Austrian Auditorium: Paul Sharp, Univ Edinburgh: Origin of human Plasmodium vivax, the neglected malaria
GENETICS FRIDAY RESEARCH TALKS
Yi-An Ko (Susztak Lab) From Risk Variants to Genes: Post-GWAS Annotation of Chronic Kidney Disease Risk Loci
Varun Aggarwala (Voight Lab) Heptanucleotide sequence context explains substantial variability in nucleotide substitution probabilities across the human genome
Abstract:The rate of single nucleotide polymorphism varies by ~1000 fold across the human genome and fundamentally impacts evolution and incidence of genetic disease. The identities of the single nucleotides that immediately flank a polymorphic site – or the site’s trinucleotide local sequence context – substantially influence the probability that a nucleotide change will occur. In human populations, the impact of local sequence context on polymorphism rate has not been fully described and is untested beyond the trinucleotide context. To examine the boundaries of the window of local sequence that impacts the probability of polymorphism, we developed a statistical framework to compare different local sequence lengths using non-coding genomic data obtained from the 1000 Genomes Project. We demonstrate that a heptanucleotide sequence context – that is, a model that incorporates the three adjacent nucleotides located both 5′ and 3′ to a polymorphic site – accounts for up to 93% of the variability in the probability of nucleotide substitution observed genome-wide. Our study also reveals previously undocumented variability in the probability of cytosine-to-thymine transition substitutions at CpG dinucleotides. Extension of our statistical framework into coding genomic data demonstrates additional context-specific variability in the probabilities of amino acid substitutions. Based on these observations, we present two statistics, informed by our best performing sequence context model, that are relevant for clinical studies: a substitution tolerance score for genes and a novel tolerance score for amino acids."
Time: 12:00 PM Location: 252 BRB II/III
Xiaowen Hu, Ph.D.
Ovarian Cancer Research Center
Center for Research on Reproduction and Women's Health
Department of Obstetrics and Gynecology
Perelman School of Medicine
University of Pennsylvania
A Functional Genomic Approach Identifies FAL1 as an Oncogenic Long Noncoding RNA that Associates with BMI1 and Represses p21 Expression in Cancer
Time: 12:00 PM Location: 252 BRB II/III
Senad Divanovic, Ph.D.
Assistant Professor of Pediatrics
Division of Cellular and Molecular Immunology
Cincinnati Children's Hospital Medical Center
Inflammation: A Regulatory Role in Premature Parturition and Metabolic Disease Pathogenesis
Time: 12:00 PM Location: 252 BRB II/III
Aaron K. Styer, M.D.
Assistant Professor of Obstetrics, Gynecology and Reproductive Biology
Harvard Medical School
Emerging MicroRNA Applications in Uterine Leiomyomata: Delineating Genetic Regulation of Clinical Disease
CRRWH Special Seminar
*note, this seminar will be held on a Friday
Time: 12:00 PM Location: 253 BRB II/III
Michael Eisenbach, Ph.D.
Jack & Simon Djanogly Chair in Biochemistry
Department of Biological Chemistry
The Weizmann Institute of Science
Sperm see it hot: sperm guidance in mammals
"Beyond the one-exposure, one-outcome paradigm for scientific discovery in environmental epidemiology"
Jennifer Feder Bobb, PhD Research Associate Department of Biostatistics Harvard School of Public Health
Abstract: The most common approach in environmental epidemiology is to hypothesize a relationship between a particular exposure and a particular outcome and then estimate the health risks. In this talk I will present two case studies from my research that move beyond this standard one-exposure, one-outcome paradigm. The first case study considers the problem of estimating the effects of multiple exposures on a single outcome. We propose a new approach for estimating the health effects of multi-pollutant mixtures, Bayesian kernel machine regression, which simultaneously estimates the (potentially high-dimensional) exposure-response function and incorporates variable selection to identify important mixture components. The second case study considers the effects of a single exposure (heat waves) on multiple outcomes (cause-specific hospitalization rates). Rather than pre-specifying a small number of individual diseases, we jointly consider all 15,000 possible discharge diagnosis codes and identify the full spectrum of diseases associated with exposure to heat waves among 23.7 million older adults. Through these case studies, we find that approaches that consider multiple exposures and/or multiple outcomes have the potential to lead to new scientific insights.
“Temperature-Depedence of Kidney Stone Presentation"
Gregory Tasian, MD, MSc, MSCE
Assistant Professor of Urology in Surgery, Perelman School of Medicine
Attending Urologist, Children’s Hospital of Philadelphia
"The Causal Inference Paradigm for Network Meta-Analysis with Implications for Feasibility and Practice" Mireille Schnitzer, PhD Assistant Professor Department of Biostatistics University of Montreal
Abstract: Standard meta-analysis pools the results of randomized controlled trials conducted on the same two treatments with the same outcome of interest. Network meta-analysis pools the results from trials comparing different treatments. It has been noted in the literature that the choice of treatment comparison made at the study-design stage is often dependent on the chosen/available population to be recruited into each study. Therefore, pooling results over studies without adjustment might lead to confounded estimates of the effect of interest and lack of clarity as to the population on which the effects are defined. Current proposed methods for network meta-analysis often place a model on the contrast (e.g. risk ratio) estimated in the studies, the results of which are heavily reliant on both parametric modeling and contrast choice.
Making explicit parallels to the causal inference framework, we instead focus on the mean outcome when assigning a specific treatment to a study arm. We seek to 1) define the population of interest in the network meta-analysis, 2) nonparametrically define the target parameter, 3) determine the requirements for identifiability of this parameter, and 4) propose modeling strategies to consistently estimate the target parameter.
This workshop is designed to teach you how to manage your regulatory documents electronically. By using a hands-on approach this workshop will teach you how to name documents, store documents and organize documents on your computer in a manner that is consistent with FDA regulations.
Melissa Bryn the manager of the Office of Clinical Research Compliance team will be presenting on preparation for an FDA inspection. This workshop will walk you through the things you need to know to prepare for an audit from the FDA and provide tips and tricks for making the process run as smoothly as possible. After the presentation attendees will hear from a panel of research staff who have gone through the FDA inspection process. There will be time for question and answer with both the presenter and the panel members.
“THIN as a Resource for Population-Based Studies of Kidney Disease and Fracture Epidemiology”
Michelle Denburg, MD, MSCE
Division of Nephrology, Assistant Professor of Pediatrics at CHOP
“Why Good Drugs Are Sometimes Bad for the Liver”
Paul Watkins, MD
Director Hamner-Institute for Drug Safety Sciences
University Of North Carolina, Eshelman School of Pharmacy
"The complexity of complexin: regulation of the exocytosis fusion machinery"
"Implementing Reproducible Research in the Collaborative Medical Environment: Challenges, Solutions, and the Future"
Leah J. Welty, PhD Associate Professor Department of Preventive Medicine Northwestern University Feinberg School of Medicine
Abstract: Reproducible research -- in which statistical programming and documentation is sufficient so that others may replicate results and the research process -- is gaining widespread practice in statistics and many areas of science. Biostatisticians have a responsibility to implement reproducible research practices, yet there are many challenges to doing so in a collaborative research environment within an academic medical center. For example, the use of Microsoft Word is ubiquitous among non-biostatisticians; it is unlikely to be replaced by plain text editors or LaTeX. Current tools for reproducible research, such as Sweave and knitR, are geared for use by biostatisticians and others who are comfortable with computer programming, and are not likely to have broad appeal or uptake within the physician scientist community. In this talk, we’ll present: (1) challenges to conducting reproducible research in the collaborative environment; (2) partial solutions for implementing reproducible research practices in biostatistics cores and collaborative research projects; and (3) future work to ensure that reproducible research is widely adopted within medical research.
"On Estimation of Optimal Treatment Regimes for Maximizing T-Year Survival Probability"
Wenbin Lu, PhD Associate Professor Department of Statistics North Carolina State University
Abstract: A treatment regime is a deterministic function that dictates personalized treatment based on patients' individual prognostic information. There is a fast-growing interest in finding optimal treatment regimes to maximize expected long-term clinical outcomes of patients for complex diseases, such as cancer and AIDS. For many clinical studies with survival time as a primary endpoint, a main goal is to maximize patients' survival probabilities given treatments. In this talk, I first present two nonparametric estimators for survival function of patients following a given treatment regime, i.e. the value function. Since the estimated value functions are very bumpy, kernel smoothed estimators of the value functions are introduced. Then, I present the proposed estimators of the optimal treatment regimes, which maximizes the smoothed value functions within a class of pre-specified regimes. The estimation of the optimal treatment regimes for both single and multiple decision time points are studied. In addition, I present the asymptotic properties of the proposed estimators of the value functions, and illustrate the numerical performance of the proposed estimators by simulations and an application to an AIDS clinical trial data.