The IGES leadership and Education Committee have accepted P-STAR’s proposal to hold a P-STAR Education Workshop in collaboration with IGES for the Education Session that IGES offers the day before the conference. IGES 2014 will be held in Vienna, Austria from August 28-30. There will be a mini-symposium on personalized medicine at a University on August 28 from 9am-1pm, then the Education Session on Pharmacogenomics from 2-5:30pm that will be sponsored jointly by the IGES Education Committee and the PGRN Statistical Analysis Resource (P-STAR).

Schedule for August 28,2014:

Brooke Fridley:  Pharmacogenetics is the study of the role of inheritance in individual genetic variation in response to drugs. In this post-genomic era, pharmacogenetics has evolved into pharmacogenomics, the study of the influence of genetic variation across the entire genome on drug–response. Pharmacogenomics has been heralded as one of the first major clinical applications of the striking advances that have occurred and continue to occur in human genomic science. In this talk, I will provide an overview of pharmacogenomics and discuss the past, present and future of pharmacogenomics in the 21st century.

Will Bush:  Electronic Health Records (EHRs) are increasingly used in epidemiological and genetic research due in part to the scale and depth of data collected in the process of routine clinical care. Due to their clinical focus, EHRs are an attractive resource for conducting pharmacogenomics studies for the discovery of genetic factors that influence both efficacy and adverse events. In this presentation, I will discuss the benefits and drawbacks of EHR-based studies and illustrate some of the challenges associated with accurate data extraction.

Hae Kyung Im:  Studies in pharmacogenomics have identified many individual variants with sufficiently large effect sizes to have clinical utility, and many of these are now the subject of implementation studies at a variety of levels. Recent research on common diseases and complex traits have, however, raised the possibility that mixed models allowing separately for the contribution of variants with larger effect sizes and a polygenic background may yield improved prediction. As we medical centers routinely move to having large-scale genome data routinely available on patients, as opposed to one-off genotyping for the prescribing of specific drugs, the opportunity to build predictors of adverse events and efficacy using large scale genome data rather than individual (or small numbers of) variants becomes a real possibility. Using real examples from large-scale studies, we will contrast prediction based on individual or small numbers of variants with predictions based on large-scale information. We will also discuss efforts to implement these alternative approaches in EMR settings.

Chris Amos:  In this presentation I contrast the discovery of genetic variants that influence smoking behavior including initiation, daily consumption and cessation. The most prominent associations are with the nicotinic acetylcholine receptor gene family on chromosome 15q25.1. These genes along with CYP2A6 strongly influence smoking behavior and also affect lung cancer risk. I will describe the striking impact that variation in these genes appears to have on the efficacy of pharmacological interventions to influence smoking cessation. Finally, I will describe studies of lung cancer risk and how these genes relate to it, along with a further discussion of the potential relevance of novel associations recently discovered for squamous lung cancer that may influence chemotherapeutic responses.

Brooke L. Fridley, PhD joined The University of Kansas Medical Center 2012 as an Associate Professor of Biostatistics and Director of the Biostatistics and Informatics Shared Resource for the NCI designated University of Kansas Cancer Center. She is also the Site Director for the Kansas-INBRE Bioinformatics Core. Her research focus is in the areas of statistical genomics, cancer genomics, and pharmacogenomics. She has extensive experience in the design and analysis of genomic studies for both candidate genes and genome-wide association studies, in addition to multiple types of genomic data (e.g., genotypic, DNA methylation, mRNA expression, copy number). Recently, with the advancements in sequencing technology, she has also gained experience in the design and analysis of RNA sequencing studies. Dr. Fridley’s statistical research focuses on the development of new statistical methods for genomic studies, which is closely integrated with her collaborations with multidisciplinary scientific teams.

William S. Bush, PhD, MS is an Assistant Professor of Epidemiology and Biostatistics in the Institute for Computational Biology at Case Western Reserve University, and is a consultant for the PSTAR resource. His research interests are focused on bioinformatics and functional annotation of the human genome, and genetic epidemiology for a variety of traits, including statin myotoxicity. Dr. Bush has also developed statistical applications that leverage existing information about genome function into the analysis of human genetic data.

Dr. Hae Kyung Im is a statistician who develops large scale methods to sift through vast amounts of genomic data with the goal of making discoveries that can be translated into clinical practice. She is focused on prediction of complex traits and the dissection of molecular mechanisms of disease risk and drug response phenotypes. She received her BS and MS in Physics from the prestigious Instituto Balseiro in Argentina. She received her PhD in Statistics in 2005 from the University of Chicago. She joined the Biostatistic Laboratory at the University of Chicago in 2009 as research faculty and joined the Section of Genetic Medicine in 2014.

Dr. Chris Amos moved in September, 2012 to the Geisel School of Medicine, where he serves as the Associate Cancer Center Director for Population Studies and leading the Center for Genomic Medicine. He is also serving on the Cancer Center Review Committee to provide assistance with review of new population-based research studies. Dr. Amos leads studies to identify genetic risk factors for lung cancer and melanoma risk using genome-wide association and sequencing approaches. He also leads a U19 grant entitled Transdisciplinary Research in Cancer of the Lung (TRICL) to identify genetic factors for lung cancer and interactions with smoking, study these factors in cell biological and animal models and to perform epidemiological studies of gene and environmental contributions to lung cancer risk. This grant includes 16 subcontracts and integrates work from an international consortium.  Dr. Amos has developed novel statistical approaches for gene-environment interaction analysis and for the identification of genes influencing complex diseases using either association based approaches or genetic linkage analysis.  He has written or coauthored more than 400 papers and was recently selected to be a fellow of the American Association for the Advancement of Science.