does this resource allow interested scientists to browse and analyze the data but also it allows researchers to augment these data. Thus this resource is really the foundation for a large scale effort to identify genetic risk factors for Parkinson’s disease and other neurological disorders.

## Data was released ~6 weeks prior to publication – nothing ‘bad’ happened
Three ongoing collaborations based on the initial work
Collaborators want to share sample series and results from their own GWAS
This has brought novel analytic techniques and perspectives to our laboratory

- Three ongoing collaborations based on the initial work
- Collaborators want to share sample series and results from their own GWAS
- This has brought novel analytic techniques and perspectives to our laboratory

- Public release – facilitating collaboration

## We analyzed CNVs manually

- Six people 3 months

- Data release directly led to a collaboration to create and test automated CNV calling

## Bottom line

- Data sharing is becoming standard
- Requires time, resources and planning to be most effective
- It repays the investment, collaborators realize you know your data best
- Funding bodies are beginning to mandate data sharing
- If there is no plan for sharing, no adequate consent, no depth to the data – will have a lower funding priority