The Flannick lab focuses on learning what large-scale genetic and genomic datasets can teach us about type 2 diabetes (T2D). We focus on techniques ranging from software engineering to statistical method development to genetic data analysis. Through these approaches, we hope to identify insights into diabetes only apparent once we combine the necessary datasets, develop the necessary methods, apply the necessary analyses, and present the necessary results in ways that any researcher can interpret. We have three major projects ongoing in the lab:

Rare coding variants and their contribution to T2D. Genome wide association studies (GWAS) of common variants are the dominant genetic study design for complex diseases like T2D. Complementary to these studies are whole exome sequence analyses, which allow us to directly implicate genes in T2D based on aggregate associations observed for the collection of rare coding variants within them. We aggregate and analyze whole exome sequence for T2D understand T2D’s genetic basis (Nature, PMC5034897), evaluate the extent to which rare coding variant associations exist throughout the genome (Nature, PMC6699738), and use rare coding variants to personalize diagnosis for T2D (Under review at Nature Genetics)

Methods to evaluate the contribution of a gene to T2D or related traits. Our work suggests that rare coding variant associations are present for many T2D-relevant genes, but they are too weak to detect with statistical significance for the foreseeable future. We develop statistical methods to model the probability that a gene is involved with T2D, given its observed rare coding variant and GWAS associations (Under review at Cell Metabolism). We are extending these methods to incorporate other non-genetic information as well as information from multiple traits to provide the best possible picture – given currently available genetic data – of if and how a gene is involved in T2D.

Disseminating the results of T2D genetic analyses to the world. Our group maintains the Type 2 Diabetes Knowledge Portal (T2DKP), which seeks to make genetic and genomic datasets for T2D and related traits more publicly accessible (Submitted to AJHG). The portal consists of a web-interface and underlying software platform that integrates numerous genomic datasets with bioinformatic methods for predicting relationships among diseases, variants, genes, and pathways. The T2DKP is the world’s clearinghouse for T2D-related genetic data and provides a foundation for numerous T2D-related genetic analyses.

Lab website