Assessing the Relationship of Ancient and Modern Populations

Schraiber, Joshua G.

doi:10.1534/genetics.117.300448

Cited by 22 publications

(21 citation statements)

References 40 publications

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“…A final direction would be to model relatedness within a layer as a spatiotemporal process, in which covariance decays both with distance in space and in time. As the number of genotyped historical or ancient samples increases, it is becoming possible to ask whether there is genetic continuity at a point in space across time, or whether populations are being replaced [73][74][75][76][77]. However, we expect allele frequencies to change through time in a population, even without replacement, simply due to drift.…”

Section: Discussionmentioning

confidence: 99%

Inferring continuous and discrete population genetic structure across space

Bradburd

Coop

Ralph

2017

Preprint

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These authors contributed equally to this work. AbstractA classic problem in population genetics is the characterization of discrete population structure in the presence of continuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignment methods may incorrectly ascribe differentiation due to continuous processes (e.g., geographic isolation by distance) to discrete processes, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of current methods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributed populations.Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns of population structure. The method estimates ancestry proportions for each sample from a set of twodimensional population layers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addresses the "clines versus clusters" problem in modeling population genetic variation. The method produces useful descriptions of structure in genetic relatedness in situations where separated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstrate the utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America. Author summaryOne of the first steps in the analysis of genetic data, and a principal mission of biology, is to describe and categorize natural variation. A continuous pattern of differentiation (isolation by distance), where individuals found closer together in space are, on average, more genetically similar than individuals sampled farther apart, can confound attempts to categorize natural variation into groups. This is because current statistical methods for assigning individuals to discrete clusters cannot accommodate spatial patterns, and so are forced to use clusters to describe what is in fact continuous variation. As isolation by distance is common in nature, this is a substantial shortcoming of existing methods. In this study, we introduce a new statistical method for categorizing natural genetic variation -one that describes variation as a combination of continuous and discrete patterns. We demonstrate that this method works well and can capture patterns in population genomic data without resorting to splitting populations where they can be described by continuous patterns of variation.

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Section: Discussionmentioning

confidence: 99%

Inferring continuous and discrete population genetic structure across space

Bradburd

Coop

Ralph

2017

Preprint

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“…We next used a maximum-likelihood approach [9] to test for continuity of ancient and modern populations of the Armenian Highland. This model assumes a scenario where changes in allele frequency from the common ancestor forward in time are explained through drift alone.…”

Section: Insights Into the Armenian Regional Continuitymentioning

confidence: 99%

“…A formal continuity test was performed using the method described in Schraiber 2018 [9]. Prior to the analysis, we fitted alpha and beta priors to the discrete reference allele frequencies .…”

Section: Population Genomic Analysesmentioning

confidence: 99%

An Admixture Signal in Armenians Around the End of the Bronze Age Reveals Widespread Population Movement Across the Middle East

Հովհաննիսյան

Jones

Delser

et al. 2020

Preprint

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The Armenians, a population inhabiting the region in West Asia known as the Armenian Highland, has been argued to show a remarkable degree of population continuity since the Early Neolithic. Here we test the degree of continuity of this population as well as its plausible origin, by collating modern and ancient genomic data, and adding a number of novel contemporary genomes. We show that Armenians have indeed remained unadmixed through the Neolithic and at least until the first part of the Bronze Age, and fail to find any support for historical suggestions by Herodotus of an input from the Balkans. However, we do detect a genetic input of Sardinian-like ancestry during or just after the Middle-Late Bronze Age. A similar input at approximately the same time was detected in East Africa, suggesting large-scale movement both North and South of the Middle East. Whether such large-scale population movement was a result of climatic or cultural changes is unclear, as well as the true source of gene flow remains an open question that needs to be addressed in future ancient DNA studies.

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“…Standard single nucleotide polymorphism (SNP) calling approaches involving the identification of heterozygous positions are likely to be error prone when applied to low coverage palaeogenome data, although methods have been developed for bypassing these problems to some extent [14]. Sophisticated methods for estimating SNPs also exist, but these are only applicable for specific datasets such as human SNPs (e.g., [15]). An alternative approach is to disregard heterozygous positions and instead aim to sample a single allele from the sampled diploid chromosomes, producing a so-called pseudohaploid sequence.…”

Section: Introductionmentioning

confidence: 99%

Consensify: A Method for Generating Pseudohaploid Genome Sequences from Palaeogenomic Datasets with Reduced Error Rates

Barlow

Hartmann

González

et al. 2020

Genes

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A standard practise in palaeogenome analysis is the conversion of mapped short read data into pseudohaploid sequences, frequently by selecting a single high-quality nucleotide at random from the stack of mapped reads. This controls for biases due to differential sequencing coverage, but it does not control for differential rates and types of sequencing error, which are frequently large and variable in datasets obtained from ancient samples. These errors have the potential to distort phylogenetic and population clustering analyses, and to mislead tests of admixture using D statistics. We introduce Consensify, a method for generating pseudohaploid sequences, which controls for biases resulting from differential sequencing coverage while greatly reducing error rates. The error correction is derived directly from the data itself, without the requirement for additional genomic resources or simplifying assumptions such as contemporaneous sampling. For phylogenetic and population clustering analysis, we find that Consensify is less affected by artefacts than methods based on single read sampling. For D statistics, Consensify is more resistant to false positives and appears to be less affected by biases resulting from different laboratory protocols than other frequently used methods. Although Consensify is developed with palaeogenomic data in mind, it is applicable for any low to medium coverage short read datasets. We predict that Consensify will be a useful tool for future studies of palaeogenomes.

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Assessing the Relationship of Ancient and Modern Populations

Abstract: Sequencing DNA from deceased individuals can inform whether the individuals that currently live in a location are descended from individuals that.....

Cited by 22 publications

References 40 publications

Inferring continuous and discrete population genetic structure across space

Inferring continuous and discrete population genetic structure across space

An Admixture Signal in Armenians Around the End of the Bronze Age Reveals Widespread Population Movement Across the Middle East

Consensify: A Method for Generating Pseudohaploid Genome Sequences from Palaeogenomic Datasets with Reduced Error Rates

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