Developing New Statisical Methods to Detect Common and Rare Variants Involved in Neuropsychiatric Disorders

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Our goal is to develop new analytic tools that can be used to identify genetic variation more efficiently and accurately than the existing methods and then test them in the datasets of psychiatric disorders or other complex human diseases. We have been actively working with both intramural and extramural collaborators. A few examples include 1) Using pathway-bases approaches to detect genetic domains underlying the pharmacotherapy options of serotonin reuptake inhibitors effect in study subjects affected with obsessive compulsive disorder (OCD); 2) Establishing efficient pipelines for exome sequencing analyses in a Latino population (schizophrenia and bipolar); and 3) Developing an efficient test for nonlinear dependence of two continuous variables. This presents a new way of testing nonlinear dependence between two continuous variables. There is a need to improve the current version of the software so that it can be used by individuals who use Linux-based computer or desktops to run their analysis. Summary We collaborated with extramural scientists and developed a new statistical method called CANOVA. Using this method, one can generalize the within category variance in traditional analysis of variance (ANOVA). Using extensive simulations, we extensively evaluated the performance of CANOVA. We then applied CANOVA to a real dataset and showed that the power of CANOVA performs better when the correlation is highly non-linear. In addition, we developed a novel mixture model to estimate the time to antidepressant effect onset and its association with covariates such as age, gender and baseline anxiety. We evaluated the model's overall utility and performance via extensive simulations. We demonstrated its use by application to a longitudinal dataset from the Sequenced treatment Alternatives to Relieve Depression (STAR*D) study. Our algorithm successfully identified age and anxiety status as significant factors in influencing the onset distribution of citalopram. And we developed a pathway-based pipeline which can be used to link genome-wide association study signals to several important biological pathways. We used our own pipelines to analyze drug response on Obsessive Compulsive Disorder (OCD) GWAS data to select significant pathways. In a recent publication, we reported the suggestive roles of genes in the glutamatergic neurotransmission system and the serotonergic system. The results presented may provide new insights into genetic mechanisms underlying treatment response in OCD. More recently, we applied the same approach to seeking genetic variants underlying antidepressants such as ketamine. The potential findings will be reported in the future.