NDA Help Center

Collection - General Tab

Fields available for edit on the top portion of the page include:

  • Collection Title
  • Investigators
  • Collection Description
  • Collection Phase
  • Funding Source
  • Clinical Trials

Funding Source

The organization(s) responsible for providing the funding is listed here. 

Supporting Documentation

Users with Submission privileges, as well as Collection Owners, Program  Officers, and those with Administrator privileges, may upload and attach supporting documentation. By default, supporting documentation is shared to the general public, however, the option is also available to limit this information to qualified researchers only. 

Grant Information 

Identifiable details are displayed about the Project of which the Collection was derived from. You may click in the Project Number to view a full report of the Project captured by the NIH. 

Clinical Trials

Any data that is collected to support or further the research of clinical studies will be available here. Collection Owners and those with Administrator privileges may add new clinical trials. 

Frequently Asked Questions

  • When a Collection is created by NDA staff and marked as Shared, an email notification will automatically be sent to the PI(s) of the grant(s) associated with the Collection to notify them.

  • During Collection creation, NDA staff determine the appropriate Permission Group based on the type of data to be submitted, the type of access that will be available to data access users, and the information provided by the Program Officer during grant award.

  • The NDA system does not allow for a single grant to be associated with more than one Collection; therefore, a single grant will not be listed in the Grant Information section of a Collection for more than one Collection.

  • In general, each Collection is associated with only one grant; however, multiple grants may be associated if the grant has multiple competing segments for the same grant number or if multiple different grants are all working on the same project and it makes sense to hold the data in one Collection (e.g., Cooperative Agreements).

Glossary

  • A privilege provided to a user associated with an NDA Collection or NDA Study whereby that user can perform a full range of actions including providing privileges to other users. 

  • Typically considered Descriptive/Raw Data unless related to the primary aims of a study, Clinical Data includes diagnostic assessments, clinical measures, medical histories, demographic data, questionnaires, etc. Each set of clinical data is submitted to the NDA using a corresponding Data Structure in the NDA Data Dictionary.

  • A Collection is a virtual container into which data will be submitted and shared.  It also provides important information about the project, funding amounts, reported enrollment amounts, data sharing schedule, and results that will help program staff better evaluate the grant.  While the general rule is that one Collection is created for each grant, it may be appropriate to associated multiple grants with a single Collection in the case of collaborative projects or projects submitting data through a single site such as a Data Coordinating Center. 

  • Generally, the Collection Owner is the contact PI listed on a grant. Only one NDA user is listed as the Collection owner. Most automated emails are primarily sent to the Collection Owner.

  • The Collection Phase provides information on data submission as opposed to grant/project completion so while the Collection phase and grant/project phase may be closely related they are often different.  Collection users with Administrative Privileges are encouraged to edit the Collection Phase.  The Program Officer as listed in eRA (for NIH funded grants) may also edit this field. Changes must be saved by clicking the Save button at the bottom of the page.  This field is sortable alphabetically in ascending or descending order. Collection Phase options include: 

    • Pre-Enrollment:  A grant/project has started, but has not yet enrolled subjects.
    • Enrolling:  A grant/project has begun enrolling subjects.  Data submission is likely ongoing at this point.
    • Data Analysis:  A grant/project has completed enrolling subjects and has completed all raw data submissions.
    • Funding Completed:  A grant/project has reached the project end date.
  • The Collection State indicates whether the Collection is viewable and searchable.  Collections can be either Private, Shared, or an Ongoing Study.  A Collection that is shared does not necessarily have shared data as the Collection State and state of data are independent of each other.  This field can be edited by Collection users with Administrative Privileges and the Program Officer as listed in eRA (for NIH funded grants). Changes must be saved by clicking the Save button at the bottom of the page.

  • An editable field with the title of the Collection, which is often the title of the grant associated with the Collection.

  • Provides the grant number(s) for the grant(s) associated with the Collection.  The field is a hyperlink so clicking on the Grant number will direct the user to the grant information in the NIH Research Portfolio Online Reporting Tools (RePORT) page.

  • Various documents and materials to enable efficient use of the data by investigators unfamiliar with the project and may include the research protocol, questionnaires, and study manuals.  

  • The total number of unique subjects for whom data have been shared and are available for users with permission to access data.

NDA Help Center

Collection - Shared Data Tab

This tab provides a quick overview of the Data Structure title, Data Type, and Number of Subjects that are currently Shared for the Collection. The information presented in this tab is automatically generated by NDA and cannot be edited. If no information is visible on this tab, this would indicate the Collection does not have shared data or the data is private.

The shared data is available to other researchers who have permission to access data in the Collection's designated Permission Group(s). Use the Download button to get all shared data from the Collection to the Filter Cart.

 

Frequently Asked Questions

  • To see what data your project have submitted are being used by a study, simply go the Associated Studies tab of your collection.  Alternatively, you may review an NDA Study Attribution Report available on the General tab.  

  • Often it becomes more difficult to organize and format data electronically after the project has been completed and the information needed to create a GUID may not be available; however, you may still contact a program staff member at the appropriate funding institution for more information.

  • Unlike completed projects where researchers may not have the information needed to create a GUID and/or where the effort needed to organize and format data becomes prohibitive, ongoing projects have more of an opportunity to overcome these challenges.  Please contact a program staff member at the appropriate funding institution for more information.

Glossary

  • A defined organization and group of Data Elements to represent an electronic definition of a measure, assessment, questionnaire, or collection of data points.  Data structures that have been defined in the NDA Data Dictionary are available at https://ndar.nih.gov/data_dictionary.html. 

  • A grouping of data by similar characteristics such as Clinical Assessments, Omics, or Neurosignal data.

  • The term 'Shared' generally means available to others; however, there are some slightly different meanings based on what is Shared.  A Shared NDA Collection or NDA Study is viewable and searchable publicly regardless of the user's role or whether the user has an NDA account.  A Shared Collection or NDA Study does not necessarily mean that data submitted to the Collection or used in the NDA Study have been shared as this is independently determined.  Data are shared according the schedule defined in a Collection's Data Expected Tab and/or in accordance with data sharing expectations in the NDA Data Sharing Terms and Conditions.  Additionally, Supporting Documentation uploaded to a Collection may be shared independent of whether data are shared, but will only be viewable and accessible if the Collection is Shared.

NDA Help Center

fMRi

fMRI stands for functional magnetic resonance imaging. fMRI tests measure blood flow, providing detailed functional images of the brain or body. 

Acquisition
The Acquisition parameters needed for an experiment include the following:

Name of the experiment is required. Please be concise and specific as possible.
Following experiment name, selection boxes are provided for the Equipment, Software, or other items specific to experiment type. At least one selection is required for each. If NDAR does not have the appropriate listing, select Add New to add the information provided. Following the selection boxes, provide additional information may be required depending on experiment type. Any required items are denoted by an asterisk (*).

Block/Event Design
At least one block/event is required. Note that any fields denoted with an asterisk (*) are required. All data must be devoid of personally identifiable data, including the contents of any files attached to the experiment.

Note: To simplify definition of multiple events, we provide an Import from XML function. This function supports importing data from all three experiment sections (Acquisition, Block/Event Design, and Post Processing), at this time files cannot be uploaded from XML A test format is provided here and our XML Schema Definition (xsd) can be found here.

Post Processing
If you have completed any post processing on your data, please choose 'Yes' for Has Postprocessing? If not, select 'No'. Depending on this selection the remaining post processing fields will be enabled (some of which will be required). If you are initially providing raw, non-analyzed data you can select 'No', then return to the experiment to add post processing steps at a later date when the analyzed data are being provided.

Please provide information about post-processing manipulations, i.e. artifact detection algorithms, segmentation used for post data collection, items denoted with an asterisk (*) are required.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments tab. 

NDA Help Center

Collection - Submissions Tab

Users with permission to access Shared data in the Collection’s assigned Permission Group may use this tab. 

Here, you can:

  • Review your uploads to your Collection, monitor their status, and download them individually to verify their contents.
  • Download individual datasets as a secondary user of the data approved for access.
  • Identify and download datasets containing errors identified by NDA's QA/QC process for review and resolution.
  • Report suspected or discovered Personally Identifiable Information in a submission via the Actions column.

Frequently Asked Questions

Glossary

  • The default view of Datasets within a Collection's Submission tab.

  • A Submission Loading Status on a Collection's Submission Tab that indicates that an issue has prevented the successful loading of the submission.  Users should contact the NDA Help Desk for assistance at NDAHelp@mail.nih.gov.

  • The NDA has two Submission Cycles per year - January 15 and July 15.

  • An interface to notify NDA that data may not be submitted during the upcoming/current submission cycle.  

  • The unique and sequentially assigned ID for a submission (e.g. a discrete upload via the Validation and Upload Tool), which may contain any number of datafiles, Data Structures and/or Data Types, regardless of the Submission Loading Status. A single submission may be divided into multiple Datasets, which are based on Data Type.

  • The total number of unique subjects for whom data have been shared and are available for users with permission to access data.

  • The total number of unique subjects for whom data have been submitted, which includes data in both a Private State and a Shared State.

NDA Help Center

Collection - Publications Tab

The number of Publications is displayed in parentheses next to the tab name. Clicking on any of the Publication Titles will open the Publication in a new internet browsing tab. 

Collection Owners, Program Officers, and users with Submission or Administrative Privileges for the Collection may mark a publication as either Relevant or Not Relevant in the Status column. 

 

Frequently Asked Questions

  • Publications are considered relevant to a collection when the data shared is directly related to the project or collection.

  • PubMed, an online library containing journals, articles, and medical research. Sponsored by NiH and National Library of Medicine (NLM). 

Glossary

  • A link to the Create an NDA Study page that can be clicked to start creating an NDA Study with information such as the title, journal and authors automatically populated.

  • Indicates that the publication has not yet been reviewed and/or marked as Relevant or Not Relevant so it has not been determined whether an NDA Study is expected.

  • A publication that is not based on data related to the aims of the grant/project associated with the Collection or not based on any data such as a review article and, therefore, an NDA Study is not expected to be created.

  • PubMed provides citation information for biomedical and life sciences publications and is managed by the U.S. National Institutes of Health's National Library of Medicine.

  • The PUBMed ID is the unique ID number for the publication as recorded in the PubMed database.  

  • A publication that is based on data related to the aims of the grant/project associated with the Collection and, therefore, an NDA Study is expected to be created.

NDA Help Center

EEG

EEG stands for electroencencephalogram and is a test used to measure electrical activity in the brain.

Acquisition
The Acquisition parameters needed for an experiment include the following:

Name of the experiment is required. Please be concise and specific as possible.
Following experiment name, selection boxes are provided for the Equipment, Software, or other items specific to experiment type. At least one selection is required for each. If NDAR does not have the appropriate listing, select Add New to add the information provided. Following the selection boxes, provide additional information may be required depending on experiment type. Any required items are denoted by an asterisk (*).

Block/Event Design
At least one block/event is required. Note that any fields denoted with an asterisk (*) are required. All data must be devoid of personally identifiable data, including the contents of any files attached to the experiment.

Note: To simplify definition of multiple events, we provide an Import from XML function. This function supports importing data from all three experiment sections (Acquisition, Block/Event Design, and Post Processing), at this time files cannot be uploaded from XML A test format is provided here and our XML Schema Definition (xsd) can be found here.

Post Processing
If you have completed any post processing on your data, please choose 'Yes' for Has Postprocessing? If not, select 'No'. Depending on this selection the remaining post processing fields will be enabled (some of which will be required). If you are initially providing raw, non-analyzed data you can select 'No', then return to the experiment to add post processing steps at a later date when the analyzed data are being provided.

Please provide information about post-processing manipulations, i.e. artifact detection algorithms, segmentation used for post data collection, items denoted with an asterisk (*) are required.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments tab. 

NDA Help Center

Collection - Data Expected

There are two types of Data Expected displayed:

  1. Data Expected from Relevant Publications: Publications reported in association with the Collection’s grant, and determined as relevant to data expected for sharing, are listed first. Any data specific to these publications are expected to be shared using the NDA Study feature. If a publication on this list is marked relevant in error, the PI of the project can correct the status. When sharing a Study, only the outcome measures for the subjects/time-points specific to the publication are shared. 
  2. Data Expected by Data Structure: The table displayed second is defined by the Investigator, and lists all the measures expected for sharing as defined in the Data Dictionary. Targeted Enrollment indicates the expected final unique subject count for that structure. Initial submission dates indicate when the NDA should expect the first upload of those data, and initial sharing dates indicate when the first round of those data is expected to be shared. Click the next to the Data Expected title to view the structures that are counted within that item.

Frequently Asked Questions

  • An NDA Data Structure is comprised of multiple Data Elements to make up an electronic definition of an assessment, measure, questionnaire, etc will have a corresponding Data Structure.

  • The NDA Data Dictionary is comprised of electronic definitions known as Data Structures.

Glossary

  • Imaging+ is an NDA term which encompasses all imaging related data including, but not limited to, images (DTI, MRI, PET, Structural, Spectroscopy, etc.) as well as neurosignal data (EEG, fMRI, MEG, EGG, eye tracking, etc.) and Evaluated Data.

  • Data specific to the primary aims of the research being conducted (e.g. outcome measures, other dependent variables, observations, laboratory results, analyzed images, volumetric data, etc.) including processed images.

  • Items listed on the Data Expected list in the Collection which may be an individual and discrete Data Structure, Data Structure Category, or Data Structure Group.

  • A defined organization and group of Data Elements to represent an electronic definition of a measure, assessment, questionnaire, or collection of data points.  Data structures that have been defined in the NDA Data Dictionary are available at https://ndar.nih.gov/data_dictionary.html. 

  • An NDA term describing the affiliation of a Data Structure to a Category, which may be disease/disorder or diagnosis related (Depression, ADHD, Psychosis), specific to data type (MRI, eye tracking, omics), or type of data (physical exam, IQ).

  • A Data Item listed on the Data Expected tab of a Collection that indicates a group of Data Structures (e.g., ADOS or SCID) for which data may be submitted instead of a specific Data Structure identified by version, module, edition, etc. For example, the ADOS Data Structure Category includes every ADOS Data Structure such as ADOS Module 1, ADOS Module 2, ADOS Module 1 - 2nd Edition, etc. The SCID Data Structure Group includes every SCID Data Structure such as SCID Mania, SCID V Mania, SCID PTSD, SCID-V Diagnosis, and more. 

  • Typically not related to te primary aims of a study, Descriptive/raw data are data used to characterize a research subject, including data from standard diagnostic assessments, standard clinical measures, family/subject medical history, demographic data, raw unprocessed images, -omics (e.g. proteomics, genomics, metabolomics) data, raw neurosignal recordings, and genetic test results that are being collected in the course of the supported research. Descriptive/raw data are expected to be submitted to NDA on a semi-annual basis (on or before January 15 and July 15). Cumulative submission of clinical data is expected during each submission cycle to enable data corrections throughout the duration of the award. Raw -omic, EEG, and neuroimaging data are expected to be submitted only once.  Descriptive/raw data are Shared within 4 months after submission.

  • A new Data Structure category, Evaluated Data is analyzed data resulting from the use of computational pipelines in the Cloud and can be uploaded directly back to a miNDAR database.  Evaluated Data is expected to be listed as a Data Item in the Collection's Data Expected Tab.

  • The earliest date on which the data related to the Data Item may expect to be Shared based on whether the data are considered Descriptive/Raw or Analyzed.  Descriptive/raw data are shared within 4 months after submission (on May 15 for data submitted during the January 15 Submission Cycle or on November 15 for data submitted during the July 15 Submission Cycle).  Analyzed data are expected to be Shared at the time a publication is released  through an NDA Study or one year after the original project completion, whichever comes first.  The Initial Share Date is used by the NDA as a trigger to automatically share data.

  • The earliest date on which the data related to the Data Item may expect to start being submitted based on whether the data are considered Descriptive/Raw or Analyzed and based on the project's data collection timeline.  Descriptive/raw data are expected to be submitted every 6 months (January 15 and July 15) while Analyzed data are expected to be submitted no later than the time a manuscript is accepted.  Data for all subjects is not expected on the Initial Submission Date and modifications may be made as necessary based on the project's conduct.

  • An NDA created Data Structure used to convey basic information about the subject such as demographics, pedigree (links family GUIDs), diagnosis/phenotype, and sample location that are critical to allow for easier querying of shared data.

  • The NDA has two Submission Cycles per year - January 15 and July 15.

  • An interface to notify NDA that data may not be submitted during the upcoming/current submission cycle.  

NDA Help Center

Collection - Permissions Tab

Collection Owners, Program Officers, and users with Administrator privileges may view this tab.

The available permission groups include:

  • Query: This read-only access is generally for NIH Program Officers
  • Submission: This will grant read access and allow the user to upload data and create experiment definitions. This is for the typical contributing personnel member.
  • Administrator: In addition to the access provided to Query and Submission users, Admins can also edit the Collection itself, create or edit the Data Expected list, and edit user permissions. This access is for the PI, data managers, and anyone they wish to delegate this to.

The PI has a special designation as the Collection Owner in addition to administrator access.

Frequently Asked Questions

  • Collection Owners and Admins may assign Collection Privileges to anyone.

  • Yes, you can assign various Privileges to other users with an NDA account.

  • If you are the Collection Owner or have Admin privileges, you can view and make changes to the list of individuals who have access to the Collection on the Collection's Permissions tab.  Information on users who have access to data Shared in your Collection because they were granted access to a Permission Group is not available.

  • Staff/collaborators who are working submitting data to the Collection, checking the quality of the data, and/or analyzing data should have access for the duration of the project until all data have been submitted, NDA Studies have been created for data used in publications, and/or a collaborative relationship with the user exists.  

  • The individual listed as an Investigator on the General tab of the NDA Collection will generally be able to provide a user access to the NDA Collection.  Additional users may also have this ability if granted Administrator access to an NDA Collection; however, these users are not viewable unless your account has access to the NDA Collection.  Given this, it is best to contact the Investigator to request access to the Collection.

  • Privileges that can be assigned to a user include:
    Submission allows a user to submit data to Collection
    Query allows the user to download data from Collection even when in a Private state
    Admin is both the Submission and Query Privilege + the ability to give privileges to other users.

  • You may have staff who are working on the submission of data or other activities associated with data sharing such as the definition of the Data Expected list or NDA Experiment creation.  Also, many projects have multiple performance sites and wish to share data among the site PIs.  Submitting to the NDA facilitates access by all investigators working on a project even before data have been shared with other users.  You can control who gets access to data while in a Private state.

Glossary

  • A privilege provided to a user associated with an NDA Collection or NDA Study whereby that user can perform a full range of actions including providing privileges to other users. 

  • The NDA has data grouped data into Collections which are associated with a Permission Groups (e.g., ABCD, NDAR, NDCT, PedsMRI, RDoCdb, OAI) so that access requests are made for a Permission Group instead of individual Collections. While each Permission Group has it's own identity, all data included are in the NIMH Data Archive regardless of Permission Group.

NDA Help Center

Eye Tracking

EyeTracking tests follow the movement of the eye. The visual trajectory or focus can help determine predictions and assist in diagnoses. 

Acquisition
The Acquisition parameters needed for an experiment include the following:

Name of the experiment is required. Please be concise and specific as possible.
Following experiment name, selection boxes are provided for the Equipment, Software, or other items specific to experiment type. At least one selection is required for each. If NDAR does not have the appropriate listing, select Add New to add the information provided. Following the selection boxes, provide additional information may be required depending on experiment type. Any required items are denoted by an asterisk (*).

Block/Event Design
At least one block/event is required. Note that any fields denoted with an asterisk (*) are required. All data must be devoid of personally identifiable data, including the contents of any files attached to the experiment.

Note: To simplify definition of multiple events, we provide an Import from XML function. This function supports importing data from all three experiment sections (Acquisition, Block/Event Design, and Post Processing), at this time files cannot be uploaded from XML A test format is provided here and our XML Schema Definition (xsd) can be found here.

Post Processing
If you have completed any post processing on your data, please choose 'Yes' for Has Postprocessing? If not, select 'No'. Depending on this selection the remaining post processing fields will be enabled (some of which will be required). If you are initially providing raw, non-analyzed data you can select 'No', then return to the experiment to add post processing steps at a later date when the analyzed data are being provided.

Please provide information about post-processing manipulations, i.e. artifact detection algorithms, segmentation used for post data collection, items denoted with an asterisk (*) are required.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments tab. 

NDA Help Center

Collection - Experiments Tab

The number of Experiments included is displayed in parentheses next to the tab name. You may download all experiments associated with the Collection via the Download button. You may view individual experiments by clicking the Experiment Name and add them to the Filter Cart via the Add to Cart button.

Collection Owners, Program Officers, and users with Submission or Administrative Privileges for the Collection may create or edit an Experiment.

Please note: The creation of an NDA Experiment does not necessarily mean that data collected, according to the defined Experiment, has been submitted or shared.

Frequently Asked Questions

  • Yes -see the “Copy” button in the bottom left when viewing an experiment. There are two actions that can be performed via this button:

    1. Copy the experiment with intent for modifications.  
    2. Associate the experiment to the collection. No modifications can be made to the experiment.

     

Glossary

  • An Experiment must be Approved before data using the associated Experiment_ID may be uploaded.

  • The ID number automatically generated by NDA which must be included in the appropriate file when uploading data to link the Experiment Definition to the subject record.

NDA Help Center

Omics

Omics is a collective group of technologies, related to a field of study in Biology such as Genomics or proteomics. 

Experiment Parameters

To define an Omics experiment, provide a meaningful name and select a single molecule. The standard molecules are listed. However, if you are doing proteomic or environmental experiments, simply “Add New” and the new selection will be created. Only one value for molecule is permitted.

Next the technology (box 2) associated with the molecule will be presented along with its application. Again, only one selection is possible. If you wish to see all of NDAR’s options for any one box, Select “Show All”.

Platform

Continue to select the Platform (box 3).

Extraction

Next, the Extraction Protocol (box 4) and Kits (box 5) are presented based upon the Molecule selected and the Processing Protocol (box 6) and Kits (box 7) are presented based upon the Molecule and Technology Application (Box 1 and 2)

Processing

Note that for each of these (boxes 4, 5, 6, and 7) multiple selections are possible.

Additional Information

Lastly, the Software (box 8) and Equipment (box 9) is expected.

 

Once saved, the experiment will be associated with the Collection and by using the returned Experiment_ID, the NDA makes it possible to associate the experiment meta data directly with the data from the experiment.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments tab. 

NDA Help Center

Filter Cart

The Filter Cart provides a powerful way to query and access data for which you may be interested.  

A few points related to the filter cart are important to understand with the NDA Query/Filter implementation: 

First, the filter cart is populated asyncronously.  So, when you run a query, it may take a moment to populate but this will happen in the background so you can define other queries during this time.  

When you are adding your first filter, all data associated with your query will be added to the filter cart (whether it be a collection, a concept, a study, a data structure/elment or subjects). Not all data structures or collections will necessarily be displayed.  For example, if you select the NDA imaging structure image03, and further restrict that query to scan_type fMRI, only fMRI images will appear and only the image03 structure will be shown.  To see other data structures, select "Find All Subject Data" which will query all data for those subjects. When a secord or third filter is applied, an AND condition is used.  A subject must exist in all filters.  If the subject does not appear in any one filter, that subjects data will not be included in your filter cart. If that happens, clear your filter cart, and start over.  

It is best to package more data than you need and access those data using other tools, independent of the NDA (e.g. miNDAR snapshot), to limit the data selected.  If you have any questions on data access, are interested in using avaialble web services, or need help accessing data, please contact us for assistance.  

Frequently Asked Questions

Glossary

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Frequently Asked Questions

Glossary

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Collection - Associated Studies

Clicking on the Study Title will open the study details in a new internet browser tab. The Abstract is available for viewing, providing the background explanation of the study, as provided by the Collection Owner. 

Primary v. Secondary Analysis: The Data Usage column will have one of these two choices. An associated study that is listed as being used for Primary Analysis indicates at least some and potentially all of the data used was originally collected by the creator of the NDA Study. Secondary Analysis indicates the Study owner was not involved in the collection of data, and may be used as supporting data. 

Private v. Shared State: Studies that remain private indicate the associated study is only available to users who are able to access the collection. A shared study is accessible to the general public. 

Frequently Asked Questions

  • Studies are associated to the Collection automatically when the data is defined in the Study. 

Glossary

  • A tab in a Collection that lists the NDA Studies that have been created using data from that Collection including both Primary and Secondary Analysis NDA Studies.

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Selected Filters
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The filters you have selected from various query interfaces will be stored here, in the 'Filter Cart'. The database will be queried using filters added to your 'Filter Cart', when multiple filters are defined, each will be executed using 'AND' logic, so with each filter that is applied the result set gets smaller.

From the 'Filter Cart' you can inspect each of the filters that have been defined, and you also have the option to remove filters. The 'Filter Cart' itself will display the number of filters applied along with the number of subjects that are identified by the combination of those filters. For example a GUID filter with two subjects, followed by a GUID filter for just one of those subjects would return only data for the subject that is in both GUID filters.

If you have a question about the filter cart, or underlying filters please contact the help desk at The NDA Help Desk

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1 Numbers reported are subjects by age
New Trial
New Project

Format should be in the following format: Activity Code, Institute Abbreviation, and Serial Number. Grant Type, Support Year, and Suffix should be excluded. For example, grant 1R01MH123456-01A1 should be entered R01MH123456

Please select an experiment type below

Collection - Use Existing Experiment

To associate an experiment to the current collection, just select an axperiment from the table below then click the associate experiment button to persist your changes (saving the collection is not required). Note that once an experiment has been associated to two or more collections, the experiment will not longer be editable.

The table search feature is case insensitive and targets the experiment id, experiment name and experiment type columns. The experiment id is searched only when the search term entered is a number, and filtered using a startsWith comparison. When the search term is not numeric the experiment name is used to filter the results.

SelectExperiment IdExperiment NameExperiment Type
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  • EEG
  • EGG
  • Eye Tracking
  • Omics
  • fMRI
Created On
1110Roundabout Minds Project measuring social salienceEye Tracking12/12/2018
1109P1-nativelangfMRI12/12/2018
1108P1_languagefMRI12/12/2018
1107MRS-Hippocampus with WaterfMRI12/11/2018
1106MRS-HippocampusfMRI12/11/2018
1105MRS-Dorsal Striatum with WaterfMRI12/11/2018
1104MRS-Dorsal StriatumfMRI12/11/2018
1103MRS-Dorsal ACC with WaterfMRI12/11/2018
1102MRS-Dorsal ACCfMRI12/11/2018
1101T2fMRI12/11/2018
1100Brain activity in assessing the role of top-down factors in threat perception in anxietyfMRI12/10/2018
1099resting state ASLfMRI12/03/2018
1098resting state BOLDfMRI12/03/2018
1097Hakim et al. Psych Science Exp 4EEG12/03/2018
1096Hakim et al. Psych Science Exp 3EEG12/03/2018
1095Hakim et al. Psych Science Exp 1EEG12/03/2018
1090T1fMRI11/27/2018
1089Double StepEye Tracking11/26/2018
1086Brain connectivity and the role of myelin in PsychosisfMRI11/20/2018
1085Hakim et al. Psych Science Exp 2EEG11/19/2018
1081BOLD RESTfMRI11/08/2018
1080Adam et al. 2018 JoCN Exp 2EEG10/19/2018
1079Adam et al. 2018 JoCN Exp 1EEG10/19/2018
1077NPU EEG Task EEG10/11/2018
1076Multi-session Stimulation Effects on fMRIfMRI10/10/2018
1075Learning Naturalistic Temporal Structure in the Posterior Medial Network.fMRI10/09/2018
1074Resting State fMRIfMRI10/03/2018
1073Yale/Mayo/Stanford_mixed_DNA_10XOmics10/01/2018
1072Yale/Mayo/Stanford_mixed_DNA_RegularWGSOmics10/01/2018
1071State distinctivenessfMRI09/25/2018
1070GeneticsOmics09/24/2018
1069State representation picturesfMRI09/24/2018
1068Representation of real-world event schemas during narrative perceptionfMRI09/20/2018
1067Social Predictive CodingfMRI09/13/2018
1066Associative prediction of visual shape in the hippocampusfMRI09/13/2018
1065Resting StatefMRI08/29/2018
1064ECoG Continuous Recognition StudyEEG08/29/2018
1063ValenceFlexibility_WordsfMRI08/23/2018
1062ValenceFlexibilty_IAPSfMRI08/23/2018
1039ruthptsd fmri 1fMRI08/22/2018
1038ValenceFlexibility_FacesfMRI08/20/2018
1037fMRI DoubleStep Task (Eye Tracking)Eye Tracking08/17/2018
1036Blanking TaskEye Tracking08/17/2018
1035fMRI DoubleStep TaskfMRI08/17/2018
1034Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis.fMRI08/15/2018
1033Task Dependence, Tissue Specificity, and Spatial Distribution of Widespread Activations in Large Single-Subject Functional MRI Datasets at 7TfMRI08/15/2018
1032UCSD_Gleeson_U01MH108898_WES_Nextera_HiSeq2500Omics08/14/2018
1031UCSD_Gleeson_U01MH108898_WES_SureSelectXT_HiSeq2500Omics08/14/2018
1030UCSD_Gleeson_U01MH108898_WES_SureSelect_HiSeq2000Omics08/14/2018
1029Reductions in retrieval competition predict the benefit of repeated testingEEG08/10/2018
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Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
Elucidating the Genetic Architecture of Autism by Deep Genomic Sequencing
Mark Daly, Richard Gibbs, Joseph Buxbaum, Gerard Schellenberg and James Sutcliffe 
ARRA Autism Sequencing Collaboration
NDAR
Funding Completed
Close Out
Shared
No
$14,982,039.00
0
0
2,103
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NIH - Extramural None

https://software.broadinstitute.org/gatk/ Software Genome Analysis Toolkit Qualified Researchers
http://picard.sourceforge.net/ Software Picard Qualified Researchers
http://samtools.sourceforge.net/ Software SAM Tools Qualified Researchers

R01MH089208-01 2/5-Elucidating the Genetic Architecture of Autism by Deep Genomic Sequencing 09/30/2009 12/31/2012 Not Reported Not Reported BROAD INSTITUTE, INC. $4,165,764.00
R01MH089175-01 1/5: Elucidating the Genetic Architecture of Autism by Deep Genomic Sequencing 09/30/2009 08/31/2011 Not Reported Not Reported BAYLOR COLLEGE OF MEDICINE $2,998,515.00
R01MH089482-01 5/5 - Elucidating the Genetic Architecture of Autism by Deep Genomic Sequencing 09/30/2009 08/31/2012 Not Reported Not Reported VANDERBILT UNIVERSITY $5,196,989.00
R01MH089004-01 4/5-Elucidating the Genetic Architecture of Autism by Deep Genomic Sequencing 09/30/2009 08/31/2012 Not Reported Not Reported UNIVERSITY OF PENNSYLVANIA $1,208,739.00
R01MH089025-01 3/5-Elucidating the Genetic Architecture of Autism by Deep Genomic Sequencing 09/30/2009 08/31/2012 Not Reported Not Reported ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI $1,412,032.00

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Shared Data

Data structures with the number of subjects submitted and shared are provided.

Autism Diagnostic Interview - Cumulative Clinical Assessments 435
Autism Diagnostic Observation Schedule (ADOS) - Module 4 Clinical Assessments 9
Autism Diagnostic Observation Schedule (ADOS)- Module 1 Clinical Assessments 154
Autism Diagnostic Observation Schedule (ADOS)- Module 2 Clinical Assessments 68
Autism Diagnostic Observation Schedule (ADOS)- Module 3 Clinical Assessments 110
CPEA STAART PPVT SUMMARY 2004 Clinical Assessments 322
Genomics Sample Genomics 2094
Genomics Subject Genomics 2097
Ravens Coloured Progressive Matrices (CPM) Clinical Assessments 322
Stanford-Binet Intelligence Scales, Fifth Edition (SB5) Clinical Assessments 18

Collection Owners and those with Collection Administrator permission, may edit a collection. The following is currently available for Edit on this page:

Publications

Publications relevant to NDAR data are listed below. Most displayed publications have been associated with the grant within Pubmed. Use the "+ New Publication" button to add new publications. Publications relevant/not relevant to data expected are categorized. Relevant publications are then linked to the underlying data by selecting the Create Study link. Study provides the ability to define cohorts, assign subjects, define outcome measures and lists the study type, data analysis and results. Analyzed data and results are expected in this way.

PubMed IDStudyTitleJournalAuthorsDateStatus
29593342Create StudyGenetic variants and pathways implicated in a pediatric inflammatory bowel disease cohort.Genes and immunityShaw KA, Cutler DJ, Okou D, Dodd A, Aronow BJ, Haberman Y, Stevens C, Walters TD, Griffiths A, Baldassano RN, Noe JD, Hyams JS, Crandall WV, Kirschner BS, Heyman MB, Snapper S, Guthery S, Dubinsky MC, Shapiro JM, Otley AR, Daly M, Denson LA, Kugathasan S, Zwick MEMarch 2018Not Determined
29358944Create StudyA Powerful Gene-Based Test Accommodating Common and Low-Frequency Variants to Detect Both Main Effects and Gene-Gene Interaction Effects in Case-Control Studies.Frontiers in geneticsChung RH, Kang CYJanuary 2017Not Determined
28344757Create StudyLeveraging blood serotonin as an endophenotype to identify de novo and rare variants involved in autism.Molecular autismChen R, Davis LK, Guter S, Wei Q, Jacob S, Potter MH, Cox NJ, Cook EH, Sutcliffe JS, Li BJanuary 2017Not Determined
26439716Create StudyInterpreting de novo Variation in Human Disease Using denovolyzeR.Current protocols in human genetics / editorial board, Jonathan L. Haines ... [et al.]Ware JS, Samocha KE, Homsy J, Daly MJ2015Not Determined
25363760Create StudySynaptic, transcriptional and chromatin genes disrupted in autism.NatureDe Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE, Kou Y, Liu L, Fromer M, Walker S, Singh T, Klei L, Kosmicki J, Shih-Chen F, Aleksic B, Biscaldi M, Bolton PF, Brownfeld JM, Cai J, Campbell NG, Carracedo A, Chahrour MH, Chiocchetti AG, Coon H, Crawford EL, et al.November 13, 2014Not Relevant
25270638Create StudyConsensus Genotyper for Exome Sequencing (CGES): improving the quality of exome variant genotypes.Bioinformatics (Oxford, England)Trubetskoy V, Rodriguez A, Dave U, Campbell N, Crawford EL, Cook EH, Sutcliffe JS, Foster I, Madduri R, Cox NJ, Davis LKJanuary 15, 2015Not Relevant
25086666Create StudyA framework for the interpretation of de novo mutation in human disease.Nature geneticsSamocha KE, Robinson EB, Sanders SJ, Stevens C, Sabo A, McGrath LM, Kosmicki JA, Rehnström K, Mallick S, Kirby A, Wall DP, MacArthur DG, Gabriel SB, DePristo M, Purcell SM, Palotie A, Boerwinkle E, Buxbaum JD, Cook EH, Gibbs RA, Schellenberg GD, Sutcliffe JS, Devlin B, Roeder K, Neale BM, et al.September 2014Not Determined
24094742Create StudyIdentification of small exonic CNV from whole-exome sequence data and application to autism spectrum disorder.American journal of human geneticsPoultney CS, Goldberg AP, Drapeau E, Kou Y, Harony-Nicolas H, Kajiwara Y, De Rubeis S, Durand S, Stevens C, Rehnström K, Palotie A, Daly MJ, Ma'ayan A, Fromer M, Buxbaum JDOctober 3, 2013Not Relevant
23979605Create StudyDe novo mutation in the dopamine transporter gene associates dopamine dysfunction with autism spectrum disorder.Molecular psychiatryHamilton PJ, Campbell NG, Sharma S, Erreger K, Herborg Hansen F, Saunders C, Belovich AN, , Sahai MA, Cook EH, Gether U, McHaourab HS, Matthies HJ, Sutcliffe JS, Galli ADaly MJGibbs RABoerwinkle EBuxbaum JDCook EHDevlin BLim ETNeale BMRoeder KSabo ASchellenberg GDStevens CSutcliffe JSDecember 2013Not Determined
23966865Create StudyIntegrated model of de novo and inherited genetic variants yields greater power to identify risk genes.PLoS geneticsHe X, Sanders SJ, Liu L, De Rubeis S, Lim ET, Sutcliffe JS, Schellenberg GD, Gibbs RA, Daly MJ, Buxbaum JD, State MW, Devlin B, Roeder K2013Not Determined
23943636Create StudyDRAW+SneakPeek: analysis workflow and quality metric management for DNA-seq experiments.Bioinformatics (Oxford, England)Lin CF, Valladares O, Childress DM, Klevak E, Geller ET, Hwang YC, Tsai EA, Schellenberg GD, Wang LSOctober 1, 2013Not Determined
23743231Create StudyWhole exome sequencing reveals minimal differences between cell line and whole blood derived DNA.GenomicsSchafer CM, Campbell NG, Cai G, Yu F, Makarov V, Yoon S, Daly MJ, Gibbs RA, Schellenberg GD, Devlin B, Sutcliffe JS, Buxbaum JD, Roeder KOctober 2013Not Determined
23711981Create StudyDisruption of the non-canonical Wnt gene PRICKLE2 leads to autism-like behaviors with evidence for hippocampal synaptic dysfunction.Molecular psychiatrySowers LP, Loo L, Wu Y, Campbell E, Ulrich JD, Wu S, Paemka L, Wassink T, Meyer K, Bing X, El-Shanti H, Usachev YM, Ueno N, Manak JR, Manak RJ, Shepherd AJ, Ferguson PJ, Darbro BW, Richerson GB, Mohapatra DP, Wemmie JA, Bassuk AGOctober 2013Not Determined
23684009Create StudySequence kernel association tests for the combined effect of rare and common variants.American journal of human geneticsIonita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin XJune 6, 2013Not Determined
23593035Create StudyAnalysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls.PLoS geneticsLiu L, Sabo A, Neale BM, Nagaswamy U, Stevens C, Lim E, Bodea CA, Muzny D, Reid JG, Banks E, Coon H, Depristo M, Dinh H, Fennel T, Flannick J, Gabriel S, Garimella K, Gross S, Hawes A, Lewis L, Makarov V, Maguire J, Newsham I, Poplin R, Ripke S, et al.April 2013Not Relevant
23386037Create StudyFamily-based association tests for sequence data, and comparisons with population-based association tests.European journal of human genetics : EJHGIonita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin XOctober 2013Not Relevant
23352160Create StudyRare complete knockouts in humans: population distribution and significant role in autism spectrum disorders.NeuronLim ET, Raychaudhuri S, Sanders SJ, Stevens C, Sabo A, MacArthur DG, Neale BM, Kirby A, Ruderfer DM, Fromer M, Lek M, Liu L, Flannick J, Ripke S, Nagaswamy U, Muzny D, Reid JG, Hawes A, Newsham I, Wu Y, Lewis L, Dinh H, Gross S, Wang LS, Lin CF, et al.January 23, 2013Not Relevant
23216583Create StudyCharacterizing polymorphisms and allelic diversity of von Willebrand factor gene in the 1000 Genomes.Journal of thrombosis and haemostasis : JTHWang QY, Song J, Gibbs RA, Boerwinkle E, Dong JF, Yu FLFebruary 2013Not Relevant
22843986Create StudyzCall: a rare variant caller for array-based genotyping: genetics and population analysis.Bioinformatics (Oxford, England)Goldstein JI, Crenshaw A, Carey J, Grant GB, Maguire J, Fromer M, O'Dushlaine C, Moran JL, Chambert K, Stevens C, , , Sklar P, Hultman CM, Purcell S, McCarroll SA, Sullivan PF, Daly MJ, Neale BMOctober 1, 2012Not Relevant
22641211Create StudyExome sequencing and the genetic basis of complex traits.Nature geneticsKiezun A, Garimella K, Do R, Stitziel NO, Neale BM, McLaren PJ, Gupta N, Sklar P, Sullivan PF, Moran JL, Hultman CM, Lichtenstein P, Magnusson P, Lehner T, Shugart YY, Price AL, de Bakker PI, Purcell SM, Sunyaev SRJune 2012Not Relevant
22610117Create StudyExtremely low-coverage sequencing and imputation increases power for genome-wide association studies.Nature geneticsPasaniuc B, Rohland N, McLaren PJ, Garimella K, Zaitlen N, Li H, Gupta N, Neale BM, Daly MJ, Sklar P, Sullivan PF, Bergen S, Moran JL, Hultman CM, Lichtenstein P, Magnusson P, Purcell SM, Haas DW, Liang L, Sunyaev S, Patterson N, de Bakker PI, Reich D, Price ALJune 2012Not Relevant
22578327Create StudyScan-statistic approach identifies clusters of rare disease variants in LRP2, a gene linked and associated with autism spectrum disorders, in three datasets.American journal of human geneticsIonita-Laza I, Makarov V, , Buxbaum JDBoerwinkle EBuxbaum JDCook EHDaly MJDevlin BGibbs RRoeder KSabo ASchellenberg GDSutcliffe JSJune 8, 2012Not Relevant
22511880Study (293)Whole-exome sequencing and homozygosity analysis implicate depolarization-regulated neuronal genes in autism.PLoS geneticsChahrour MH, Yu TW, Lim ET, Ataman B, Coulter ME, Hill RS, Stevens CR, Schubert CR, , Greenberg ME, Gabriel SB, Walsh CA2012Relevant
22499558Create StudyNetwork- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability.American journal of medical genetics. Part C, Seminars in medical geneticsKou Y, Betancur C, Xu H, Buxbaum JD, Ma'ayan AMay 15, 2012Not Relevant
22495311Study (317)Patterns and rates of exonic de novo mutations in autism spectrum disorders.NatureNeale BM, Kou Y, Liu L, Ma'ayan A, Samocha KE, Sabo A, Lin CF, Stevens C, Wang LS, Makarov V, Polak P, Yoon S, Maguire J, Crawford EL, Campbell NG, Geller ET, Valladares O, Schafer C, Liu H, Zhao T, Cai G, Lihm J, Dannenfelser R, Jabado O, Peralta Z, et al.May 10, 2012Relevant
22257670Create StudyAnnTools: a comprehensive and versatile annotation toolkit for genomic variants.Bioinformatics (Oxford, England)Makarov V, O'Grady T, Cai G, Lihm J, Buxbaum JD, Yoon SMarch 1, 2012Not Relevant
22137099Create StudyFinding disease variants in Mendelian disorders by using sequence data: methods and applications.American journal of human geneticsIonita-Laza I, Makarov V, Yoon S, Raby B, Buxbaum J, Nicolae DL, Lin XDecember 9, 2011Not Relevant
21408211Create StudyTesting for an unusual distribution of rare variants.PLoS geneticsNeale BM, Rivas MA, Voight BF, Altshuler D, Devlin B, Orho-Melander M, Kathiresan S, Purcell SM, Roeder K, Daly MJMarch 2011Not Relevant
20876472Create StudyA comprehensive analysis of deletions, multiplications, and copy number variations in PARK2.NeurologyKay DM, Stevens CF, Hamza TH, Montimurro JS, Zabetian CP, Factor SA, Samii A, Griffith A, Roberts JW, Molho ES, Higgins DS, Gancher S, Moses L, Zareparsi S, Poorkaj P, Bird T, Nutt J, Schellenberg GD, Payami HSeptember 28, 2010Not Relevant

Relevant Publications
PubMed IDStudyTitleJournalAuthorsDate
No records found.

You can use "Add New Data Expected" to add exsiting structures and create your project's list. However, this is also the method you can use to request new structures be created for your project. When adding the Data Expected item, if the structure already exists you can locate it and specify your dates and enrollment. To add a new structure and request it be defined in the Data Dictionary, select Upload Definition and attach the definition or material needed to create it, including manual, codebooks, forms, etc. If you have multiple files, please upload a zipped archive containing them all.

Expected dates should be selected based on the standard Data Sharing Regimen and are restricted to within date ranges based on the project start and end dates.

Data Expected
Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
genomics/omics info icon08/31/2012Approved
Structure not yet defined

Collection Owners and those with Collection Administrator permission, may edit a collection. The following is currently available for Edit on this page:

Associated Studies

Studies that have been defined using data from a Collection are important criteria to determine the value of data shared. The number of subjects column displays the counts from this Collection that are included in a Study, out of the total number of subjects in that study. The Data Use column represents whether or not the study is a primary analysis of the data or a secondary analysis. State indicates whether the study is private or shared with the research community.

Study NameAbstractCollection/Study SubjectsData UsageState
No Evidence for Association of Autism with Rare Heterozygous Point Mutations in Contactin-Associated UNIQUENAME Protein-Like 2 (CNTNAP2), or in Other Contactin-Associated Proteins or ContactinsContactins and Contactin-Associated Proteins, and Contactin-Associated Protein-Like 2 (CNTNAP2) in particular, have been widely cited as autism risk genes based on findings from homozygosity mapping, molecular cytogenetics, copy number variation analyses, and both common and rare single nucleotide association studies. However, data specifically with regard to the contribution of heterozygous single nucleotide variants (SNVs) have been inconsistent. In an effort to clarify the role of rare point mutations in CNTNAP2 and related gene families, we have conducted targeted next-generation sequencing and evaluated existing sequence data in cohorts totaling 2704 cases and 2747 controls. We find no evidence for statistically significant association of rare heterozygous mutations in any of the CNTN or CNTNAP genes, including CNTNAP2, placing marked limits on the scale of their plausible contribution to risk.2094/4118Primary AnalysisShared
Elucidating the Genetic Architecture of Autism by Deep Genomic SequencingARRA Autism Sequencing Collaboration The VCF files provided as Study Results for this study are what was provided at the time the study was created and consist of the Autism Only consent group. There is an additional General Research Use cohort, but those data are not provided here in this study. To obtain data from the General Research Use cohort please visit the dbGaP Study phs000298. It should be noted that the dbGaP study has been updated since the time this study was created, and the update includes genomics data on additional subjects. http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000298.v2.p2 2095/2095Primary AnalysisShared
Unravelling the Collective Diagnostic Power Behind the Features in the Autism Diagnostic Observation ScheduleBackground: Autism is a group of heterogeneous disorders defined by deficits in social interaction and communication. Typically, diagnosis depends on the results of a behavioural examination called the Autism Diagnostic Observation Schedule (ADOS). Unfortunately, administration of the ADOS exam is time-consuming and requires a significant amount of expert intervention, leading to delays in diagnosis and access to early intervention programs. The diagnostic power of each feature in the ADOS exam is currently unknown. Our hypothesis is that certain features could be removed from the exam without a significant reduction in diagnostic accuracy, sensitivity or specificity. Objective: Determine the smallest subset of predictive features in ADOS module-1 (an exam variant for patients with minimal verbal skills). Methodology: ADOS module-1 datasets were acquired from the Autism Genetic Resource Exchange and the National Database for Autism Research. The datasets contained 2572 samples with the following labels: autism (1763), autism spectrum (513), and non-autism (296). The datasets were used as input to 4 different cost-sensitive classifiers in Weka (functional trees, LADTree, logistic model trees, and PART). For each classifier, a 10-fold cross validation was preformed and the number of predictive features, accuracy, sensitivity, and specificity was recorded. Results & Conclusion: Each classifier resulted in a reduction of the number of ADOS features required for autism diagnosis. The LADtree classifier was able to obtain the largest reduction, utilizing only 10 of 29 ADOS module-1 features (96.8% accuracy, 96.9% sensitivity, and 95.9% specificity). Overall, these results are a step towards a more efficient behavioural exam for autism diagnosis. 121/1835Secondary AnalysisShared
Germline Mutations in Predisposition Genes in Pediatric CancerBackground The prevalence and spectrum of predisposing mutations among children and adolescents with cancer are largely unknown. Knowledge of such mutations may improve the understanding of tumorigenesis, direct patient care, and enable genetic counseling of patients and families. Methods In 1120 patients younger than 20 years of age, we sequenced the whole genomes (in 595 patients), whole exomes (in 456), or both (in 69). We analyzed the DNA sequences of 565 genes, including 60 that have been associated with autosomal dominant cancer-predisposition syndromes, for the presence of germline mutations. The pathogenicity of the mutations was determined by a panel of medical experts with the use of cancer-specific and locus-specific genetic databases, the medical literature, computational predictions, and second hits identified in the tumor genome. The same approach was used to analyze data from 966 persons who did not have known cancer in the 1000 Genomes Project, and a similar approach was used to analyze data from an autism study (from 515 persons with autism and 208 persons without autism). Results Mutations that were deemed to be pathogenic or probably pathogenic were identified in 95 patients with cancer (8.5%), as compared with 1.1% of the persons in the 1000 Genomes Project and 0.6% of the participants in the autism study. The most commonly mutated genes in the affected patients were TP53 (in 50 patients), APC (in 6), BRCA2 (in 6), NF1 (in 4), PMS2 (in 4), RB1 (in 3), and RUNX1 (in 3). A total of 18 additional patients had protein-truncating mutations in tumor-suppressor genes. Of the 58 patients with a predisposing mutation and available information on family history, 23 (40%) had a family history of cancer. Conclusions Germline mutations in cancer-predisposing genes were identified in 8.5% of the children and adolescents with cancer. Family history did not predict the presence of an underlying predisposition syndrome in most patients.723/723Secondary AnalysisShared
Data-Driven Generation of Synthetic Behavioral Feature Vectors Modeling Children with Autism Spectrum DisordersBehavioral data on children with Autism Spectrum Disorders (ASD) are available thanks to standardized diagnostic tools, such as the Autism Diagnostic Observation Schedule (ADOS). This data can be of great use to enhance the learning and reasoning of agents interacting with children with ASD. However, the amount of such available data is limited and may not prove useful by itself to inform the algorithms of complex agents. To address this data scarcity problem, we present a method for generating synthetic behavioral data in the form of feature vectors characterizing a wide range of children with ASD. Our method relies on a thorough analysis and partition of the feature space based on a real dataset containing the ADOS scores of 279 children. We first analyze the real dataset using dimensionality reduction techniques, then introduce data-driven descriptors that partition the feature space into regions naturally arising from the data. We end by presenting a descriptor-based sampling method to generate synthetic feature vectors that successfully preserves the correlation structure of the real dataset.47/173Secondary AnalysisShared
Patterns and rates of exonic de novo mutations in autism spectrum disorders.Notes: Data submitted to NDAR did not include interview age. Publication Abstract: Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified. To identify further genetic risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case-control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.104/104Primary AnalysisShared
* Data not on individual level
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