Genome-Wide Inference of Ancestral Recombination Graphs

By Matthew D. Rasmussen, Melissa J. Hubisz, Ilan Gronau, Adam C. Siepel. PLoS Genetics, 2013

347 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/ba7a79e9daa8da0f51d5014c68d24f0962dd36ec

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. G. McVean
2. Jerome Kelleher
3. A. Etheridge
4. C. Ryan Campbell
5. Sinan Shi
6. Leo Speidel
7. S. Myers
8. J. Poelstra

In Molecular Biology

1. C. Ryan Campbell
2. Jerome Kelleher
3. G. McVean
4. Sinan Shi
5. Leo Speidel
6. A. Etheridge
7. S. Myers
8. J. Poelstra

In Plant Science

1. C. Ryan Campbell
2. G. McVean
3. J. Poelstra
4. A. Yoder

Citations in the corpus

• Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes (2015, methodology): “Nonetheless, there has been much work in this area [120] with several heuristic [121] and more principled approaches that may be adopted [19, 122].”
• What is Speciation Genomics? The roles of ecology, gene flow, and genomic architecture in the formation of species (2018, methodology): “Moreover, this approach has been used to estimate the age of genomic admixture blocks (Rasmussen et al., 2014), therefore adding tremendous sophistication and precision to temporal estimates of lineage diversification.”
• A method for genome-wide genealogy estimation for thousands of samples (2019, background): “One complexity here is that such samples may have substantially higher error rates or more missing data than modern-day individuals, and an approach to handle this might involve “threading” of additional (ancient) sequences through genealogies initially built using sequenced individuals 2 .”