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STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method

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TLDR
STRUCTURE HARVESTER is presented, a web-based program for collating results generated by the program STRUCTURE, which provides a fast way to assess and visualize likelihood values across multiple values of K and hundreds of iterations for easier detection of the number of genetic groups that best fit the data.
Abstract
We present STRUCTURE HARVESTER (available at http://taylor0.biology.ucla.edu/structureHarvester/ ), a web-based program for collating results generated by the program STRUCTURE. The program provides a fast way to assess and visualize likelihood values across multiple values of K and hundreds of iterations for easier detection of the number of genetic groups that best fit the data. In addition, STRUCTURE HARVESTER will reformat data for use in downstream programs, such as CLUMPP.

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Citations
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Clumpak: a program for identifying clustering modes and packaging population structure inferences across K

TL;DR: Clumpak, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology by automating the postprocessing of results of model‐based population structure analyses.
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pophelper: an R package and web app to analyse and visualize population structure.

TL;DR: The pophelper r package and web app are software tools to aid in population structure analyses which can be used for the analyses and visualization of output generated from population assignment programs such as admixture, structure and tess.
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Short-Term Local Adaptation of Historical Common Bean (Phaseolus vulgaris L.) Varieties and Implications for In Situ Management of Bean Diversity.

TL;DR: Results indicate that three years of multiplication suffice for local adaptation to occur, and the microevolution of bean populations within networks of gardens and farms emerges as a research perspective.
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The K = 2 conundrum.

TL;DR: This review suggests that many studies may have been over‐ or underestimating population genetic structure; both scenarios have serious consequences, particularly with respect to conservation and management.
References
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Journal ArticleDOI

Inference of population structure using multilocus genotype data

TL;DR: Pritch et al. as discussed by the authors proposed a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations, which can be applied to most of the commonly used genetic markers, provided that they are not closely linked.
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Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.

TL;DR: It is found that in most cases the estimated ‘log probability of data’ does not provide a correct estimation of the number of clusters, K, and using an ad hoc statistic ΔK based on the rate of change in the log probability between successive K values, structure accurately detects the uppermost hierarchical level of structure for the scenarios the authors tested.
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Inference of Population Structure Using Multilocus Genotype Data: Linked Loci and Correlated Allele Frequencies

TL;DR: Extensions to the method of Pritchard et al. for inferring population structure from multilocus genotype data are described and methods that allow for linkage between loci are developed, which allows identification of subtle population subdivisions that were not detectable using the existing method.
Journal ArticleDOI

CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure

TL;DR: Three algorithms for aligning multiple replicate analyses of the same data set using the computer program CLUMPP (CLUster Matching and Permutation Program) are described.
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distruct: a program for the graphical display of population structure

TL;DR: Distortion as discussed by the authors provides a general method for visualizing membership coefficients in multilocus genotypes from structured populations, and can be used to display subpopulation assignment probabilities when individuals are assumed to have ancestry in only one group.
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