Dongwon Lee, PhD
Transcriptional Regulatory Maps for Nephrotic Syndrome
Nephrotic syndrome (NS) is a rare kidney disease caused by damage to glomeruli, microstructures that filter the blood in the kidney. Among many different factors that can induce damage to the glomeruli, genetic factors play an essential role in the development and progression of NS. To date, more than 50 genes have been identified to cause NS when mutated. However, more than two-thirds of NS patients do not have a known mutation in any of these NS genes and, thus, remain unsolved. Our primary hypothesis is that genetic variants altering the abundance and/or balance of gene transcripts can be alternative risk factors for NS. Empowered by single-cell genomics and machine-learning technology, we aim to identify functional regulatory variants that can change gene expression in kidney cells. We will then evaluate their impacts on the development of NS using large NS cohorts. The underlying genetic and molecular mechanisms for NS uncovered in this research proposal will ultimately help us to identify genes and pathways for the development of targeted therapies.
Anne O’Donnell-Luria, MD, PhD
Incomplete Penetrance in Mendelian Disease
Sequencing of genomes and exomes has far outpaced our ability to interpret the functional and clinical impact of human genetic variation. To date, we have discovered hundreds of millions of genetic variants in humans; however, the clinical significance of the vast majority of these variants is unknown. The lack of penetrance estimates of putative pathogenic variants associated with genetic disease is a major challenge when counseling affected families, estimating disease prediction in currently healthy individuals, and prenatal screening. Understanding the extent and biological mechanisms of variable penetrance is crucial for variant interpretation and has the potential to dramatically improve disease risk prediction for a wide range of severe genetic diseases. We are investigating the underlying biological mechanisms of incomplete penetrance by large-scale analysis of the world’s largest dataset: the Genome Aggregation Database (gnomAD). The gnomAD dataset is depleted of individuals with severe pediatric disorders, still, there are observations of individuals with early-onset, highly penetrant pathogenic variants for dominant disease in this dataset, making gnomAD a valuable resource for investigating penetrance mechanisms. By investigating this cohort our goal is to unravel some of the mechanisms underlying incomplete penetrance.