Nicholas B. Larson, M.S., Ph.D., is biostatistician with an interdisciplinary background in the areas of statistical genetics, bioinformatics and computational biology. His research interests are motivated by the unique opportunities and challenges provided by high-dimensional molecular data in addressing outstanding questions about complex human phenotypes.
Dr. Larson works with Mayo Clinic collaborators to improve understanding of a variety of complex diseases, including cardiovascular disease and cancer. His long-term research goals involve empowering genomic studies through the integration of multiple sources of information.
- Statistical methods development. Dr. Larson develops statistical methods for handling the high-dimensional nature of molecular data in the analysis of complex traits, with an emphasis on next-generation sequencing data. These methods include analyses of gene-gene interactions, rare-variant association analysis and gene expression studies.
- Prostate cancer. Dr. Larson examines how genetics contribute to the risk of prostate cancer. He is specifically interested in how identified genetic risk factors influence complex patterns of gene expression in prostate tissue, revealing insights into the biology of prostate cancer etiology.
- Cardiovascular disease. Dr. Larson investigates how genetic factors contribute to atherosclerosis and related biomarkers. He also is interested in how the wealth of information stored in electronic health records can be leveraged to improve individualized risk prediction for cardiovascular disease.
- Pharmacogenomics. Dr. Larson pursues novel insights into how genetics contribute to variation in drug response, including therapeutic effectiveness and adverse side effects. These research interests range from prescribed opioids to novel therapeutics in cancer clinical trials.
Significance to patient care
Dr. Larson's research efforts are ultimately orientated to furthering the understanding, prediction and prevention of common complex diseases through the use of high-dimensional molecular data sets. Analyses that uncover genetic factors associated with disease risk, prognosis and treatment response provide additional information on relevant biological mechanisms and may lead to new strategies for patient risk stratification and individualized therapies.
- Recipient, James V. Neel Young Investigator Award, International Genetic Epidemiology Society, 2012