Research
Objectives
My research group’s broad research interest lies in the development of computational tools and algorithms and also the integration and analysis of different kinds of large-scale genomic datasets to facilitate a better understanding of Mendelian disease biology. As a part of the Genomics Research to Elucidate the Genetics of Rare diseases (GREGoR) consortium, our efforts have been mainly focused on developing novel computational tools that enhance discovery of novel disease genes underlying Mendelian phenotypes due to either production of truncated or altered proteins or small and intragenic copy number variant (CNV) deletions.
Projects
Identification of disease-associated genes through NMD-escape alleles
Our proposed studies aim to develop enhanced predictive models and computational tools for a more accurate annotation of nonsense-mediated decay (NMD) outcome (NMD-triggering vs. NMD-escape) of protein truncating variants by melding knowledge of both canonical and non-canonical NMD rules. The proposed research will also have the potential to systematically identify and characterize transcripts associated with a wide variety of human phenotypes through NMD-escape alleles. These studies will also contribute to understanding the molecular mechanisms behind genetic diseases and provide potential therapeutic targeting mutant proteins through leveraging NMD-escape mechanisms.
Addressing Gaps in Congenital Heart Disease Genetics
Congenital heart disease (CHD) is the most common birth defect, affecting approximately 0.8% of all liveborn infants. It has long been suspected that genetic alterations are responsible for the majority of CHD. Indeed, a genetic contribution to a considerable proportion of CHD lesions (30-35%) has been elucidated over the past decade. The main obstacles to a complete understanding of the CHD genetics are the observed genetic heterogeneity coupled with shallow phenotyping and as of yet underexplored variant types (e.g., small and intragenic CNVs, potential gain-of-function (GoF) alleles, non-coding and structural variation), more complex inheritance models and analysis tools. We are applying and developing novel computational and analytical approaches, deep learning-based models, alternative sequencing technologies and deep phenotyping to expand the repertoire of novel genotype-phenotype correlations underlying CHD and drive understanding of more complex inheritance models of CHD.
