Jason Mezey

Dr. Jason Mezey is a tenured faculty member in the Department of Biological Statistics and Computational Biology (Cornell University) and a statistical geneticist working in the area of quantitative genetics (i.e. the genetics of complex phenotypes). Dr. Mezey has spent the last decade developing cutting edge statistical and computational analysis methodology. Among other areas, this research has contributed to new methodology now applied to genome-wide association studies (GWAS), for identifying expression quantitative trait loci (eQTL), and for network discovery. He also collaborates on the analysis of next-generation sequencing and other high-throughput data collected by researchers working on applied and basic problems in statistics, genomics, and medicine.

Research Focus

My research is driven by the question: How do we optimally apply computational statistics and machine learning to big biological data to produce actionable insights and predictions?  Our work on this question involves core research projects, management and analysis of big biological data, and development of new analysis methods and associated algorithms. Our core research projects fall within four major areas: (1) Genome Variation - where our published work includes papers on inferring relatedness, whole-genome analysis of human migration, and analysis of genome admixture, (2) Statistical Genetics - where our published work includes papers on association analysis of phenotypes ranging from molecular (including expression quantitative trait loci (eQTL) and related), to complex diseases (including pedigree analysis, genome-wide association studies (GWAS), and analysis of rare diseases), (3) Network Discovery - where our published work includes papers on causal modeling and network analysis of mixed genomic data types, (4) Disease Risk Prediction - where our published work includes papers on behavioral and environmental biomarkers, identification of disease subtypes, improvement of polygenic risk scores (PRS), and clinical predictors of cancer risk.  As a group, we are committed to providing a supportive and inclusive training environment for all members to perform original research and develop in-demand skills for their future careers.

Teaching Focus

Every year, I teach a four credit course in Quantitative Genomics and Genetics, which provides a rigorous treatment of analysis techniques used to understand complex genetic systems. This course covers both the fundamentals and advances in statistical methodology that are now used to analyze disease, agriculturally relevant, and evolutionarily important phenotypes. The course introduces the analysis of Genome-wide Association Study (GWAS) data from first principles, as well as an intuition for how extensions of GWAS are applied in almost every genetic discipline. Application of classic inference and Bayesian analysis approaches are covered, with an emphasis on computational methods. Both a rigorous and intuitive understanding of concepts is emphasized throughout the course.

Research Interests

• Quantitative Genetics/Genomics
• Statistical Genetics
• Computational Statistics
• Machine Learning
• Computational Biology

Graduate Fields

• Applied Mathematics
• Computational Biology
• Epidemiology
• Genomics
• Statistics
• Tri-Institutional Program in Computational Biology and Medicine (CBM)