Inferring genomic landscapes with the integrative sequentially Markov coalescent (iSMC)

Gustavo Valadares Barroso, Julien Yann Dutheil 

The integrative Sequentially Markovian Coalescent (iSMC) is an extension of the sequentially Markovian Coalescent (SMC) model allowing for parameter heterogeneity along the genome, such as recombination and mutation rates. Heterogeneous parameters follow an autocorrelation process that modulates the genealogical process, extending the hidden state space and adding as few as two extra parameters per heterogeneous rate. Classical hidden Markov chain methodology is used to infer the posterior estimate of the rate landscape. In this chapter, we demonstrate the use of iSMC to infer both recombination and mutation landscapes, using data from Homo sapiens and Homo neanderthalensis genomes. We further indicate how to use simulations to assess statistical power and investigate possible sources of inference noise.