【Speaker】Dr.Li Shen (沈理)

【Host】Dr.Qian Wang (王乾)

【Time】3PM,Aug 6

【Venue】Med-X 218

【Abstract】Today's generation of multi-modal imaging systems produces massive high dimensional data sets, which when coupled with high throughput genotyping data such as single nucleotide polymorphisms (SNPs), provide exciting opportunities to enhance our understanding of phenotypic characteristics and the genetic architecture of human diseases. However, the unprecedented scale and complexity of these data sets have presented critical bottlenecks requiring new concepts and enabling tools. In this talk, using the quantitative genetics study of the Alzheimer's Disease Neuroimaging Initiative (ADNI) data as an example, we discuss the recent development of bioinformatics strategies for multidimensional brain imaging genomics. We review and synthesize ADNI genetic associations with disease status or quantitative disease endophenotypes including structural and functional neuroimaging, fluid biomarker assays, and cognitive performance. We briefly discuss the diverse analytical strategies used in these studies, and present a very recent study on transcriptome-guided amyloid imaging genetic analysis via a novel structured sparse learning algorithm. We perform pathway and network enrichment analyses of these ADNI genetic associations to highlight key mechanisms that may drive disease onset and trajectory. We show that the broad availability and wide scope ofm ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future studies.

【Bio】Dr. Li Shen holds a B.S. degree from Xi'an Jiao Tong University, an M.S. degree from Shanghai Jiao Tong University, and a Ph.D. degree from Dartmouth College, all in Computer Science. He is an Associate Professor of Radiology and Imaging Sciences at Indiana University (IU) School of Medicine. He is a member of both the IU Center for Neuroimaging (CfN) and Center for Computational Biology and Bioinformatics (CCBB). He is also affiliated with Department of Computer and Information Science, School of Informatics and Computing, and Department of Biostatistics. His research interests include medical image computing, bioinformatics, data mining, and morphometric analysis. The central theme of his lab is focused on developing computational and informatics methods for integrative analysis of multimodal imaging data, high throughput "omics" data, fluid and cognitive biomarker data, and rich biological knowledge such as pathways and networks, with applications to various complex disorders. The ultimate goal is to improve early diagnosis and mechanistic understanding of disease processes and treatment response. His research is primarily funded by NIH (NLM, NIA, NIBIB, NIAAA), NSF and DOD. Further information about Dr. Shen's research activities is available at http://www.iu.edu/~shenlab/.