Research Interests

Our team is committed to apply genomics, statistical genetics and systems biology approaches to mechanism research of complex diseases by integrating high-throughput and multi-dimensional genomics data. Besides, combined with molecular biology experiments, we strive to explore the molecular genetic mechanisms and pathogenesis of complex diseases (including psychiatric disorders and autoimmune diseases), which can provide scientific basis for the early diagnosis of complex diseases, development of disease intervention strategy and detection of new drug targets.

Main Research Contents

1. Candidate-gene research of complex diseases

To study the genetic mechanism of complex diseases, our team has established relevant technical methods and strategies in many aspects, for example, construction of genetic database of diseases, candidate gene prioritization, candidate gene selection and target region selection. Meanwhile, based on the collected data, we would carry out specific candidate gene association studies, target region sequencing and genome-wide association studies on psychiatric disorders and autoimmune diseases. Finally, we would analyze functions of the obtained reliable loci and genes by combining with experimental verification.

2. Pathway/network based analysis for multidimensional omics data of complex diseases

Multi-level research of complex diseases has accumulated a large number of multi-dimensional genomic data, including genetic data, expression data and network data. To further explore the pathogenesis of complex diseases, we have established a variety of algorithms and tools about pathway and network based analysis on -omics data, including pathway-based analysis to search for disease-related pathways, disease-related network module analysis, and integration analysis on genetic data and expression data. On this basis, we have performed disease-related pathway and network analysis in several psychiatric disorders, such as schizophrenia and bipolar disorder.

3. Gene and environment interaction analysis of complex diseases

Except for genetic factors, complex diseases were also affected by environmental factors. The complicated interaction between environmental and genetic factors is one of the most important reasons leading to disease heterogeneity. We start from the environment-associated expression data to investigate the interactions between gene and environment. We constructed gene regulation and expression network subjected to specific environment through mining of public data, and further performed systems biology analysis of its networks, with an ultimate purpose of exploring experimental verification of molecular biology for core parts of the networks.