A method for genome-wide genealogy estimation for thousands of samples

Speidel, Leo; Forest, Marie; Shi, Sinan; Myers, Simon

doi:10.1038/s41588-019-0484-x

Cited by 404 publications

(715 citation statements)

References 78 publications

(17 reference statements)

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“…Obviously, the length of loci will be constrained by recombination. Nonetheless, with loci from across the genome, each with more information about the gene tree they evolved along (Speidel et al, 2019), perhaps more information can be captured about temporally clustered changes in the rate of coalescence across populations.…”

Section: Discussionmentioning

confidence: 99%

Insights from a general, full-likelihood Bayesian approach to inferring shared evolutionary events from genomic data: Inferring shared demographic events is challenging

Oaks

L'Bahy

Cobb

2019

Preprint

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Factors that influence the distribution, abundance, and diversification of species can simultaneously affect multiple evolutionary lineages within or across communities. These include environmental changes and inter-specific ecological interactions that cause ranges of multiple, co-distributed species to contract, expand, or become fragmented. Such processes predict genetic patterns consistent with temporally clustered evolutionary events across species, such as synchronous population divergences and/or changes in population size. There have been a number of methods developed to infer shared divergences or changes in effective population size, but not both, and the latter has been limited to approximate Bayesian computation (ABC). We introduce a general, full-likelihood Bayesian method that can use genomic data to estimate temporal clustering of an arbitrary mix of population divergences and populationsize changes across taxa. Applying this method to simulated data, we find that estimating the timing and sharing of demographic changes is much more challenging than divergences. Even under favorable simulation conditions, the ability to infer shared demographic events is quite limited and very sensitive to prior assumptions, which is in sharp contrast to accurate, precise, and robust estimates of shared divergence times. Our results also suggest that previous estimates of co-expansion among five Alaskan populations of threespine sticklebacks (Gasterosteus aculeatus) were likely driven by a combination of prior assumptions and the lack of information about the timing of demographic changes when invariant characters are ignored. We conclude by discussing potential avenues to improve the estimation of synchronous demographic changes across populations.

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Section: Discussionmentioning

confidence: 99%

Insights from a general, full-likelihood Bayesian approach to inferring shared evolutionary events from genomic data: Inferring shared demographic events is challenging

Oaks

L'Bahy

Cobb

2019

Preprint

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“…These subtle patterns are hard to pick up by simple latent mixed-membership models (Daniel J Lawson, Van Dorp, and Falush, 2018), although there has recently been some progress in this regard (Joseph and Pe'er, 2018;Bradburd, Coop, and Ralph, 2018). Other types of population genetic frameworks are able to better detect some of these more subtle signals by, for example, modelling patterns of haplotype sharing (Hellenthal et al, 2014;Daniel John Lawson et al, 2012), the full site-frequency spectrum (Excoffier et al, 2013;Kamm et al, 2019) or an approximation to the full ancestral recombination graph (Kelleher et al, 2018;Speidel et al, 2019). Nevertheless, these also have their own limitations and assumptions.…”

Section: Discussionmentioning

confidence: 99%

A geostatistical approach to modelling human Holocene migrations in Europe using ancient DNA

Racimo

Woodbridge

Fyfe

et al. 2019

Preprint

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The European continent was subject to two major migrations of peoples during the Holocene: the northwestward movement of Anatolian farmer populations during the Neolithic and the westward movement of Yamnaya steppe peoples during the Bronze Age. These movements changed the genetic composition of the continent's inhabitants, via admixture and population replacement processes. The Holocene was also characterized by major changes in vegetation composition, which altered the environment occupied by the original hunter-gatherer populations. Here, we use a combination of paleogenomics and geostatistical modelling to produce detailed maps of the movement of these populations over time and space, and to understand how these movements impacted the European vegetation landscape. We find that the dilution of hunter-gatherer ancestries and the Yamnaya steppe migration coincided with a reduction in the amount of broad-leaf forest and an increase in the amount of pasture lands in the continent. We also show that climate played a role in these vegetational changes. Additionally, we find that the spread of Neolithic farmer ancestry had a two-pronged wavefront, in agreement with similar findings based on patterns of the cultural spread of farming from radiocarbon-dated archaeological sites. With thousands of ancient genomes publicly available and in production, we foresee that the integration of ancient DNA with geostatistical techniques and large-scale archaeological datasets will revolutionize the study of ancient populations movements, and their effects on local fauna and flora.

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“…We do not know the true genealogies underlying real data, but recent methods are available to estimate them at scale [Kelleher et al, 2019, Speidel et al, 2019. In Figure 3, we showed that Branch and Site statistics matched well in simulated data.…”

Section: Application To 1000 Genomes Tree Sequencesmentioning

confidence: 91%

“…However, these simulations make many simplifying assumptions, and, moreover, the underlying tree topologies and branch lengths are exactly correct (which inference methods can only hope to approximate). To evaluate the correspondence between Site and Branch statistics in tree sequences inferred from real data, we calculated statistics for chromosome 20 of the 1000 Genomes dataset [1000Genomes Project Consortium, 2015 using dated trees estimated by Relate [Speidel et al, 2019]. Specifically, we calculated diversity using the Site statistic described in Example 2, and compared In each case, "site" is mean genetic diversity (Tajima's π) divided by mutation rate, and "branch" is the corresponding Branch statistic.…”

Section: Application To 1000 Genomes Tree Sequencesmentioning

confidence: 99%

Efficiently summarizing relationships in large samples: a general duality between statistics of genealogies and genomes

Ralph¹,

Thornton

Kelleher

2019

Preprint

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AbstractAs a genetic mutation is passed down across generations, it distinguishes those genomes that have inherited it from those that have not, providing a glimpse of the genealogical tree relating the genomes to each other at that site. Statistical summaries of genetic variation therefore also describe the underlying genealogies. We use this correspondence to define a general framework that efficiently computes single-site population genetic statistics using the succinct tree sequence encoding of genealogies and genome sequence. The general approach accumulates “sample weights” within the genealogical tree at each position on the genome, which are then combined using a “summary function”; different statistics result from different choices of weight and function. Results can be reported in three ways: by site, which corresponds to statistics calculated as usual from genome sequence; by branch, which gives the expected value of the dual site statistic under the infinite-sites model of mutation, and by node, which summarizes the contribution of each ancestor to these statistics. We use the framework to implement many currently-defined statistics of genome sequence (making the statistics’ relationship to the underlying genealogical trees concrete and explicit), as well as the corresponding “branch” statistics of tree shape. We evaluate computational performance using simulated data, and show that calculating statistics from tree sequences using this general framework is several orders of magnitude more efficient than optimized matrix-based methods in terms of both run time and memory requirements. We also explore how well the duality between site and branch statistics holds in practice on trees inferred from the 1000 Genomes Project dataset, and discuss ways in which deviations may encode interesting biological signals.

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A method for genome-wide genealogy estimation for thousands of samples

Cited by 404 publications

References 78 publications

Insights from a general, full-likelihood Bayesian approach to inferring shared evolutionary events from genomic data: Inferring shared demographic events is challenging

Insights from a general, full-likelihood Bayesian approach to inferring shared evolutionary events from genomic data: Inferring shared demographic events is challenging

A geostatistical approach to modelling human Holocene migrations in Europe using ancient DNA

Efficiently summarizing relationships in large samples: a general duality between statistics of genealogies and genomes

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