Computational tools to analyze SNP data from patients with mental illness
DESCRIPTION (provided by applicant): The broad, long-term objective of the proposed research is to develop and market a commercial software product that can be used to facilitate the analysis of genetic changes in order to elucidate chromosomal abnormalities that underlie diseases such as autism spectrum disorder, bipolar disorder, and schizophrenia. Recent technological advances allow samples of DNA from patients to be analyzed on single nucleotide polymorphism (SNP) arrays, generating up to millions of data points from each sample. In parallel, next- generation sequencing (NGS) of whole genomes (or whole exomes) allows the determination of sequence data from individuals with mental health (or other) diseases, as well as sequence data from affected and unaffected family members. These data must be analyzed to identify chromosomal abnormalities (e.g. DNA mutations, hemizygous or homozygous deletions, or translocations) that confer risk for these diseases. Software such as Partek(R) Genomics Suite (GS) offersa robust set of tools to perform data analysis and visualization. A goal of this proposal is to enhance the Partek GS and Partek Flow commercial products by introducing innovative, practically useful software modules that define genetic relatedness in studies based on SNP and/or NGS data. Specific Aim 1 is to develop and incorporate methods for the determination of genetic relatedness based on SNP data (including data sets of pedigrees and large populations). These methods allow the relationship between all pairs of individuals in a data set to be determined with high accuracy (even for large studies with thousands of samples). Specific Aim 2 is to develop and incorporate methods for the determination of genetic relatedness based on NGS data, including whole genome sequences of individuals. These methods will provide a significant new dimension to the analysis of genome sequence data, facilitating the identification of variants that are relevant to disease. For Specific Aim 3 we will apply these novel methods to two data sets: whole exome sequence data from individuals with autism (data from over 800 trios obtained from dbGaP), and SNP and whole genome or whole exome sequences from quintets of father/mother/child1/child2/child3 in which at least one child is diagnosed with autism. These studies will demonstrate the utility of the novel software methods and demonstrate how they can facilitate the discovery of genetic variants that underlie autism and other mental health disorders. PUBLIC HEALTH RELEVANCE PUBLIC HEALTH RELEVANCE: Newly available technologies allow the measurement of millions of variations in DNA sequence between samples from individuals with diseases (such as autism and schizophrenia) relative to unaffected individuals (controls). The proposed research is designed to create software analysis tools that will facilitate the discovery of chromosomal abnormalities in diseases. This may lead to improved diagnosis and treatments for these disorders, serving a large public health need.
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