Time: 12:00 PM Location: 252 BRB II/III
Gary D. Wu, Ph.D.
Professor of Medicine
Ferdinand G. Weisbrod Chair in Gastroenterology
Perelman School of Medicine
University of Pennsylvania
Metabolic Interactions Between the Host and its Gut Microbiome: Implications for Health and Disease
Narayan G. Avadhani, Ph.D.
Harriet Ellison Woodward Professor of Biochemistry
Chair, Department of Animal Biology
University of Pennsylvania School of Veterinary Medicine
Mechanisms of Mitochondrial Targeting of Cytochrome P450s and Their Role in Drug and Alcohol Toxicity
*co-sponsored seminar with the Center for Excellence in Environmental Toxicology (CEET)
"Regulatory interactions between epigenetic complexes and their RNA interactomes"
Monday, April 6, 2015, 4:00 PM Main Auditorium, Biomedical Research Building
Jeannie T. Lee is an Investigator of the Howard Hughes Medical Institute and Professor of Genetics at Harvard Medical School. Dr. Lee specializes in the study of epigeneticregulationby long noncoding RNAs using X-chromosome inactivation as a model system. For her work on RNA-mediated chromatin change, Dr. Lee became the recipient of the 2010 Molecular Biology Prize from the National Academy of Sciences, U.S.A, and is a Fellow of the American Association for the Advancement of Science. Dr. Lee has also been named a Distinguished Graduate Award of the University of Pennsylvania School of Medicine and served on the Board of Directors of the Genetics Society of America. She received her A.B. in Biochemistry and Molecular Biology from Harvard University and obtained M.D.-Ph.D degrees from the University of Pennsylvania School of Medicine. Dr. Lee began her work on epigenetic regulation at the Whitehead Institute/MIT with Rudolf Jaenisch and served as Chief Resident of Clinical Pathology at the Massachusetts General Hospital.As a young investigator, she received the Basil O’Connor Scholar Award from the March of Dimes and the Pew Scholars Award. In 2011, she co-founded RaNA Therapeutics to harness the potential of long noncoding RNAs to treat disease. She is also a founder and Co-Director of the Harvard Epigenetics Initiative.
"Non-Parametric Analysis of Competing Risks Data with Event Category Missing at Random"
Natalia Gouskova, PhD
Department of Biostatistics
UNC Gillings School of Global Public Health
Abstract: In competing risks setup, the data for each subject consists of event time, censoring indicator, and event category. However, sometimes the information about the event category can be missing, as, for example, in a case when the date of death is known but the cause of death is not available. In such situations, treating subjects with missing event category as censored leads to the underestimation of the hazard functions. We suggest non-parametric estimators for the cumulative cause-specific hazards and the cumulative incidence functions which use local polynomial regression techniques to estimate the contribution of an event with missing category to each of the cause-specific hazards, and derive the propertied of these estimators. The method is illustrated using the data on infections in patients from the United States Cystic Fibrosis Foundation Patient Registry.
“Cancer Absolute Risk Projection with Incomplete Predictor Variables”
Lu Chen PhD Candidate Graduate Program in Biostatistics Graduate Group in Epidemiology and Biostatistics
Dissertation Advisor: Jinbo Chen, PhD Committee Chair: Hongzhe Li, PhD Committee Members: Emily F. Conant, MD, Daniel F. Heitjan, PhD, and Andrea B. Troxel, ScD
Abstract: A popular approach to projecting cancer absolute risk is to integrate a relative risk function of predictors with hazard rates obtained from different sources. To assess added values of candidate risk predictors, it is very common that data for standard risk predictors is fully available from a frequency-matched case-control study, but that of candidate predictors is available only for a subset of cases and controls. In the first project, we developed statistical measures for quantifying predictive accuracy of cancer absolute risk prediction models, accommodating incomplete predictor variables. We particularly focused on a measure that is useful for evaluating efficiency of model-based cancer screening, the proportion of cases that can be captured by screening only people with high projected risk. In the second project, using a logistic regression model to describe the relationship between cancer status and all risk predictors, we developed a novel semiparametric maximum likelihood approach under rare disease approximation for the estimation of relative risk parameters and the distribution of candidate predictors. Through theoretical and simulation studies, we showed that our estimator is consistent with an asymptotically normal distribution and has improved statistical efficiency. In the third project, we applied the statistical methods developed in the first two to evaluate the added values of percent mammographic density and breast cancer risk SNPs in breast cancer absolute risk projection. Our results showed that the two sets of predictors had similar added values and can lead to more efficient model-based screening for breast cancer.
“Case Fatality and Population Mortality Associated with Anaphylaxis in the United States”
Larry MA, PhD
Johnson & Johnson Consumer Products Company
"Issues and challenges to analyze Genome-wide Methylation data and possible solutions.”
Hemant K. Tiwari PhD William "Student" Sealy Gosset Professor Head, Section on Statistical Genetics Department of Biostatistics University of Alabama at Birmingham School of Public Health
Abstract: For complex diseases there often exists the problem of missing heritability. It is continually debated whether this is because of highly complex genetic architecture that is not accounted for, or if there are actually greater heritable contributions unrelated to the actual DNA sequence itself in the study of epigenetics. An epigenetic modification is defined as any alteration to DNA that does not affect the sequence itself but serves a function and is retained when the cell divides. The study of epigenetics may provide vital information that can be used to provide a better understanding of this phenotypic variability among individuals. The most widely studied and perhaps the foundational epigenetic modification is DNA methylation. However, there are challenges to DNA methylation data analysis, specifically the analysis of DNA from the Illumina Methyl450 array which is a relatively new area that presents both computational and statistical challenges. This talk will cover issues with quality control, genome-wide heritability estimation, and association methods for DNA methylation as well provide possible solutions.
PMI SEMINAR SERIES- AJIT JOGLEKAR, PHD- "Triggering and tuning the Spindle Assembly Checkpoint"
2PM CRB AUSTRIAN AUDITORIUM
“How Much Can Blocking and Randomization Improve Molecular Biomarker Discovery?”
A Block Randomized Study of microRNAs in Gynecologic Tumors