Dr. Xia’s research focuses on Genome wide association study (GWAS) of early brain development in humans; Meta-analysis of eQTL studies with independent and correlated subjects; Development of efficient tools for GWAS with twin/correlated subjects; and Modular regulation in protein-protein interaction network.

Dr. Xia’s main research is to apply advanced computational and statistical tools to identify genetic factors, microbiome and environmental factors which impact brain development in infancy and early childhood. This time period is critically important in disorders such as autism, bipolar disorder, schizophrenia and intellectual disability. He has the primary responsibility for managing and analyzing genome-wide association (GWAS) data including the development of new methods for dealing with complex genetic and neuroimaging data. The state-of-the-art data science with the most advanced machine learning algorithm, such as deep learning, provides us new opportunity to uncover the genetic code underlying in millions of genetic markers across the human genome. Dr. Xia’s current research projects are building predictive models such as deep convolutional neural network utilizing DNA markers to predict cognitive function of early age. His research will also adapt this deep learning framework to predict other psychiatric disorders such as autism, schizophrenia, and bipolar disorder so that the DNA code of human genome about mental disorders can be decoded.