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One of the challenges in psychiatric genetics is the heterogeneous nature of these disorders. To reduce the complexity of phenotypes, we aim to use neuroimaging-based endophenotypes to study the genetics of psychiatric disorders (with a current focus on alcoholism). Compared with clinical symptoms, endophenotypes are believed to be more homogeneous, simpler in genetic architecture, and closer to the action of risk genes, and hence afford more power for detecting genetic associations.
With advances in sequencing technologies, a huge amount of genetic variation information can be obtained, however, an increasing level of noise will also be inevitably introduced. This translates into challenges for the analysis and interpretation of large data sets, especially those generated by whole genome sequencing. We are interested in integrating prior information (e.g., linkage evidence, gene expression studies, functional annotations, etc.) in genetic analysis to improve the signal-to-noise ratio and boost the power of next-generation genetic association studies.
We are also interested in applying system genetic approaches to the genetic study of psychiatric disorders. With advances in genomic technologies, multiple layers of molecular data can be collected for the same samples, such as genetic variations, gene expressions, and protein levels—all in a high-throughput manner. Integrating multiple layers of molecular profiles could enhance the identification of the associations between genetic variations and phenotypes.
Additionally, we are interested in revisiting the linkage regions and candidate genes for psychiatric disorders via targeted deep sequencing, exploring gene-gene and gene-environment interactions, and investigating the epigenetics of psychiatric disorders. We are also interested in gene mapping studies in admixed populations, such as African-Americans, for admixture mapping or trans-racial mapping, and in isolated populations for mapping rare risk variants.