Strategic Plan Objective Detail
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Question 3: Long-term Objective B  

Fiscal Year: 2009

Green dot: Objective has greater than or equal to the recommended funding.3LB. Identify genetic risk factors in at least 50% of people with ASD by 2014. IACC Recommended Budget: $33,900,000 over 6 years.

Download 2009 Question 3: Long-term Objective B projects (EXCEL)
Note: Initial Sort is by Principal Investigator. Sorting by other columns is available by clicking on the desired column header.
Project Title Principal Investigator Institution
Genes disrupted by balanced genomic rearrangements in autism spectrum disorders Gusella, James Massachusetts General Hospital
Genetic basis of autism Wigler, Michael Cold Spring Harbor Laboratory
Genetic dissection of restricted repetitive behavior (RRB) Kim, Soo-Jeong University of Florida
Genetic investigation of cognitive development in autistic spectrum disorders Morrow, Eric Brown University
Genetics of autism intermediate phenotypes Coon, Hilary University of Utah
Genetic studies in autism on chromosome 7 (supplement) Pericak-Vance, Margaret Duke University
Genome-wide analyses of DNA methylation in autism Chess, Andrew Massachusetts General Hospital
Genomic hotspots of autism Eichler, Evan University of Washington
Genomic imbalances in autism Kumar, Ravinesh University of Chicago
Identification and functional assessment of autism susceptibility genes Brzustowicz, Linda Rutgers, The State University of New Jersey - New Brunswick
Identification and functional assessment of autism susceptibility genes Vieland, Veronica The Research Institute at Nationwide Children's Hospital
Identification and functional assessment of autism susceptibility genes Millonig, James University of Medicine & Dentistry of New Jersey - Robert Wood Johnson Medical School
Identification of aberrantly methylated genes in autism: The role of advanced paternal age Gingrich, Jay Research Foundation for Mental Hygiene, Inc.
Identifying and understanding the action of autism susceptibility genes Monaco, Anthony University of Oxford
Identifying autism susceptibility genes by high-throughput chip resequencing Zwick, Michael Emory University
Illumina, Inc. No PI listed Illumina, Inc.
Integrative genetic analysis of autistic brains Arking, Dan Johns Hopkins University School of Medicine
Investigation of genes involved in synaptic plasticity in Iranian families with ASD Santangelo, Susan Massachusetts General Hospital
Isolation of autism susceptibility genes Stefansson, Kari deCODE Genetics, Inc.
Molecular Analysis Core (supplement) Pericak-Vance, Margaret Duke University
Molecular and genetic epidemiology of autism Pericak-Vance, Margaret University of Miami Miller School of Medicine
Neurogenetics of candidate systems in autism (supplement) Haines, Jonathan Duke University
Pathway-based genetic studies of autism spectrum disorder Bucan, Maja University of Pennsylvania
Pilot project to assess web-based family recruitment for autism genetics studies Nelson, Stan; Constantino, John; Law, Paul University of California, Los Angeles; Washington University in St. Louis; Kennedy Krieger Institute
Potential role of non-coding RNAs in autism Talebizadeh, Zohreh Children's Mercy Hospitals And Clinics

Objective Multiyear Funding Table

IACC Strategic Plan Objective 2008 2009 2010 2011 2012 Total
Identify genetic risk factors in at least 50% of people with ASD by 2014.

IACC Recommended Budget: $33,900,000 over 6 years
83 projects

79 projects

60 projects

59 projects

74 projects

3.L.B. Funding: The recommended budget was met. Significantly more than the recommended minimum budget was allocated to projects specific to this objective.

Progress: Further work is needed to identify genetic risk factors in at least 50% of people. Currently, whole exome analysis predicts that a genetic risk factor can be identified for 20% of people; inclusion of CNV data might push this toward 30%.

Remaining Gaps, Needs, and Opportunities: The initial budget recommendation for this objective was made based on the assumption that GWAS studies would provide risk factor identification, but sequencing has proven more fruitful. Since this technique is more expensive, a higher budget will be required to meet the goal of 50%.