January 3-7, 2018
Fairmont Orchid Resort
Kohala Coast, Big Island, Hawaii, USA
|Puako Petroglyph Archeological District|
Precision medicine is often described as providing a patient the optimal tailored treatment the first time as opposed to standard treatment or trial and error. The concept of precision medicine is not new, yet the implementation of precision medicine is relatively nascent. Despite technological advances, the adoption of 'omic data in routine clinical practice has been slow. To accelerate its clinical adoption, additional data relevant for implementation are required, including basic discovery and the translation of these findings into better prevention or treatment strategies. Further layered on to this is a real need for the inclusion of diversity from multiple angles including race/ethnicity, geography, and socioeconomic status.
This session aims to emphasize new and emerging methods development and analyses across multiple data types that inform precision medicine discovery or implementation efforts including genotype-phenotype data; other 'omic data; lifestyle and environmental variables; and electronic health records data.
The session is open to a broad range of computational topics applicable to precision medicine research. As precision medicine research is active in both discovery and implementation encompassing a broad range of topics, the session is expected to review original research and methodology related to precision medicine's most pressing or anticipated problems or needs. We welcome submissions that advance the field of precision medicine, ranging from basic scientific understanding of relevant biology through technical approaches to clinical implementation. We particularly encourage submissions that address opportunities and challenges relevant to the generalization of precision medicine to diverse groups or that directly address health disparities. We appreciate submissions that involve multiple data types and have applications to real world data. We will also welcome submissions from biologists and clinicians who have identified interesting new analysis problems such as new measurement technologies or datasets requiring novel computational analyses.
Examples of topics and problems within the scope this session include (but are not limited to)
- Methods for multi-ethnic analyses to identify genomic contributors that explain or inform observed health disparities.
- Methods or approaches to clinically annotate and interpret whole exome or whole genome sequencing data, including in populations from diverse genetic and environmental backgrounds.
- Methodology for making use of rare and low frequency noncoding variants arising from whole-genome and exome sequencing.
- Approaches for incorporating methods or data from biological perturbations generated by advanced genome editing techniques into predictive or integrative models, ideally across many phenotype layers including electronic health records.
- Methods for the incorporation of lifestyle, environmental, and electronic health record data to identify contributors alone and/or interacting with the genome that explain observed health disparities.
- Development of causal or predictive models for genotype, gene expression, disease labels, and intermediate phenotypes across populations.
- Computational methods for estimating and/or incorporating genetic ancestry for genomic discovery or precision medicine implementation.
All submissions are reviewed and accepted on a competitive basis.
Bruce J. Aronow is Co-Director of the Computational Medicine Center and Professor of Pediatrics at Cincinnati Children's Hospital. Dr. Aronow has developed bioinformatics applications and databases that facilitate the detection of conserved structural features that contribute to gene regulation and function, harmful SNPs or mutations that confer disease risk, and improved recognition of gene-anatomy and gene-disease associations.
Steven E. Brenner is a computational biologist and Professor at the University of California, Berkeley with an Adjunct appointment at the University of California, San Francisco. He has interests in protein function prediction, RNA gene regulation, and personal genome interpretation—particularly as applied to newborns.
Dana C. Crawford is Associate Professor of Epidemiology and Biostatistics and Assistant Director in the Institute for Computational Biology at Case Western Reserve University. As a genetic epidemiologist, she accesses epidemiologic and clinical collections to characterize common and rare genetic variants associated with human common disease and traits with an emphasis on diverse populations.
Joshua C. Denny is is Professor of Biomedical Informatics and Medicine and Director of the Center for Precision Medicine at Vanderbilt University Medical Center. His interests include natural language processing, accurate phenotype identification from electronic health record data, and using the electronic health record to discover genome-phenome associations to better understand disease and drug response, including the development of the EHR-based phenome-wide association studies.
Alexander A. Morgan is is a biomedical informatics researcher and medical student at Stanford University. He is working in genome interpretation, the use of wearables and consumer electronic devices for health surveillance, and improving models of disease.
September 15, 2017 Notification of paper acceptance
October 2, 2017 Camera-ready final paper deadline