Advances in computing have led to a data-driven revolution in biology and promise to guide progress in precision medicine. This session will explore the spectrum of challenges and opportunities in precision medicine, including genomics, electronic health record analytics, and drug discovery. Confirmed speakers apply systems approaches to disease, biomarker, and other complex trait prediction by building computational models that leverage and integrate similarity in genetic, transcriptomic and other omics-level data. The intended audience for this session includes those interested in integrative genomics, statistical modeling, machine learning, and human population genetics applications in medicine
This session will accept submitted abstracts, but not full papers.
Guidelines for submitting abstracts: coming soon
Heather Wheeler - Loyola University Chicago
Lipid level transcriptome association study in Hispanic populations implicates novel genes
Angela Andaleon - Loyola University Chicago
Integrative analysis of transcriptomic annotation data and biobank-scale GWAS summary statistics identifies risk factors for Alzheimer’s disease
Qiongshi Lu - University of Wisconsin-Madison
Probabilistic Egger regression for two sample Mendelian randomization analysis in genome-wide association studies
Xiang Zhou - University of Michigan
The cell type specific effect of inherited genetic variation on gene expression improves the understanding the biology of cancer risk and drug response
Paul Geeleher - St. Jude Children's Research Hospital
Pharmacogenomic landscape of long non-coding RNAs in human cancers
Aritro Nath - University of Minnesota
Computational approaches for advancing cancer genomics: from basic research to clinical diagnostics
Rendong Yang - The Hormell Institute
Structured analysis of diet reveals personalized diet-microbiome associations
Abigail Johnson - University of Minnesota
Peering into germline and somatic breast cancer genomes in women of African decent
Yonglan Zheng - The University of Chicago
Heather Wheeler, PhD
Loyola University Chicago
Heather E. Wheeler is an Assistant Professor at Loyola University Chicago with a joint appointment in the Departments of Biology and Computer Science. She teaches courses within the Bioinformatics Program and also has a secondary appointment in the Department of Public Health Sciences. The broad goal of her research is to better understand how genetic variation leads to phenotypic variation for complex traits including disease susceptibility and drug response. Her current focus is understanding the degree of transferability of genetic
association results and implicated genes across diverse populations with the goal of reducing the contribution of genomics to health disparities. Before joining Loyola, she was a postdoctoral fellow at The University of Chicago, where she worked in statistical genetics and cancer pharmacogenomics. Heather holds a PhD in Genetics from Stanford University and a BA in Biology from Hamline University.
Aritro Nath, PhD
University of Minnesota
Aritro Nath is a postdoctoral fellow in the R. Stephanie Huang lab at the University of Minnesota in the Department of Experimental and Clinical Pharmacology, and the Masonic Cancer Center. Aritro’s research broadly focuses on utilizing computational and experimental approaches to improve our understanding of the mechanisms therapeutic response to anticancer agents. His research revolves around establishing a role of the non-coding transcriptome in cancer pharmacogenomics, translating pre-clinical models to predict drug response in patients and studying the longitudinal effects of drug treatment on the genome and transcriptome of patients. He previously worked as a postdoc in the Huang lab at the University of Chicago, following a PhD in Genetics from Michigan State University.
Qiongshi Lu, PhD
University of Wisconsin-Madison
Qiongshi Lu is an Assistant Professor in the Department of Biostatistics and Medical Informatics at University of Wisconsin-Madison. Dr. Lu's research focuses on developing statistical and computational methods to functionally annotate the human genome and dissectthe genetic architecture of complex human diseases. In particular, he is interested in leveraging functional annotation information in genetic association studies to improve functional gene
fine-mapping, genetic risk prediction, and genetic correlation estimation. Qiongshi holds a PhD in Biostatistics from Yale University and a BS in Mathematics from Tsinghua University.
Time & Place
UNION SOUTH BUILDING
1308 W Dayton St, Madison, WI 53715
May 19 - 22, 2019
Let us know if you have any questions!