Dr. Jack Pattee is a research associate in the Center for Innovative Design & Analysis. He works collaboratively with investigators in the School of Medicine and the Skaggs School of Pharmacy at the University of Colorado Anschutz Medical Campus. He pursues methodological research relating to risk prediction and integrative analysis for genetic and genomic data. Jack's other interests include machine learning, high dimensional data, and model selection. He received a BA in Mathematics from Carleton College and a PhD in Biostatistics from the University of Minnesota.
Areas of Expertise
- Statistical genetics
- Machine learning
- Integrative genomics
- Model selection
- Instrumental variables
Education, Licensure & Certifications
- PhD, Biostatistics, University of Minnesota, 2020
- BA, Mathematics, Carleton College, 2014
Publications and Presentations
- "Modeling recommendations and simulated power for the analysis of expression quantitative trait loci in the Hybrid Rat Diversity Panel." Contributed poster (virtual) at NIDA Genetics and Epigenetics Cross-Cutting Research Team, March 2021.
- "Identifying Risk Factors for Alzheimer's Disease using a Penalized Regression Framework for Polygenic Scores." Contributed presentation (virtual) at the ENAR spring meetings, March 2021.
- "Penalized regression and model selection for polygenic scores on GWAS summary statistics." Pattee J and Pan W. PLoS Computational Biology 2020. 16(10):e1008271.
- "Integrating germline and somatic genetics to identify genes associated with lung cancer." Pattee J, Zhan X, Xiao G, and Pan W. Genetic Epidemiology 2020. 44(3): 233-247.
- "Abnormal endothelial gene expression associated with early atherosclerosis." Hebbel RB, Wei P et al (including Pattee J). J Am Heart Assoc 2020. 9(14):e016134.