Integrative Approaches to Mapping Susceptibility Genes of Complex Neuropsychiatric Disorders

University Of Chicago, Chicago IL

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Abstract

Project Summary Identifying the susceptibility genes and variants of neurodevelopmental and psychiatric diseases will not only contribute to our understanding of these diseases, but also point to potential therapeutic targets. Genome-wide association studies (GWAS) are commonly used to study complex diseases, including neuro-psychiatric diseases. Nevertheless, GWAS focus on common variants, and have not been successful in studying early- onset diseases, including many developmental disorders, whose risk alleles are generally kept at very low frequencies in population. Additionally, the results of GWAS often cannot be directly translated into knowledge of risk genes and disease mechanisms. The goal of this project is to develop comprehensive statistical methods for analyzing genetic data of neuropsychiatric diseases to map their susceptibility genes and gain insights of the disease genetics. (1) We propose methods to analyze exome and genome sequencing data from patient families. Unlike existing methods for genetic studies which often focus on type of data per time, our methods will integrate a broad spectrum of genetic variations at the level of genes, including non-synonymous and regulatory non-coding mutations, both de novo and inherited from parents in origin. This leads to a higher power of detecting risk genes. (2) Copy number variants (CNVs) make substantial contribution to neurodevelopmental disorders. But CNVs often overlap multiple genes and it is difficult to identify risk genes within disease-related CNVs. A new algorithm is proposed to extract gene-level information from CNVs. This allows us to combine CNV data and nucleotide variation data from sequencing, to better detect disease genes. (3) Importance of non-coding variants to complex disease has now been firmly established and expression QTL (eQTL) is a promising strategy to map non-coding variants that have functional effects on gene expression levels. We propose a novel statistical approach to joint analysis of eQTL and GWAS data. The method is unique in that it uses information of all eQTL of a gene to test its role in disease, including both cis- and trans-eQTL, across the entire range of effect sizes. (4) A key component of our effort is the integration of the methods we develop into user-friendly software that could benefit the broad psychiatric genetic community.