A computational genomics approach to identify roles of rare genetic variants in psychiatric disorders and gene expression

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Project Summary This is a proposal for the K01 Mentored Career Development Award in Biomedical Big Data Science. The goal of this proposal is to obtain training in biomedical science with focus on psychiatric disorders, and perform research to discover rare genetic variants that influence human complex traits including two psychiatric disorders, bipolar disorder (BP) and schizophrenia (SCZ). Identifying those rare variants is critical for both biology and human health as it will elucidate the genetic basis of those disorders and facilitate development of treatment. Recently, as the cost of next-generation sequencing decreases at a rate faster than that described by Moore's law for computer chips, many genetic studies are utilizing whole-genome sequencing (WGS) to identify roles of rare variants in human complex traits. However, these studies have had limited success most likely due to the small sample size. In this proposal, I will analyze three WGS data sets that provide unique opportunities to find effect of rare variants. The first is WGS data of large pedigrees with BP in which rare variants may be enriched in a certain large family, increasing our chance to detect their effect. The second is expression quantitative trait loci data that contain WGS and RNA-Seq from Genotype-Tissue Expression (GTEx) initiative. GTEx collected gene expression from multiple human tissues, which would enable discovery of functional effects of rare variants on different tissues. The third is WGS data of 4,000 BP and SCZ case-control samples from two recently bottlenecked populations. Deleterious rare variants may have elevated allele frequency in these populations, which increases statistical power to detect their effect. To effectively analyze the three WGS data sets, I will develop a new statistical approach and also utilize methods that I already developed. These methods combine effects of multiple rare variants in a gene to increase statistical power. I will apply these methods to the three WGS data sets to identify rare variants that influence psychiatric disorders (BP and SCZ) and gene expression. Although I have considerable knowledge and expertise in computer science and statistics, I seek to obtain additional training in biomedical science, especially in psychiatric disorders and clinical research to better interpret results of the rare variant analyses and extract biologically meaningful information from results. I will participate in several courses and workshops offered at UCLA and other institutions to obtain this training. This training will enable me to design and lead genomic studies for psychiatric disorders and to develop a niche as a statistical geneticist. These immediate goals will be the basis for my long-term career goal, which is to enhance understanding of how genome sequences influence one's susceptibility to diseases and to develop personalized treatments. I will be mentored by Drs. Nelson Freimer, Jonathan Flint, and Giovanni Coppola who are experts in neuropsychiatric disorders and genomics. They will provide guidance on my education and research training throughout the award period.