Matthew runs a lab at the University of Chicago that works on a wide variety of problems at the interface of statistics and genetics. His main research interests include statistical analysis of genetic association studies, gene expression and gene regulation, and analysis of population genetic structure. As part of its research mission, Matthew's lab is committed to developing cutting-edge statistical and computational methods to address these questions, and his lab has released several widely used software packages.
The impact of this work is to accelerate the pace of discovery across multiple scientific domains, by building more effective analysis tools for scientists across the world to use. To take a specific example, Matthew’s lab lead the way in the development of methods for “imputing” (estimating) missing genotypes in genetic studies. These tools have allowed scientists all over the world to combine data from multiple genetic studies that would otherwise be very difficult to combine. Combining data across studies in this way has been a crucial step in making scientific progress, because the combined data produce new discoveries — like genes affecting cholesterol levels, and other medically-relevant outcomes — that could not be found in any individual study.
Matthew was awarded the Guy Medal in Bronze by the Royal Statistical Society in 2006, and was recognized as a Thomson Reuters Highly Cited Researcher, 2014. Matthew earned his PhD from the University of Oxford and also trained at the University of Cambridge.
Google Scholar Profile
Lab Group on GitHub
University of Chicago, Department of Human Genetics