| Literature DB >> 29672627 |
Petr Smýkal, Oldřich Trněný, Jan Brus, Pavel Hanáček, Abhishek Rathore, Rani Das Roma, Vilém Pechanec, Martin Duchoslav, Debjyoti Bhattacharyya, Michalis Bariotakis, Stergios Pirintsos, Jens Berger, Cengiz Toker.
Abstract
[This corrects the article DOI: 10.1371/journal.pone.0194056.].Entities:
Year: 2018 PMID: 29672627 PMCID: PMC5908173 DOI: 10.1371/journal.pone.0196376
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 2Discriminant Analysis of Principal Components (DAPC) analysis.
(A) k number is selected based on BIC value for clusters up to k = 50; (B) scatter plot shows genetic patterns of SNP data. The scree plots of eigenvalues (inset) indicates eigenvalues of discriminant analysis and the amount of variation contained in the different principal components; (C) bar plot showing the probabilities of assignment of individuals to K = 17 genetic DAPC clusters. Arrows show clusters that are more differentiated according discriminant analysis scatter plot from other clusters and connect them with barplot.
Fig 4Selfing rate estimation by identity disequilibrium analysis.
Black lines are value of g2 that expresses level of Identity Disequilibrium with 95% confident intervals computed using 100 bootstraps. Red bars show estimation of selfing rate based on g2 values.
Fig 6Boxplot for expected heterozygosity (Hexp) in population computed for polymorphic loci.
Lines in boxes indicates median. Bottom and top of boxes indicate I. and III. quartiles of dataset, whiskers indicate range of data but maximally 1.5 times higher than high of box. Remaining points are outliers. The boxes are drawn with widths proportional to the square-roots of the number of polymorphic loci in the populations.