School of Public Health
These researchers develop and evaluate various statistical and computational methods for genome-wide association studies (GWASs), next-generation sequencing data, multiple types of -omic data, and neuroimaging data. They conduct frequent and wide-ranging studies with large-scale simulated and real genetic and -omic data, which require both CPU-intensive computing and a large disk space to store big data such as the U.K. Biobank data of GWAS, WES, imaging, and phenomes. More recently the researchers have expanded their study into neuroimaging involving large-scale MRI data and deep learning. For example, they would like to develop and evaluate new computational methods to construct functional networks or connectomes based on resting-state functional MRI (fMRI) data. They have recently been funded with multiple projects by NIA and plan to use the ADSP whole-genome sequencing (WGS) and otheromic data that are being collected. One of their main goals is to integrate multiple types and multiple sources of -omic and neuroimaging data, for which they will continue downloading huge datasets such as the U.K. Biobank GWAS and neuroimaging data with almost half a million participants.
Research by this group was featured on the MSI website in April 2023: Improving Imaging Wide Association Studies.