Research in the Computational and Statistical Genomics Branch (CSGB) focuses on the development and application of computationally intensive approaches to analyze large-scale genetic and genomic data, with a particular focus on identifying genetic contributions to human disease. CSGB investigators specialize in statistical genetics and genetic epidemiology. These disciplines combine statistics, epidemiology, mathematics, molecular genetics and computer science to identify genetic variants responsible for increased susceptibility to disease and variation of phenotypic traits.
CSGB scientists conduct studies aimed at understanding complex genetic diseases and developing new statistical methods and software to analyze data sets emanating from large-scale genetic association and linkage studies. They also use innovative approaches to distinguish genuine genetic influences from random background noise. As computational biologists, they employ comparative genomic approaches to understand the evolution and function of protein families and their ultimate role in human disease.
The branch serves as a focal point at the NIH campus for the analysis of a wide variety of large-scale genomic data generated in the course of laboratory and clinical studies, with branch members actively involved in efforts aimed at developing new bioinformatic approaches for the analysis and visualization of these data. The branch also serves as a major link between NHGRI and the Center for Inherited Disease Research (CIDR), an NIH-supported facility located at The Johns Hopkins University in Baltimore, Maryland. CIDR provides high-throughput genotyping to scientists at NIH and at research institutions around the world.