Headshot photo of Beth Wilmot, Ph.D.

Beth Wilmot, Ph.D.

  • Research Assistant Professor, Oregon Clinical and Translational Research Institute
  • Biomedical Informatics Graduate Program, School of Medicine


Dr. Wilmot is a statistical geneticist at Oregon Health and Science University where she plays a key role in interdisciplinary collaborations within the Oregon Clinical and Translational Research Institute (OCTRI) and the OHSU Knight Cancer Institute. Her primary interest is the study of the etiology of common complex traits including complex disease. The focus of her research is on the development and application of statistical and computational methodologies for analysis of genomic data (gene expression, methylation, SNP variation and copy number variation) in both unrelated individuals and pedigrees in order to understand the role of genomic variation in common, complex disease. 

She has overseen the experimental design, statistical protocol development, QA/QC and analysis of candidate gene studies, genome wide association studies (GWAS) analyses, as well as functional genomics studies (microarray, methylation, proteomics, NGS) and copy number analyses. She routinely collaborates with large genetic consortiums, including leading the data cleaning, initial analysis efforts and imputation for the MrOS (Osteoporotic Fractures in Men) and SOF (Studies of Osteoporotic Fractures) genome wide association studies (GWAS), which has over 10,000 subjects. She also collaborates as an analyst and co-investigator in the World Consortium on the Molecular Genetics of ADHD, and the OHSU ADHD Program genetic study, as well as the multi-site Alzheimer's Disease Neuroimaging Initiative. Her intellectual contributions have included helping to shape novel methodologies in genomic pathway analysis, detecting individual patient outliers in functional data, and the development of a personalized cancer genomic diagnostic.

One of her more recent interests has been in visualization of multiple- omics data for which she has conceived of and is leading a team in developing a novel paradigm for the optimization of human genome annotation and visualization.




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