I am strongly interested in genetic basis of type 2 diabetes (T2D) and related metabolic traits. Specifically, I aim to elucidate biological processes implicated by large-scale genetic studies in light of the fact that most disease-associated genetic markers (i.e. SNPs) map to non-coding regions of the genome.
Currently, I am working as a joint member of the Gloyn and McCarthy labs to integrate epigentic maps of chromatin accessibility, regulatory state, and physical interactions with eQTL maps in order to elucidate genes and pathways mediating the effects of disease-promoting alleles in primary human islets and glucose-responsive beta cell lines. By leveraging high-performance computing in combination with advanced statistical methods, I aim to spotlight candidate genes and regulatory mechanisms that can be targeted in experimental validation studies and guide the discovery of novel therapies.
Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls.
Flannick J. et al, (2019), Nature
Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data.
Fernández-Tajes J. et al, (2019), Genome Med, 11
Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics
Barbeira AN. et al, (2018), Nature Communications, 9
Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps.
Mahajan A. et al, (2018), Nat Genet, 50, 1505 - 1513
Integration of human pancreatic islet genomic data refines regulatory mechanisms at Type 2 Diabetes susceptibility loci.
Thurner M. et al, (2018), Elife, 7