W. Evan Johnson
Assistant Professor of Medicine
Ph.D., Biostatistics, Harvard University, Cambridge, Massachusetts; June 2007
M.A., Biostatistics, Harvard University, Cambridge, Massachusetts, June 2006
M.S., Statistics, Brigham Young University, Provo, Utah; August 2003
B.S., Summa Cum Laude, Mathematics, Physics Minor, Southern Utah University, Cedar City, Utah; May 2002
General Field of Work:
Computational Biology; Biostatistics
Affiliations other than medicine:
Adjunct Assistant Professor, Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah
Office and Lab: 72 E. Concord St., E-645, Boston MA 02118
Office Phone: 617-638-2541
Lab Phone: 617-414-6947
Genomic medicine, computational biology, epigenetics, transcription regulation, Bayesian statistics, biostatistics
Summary of academic interest:
The development of personalized treatment regimes is an active area of current research in genomics. The focus of our research is to investigate core biological components that contribute to disease prognosis, development, and early detection and to develop latent variable models to accurately determine optimal therapeutic regimens for individual patients. Because biological processes do not act in isolation but as parts of complex interactive systems, we are computationally evaluating interactions between these systems at multiple levels. At the sequence and cellular level, we have developed latent variable models for probabilistically determining gene expression profiles that are linked to individual response to treatment. In addition, we are experimentally perturbing subcomponents of larger biological systems or pathways and linking pathway activation status to genet! ic disease risk and drug sensitivity.
Our lab’s research consists of the development of methods for analyzing a variety of genome-wide
data types, currently focusing on the analysis of data from next-generation sequencing (NGS)
experiments. We are developing a comprehensive and coordinated set of statistical methods for NGS data analysis and data integration that directly address many important problems in epigenetics and translational medicine. There is a great need for biologically motivated and mathematically justified methods able to efficiently handle the massive data sets generated by high-throughput experiments. Our goal is to conduct cutting-edge research, making an impact in genomics and translational science while developing straightforward computational tools to facilitate others conducting similar work. In order to ensure the methods being developed are appropriate and useful, we are aggressively working to establish and maintain strong collaborations with applied scientists in genomics and translational research. We are firmly of the opinion that the research conducted in our lab is timely, of high importance, and relevant to the current needs in these fields.
Cohen AL, Soldi R, Zhang H, Gustafson AM, Wilcox R, Welm BE, Chang JT, Johnson WE, Spira A, Jeffrey SS, Bild AH (2011). MATCH: Merging genomic and pharmacologic Analyses for Therapy CHoice. Molecular Systems Biology 7: 513.
Rope AF, Wang K, Evjenth R, Xing K, Johnston JJ, Swenson JJ, Johnson WE, Moore B, Huff CD, Bird LM, Carey JC, Opitz JM Stevens CA, Jiang T, Schank C, Fain HD, Robison R, Dalley B, Chin S, South TS, Pysher TJ, Jorde LB, Hakonarson H, Lillehaug JR, Biesecker LG, Yandell M, Arnesen T, Lyon GJ (2011). Using VAAST to Identify an X-Linked Disorder Resulting in Lethality in Male Infants Due to N-Terminal Acetyltransferase Deficiency. American Journal of Human Genetics 10.1016/j.ajhg.2011.05.017.
Johnson WE, Welker NC, Bass BL (2011). Dynamic Linear Model for the Identification of miRNAs in Next-generation Sequencing Data. Biometrics 67: doi: 10.1111/j.1541-0420.2010.01570.x.
Leek JT, Scharpf R, Corrada-Bravo H, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA (2010). Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Reviews Genetics 11, 733-739.
Clement N, Snell Q, Clement M, Hollenhorst PC, Parwar J, Graves BJ, Cairns BR, Johnson WE (2010). The GNUMAP Algorithm: Unbiased Probabilistic Mapping of Oligonucleotides from Next-Generation Sequencing. Bioinformatics, 26(1): 38-45
Johnson WE, Liu JS, Liu XS (2009). Doubly-stochastic continuous-time Hidden Markov approach for applications on genome tiling arrays. Annals of Applied Statistics, 3:1183-1203.
Johnson WE, Rabinovic A, Li C (2007). Adjusting batch effects in microarray expression data using Empirical Bayes methods. Biostatistics, 8 (1):118-127.
Johnson WE, Li W, Meyer CA, Gottardo R, Carroll JS, Brown M, Liu XS (2006). Model-based analysis of tiling-arrays for ChIP-chip. Proceedings of the National Academy of Sciences 103 (33):12457-12462