Type 2 diabetes is a complex, non-Mendelian disease affecting nearly 10% of the US population. Thus far, GWAS have only explained a small proportion of disease heritability. This research attempts to shed light on the missing heritability by analyzing rare variants in non-coding genomic regions. Previous rare-variant association studies have lacked sufficient power, which this research addresses by utilizing the Roadmap Epigenomics data for specific cell types of interest and the IGR algorithm to predict the effects of mutations on transcription factor binding affinity. By testing a smaller set of enriched regulators and collapsing variants based on their effects, this research aims to boost the power of sequence association studies and improve our understanding of the genetics of T2D.

From collecting pond water samples as a kid to burning the midnight oil on 6.047 psets, I’ve always loved biology. My previous work developing a model to predict the functionality of non-synonymous T2D variants from empirical data gave me prior experience in the field. I hope to continue learning in this space, and I am thrilled by the potential to make a significant contribution to our understanding of T2D genetics.