The International Genetic Epidemiology Society (IGES) and The Genomics of Common Diseases (GCD) committee are pleased to announce a joint symposium titled “Big Data Analytics for Genetics in Personalised Medicine”
The integration of large-scale human genomics and disease phenotypes data is offering new opportunities for personalised medicine. The joint symposium will focus on recent developments in analytical tools and resources that are facilitating big data integration, exploration, and usage in medicine. It will offer a platform for scientists interested in learning and sharing innovative tools and interfaces that improve up-to-date access to genetic data for making clinical decisions.
The symposium will offer a valuable training ground and rich learning experience for students and trainees, and provide networking opportunities for scientists across disciplines relevant to complex trait analysis and human diseases. It will be relevant for clinicians, research scientists, bioinformaticians and data users interested in integrating genomic results into the clinical setting.
George Davey Smith University of Bristol, UK
Haky Im University of Chicago, USA
Heidi Rehm The Broad Institute, USA
Peter Robinson The Jackson Laboratory, USA
Kimberly Siegmund University of Southern California, USA
Info on the speakers
George Davey Smith
Professor George Davey Smith is a clinical epidemiologist, director of the MRC Integrative Epidemiology Unit (IEU) at the University of Bristol and Scientific Director of the Avon Longitudinal Study of Parents and Children. His research has pioneered understanding of the causes and alleviation of health inequalities; lifecourse epidemiology; systematic reviewing of epidemiological evidence; and the study of population health contributions of the new genetics. He is particularly interested in developing and applying Mendelian randomization approaches, interrogating the causal role of modifiable exposures on health outcomes, including disease progression.
Hae Kyung Im develops statistical methods to make sense of large amounts of genomic and other high dimensional data with the ultimate goal of making discoveries that can be translated into improving human health. Her current focus is in the integration of GWAS and functional genomics studies and prediction of complex traits to understand the etiology of complex diseases. After a meandering career through physics, manufacturing, information security, finance, and statistics she found a home in the intersection of statistics, genomics, medicine, and big data analytics. She is currently an Assistant Professor in the Section of Genetic Medicine at the University of Chicago.
Heidi L. Rehm, PhD, FACMG is the Director of the Partners Healthcare Laboratory for Molecular Medicine (LMM), the Medical Director of the Broad Institute’s Clinical Research Sequencing Platform and Associate Professor of Pathology at Brigham & Women’s Hospital and Harvard Medical School. Both clinical labs focus on the rapid translation of new genetic discoveries into clinical tests and bringing novel technologies and software systems into molecular diagnostics to support the integration of genomics into clinical use. Dr. Rehm has also been a leader in genomic medicine research, supporting several programs from discovery (Center for Mendelian Genomics), to translation (MedSeq, BabySeq, eMERGE) to building standards to support genomics (ClinGen, GA4GH, ACMG).
Peter Robinson studied Mathematics and Computer Science at Columbia University and Medicine at the University of Pennsylvania. He completed training as a Pediatrician at the Charité University Hospital in Berlin, Germany. His group developed the Human Phenotype Ontology (HPO), which is now an international standard for computation over human disease that is used by the Sanger Institute, several NIH-funded groups including the Undiagnosed Diseases Program, Genome Canada, the rare diseases section of the UK’s 100,000 Genomes Project, and many others. The group develops algorithms and software for the analysis of exome and genome sequences and has used whole-exome sequencing and other methods to identify a number of novel disease genes, including CA8, PIGV, PIGO, PGAP3, IL-21R, PIGT, and PGAP2.
Kimberly Siegmund is Professor of Biostatistics in the Department of Preventive Medicine and Member of the USC Norris Comprehensive Cancer Center at the University of Southern California Keck School of Medicine. Her research interests focus on understanding the etiology of cancer, both through mathematical modeling of cancer growth and the development and application of statistical methods for signal processing, class discovery, and classification of high-throughput molecular data. Her recent work modeling colon cancer determined that detectable branch mutations must have occurred in the first few divisions of cancer growth and that certain patterns of intratumor heterogeneity can identify early abnormal cell motility, a potential phenotype of tumor aggressiveness, from a time period before the tumor is clinically detectable. In other work, she developed DNA methylation marker panels to monitor bladder cancer recurrence in urine samples and diagnose subtype of kidney cancer from needle biopsy.