2010 B.S. Biology, University of Massachusetts Boston, Boston, MA
2015 M.S. Biostatistics, University of Massachusetts Amherst, Amherst, MA
Network-Based Analysis of Transcriptomic Data
I utilize gene expression data to investigate the conditional response of biological network activity to a variety of environmental exposures. This work compliments gene-by-gene differential expression analyses, but allows for a broader net of inference of cellular disruption caused by specific perturbations. Specifically, I explore the different networks affected by a variety of chemical perturbations known to cause similar phenotypic response, such as cancer and obesity, with the overall goal of sub-classifying these “perturbagens” and build predictive models for characterizing chemicals of unknown effect.
Ballantyne, R., Zhang, X., Nuñez, S., Xue, C., Zhao, W., Reed, E., … Reilly, M. (2016). Genome-wide interrogation reveals hundreds of long intergenic noncoding RNAs that associate with cardiometabolic traits. Human Molecular Genetics. http://doi.org/10.1093/hmg/ddw154
Qian, J., Nunez, S., Reed, E., Reilly, M. P., & Foulkes, A. S. (2016). A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci. Plos One, 11(2), e0148218. http://doi.org/10.1371/journal.pone.0148218
Yazdani, N., Parker, C. C., Shen, Y., Reed, E. R., Guido, M. A., Kole, L. A., … Bryant, C. D. (2015). Hnrnph1 Is A Quantitative Trait Gene for Methamphetamine Sensitivity. PLoS Genetics, 11(12). http://doi.org/10.1371/journal.pgen.1005713
Reed, E., Nunez, S., Kulp, D., Qian, J., Reilly, M. P., & Foulkes, A. S. (2015). A guide to genome-wide association analysis and post-analytic interrogation. Statistics in Medicine, 34(28), 3769–3792. http://doi.org/10.1002/sim.6605