Results for Genotyping

Dr. Grant uses high-throughput genotyping and sequencing technologies, combined with statistical and bioinformatic approaches, to unravel genomic puzzles related to multiple common complex traits.

Dr. Aplenc investigates ways to improve the outcomes of children with cancer, particularly acute myeloid leukemia, with clinical epidemiology studies and translational research.

Dr. Diskin's research is focused on translational genomics in childhood cancers. Her laboratory seeks to identify the genetic basis of childhood cancers by combining quantitative computational methods with rigorous "wet-lab" experimental approaches. In parallel, she has developed, and is applying, a proteogenomic approach to identify novel immunotherapeutic targets for high-risk and relapsed pediatric malignancies.

Dr. Stanley’s lab has identified many of the genes and syndromes associated with congenital hyperinsulinism including ABCC8, GCK, GLUD1, and Turner and Beckwith syndromes. Working with clinical and rodent model studies, his lab team has identified distinctive phenotypes of these disorders, including diazoxide unresponsiveness, leucine sensitivity, and protein sensitivity. Dr. Stanley continues to seek new diagnostic and treatment paradigms for infants with acquired and genetic disorders of hyperinsulinism.

Dr. Barzilay aims to understand what drives variability in the development of brain and behavior of youths growing up under stress. He studies biological and environmental mechanisms leading to suicidal behavior, using big datasets of diverse genotyped youths with deep phenotyping of environment.

Dr. Hakonarson is director of the Center for Applied Genomics and professor of Pediatrics at the Perelman School of Medicine, University of Pennsylvania. He leads a $40 million commitment from Children’s Hospital of Philadelphia to genomically characterize approximately 100,000 children, an initiative that has gained nationwide attention in the Wall Street Journal, New York Times, Time Magazine, Nature, and Science.

Dr. Shults works to develop statistical methods for longitudinal data that include semi-parametric approaches to account for subject/cluster level associations and maximum likelihood-based approaches for simulation and analysis of discrete longitudinal outcomes that may have overdispersion.