Reconstructing Population-Level History from Genetic Variation Data
Russell Schwartz
Department of Biological Sciences and Lane Center for Computational Biology
Carnegie Mellon University
Reconstructing the process by which modern human populations emerged by successive
divergence and admixture events from our common ancestors is a fundamental question in
human genetics as well as an important practical issue in biomedical research. Existing
approaches for learning the history of human populations have often been limited by restrictive
assumptions of their underlying population genetics models or inference algorithms and by the
need for manual expert intervention at key steps of analysis. As large-scale DNA sequencing
studies have become easier to conduct, large volumes of human genetic variation data have been
collected, creating a need for more automated methods capable of drawing inferences about
population-scale history from these large variation data sets. This talk will describe efforts to
develop computational models and algorithms for inferring histories of population-level
evolution in terms of divergence and admixture events by which population subgroups divide
and merge together over time. We will first examine an approach to use phylogenetic methods
on a genomic scale to reconstruct historical population groups and possible events in their
emergence. We will then extend that approach through machine learning methods to identify
and model admixture events. Finally, we will examine how such models might be used to
improve our ability to identify meaningful correlations between genetic variants and disease.
joint work with Ming-Chi Tsai, R. Ravi, and Guy Blelloch