MSU scientist lands NIH grant to study population genetics

Release Date: Jul 8, 2020

Michigan State University population geneticist Gideon Bradburd has received a five-year, $1.84 million National Institutes of Health (NIH) Maximizing Investigators' Research Early Stage Investigator Award.

The grant, which began July 1, 2020 supports Bradburd, an assistant professor in the Department of Integrative Biology and the Ecology, Evolution, and Behavior Program, and his lab in their studies of spatial population genetics related to how humans have historically adapted to their environment.

Bradburd’s research is grounded in the observation that genetic variation is often distributed geographically and that patterns of genetic variation can therefore be used to learn about past population movements. In his lab, he works to develop statistical methods to understand these spatial patterns, which are critical to analyzing human evolutionary history and linking genotypes and phenotypes.

In modern population genetics, the genomic sequencing revolution is creating enormous data sets. MSU population geneticist Gideon Bradburd (pictured above) is particularly excited to use ancient DNA to study the phenomenon of population replacement. His research will generate novel statistical methods that incorporate geography into the study of population genetic structure, demography and natural selection. Courtesy photo

Pedigree diagram that shows pedigree across time and space for a sample individual — An example pedigree of a focal individual from the modern day, placed in its geographic context to make a spatial pedigree. Dashed lines denote matings, and solid lines denote parentage, with red hues for the maternal ancestors, and blue hues for the paternal ancestors. Each plane represents a sampled region in a discrete generation, and each dot shows the birth location of an individual. The pedigree of the focal individual is highlighted back through time and across space. Credit: Gideon lab

In modern population genetics, the genomic sequencing revolution is creating enormous data sets. Historically, limitations in the size and scope of empirical datasets allowed researchers to employ statistical models that ignored geography, but modern genomic datasets demand population genetic analysis methods that incorporate geographic space.

Bradburd’s research will generate novel statistical methods that incorporate geography into the study of population genetic structure, demography and natural selection. These methods will be developed and implemented as open-source software.