Biostatistics & Computational Biology Branch
- Alison A. Motsinger-Reif, Ph.D.
Chief, Biostatistics & Computational Biology Branch and Principal Investigator
- Tel 984-287-3705
- 111 T W Alexander Dr
Research Triangle Park, NC 27709
Alison Motsinger-Reif, Ph.D., is Chief and a principal investigator in the Biostatistics and Computational Biology Branch (BCBB). The primary goal of the BCBB is the development of computational methods to detect genetic risk factors of common, complex traits in human populations. As the field of human genetics increasingly accepts a complex model of phenotypic development involving many genetic and environment factors, it is increasingly important to develop analytical strategies that incorporates this complexity. Data collected from different physiological compartments that represent biological flux across time and space, such as genetic, metabolomics, and environmental data, will need to be incorporated to gain a fuller understanding of the biological mechanism underlying complex phenotypes. The Branch’s research is focused on the development of methods to detect such complex predictive models in high-throughput genomic data.
Motsinger-Reif and her staff work on the development and extension of methods to detect gene-gene and gene-environment interactions. These methods include Multifactor Dimensionality Reduction and Grammatical Evolution Neural Networks. They also work on methods for dose-response curve modeling using evolutionary algorithms and methods for variable selection and dimensionality reduction in genome-wide association studies.
While methods development is a key component of their research, real data applications are the driving factor. In particular, Branch members work on performing association mapping to detect genes that are associated with differential response to pharmaceutical agent exposure. They work both in clinical trials and in cell line models of response. The scientists also collaborate with a number of investigators to understand complex human diseases, compare disease etiology across species, and perform gene mapping for a range of common, complex diseases.
For a complete list of publications, please see:
Google Scholar Profile
Relevance to NIEHS Mission
The development and application of methods for gene-gene and gene-environment interactions readily addresses the NIEHS mission to better understand aspects of individual susceptibility. Our methods development and applications directly serve the data science and big data mission. Additionally, environmental exposure are major contributors to the disease that we study — including Type 2 Diabetes and cancer. The methods that we develop and apply contribute to advancing environmental health science research.