Abstract
When cells differentiate, they undergo changes in gene regulatory networks. These shifts in gene expression are often modulated by genetic variants, which are known to contribute to disease risk. Usually, population-level studies are required to identify these markers – however, this is not feasible for small sample sizes or to infer the effects of rare variation. This project aims to develop computational methods to detect gene-level expression biases (allele specific expression – ASE) in human induced pluripotent stem cells, study how well personalized detection of ASE signals recapitulate effects on a population-scale and identify important genetic variation influencing cellular differentiation pathways.
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