Title: Modeling the Complex Impact of Genetic Variation on Gene Expression
Non-coding and regulatory genetic variation plays a significant role in human health, but the impact of regulatory variants has proven difficult to predict from sequence alone. Further, genetic effects can be modulated by context, such as cell type and environmental factors. We have developed machine learning approaches to model the effects of regulatory variation, including predicting the impact of rare regulatory variants on gene expression, modeling the interaction between environmental factors and genetic variation, and detecting regulatory effects that vary over time. I will present recent results evaluating the complex impact of both rare and common genetic variation on gene regulation in diverse contexts including changes in genetic effects evident across cellular differentiation.
Alexis Battles research focuses on unraveling the impact of genetics on the human body, using machine learning and probabilistic methods to analyze large scale genomic data. She is interested in applications to personal genomics, genetics of gene expression, and gene networks in disease, leveraging diverse data to infer more comprehensive models of genetic effects on the cell. She earned her Ph.D. and Masters in Computer Science in 2014 from Stanford University in 2014, where she also received her Bachelors in Symbolic Systems (2003). Alexis also spent several years in industry as a member of the technical staff at Google. Prior to joining Hopkins, Alexis spent a year as a postdoc with Jonathan Pritchard with HHMI and the Genetics Department at Stanford. She joined John's Hopkins in July 2014.
Light refreshments will be provided at reception from 4:00pm - 4:30pm, 125 LKS foyer.