Genetic Variation and Its Contribution to Human Health
|When||Oct 02, 2013
Understanding genetic variation in human genomes and its contribution to human health is one of the central goals in biology and medicine. Substantial progress has already been made to associate genetic variants with various human phenotypes including diseases. Moreover, the advent of large amounts of genomic and transcription data has created opportunities to integrate these datasets to identify expression quantitative trait loci (eQTLs) and dissect the underlying genetic architecture for regulatory variation. However, the plethora of genetic variants discovered through recent technological advances and the complexity brought about by the large number of potential interactions of these variants and genes present computational challenges at a unprecedented level. In this talk, Dr. Shi will first review the current status of the 1000 Genomes Project that aims to provide a comprehensive resource on human genetic variation through whole-genome sequencing at a population level. Second, she will present a novel approach that combines sparse learning with biological networks to characterizing the joint effect of multiple genetic variants on phenotypes. Finally, she will describe a graph-guided sparse learning model to estimate subnetwork-to-subnetwork associations in eQTL mapping, where interacting genetic variants in a subnetwork affect the expression levels of multiple genes in another subnetwork.
Xinghua (Mindy) Shi is an assistant professor in the Department of Bioinformatics and Genomics, College of Computing and Informatics, University of North Carolina at Charlotte. Before joining UNC Charlotte, she was a postdoctoral research fellow at Brigham and Women’s Hospital and Harvard Medical School, a NIH T32 genetics training fellow at Harvard Medical School, a visiting research fellow in the Medical and Population Genetics program at Broad Institute, and an associate in the Quantitative Genetics Program at Harvard School of Public Health.
Dr. Shi received her Ph.D. and M.S. degrees in Computer Science from the University of Chicago and M.Eng and B. Eng degrees in Computer Science from Beijing Institute of Technology, China. She is a recipient of the Wells Fargo Foundation Fund for Faculty Excellence from Charlotte Research Institute in 2013.
Her research interest is in computational systems biology; particularly, the design and development of tools and algorithms to solve large-scale computational problems in biology and biomedical research. She is currently focused on integrating large-scale “-omics” datasets to study how genetic architecture affects biological processes and complex phenotypes at the systems level. She is also interested in complex network analysis and mathematical modeling of biological systems. Other broad interests of her research include genetic privacy, big data analytics, and high performance computing.
Broadcast Stream (live @ 4:00 p.m. on Wed, Oct 2)
Archived Presentation (active beginning Thu, Oct 3)