Srijan Sen

By |

Srijan Sen, MD, PhD, is the Frances and Kenneth Eisenberg Professor of Depression and Neurosciences. Dr. Sen’s research focuses on the interactions between genes and the environment and their effect on stress, anxiety, and depression. He also has a particular interest in medical education, and leads a large multi-institution study that uses medical internship as a model of stress.

Jun Li

By |

Jun Li, PhD, is Professor and Chair for Research in the department of Computational Medicine and Bioinformatics and Professor of Human Genetics in the Medical School at the University of Michigan, Ann Arbor.

 Prof. Li’s areas of interest include genetic and genomic analyses of complex phenotypes, including bipolar disorder, cancer, blood clotting disease, and traits involving animal models and human microbiomes. Our approach emphasizes statistical analysis of genome-scale datasets (e.g, gene expression and genotyping data, results from next-generation sequencing), evolutionary history, bioinformatics, and pattern recognition.

Bhramar Mukherjee

By |

Bhramar Mukherjee is  a Professor in the Department of Biostatistics, joining the department in Fall, 2006. Bhramar is also a Professor in the Department of Epidemiology. Bhramar completed her Ph.D. in 2001 from Purdue University. Bhramar’s principal research interests lie in Bayesian methods in epidemiology and studies of gene-environment interaction. She is also interested in modeling missingness in exposure, categorical data models, Bayesian nonparametrics, and the general area of statistical inference under outcome/exposure dependent sampling schemes. Bhramar’s methodological research is funded by NSF and NIH.   Bhramar is involved as a co-investigator in several R01s led by faculty in Internal Medicine, Epidemiology and Environment Health sciences at UM. Her collaborative interests focus on genetic and environmental epidemiology, ranging from investigating the genetic architecture of colorectal cancer in relation to environmental exposures to studies of air pollution on pediatric Asthma events in Detroit. She is actively engaged in Global Health Research.

Sebastian Zoellner

By |

Sebastian Zöllner is a Professor of Biostatistics. He also holds an appointment in the Department of Psychiatry. Dr. Zöllner joined the University of Michigan after a postdoctoral fellowship in the Department of Human Genetics at the University of Chicago. His research effort is divided between generating new methods in statistical genetics and analyzing data. The general thrust of his work is problems from human genetics, evolutionary biology and statistical population biology.

Gilbert S. Omenn

By |

Gilbert Omenn, MD, PhD, is Professor of Computational Medicine & Bioinformatics with appointments in Human Genetics, Molecular Medicine & Genetics in the Medical School and Professor of Public Health in the School of Public Health and the Harold T. Shapiro Distinguished University Professor at the University of Michigan, Ann Arbor.

Doctor Omenn’s current research interests are focused on cancer proteomics, splice isoforms as potential biomarkers and therapeutic tar- gets, and isoform-level and single-cell functional networks of transcripts and proteins. He chairs the global Human Proteome Project of the Human Proteome Organization.

Michael Boehnke

By |

My research focuses on developing statistical methods and software tools for the analysis of human genetic data and application of those methods to understand the genetic basis of human health and disease. Our methods and tools are used by statisticians and geneticists worldwide. My disease research is focused on type 2 diabetes (T2D) and related traits and on bipolar disorder and schizophrenia. Our studies are generating and analyzing genome or exome sequence data on 10,000s of individuals, requiring the efficient handling of petabyte-scale data.

Jeremy M G Taylor

By |

Jeremy Taylor, PhD, is the Pharmacia Research Professor of Biostatistics in the School of Public Health and Professor in the Department of Radiation Oncology in the School of Medicine at the University of Michigan, Ann Arbor. He is the director of the University of Michigan Cancer Center Biostatistics Unit and director of the Cancer/Biostatistics training program. He received his B.A. in Mathematics from Cambridge University and his Ph.D. in Statistics from UC Berkeley. He was on the faculty at UCLA from 1983 to 1998, when he moved to the University of Michigan. He has had visiting positions at the Medical Research Council, Cambridge, England; the University of Adelaide; INSERM, Bordeaux and CSIRO, Sydney, Australia. He is a previously winner of the Mortimer Spiegelman Award from the American Public Health Association and the Michael Fry Award from the Radiation Research Society. He has worked in various areas of Statistics and Biostatistics, including Box-Cox transformations, longitudinal and survival analysis, cure models, missing data, smoothing methods, clinical trial design, surrogate and auxiliary variables. He has been heavily involved in collaborations in the areas of radiation oncology, cancer research and bioinformatics.

I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research  in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data.  Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation.  I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.

Kerby Shedden

By |

Kerby Shedden has broad interests involving applied statistics, data science and computing with data.  Through his work directing the data science consulting service he has worked in a wide variety of application domains including numerous areas within health science, social science, and transportation research.  A current major focus is development of software tools that exploit high performance computing infrastructure for statistical analysis of health records, and sensor data from vehicles and road networks.

Stephen Smith

By |

The Smith lab group is primarily interested in examining evolutionary processes using new data sources and analysis techniques. We develop new methods to address questions about the rates and modes of evolution using the large data sources that have become more common in the biological disciplines over the last ten years. In particular, we use DNA sequence data to construct phylogenetic trees and conduct additional analyses about processes of evolution on these trees. In addition to this research program, we also address how new data sources can facilitate new research in evolutionary biology. To this end, we sequence transcriptomes, primarily in plants, with the goal of better understanding where, within the genome and within the phylogeny, processes like gene duplication and loss, horizontal gene transfer, and increased rates of molecular evolution occur.

A rough draft of the first comprehensive tree of life, showing the links between all of the more than 2.3 million named species of animals, plants and microorganisms. The draft was constructed by combining more than 450 existing trees to a comprehensive taxonomy. Because the tree is large, only lineages with at least 500 species are shown. The colors correspond to the amount of publicly available DNA data for each lineage (red = high, blue = low, giving an idea of the amount of available information).

A rough draft of the first comprehensive tree of life, showing the links between all of the more than 2.3 million named species of animals, plants and microorganisms. The draft was constructed by combining more than 450 existing trees to a comprehensive taxonomy. Because the tree is large, only lineages with at least 500 species are shown. The colors correspond to the amount of publicly available DNA data for each lineage (red = high, blue = low, giving an idea of the amount of available information).

Brian D. Athey

By |

Data science applications: Bioinformatics and ‘Big Data’ based customizable pipelines that predict pharmacogenomic response elements for clinical testing in psychiatric disease; Integrative Biomedical Informatics platforms such as tranSMART (see tranSMART Foundation.org); and High-throughput multi-scale imaging and analysis platforms directed to understanding the ‘4D Nucleome’.

The Athey Lab in the Department of Computational Medicine and Bioinformatics (DCM&B) University of Michigan Medical School, is led by Dr. Brian Athey (see Atheylab.ccmb.med.umich.edu).

The lab is working on two complementary domains of research and development.

1. The Athey Lab’s recent research interests are in the creation and use of bioinformatics pipelines and machine learning methods to radically improve the efficacy of psychiatric pharmacogenomics—allowing patients to take the most effective drug for their illness and suffer the fewest side effects. This area of research centers on the exploration of the ‘pharmacoepigenome’ in psychiatry, neurology, anesthesia and addiction medicine. This research employs high-throughput 4D microscopic imaging of enhancers, promoters and chromatin features, using fluorescence in situ hybridization (FISH). These methods are coupled with Hi-C chromatin conformation capture, chromatin state annotation, localization in postmortem human brain tissue and induced neuronal pluripotent stem cells, and machine learning for identification of regulatory variants, to provide insight into the genetic and epigenetic mechanisms of inter-individual and inter-cohort differences in psychotropic drug response

2. The Athey Lab is also developing new high-throughput methods to analyze images of genes in the context of the cellular nucleus to better understand the machinery of bioinformatics in context. One main area of research is the application of high resolution fluorescence optical microscopy coupled with high-throughput analysis, 3D imaging and machine learning to explore the chromatin structure and nuclear architecture of cells. This research emphasizes the convergence between 3D structural predictions and 3D structural measurements with microscopy, to provide insight into the transcriptional architecture of the interphase nucleus.

This area of research centers on the exploration of the ‘pharmacoepigenome’ in psychiatry, neurology, anesthesia and addiction medicine. This research employs high-throughput 4D microscopic imaging of enhancers, promoters and chromatin features, using fluorescence in situ hybridization (FISH). These methods are coupled with Hi-C chromatin conformation capture, chromatin state annotation, localization in postmortem human brain tissue and induced neuronal pluripotent stem cells, and machine learning for identification of regulatory variants, to provide insight into the genetic and epigenetic mechanisms of inter-individual and inter-cohort differences in psychotropic drug response.

Collaborations: The lab works very closely with Assurex Health, Inc. (Mason, Ohio) on project 1. This work is governed by a Regents-approved Master Agreement between U-M and Assurex Health, Inc. Similarly, the lab collaborates closely with the tranSMART Foundation (tF), and this is also governed by a Master Agreement between U-M and tF.

The lab collaborates with the Brady Urological Institute at Johns Hopkins Medical School, lead by Dr. Ken Pienta, to build on their extensive 2D characterization of prostate tumors, by the introduction of simple chromatin dyes, advanced biomarkers, and 3D imaging systems.

The lab works closely with Dr. John Wiley of University of Michigan Health System, studying the effect of glucocorticoids on the neuroblastoma based cell line Sy5y before and after treatment with retinoic acid and BDNF, particularly in their terminally differentiated condition.

The lab also collaborates with Dr. Christoph Cremer from the Institute of Molecular Biology in Mainz, Germany, investigating super-resolution microscopy techniques.

Lithium response network in human brain in bipolar disorder: A regulatory network in human brain mediating lithium response in bipolar patients was revealed by analysis of functional single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) and published gene association studies, followed by epigenome mapping. Noncoding SNPs characterized as altering enhancer and promoter function were imputed using fine epigenetic mapping, followed by bioinformatics analysis. Following gene set enrichment and pathway analysis, these genes were found to be significantly associated (p < 10-27; Fisher’s exact test) with this ionotropic AMPA2 glutamate receptor network in human brain67. Higgins GA, Allyn-Feuer A,Barbour E and Athey BD. “A glutamatergic network mediates lithium response in bipolar disorder as defined by epigenome pathway analysis.” Accepted, Journal of Pharmacogenomics. August 2015.

Lithium response network in human brain in bipolar disorder: A regulatory network in human brain mediating lithium response in bipolar patients was revealed by analysis of functional single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) and published gene association studies, followed by epigenome mapping. Noncoding SNPs characterized as altering enhancer and promoter function were imputed using fine epigenetic mapping, followed by bioinformatics analysis. Following gene set enrichment and pathway analysis, these genes were found to be significantly associated (p < 10-27; Fisher’s exact test) with this ionotropic AMPA2 glutamate receptor network in human brain67. Higgins GA, Allyn-Feuer A,Barbour E and Athey BD. “A glutamatergic network mediates lithium response in bipolar disorder as defined by epigenome pathway analysis.” Accepted, Journal of Pharmacogenomics. August 2015.