September 16, 2016

Colloquium Speaker: Jose C. Florez, MD, PhD


Jose C. Florez, M.D., Ph.D. is the Chief of the Diabetes Unit at the Massachusetts General Hospital, an Associate Professor at Harvard Medical School, and an Institute Member at the Broad Institute, where he leads the Diabetes Research Group, co-leads the Program in Metabolism, and is active in the Program in Medical and Population Genetics.

He and his group have contributed to the performance and analysis of high-throughput genome-wide association and sequencing studies in type 2 diabetes and related traits, in the Diabetes Genetics Initiative, the Framingham Heart Study, and other international consortia such as MAGIC, GENIE, DIAGRAM, T2D-GENES and SIGMA, where he plays management roles. He leads the genetic research efforts of the Diabetes Prevention Program, where the effects of genetic variants on the development of diabetes can be examined prospectively, and their impact on specific behavioral and pharmacological preventive interventions can be assessed. He is the Principal Investigator of the Study to Understand the Genetics of the Acute Response to Metformin and Glipizide in Humans (SUGAR-MGH), and also conducts other pharmacogenetic studies at MGH. He is an author on 150+ original publications and 45+ reviews/book chapters.

In addition to his research and teaching duties, he is clinically active in the MGH Diabetes Center, the Endocrine inpatient consult service, and the Down Syndrome Program. He serves on the Editorial Boards for Human Genetics and the Journal of Clinical Endocrinology and Metabolism, and has been on Editorial Boards for Diabetes and Diabetologia; he is also the Editor-in-Chief for Current Diabetes Reports. He is the recipient of the MGH Physician Scientist Development Award, a Doris Duke Charitable Foundation Clinical Scientist Development Award, the MGH Department of Medicine Stephen Krane Award, the MGH Research Scholars Award, and the 2010 Presidential Early Career Award for Scientists and Engineers, the highest honor bestowed by the United States government on science and engineering professionals in the early stages of their independent research careers.

Lab website: http://chgr.org/index-faculty_florez.html




Colloquium Topic: Clinical Translation of Genetic Predictors for Type 2 Diabetes

Over the past five years there has been an explosion of whole-genome studies for type 2 diabetes and related traits. Genome-wide association studies (GWAS) first occurred on the background of genotyping arrays populated by millions of common genetic variants, deployed in various cohorts that have coalesced to form large international consortia. As a result, we began to accumulate lists of genetic loci that influence type 2 diabetes and related quantitative glycemic traits. Genome-wide association findings have typically illustrated novel pathways, pointed toward fundamental biology, confirmed prior epidemiological observations, drawn attention to the role of β-cell dysfunction in type 2 diabetes, explained a fraction of disease heritability, tempered our expectations with regard to their use in clinical prediction, and  provided possible targets for pharmacotherapy and pharmacogenetic clinical trials. On the other hand, the causal variants have only been identified for a handful of these loci, and a substantial proportion of the heritability of these phenotypes remained unexplained. The latter was likely due to insufficient sample sizes to detect small effects, a nearly exclusive focus on populations of European descent, an imperfect capture of uncommon genetic variants, an incomplete ascertainment of alternate forms of genetic variation, and the lack of exploration of additional genetic models. As the community embraced complementary approaches that include systematic fine-mapping, custom-made replication, denser genotyping arrays, platforms that focus on functional variation, next-generation sequencing techniques, and expansion to non-European populations, we have begun to unravel the genetic architecture of metabolic phenotypes. Whether these findings will prove useful in disease prediction or therapeutic decision-making must be tested in rigorously designed clinical trials.