The promise of inferring the past using the ancestral recombination graph

The ancestral recombination graph (ARG) is a structure that represents the history of coalescent and recombination events connecting a set of genomic sequences. The study of the ARG can yield valuable insights about past evolutionary processes. The ARG contains as much information about evolutionary processes, if not more, than the combination of all independent summary statistics that could be derived from genotypes. Consistent with this idea, some of the first ARG-based analyses to infer the past have proven to be more powerful than analyses that do not employ the ARG. Here I will present three projects that use the ARG to study the action of natural selection. First, I will present a new method to estimate the distribution of fitness effects of new mutations using the ARG. I show that our method provides better estimates of the distribution of fitness effects on some demographic scenarios compared to methods that do not use the ARG. Second, I present a methodology to infer the mutational model of STRs employing the ARG. I show that the method can estimate the mutation rate of STRs as well as parameters that determine the dynamics of changes in the number of STR motifs due to a single mutation. I present ideas on how we could use this method to study the distribution of fitness effects acting on STRs. Finally, I present a method that employs the ARG to estimate the strength of stabilizing selection acting on a complex trait.