Genome-Wide Inference of Ancestral Recombination Graphs

Abstract

The complex correlation structure of a collection of orthologous DNA sequences is uniquely captured by the “ancestral recombination graph” (ARG), a complete record of coalescence and recombination events in the history of the sample. However, existing methods for ARG inference are computationally intensive, highly approximate, or limited to small numbers of sequences, and, as a consequence, explicit ARG inference is rarely used in applied population genomics. Here, we introduce a new algorithm for ARG inference that is efficient enough to apply to dozens of complete mammalian genomes. The key idea of our approach is to sample an ARG of chromosomes conditional on an ARG of chromosomes, an operation we call “threading.” Using techniques based on hidden Markov models, we can perform this threading operation exactly, up to the assumptions of the sequentially Markov coalescent and a discretization of time. An extension allows for threading of subtrees instead of individual sequences. Repeated application of these threading operations results in highly efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these methods in a computer program called ARGweaver. Experiments with simulated data indicate that ARGweaver converges rapidly to the posterior distribution over ARGs and is effective in recovering various features of the ARG for dozens of sequences generated under realistic parameters for human populations. In applications of ARGweaver to 54 human genome sequences from Complete Genomics, we find clear signatures of natural selection, including regions of unusually ancient ancestry associated with balancing selection and reductions in allele age in sites under directional selection. The patterns we observe near protein-coding genes are consistent with a primary influence from background selection rather than hitchhiking, although we cannot rule out a contribution from recurrent selective sweeps.

Detecting ancient positive selection in humans using extended lineage sorting

“…We added to this set of regions the top reported regions from a HKA scan (Cagan et al 2016). Allele age estimates were obtained from ARGweaver (Rasmussen et al 2014).…”

“…The color coding indicates the type of signal detected by each method. Ages were estimated by ARGweaver (Rasmussen et al 2014). We only report events between 0 and 600 kya.…”

A Coalescent Model for a Sweep of a Unique Standing Variant

“…One approach to building inference machines to distinguish these different varieties may be to build upon recent developments in coalescent hidden Markov models (Li and Durbin 2011;Paul et al 2011;Sheehan et al 2013;Rasmussen et al 2014) that provide efficient algorithms to explore the space of gene trees consistent with the sequence data and leverage these algorithms to explicitly model the effect of selective sweeps on the ancestral recombination graph. To do this effectively, we need a way to evaluate the likelihood of a particular pattern of coalescence under a variety of sweep models.…”

Coalescent Inference Using Serially Sampled, High-Throughput Sequencing Data from Intrahost HIV Infection

“…There is now a sizeable literature on these and related approaches, focusing on various complications of the basic coalescent, depending on the species under study. For example, there are methods to account for recombination [MCMC (Kuhner et al 2000;Wang and Rannala 2008;Rasmussen et al 2014) and IS (Griffiths and Marjoram 1996;Fearnhead and Donnelly 2001;McVean et al 2002;Griffiths et al 2008;Jenkins and Griffiths 2011)], changing population size [MCMC (Beaumont 1999;Drummond et al 2002Drummond et al , 2005Wilson et al 2003;Minin et al 2008) and IS (Griffiths and Tavaré 1994a;Beaumont 2003; Leblois et al 2014)], and heterochronous sequence data [MCMC (Drummond et al 2002(Drummond et al , 2005Minin et al 2008) and IS (Beaumont 2003;Anderson 2005;Fearnhead 2008)]. …”