Strategic Plan Objective Detail
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Question 3: Objective 8  

Fiscal Year: 2008

Green dot: Objective has greater than or equal to the recommended funding.3.8 Identify genetic risk factors in at least 50% of people with ASD by 2014. IACC Recommended Budget: $33,900,000 over 6 years.

Download 2008 Question 3: Objective 8 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 that deregulate mTOR signaling as candidates for autism spectrum disorders Ramesh, Vijaya Massachusetts General Hospital
Genetic analysis of 15q11-q13 in autism Sutcliffe, James Vanderbilt University
Genetic basis of autism Wigler, Michael Cold Spring Harbor Laboratory
Genetic contributions to endophenotypes of autism Wijsman, Ellen University of Washington
Genetic dissection of restricted repetitive behavior (RRB) Kim, Soo-Jeong University of Florida
Genetic studies in autism on chromosome 7 Pericak-Vance, Margaret Duke University
Genetic studies of autism susceptibility Brzustowicz, Linda Rutgers University
Genetic study of restricted repetitive behavior in autism spectrum disorders Kim, Soo-Jeong University of Florida
Genomic imbalances in autism - AS Kumar, Ravinesh University of Chicago
Genomic imbalances in autism - NIH Christian, Susan University of Chicago
Genomic resources for identifying genes regulating social behavior Young, Larry Emory University
Genotype-phenotype relationships in fragile X families Hagerman, Randi University of California, Davis
Hindbrain dysgenesis in Rett syndrome and other autism spectrum disorders Swanberg, Susan University of California, Davis
Identification and functional assessment of autism susceptibility genes - 1 Brzustowicz, Linda Rutgers, The State University of New Jersey - New Brunswick
Identification and functional assessment of autism susceptibility genes - 3 Vieland, Veronica The Research Institute at Nationwide Children's Hospital
Identification and functional assessment of autism susceptibity genes - 2 Millonig, James University of Medicine & Dentistry of New Jersey - Robert Wood Johnson Medical School
Identification of autism candidate genes on the X-chromosome from copy number variants identified by 500K SNP-CHIP analysis Vincent, John Centre For Addiction And Mental Health
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
Isolation of autism susceptibility genes Stefansson, Kari Decode Genetics, Inc.
Molecular analysis core Pericak-Vance, Margaret Duke University
Mutation analysis of candidate genes derived from an autism protein interaction network in SSC autism samples Zoghbi, Huda Baylor College of Medicine
Neural circuitry of social cognition in the broad autism phenotype Piven, Joseph University of North Carolina at Chapel Hill
Neurobiology of sociability in a mouse model system relevant to autism Brodkin, Edward University of Pennsylvania
Neurogenetics of candidate systems in autism Haines, Jonathan Duke University

Objective Multiyear Funding Table

IACC Strategic Plan Objectives 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%.