Assessments of population genetic structure have become an increasing focus as they can provide valuable insight into patterns of migration and gene flow structure, the most highly cited of several clustering-based methods, was developed to provide robust estimates without the need for populations to be determined a priori structure introduces the problem of selecting the optimal number of clusters, and as a result, the ΔK method was proposed to assist in the identification of the "true" number of clusters In our review of 1,264 studies using structure to explore population subdivision, studies that used ΔK were more likely to identify K = 2 (54%, 443/822) than studies that did not use ΔK (21%, 82/386) A troubling finding was that very few studies performed the hierarchical analysis recommended by the authors of both ΔK and structure to fully explore population subdivision Furthermore, extensions of earlier simulations indicate that, with a representative number of markers, ΔK frequently identifies K = 2 as the top level of hierarchical structure, even when more subpopulations are present 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 We recommend publication standards for population structure results so that readers can assess the implications of the results given their own understanding of the species biology

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mec.14187

The K = 2 conundrum.

Inferences of population genetic structure are of great importance to the fields of ecology and evolutionary biology. The program structure has been widely used to infer population genetic structure. However, previous studies demonstrated that uneven sampling often leads to wrong inferences on hierarchical structure. The most widely used ΔK method tends to identify the uppermost hierarchy of population structure. Recently, four alternative statistics (medmedk, medmeak, maxmedk and maxmeak) were proposed, which appear to be more accurate than the previously used methods for both even and uneven sampling data. However, the lack of easy-to-use software limits the use of these appealing new estimators. Here, we developed a web-based user-friendly software structureselector to calculate the four appealing alternative statistics together with the commonly used Ln Pr(X|K) and ΔK statistics. structureselector accepts the result files of structure, admixture or faststructure as input files. It reports the "best" K for each estimator, and the results are available as HTML or tab separated tables. The program can also generate graphical representations for specific K, which can be easily downloaded from the server. The software is freely available at http://lmme.qdio.ac.cn/StructureSelector/.

StructureSelector: A web-based software to select and visualize the optimal number of clusters using multiple methods.

Ferns are the closest sister group to all seed plants, yet little is known about their genomes other than that they are generally colossal. Here, we report on the genomes of Azolla filiculoides and Salvinia cucullata (Salviniales) and present evidence for episodic whole-genome duplication in ferns—one at the base of ‘core leptosporangiates’ and one specific to Azolla. One fern-specific gene that we identified, recently shown to confer high insect resistance, seems to have been derived from bacteria through horizontal gene transfer. Azolla coexists in a unique symbiosis with N2-fixing cyanobacteria, and we demonstrate a clear pattern of cospeciation between the two partners. Furthermore, the Azolla genome lacks genes that are common to arbuscular mycorrhizal and root nodule symbioses, and we identify several putative transporter genes specific to Azolla–cyanobacterial symbiosis. These genomic resources will help in exploring the biotechnological potential of Azolla and address fundamental questions in the evolution of plant life.

https://biblio.ugent.be/publication/8575736/file/8575738.pdf

Fern genomes elucidate land plant evolution and cyanobacterial symbioses

https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.13698

On the relative abundance of autopolyploids and allopolyploids.

The sustainability of malaria control in Africa is threatened by the rise of insecticide resistance in Anopheles mosquitoes, which transmit the disease1. To gain a deeper understanding of how mosquito populations are evolving, here we sequenced the genomes of 765 specimens of Anopheles gambiae and Anopheles coluzzii sampled from 15 locations across Africa, and identified over 50 million single nucleotide polymorphisms within the accessible genome. These data revealed complex population structure and patterns of gene flow, with evidence of ancient expansions, recent bottlenecks, and local variation in effective population size. Strong signals of recent selection were observed in insecticide-resistance genes, with several sweeps spreading over large geographical distances and between species. The design of new tools for mosquito control using gene-drive systems will need to take account of high levels of genetic diversity in natural mosquito populations.

/pdf/genetic-diversity-of-the-african-malaria-vector-anopheles-2ilit3kwyz.pdf

Genetic diversity of the African malaria vector Anopheles gambiae.

The identification of the genetic structure of populations from multilocus genotype data has become a central component of modern population-genetic data analysis. Application of model-based clustering programs often entails a number of steps, in which the user considers different modelling assumptions, compares results across different predetermined values of the number of assumed clusters (a parameter typically denoted K), examines multiple independent runs for each fixed value of K, and distinguishes among runs belonging to substantially distinct clustering solutions. Here, we present CLUMPAK (Cluster Markov Packager Across K), a method that automates the postprocessing of results of model-based population structure analyses. For analysing multiple independent runs at a single K value, CLUMPAK identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software CLUMPP. Next, CLUMPAK identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in CLUMPP and simplifying the comparison of clustering results across different K values. CLUMPAK incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. CLUMPAK, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology.

/pdf/clumpak-a-program-for-identifying-clustering-modes-and-4ljqxgdc53.pdf

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

For nearly a century, biologists, and botanists in particular, have been interested in the determination and documentation of chromosome numbers for extant taxa (reviewed in Goldblatt & Lowry, 2011) as well as extinct ones (Laane & Hoiland, 1986; Masterson, 1994). These data have beenwidely used to evaluate the evolutionary pattern of chromosome number change and to estimate the base chromosome number of clades of interest. Chromosome numbers have also been extensively utilized as an important phylogenetic character in the context of cytotaxonomy (Chatterjee & Kumar Sharma, 1969; Schlarbaum & Tsuchiya, 1984; Guerra, 2012). Perhaps the most influential use of chromosome number data has been in the inference of major genomic events such aswhole genomeduplications (polyploidy), as well as changes in single chromosome numbers (e.g. dysploidy). Early researchers analyzed the distribution of chromosome numbers within a group of interest and employed various threshold techniques to estimate ploidy levels for the analyzed taxa (Stebbins, 1938; Grant, 1963; Goldblatt, 1980).More recently, phylogenetic information was incorporated into the analyses, allowing researchers to infer transitions in chromosome numbers along branches of the tree using either the maximum parsimony principle (Schultheis, 2001; Hansen et al., 2006; Ohi-Toma et al., 2006; Wood et al., 2009) or by using a probabilistic evolutionary model within the likelihood paradigm (Mayrose et al., 2010; Cusimano et al., 2012; Glick & Mayrose, 2014). Due to their significance and the relative ease by which chromosome numbers can be obtained, it is not surprising that chromosome number is the most extensively and consistently recorded cytological property in most plant families and genera (Guerra, 2008). These data have been documented along the years in an array of journal manuscripts, printed books (L€ove & L€ove, 1948; Darlington & Wylie, 1955; Fedorov, 1969) and, more recently, in the form of online databases (Goldblatt & Johnson, 1979; Watanabe, 2002; Bennett & Leitch, 2011). To date, the most comprehensive data source is the Index to PlantChromosome Numbers (IPCN; Goldblatt & Johnson, 1979), which provides reference point to original chromosome counts reported in the literature. IPCN was initially established at the University of California Berkeley in the 1950s and was later maintained by Canada Department of Agriculture, Missouri Botanical Garden, and currently by the International Association for Plant Taxonomy (IAPT). A large portion of the counts referenced during 1979– 2006, the years that IPCN has been housed in the Missouri Botanical Garden, can be accessed and searched online. Counts reported in more recent years are currently published under IAPT/ IOPBChromosomeData series (Marhold, 2006) but are not stored within a central, easily searched, database. In addition to IPCN, several other online data sources are available, most of which are dedicated to either a specific geographical region (Slovakia – Marhold et al., 2007; Poland –G oralski et al., 2009 onwards) or to a certain taxonomic group (e.g. Hieracium – Schuhwerk, 1996; Asteraceae –Watanabe, 2002). The amount of chromosome counts that exist to date is extensive, and searching the large number of resources that contain such information is a daunting task, particularly when a large number of taxa is examined. Consequently, many researchers search for chromosome number information only through the largest online database(s), while smaller but nonetheless valuable sources are ignored.This usually results inmissing data for someof the species in question, which may lead to erroneous conclusions drawn from the analysis. Obviously, a large accessible database that unifies all currently known databases, including both printed and online sources, would be of great value to the botanical community and wouldmake the task of data collectionmuch easier. In addition, such a central resource would enable researchers to add new counts as soon as they are being reported, facilitating the task of data sharing. Here, we present the Chromosome Counts Database (CCDB), as a community resource of plant chromosome numbers. The database incorporates data from dozens of sources, more than doubling the amount of data available within any single resource. The online database additionally enables researchers to add new counts or to comment on existing data entries, thereby facilitating data sharing. The extensive amount of data currently available in CCDB further allowed us to analyze the patterns of chromosome number distribution among major plant groups. We estimate the percentage of plant species exhibiting intraspecific variation in chromosome numbers as well as in their ploidy levels.

https://nph.onlinelibrary.wiley.com/doi/pdf/10.1111/nph.13191

Jonathan Mayzel

Papers

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

The Chromosome Counts Database (CCDB) – a community resource of plant chromosome numbers