National Database for Autism Research (NDAR)

Central Repository Access Request Procedure

What is NDAR?

• NDAR is a biomedical informatics system and central repository developed by the NIH. NDAR provides a common platform for data collection, retrieval and archiving, while allowing for flexibility in data entry and analysis.

• A central function of NDAR is to store and to link together genetic, phenotypic, images and other data derived from individuals who participate in autism research studies.

How does one access NDAR data?

• Researchers need to apply to the NDAR Data Access Committee (DAC) in order to be granted access to the NDAR collection.

• The DAC approves access to data and/or images from the NDAR Central Repository for research purposes only.

• The DAC will review the Central Repository Access Request and the Data Use Certification (DUC) of each Recipient requesting data and provide access based on the expectations outlined in the NDAR policy.

These policy expectations include:

• Use the data only for the approved research; • Protect data confidentiality; • Follow appropriate data security protections; • Follow all applicable laws, regulations and local institutional

policies and procedures for handling NDAR data; • Not attempt to identify individual participants from whom data

within a dataset were obtained; • Not sell any of the data elements from datasets obtained from

the NIH NDAR data repository;

Expectations (cont.)• Not share with individuals other than those listed in the request any of the data elements from datasets obtained from the NIH NDAR data repository;

• Agree to the listing of a summary of approved research uses within the NIH NDAR data repository along with his or her name and organizational affiliation;

• Agree to report, in real time, violations of the NDAR policy to the DAC;

• Acknowledge the NDAR policy with regard to publication; and,

• Provide annual progress reports on research using NDAR data.

• In the event that requests raise concerns related to privacy and confidentiality, risks to populations or groups, or other concerns, the DAC will consult with other experts as appropriate.

• Recipients seeking access to data or images from NDAR are expected to submit their Central Repository Access Request, including a DUC, certified and co-signed by the Principal Investigator and the designated Institutional Official(s).

• Completing this Central Repository Data Access Request is a necessary step to access data or images from NDAR. Submission of data to NDAR may be subject to the NDAR Central Repository Submission Request and procedures.

Steps to Request General Access to the NDAR Central Repository

• Read the NDAR Central Repository Data Use Certification (DUC).

• Complete Recipient Information and Certifications pages, including your Institution’s Federal-wide Assurance number and a Research Use Statement:– a brief description of your Research Project in the text box provided (this can be on a separate piece

of paper) that includes the objectives, design, and analysis plan.

– Provide a statement as to whether you have/will apply for, obtain, or do not have a Certificate of Confidentiality for the Research Project.

– List all the collaborating investigators at your organization. By submitting an individual’s name on the form, you and your Institutional Official affirm that the collaborators have read and agreed to the terms and conditions within the Data Use Certification.

– Your collaborators at different organizations must complete separate requests for the data. Coordinated requests by collaborating organizations should all use the same title in their request and each should reference the others in the Research Use Statement.

University of Illinois at Chicago’s Research Project Description:

Integrated Informatics and Modeling of Multiple Data Types to Predict Novel Genetic and Environmental Risk and Protective Factors for Neuropsychiatric Disorders and Phenotypes

The Conte Center for Computational Neuropsychiatric Genomics comprises 17 project and core leaders distributed across seven institutions, including The University of Chicago, The University of Illinois at Chicago, Northwestern University, Stanford University, Columbia University, Harvard University, and the University of Haifa in Israel. Our overall project goal is to mathematically model and predict phenotype-gene-environment associations using multiple data types relevant to neuropsychiatric disorders, including autism. While numerous groups around the globe tackle the bigger problem of tracing environmental and genetic factors influencing complex human traits, only a small fraction of these studies is relevant to neuropsychiatry. To our knowledge, no holistic approach of mathematical modeling and computational scrutiny of multiple threads of experimental evidence exists (e.g. pharmacogenomic and prescription data, legacy genetic association and linkage datasets, electronic medical records, text mining of enormous biomedical corpora). This project is designed to fill this gap. We will develop, test, and apply a drastically new computational methodology for the analysis of more than one complex phenotype at a time. Specifically, we propose to design and validate a battery of novel analytical tools for the inference of causal relationships among human genomic variations, environmental factors, and more than one mental health phenotype, explicitly exploiting the genetic and environmental non-independence of complex (multigenic) disorders. Strategically, we are counting on extracting more and higher-quality information for multiple types of existing and newly generated data by considering it within one unified probabilistic model - as opposed to what can be extracted from any data type and for any single phenotype alone. This study is designed to determine whether we can detect genetic variants that influence more than one disorder, whether we can identify environmental factors that influence risk for more than one pathological phenotype or are very specific to a single phenotype, and whether we can infer a probability distribution (i.e., the probability that each specific gene can harbor variations that predispose or protect an individual from disease) from a molecular network of factors relevant to disease phenotype. To evaluate performance of our methods, we will use cross-validation, one of the computational techniques designed for assessing the empirical validity of prediction algorithms and commonly used by the machine learning and statistics communities. The ultimate validation of our predictions can come only from experimental approaches including molecular, biochemical, proteomic, and animal models that our Center is funded to perform.

• Sign the Recipient Information and Certifications page, and obtain your Institutional Official’s signature and date.

• Provide a scanned copy of the completed DUC Recipient Information and Certifications pages, including signatures, when requesting an account or requesting additional access to NDAR data at http://ndarportal.nih.gov.

• Access Request Review: The DAC will review requests to access the NDAR Central Repository. Such reviews are generally completed within 10 business days.

• The DAC will notify NDAR staff if the access request has been approved, and an account will then be provided. The user will receive an automated notification of their account update with any modified user name, passwords, or instructions for accessing the NDAR Central Repository.

• Optional: NDAR System Training (if request approved): Contact NIH through NDAR@mail.nih.gov to discuss specific training needs the user may have and schedule the training.

• The majority of the preceding was taken directly from NDAR’s policy statement and instructions which can be found at:

• The policy statement for NDAR can be found at:http://ndar.nih.gov/ndarpublicweb/Documents/NDAR_Policy.pdf

• NDAR Data Access Request Form (and instructions) can be found

at: http://tinyurl.com/NDAR-DAR (http://ndar.nih.gov/ndarpublicweb/Documents/NDAR%20Data%20Access%20Request%20DUC%20FINAL.pdf)

• Once you have been approved you will have access to the NDAR collections.

• Access to data from Pediatric MRI, ATP Federated Clinical Assessments, ATP Brain MRI Data and Images, IAN, and the AGRE Restricted Access Permissions Group requires a separate application for each of those datasets.

• Full access to SSC data are not available through NDAR at this time; NDAR is working on getting that data federated; the process on how to access SSC data is outlined on the SFARI website.

• General descriptive subject information from NIMH Genetics, Release 11, is currently available as part of the NDAR Collections; however, Release 12, is not currently available although it should be soon.

• Full access to Release 12 (which includes TASC families) may be accessed by applying directly to NIMH Genetics.