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Northwestern University Feinberg School of Medicine

Faculty Profile: M. Geoffrey Hayes, PhD

M. Geoffrey Hayes, PhD

What are your research interests?

My research interests lie in both evolutionary population genetics and genetic epidemiology. The evolutionary population genetic projects include the examination of genetic profiles of prehistoric populations from the North American Arctic and Subarctic to better understand human population histories in these regions. This research has afforded me the opportunity to conduct field research in the Aleutian Islands and the Alaskan North Slope. My genetic epidemiology projects involve the identification of genetic risk factors underlying common, complex genetic traits and diseases such as diabetes and asthma, as well as the development of new methods to conduct such studies.

What made you decide to pursue that type of research?

Given my graduate training in biological anthropology I have had a long standing interest in the human evolution, particularly from a genetic perspective. For my postdoctoral training I wanted to explore the genetics of disease related traits, particularly those with a complex pattern of inheritance. These projects could then circle back to evolutionary population genetics by examining the patterns of variation in genes linked and/or associated with the complex traits to understand their evolutionary forces that shaped them in the history of our species.

What are some of your current research projects?

Most recently, I conducted a genomewide association study (GWAS) of type 2 diabetes in a Mexican American population, and was involved in a GWAS of diabetes complications (e.g. nephropathy, retinopathy, etc.) in the Genetics of Kidneys in Diabetes (GoKinD) cohort.
In addition to continuing these lines of investigation, I will lead the statistical genetic analysis of two recently funded GWAS projects here at Northwestern University. The first GWAS project involves the Hyperglycemia & Adverse Pregnancy Outcome (HAPO) study (Bill Lowe, PI), and examines the complex interaction between a mother’s blood glucose levels during pregnancy and the genes of both the mother and offspring, and how these shape the offspring’s birth weight. The second GWAS uses phenotypes derived from electronic medical records (EMR) coupled to DNA samples from the NUgene biorepository (Rex Chisholm, PI) to investigate the feasibility of the EMR-biobank design compared to more traditional design of a purposed collection of individuals with the particular phenotype of interest. I was also recently awarded an International Polar Year grant “Reconstruction of Human Genetic History Along the North Slope” by NSF. This project seeks to document the geographic patterns of genetic variation in contemporary human populations along the North Slope of Alaska, determine if the observed patterns were present in prehistory, and assess how modern and ancient residents are related to other circumarctic populations.

Why attracted you to Northwestern University Feinberg School of Medicine?

I was attracted by the wealth of incredibly impressive faculty with very interesting projects that were looking for collaborators to contribute a genetic epidemiology and/or population genetics perspective to their research.

What is the biggest challenge you have experienced so far?

So far I’ve failed miserably at declining even a single invitation to collaborate on these projects. It is simply impossible to say no to interesting research.

What do you see for the future?

The incredible growth of genotyping capacity is revolutionizing genetic epidemiology. We now have the ability to genotype orders of magnitude more markers in many more study subjects for far less time and money than we could even a few years ago. With the development of the next-generation sequencers comes the ability to generate the complete genome sequence for all participants in a study at an affordable price (the so-called $1000 genome). These are likely to become a reliable part of our tool-kit in the next few years, and the computational challenges of managing and analyzing the incredibly vast amounts of data will be the next hurdle we must overcome.

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