admixturegraph: an R package for admixture graph manipulation and fitting

Leppälä, Kalle; Nielsen, Sandro; Mailund, Thomas

doi:10.1093/bioinformatics/btx048

Cited by 78 publications

(82 citation statements)

References 5 publications

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“…We use the R packages admixturegraph (Leppälä et al 2017) and igraph (Csardi and Nepusz 2006) to visualize and manipulate various aspects of a graph. The R scripts to run PolyGraph can be downloaded here:…”

Section: Methodsmentioning

confidence: 99%

Detecting Polygenic Adaptation in Admixture Graphs

2018

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An open question in human evolution is the importance of polygenic adaptation: adaptive changes in the mean of a multifactorial trait due to shifts in allele frequencies across many loci. In recent years, several methods have been developed to detect polygenic adaptation using loci identified in genome-wide association studies (GWAS). Though powerful, these methods suffer from limited interpretability: they can detect which sets of populations have evidence for polygenic adaptation, but are unable to reveal where in the history of multiple populations these processes occurred. To address this, we created a method to detect polygenic adaptation in an admixture graph, which is a representation of the historical divergences and admixture events relating different populations through time. We developed a Markov chain Monte Carlo (MCMC) algorithm to infer branch-specific parameters reflecting the strength of selection in each branch of a graph. Additionally, we developed a set of summary statistics that are fast to compute and can indicate which branches are most likely to have experienced polygenic adaptation. We show via simulations that this method -which we call PolyGraph -has good power to detect polygenic adaptation, and applied it to human population genomic data from around the world. We also provide evidence that variants associated with several traits, including height, educational attainment, and self-reported unibrow, have been influenced by polygenic adaptation in different populations during human evolution.

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Section: Methodsmentioning

confidence: 99%

Detecting Polygenic Adaptation in Admixture Graphs

2018

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“…Experienced researchers, particularly those interested in population structure, typically present STRUCTURE results alongside other methods that make different modelling assumptions. These include TreeMix [22] ADMIXTUREGRAPH [23], fineSTRUCTURE [10], GLOBETROTTER [15], f3 and D statistics [24], amongst many others. These models can be used both to probe whether assumptions of the admixture model are likely to hold and to validate specific features of the results.…”

Section: Badmixture: a Tool To Visualise Goodness Of Fit For Admixturmentioning

confidence: 99%

A tutorial on how (not) to over-interpret STRUCTURE/ADMIXTURE bar plots

Falush

Dorp

Lawson

2016

Preprint

174

208

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Genetic clustering algorithms, implemented in popular programs such as STRUCTURE and ADMIXTURE, have been used extensively in the characterisation of individuals and populations based on genetic data. A successful example is the reconstruction of the genetic history of African Americans who are a product of recent admixture between highly differentiated populations. Histories can also be reconstructed using the same procedure for groups which do not have admixture in their recent history, where recent genetic drift is strong or that deviate in other ways from the underlying inference model. Unfortunately, such histories can be misleading. We have implemented an approach (badMIXTURE, available at github.com/danjlawson/badMIXTURE) to assess the goodness of fit of the model using the ancestry "palettes" estimated by CHROMOPAINTER and apply it to both simulated data and real case studies. Combining these complementary analyses with additional methods that are designed to test specific hypotheses allows a richer and more robust analysis of recent demographic history based on genetic data.

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“…where T represents the set of all valid topologies. Topologies have previously been explored by enumeration over small graphs (Leppälä et al, 2017), but this approach is intractable for larger graphs. Here we present a novel formulation of the problem using mixed-integer quadratic optimization (MIQO), where we model the problem of determining a best-fit graph topology as assignment of populations to leaf nodes of a binary tree.…”

Section: Methodsmentioning

confidence: 99%

“…An admixture graph provides a way to represent the complex relationship between populations, including their separation, subsequent drift, and possible merging by using distributions of multiple trees. Several methods exist to build and visualize admixture graphs as well as to infer optimal parameters of drift lengths and admixture edges, such as TreeMix (Pickrell and Pritchard, 2012), AdmixTools (Patterson et al, 2012), MixMapper (Lipson et al, 2013), and admixturegraph (Leppälä et al, 2017). Some of these methods cannot simultaneously infer the optimal topology with the parameters of the graph under that topology.…”

Section: Introductionmentioning

confidence: 99%

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miqoGraph: Fitting admixture graphs using mixed-integer quadratic optimization

Yan

Patterson²,

Narasimhan³

2019

Preprint

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Admixture graphs represent the genetic relationship between a set of populations through splits, drift and admixture. In this paper we present the Julia package miqoGraph, which uses mixed-integer quadratic optimization to fit topology, drift lengths, and admixture proportions simultaneously. Inference of topology is particularly powerful, with integer optimization automating what is usually an arduous manual process.

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admixturegraph: an R package for admixture graph manipulation and fitting

Cited by 78 publications

References 5 publications

Detecting Polygenic Adaptation in Admixture Graphs

Detecting Polygenic Adaptation in Admixture Graphs

A tutorial on how (not) to over-interpret STRUCTURE/ADMIXTURE bar plots

miqoGraph: Fitting admixture graphs using mixed-integer quadratic optimization

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