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Jason Mezey

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101B Biotechnology Building

I am a statistical geneticist working in the area of quantitative genetics (i.e. the genetics of complex phenotypes). 

Research Focus

The primary focus of my research group is developing computational statistics and machine learning methodology to answer questions about complex phenotypes. Among other contributions, our research has produced new methodology now applied to the analysis of Genome-Wide Association Study (GWAS) data, to identify expression Quantitative Trait Loci (eQTL), and for eQTL based network discovery. My group also collaborates extensively on the analysis of genomic and other big biological data types collected by scientists working on both basic and applied problems in medicine and beyond. We publish in statistics/machine learning, computational biology, general interest genetics / genomics, and medical research journals.  The research in our group is driven by Senior Associate Research Scientists, Postdoctoral Associates, PhD Graduate Students and Scientific Programmers, where our alums have gone on to careers in academia, non-profits, and industry. Our group is committed to providing a supportive and inclusive training environment for all members to perform original research (including instruction in all aspects of scientific method and data analysis, including maintenance of records) as well as future career development (including timely completion of their PhD and building of research and job skill sets to obtain a desired career).

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.

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