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Advanced Autism Genetics: Biological Subgroups, Diagnostic Classification, and Resilience.

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Completed

Tylee, Daniel

Glatt, Stephen

State University of New York, Upstate Medical University

$60,000.00

2 years

Weatherstone Predoctoral Fellowship

Syracuse

New York

United States

2015

http://www.upstate.edu

https://plu.mx/autismspeaks/grant/autismspeaks-9645

ASDs are heritable, yet gene-based diagnosis of ASD has not materialized. Why do some individuals develop ASDs while some of their siblings are resilient? Biological heterogeneity may obscure the answer. Identification of genetic ASD subgroups will aid classification of ASD and typically developing (TD) subjects. Resilience genes may buffer against development of ASDs in subjects with high genetic risk. Genetic subgrouping may improve our ability to detect resilience factors. This project will: 1) Characterize genetic ASD subgroups using GWAS methods on data from the Autism Genome Project (AGP) to identify risk SNPs for ASD, cluster those SNPs, and assign subjects to genetic subgroups. 2) Compare classifiers of ASD and biological subgroups. Using AGP SNP data, support vector machines (SVMs) will be used to classify ASD and TD subjects. A binary classifier will be compared to multi-group classifiers corresponding to Aim-1 subgroups. Classifier generalizability will be tested in a larger, independent ASD dataset from the Psychiatric Genomics Consortium. 3) Identify ASD resilience genes, creating polygenic risk scores for AGP subjects, and identifying TD subjects with high ASD risk scores (resilient TDs). A GWAS contrasting resilient TDs and ASD subjects will identify SNPs that may buffer against ASDs.

Biomarkers, Genetics, Computational Biology, Genetic Epidemiology, Quantitative/ Statistical Genetics, Machine Learning Classifiers, GWAS, Biology, Screening/ Diagnosis/ Phenotyping, Etiology/ Risk Factors

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