Nathan W. Anderson
Postdoctoral fellow in the Simons lab UChicago
PhD in Integrative Biology UW - Madison 2026
BS in Applied Mathematics Texas A&M December 2019
Research
I am currently a postdoctoral researcher working with Yuval Simons (Section of Genetic Medicine) at the University of Chicago. The Simons lab studies how the genetic architecture of complex traits has been shaped by their evolutionary history such as population structure and selective pressures. My current research interests lie in understanding how selection on high-level, complex phenotypes translates to interactions and/or interference at the genomic level. I develop mathematical models of how changing demography, selection, and linkage shape genomic variation. Such models can be leveraged to find the most likely demographic and selective history of a population as well as identify the loci underlying complex traits.
Previously, I did my PhD under the advisement of Dr. Aaron Ragsdale (Dept. of Integrative Biology) at UW - Madison. The Ragsdale lab studies human history, demographic inference, and broad areas of theoretical population genetics. My work in his lab focused on the development of models of background selection and selection on quantitative traits in out-of-equilibrium populations and changing environments. I also worked with Dr. Carol Lee (UW-Madison, Dept. of Integrative Biology) studying climate-adaptation and range expansions in marine copepods. My work focused on the development of time-series methods to infer gene-gene interactions and the modelling of complex trait evolution in a changing environment (Stern, Anderson, et al 2022). As an undergraduate researcher, I worked under Dr. Heath Blackmon (Texas A&M University; Dept. of Biology) from 2017 to Spring 2020. The Blackmon lab has a broad focus in evolutionary biology but much of the research focuses on sex chromosomes, genome structure, and population genetics. My research project in this lab involved phylogenetics and population genetic modelling, namely, the development of methods to partition phenotypes into dominance components (Armstrong, Anderson, and Blackmon 2019) and the statistical analysis of discrete and continuous trait evolution in a phylogenetic framework (Anderson et al 2020).