Integrative statistical models for pathway analysis of GWAS data

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DESCRIPTION (provided by applicant): Genome-Wide Association Studies (GWAS) have become a popular approach for identifying genetic variants underlying complex diseases. However, the variants identified so far, individually or in combination, account for only a small proportion of the inherited component of disease risk. One possible reason is that complex diseases are likely to be caused by changes at the systems level, such as in a biological network or pathway, in which individual genetic variants only have weak marginal effects on disease risk. In this proposal, we will combine statistics, bioinformatics, and genetics to develop integrative approaches aimed at understanding the genetic architecture underlying complex diseases. We will examine copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) detected by the recently developed high density genotyping arrays, and then we will integrate prior biological knowledge to formally test disease association with joint effects of groups of common and rare genetic variants (CNVs and SNPs) in the same pathway, or more broadly, gene set. We will apply our novel methods to analyze two schizophrenia (GAIN and nonGAIN) and one bipolar disorder (GAIN) GWAS datasets, all of which were generated using Affymetrix 6.0 chips. Our Specific Aims are as follows: (1) Develop novel statistical method to identify genes and pathways (or gene sets) with enriched association signals in GWAS by leveraging information from different types of genetic variants: common and rare, CNVs and SNPs. We will model all the genes, SNPs, and CNVs within a pathway in a hierarchical fashion using random gene effects, which provide the ability to borrow information across genes in the same pathway. (2) Apply the proposed model to two complex diseases (schizophrenia and bipolar disorder) and develop a user friendly software package that implements the proposed methodology. The successful completion of Aim 1 will provide us with critical statistical tools for current and future GWA studies. The successful completion of Aim 2 will significantly enhance our understandings of the genetic architecture underlying schizophrenia and bipolar disorder, including their common genetic components, and will lead to more effective treatment strategies for mental disorders.