Date of Graduation
Masters of Science (MS)
Autism is a spectrum of neurological disorders that is characterized by repetitive and stereotyped behaviors, lack of social skills in verbal and non-verbal communications, and intellectual disability. Recent statistics shows that 1 out of every 88 children in the US is affected by autism.
In this thesis, I first review previous studies on genetic association analyses of autism spectrum disorder. A large number of these studies fall into two categories: Genome Wide Association Studies (GWAS) and sequencing studies. Although GWAS are able to identify multiple common risk variants associated with different diseases, these common variants explain only a small portion of disease occurrence. In the case of autism, GWAS has had only limited success in identification of common risk variants. Recent studies suggest that rare variants have an important role in causing this disorder. There are a number of sequence-based association studies that have analyzed the relationship of rare variants with autism.
Chapter 2 of my thesis presents some of the most popular sequencing-based rare variant association analysis methods along with their advantages and disadvantages. From previously published methods, the Weighted Sum Method (WSM) and Sequence Kernel Association Test (SKAT) are used to analyze the autism disease sequencing data since they have some advantages over similar methods. A new method called the Single Variant Method (SVM) is introduced in section 2.4. This new method provides a better understanding of the data and identifies associated variants not found by the previous methods.
In Chapter 3, the results of the analysis of the autism data using WSM, SKAT and SVM are presented and discussed. Although there is no gold standard, there is insightful information to be learned by applying multiple methods, including the novel SVM method proposed here.