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Research Overview

The Flannick Lab develops computational approaches to use human genetic and broader genomic data to understand or better treat human diseases, with a current focus on diabetes. Our research lies at the intersection of computer science, statistical genetics, and computational biology, and it includes the development of fundamental statistical methods and computational algorithms, large-scale exome sequence association analysis, and computational disease modeling. We are interested specifically in developing methods to quantify the phenotypic and molecular effects of coding mutations, so that they might be used to identify or prioritize novel therapeutic targets. We also have a commitment to make genetic data and tools more useful to communities who traditionally lack the means to access or interpret it.

Laboratory Projects:

  1. Interpretation of coding mutations from large-scale exome sequence data:We are involved in international consortia to aggregate and analyze exome sequence data for associations with metabolic traits, with sample sizes currently exceeding 50,000 and expected to exceed hundreds of thousands in the next few years. Our goals are to develop methods to use these data to quantify the likelihood that molecular perturbations of hypothesized disease genes impact disease-relevant phenotypes.
  2. Using data from common diseases to understand rare diseases:Because of their higher prevalence, more genetic data is often available for common diseases than for rare diseases. We are interested to explore whether data collected and analyzed for a common disease can be used to better understand rare diseases with overlapping clinical symptoms.
  3. Knowledge portals for human genetic disease:Our group develops and maintains the Knowledge Portal Network, a series of portals that aggregate and disseminate genetic association for a range of common diseases. Our goals, over time, are to extend these portals to rare diseases as well.
  4. Disease modeling and data integration:We are interested in computational models of the human disease process and using a variety of ‘omic datasets to calibrate these models, both to understand the molecular and cellular mechanisms of disease associations and to classify diseases based on shared pathophysiology. Our work on the NCATS Biomedical Translator is a first step toward this goal.

Research Background

Jason Flannick is an Assistant Professor of Pediatrics at Harvard Medical School and the Division of Genetics and Genomics at Boston Children’s Hospital, and an Associate Member of the Broad Institute of Harvard and MIT. Dr. Flannick obtained his PhD in Computer Science at Stanford University before training as a Postdoctoral Fellow in Human Genetics at Massachusetts Hospital and the Broad Institute. Dr. Flannick plays a leadership role in several national and international genetics and bioinformatics consortia, including the Accelerating Medicines Partnership for Type 2 Diabetes, a public/private partnership that funds his group to develop a public knowledge portal to make human genetic data broadly accessible to the global research community. Dr. Flannick’s research has spanned pure computer science, comparative genomics, and finally human genetics, with a current focus on using computational and statistical methods to impact human health.

Selected Publications

  1. Fuchsberger C*, Flannick J*, Teslovich TM*, Mahajan A*, Agarwala V*, Gaulton KJ*, et al. The genetic architecture of type 2 diabetes. Nature. 2016;536(7614):41-7.
  2. Flannick J, Florez JC. Type 2 diabetes: genetic data sharing to advance complex disease research. Nature Reviews Genetics. 2016;17(9):535-49.
  3. Flannick J, Johansson S, Njolstad PR. Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes. Nature Reviews Endocrinology. 2016;12(7):394-406.
  4. Flannick J, Thorleifsson G, Beer NL, Jacobs SB, Grarup N et al. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nature Genetics. 2014;46(4):357-63.
  5. Flannick J*, Beer NL*, Bick AG, Agarwala V, Molnes J et al. Assessing the phenotypic effects in the general population of rare variants in genes for a dominant Mendelian form of diabetes. Nature Genetics. 2013;45(11):1380-5.
  6. Agarwala V*, Flannick J*, Sunyaev S, GoTD Consortium, Altshuler D. Evaluating empirical bounds on complex disease genetic architecture. Nature Genetics. 2013;45(12):1418-27.

Publications