Computational protocol: Short-Term Local Adaptation of Historical Common Bean (Phaseolus vulgaris L.) Varieties and Implications for In Situ Management of Bean Diversity

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Protocol publication

[…] Only data obtained from consistent and reproducible SSR were considered for any analyses. Mean allelic richness was calculated up to the number of individuals present in each population using the R package ARES from Van Loon et al. [] and mean accumulation curves of estimated allelic richness were plotted. Total number of polymorphic markers was assessed considering 95% of major allele frequency as criterion. Consequently, allele frequency (Na) and private alleles by populations were determined using GenAlEx 6.5 software [], and graphically illustrated by Venn diagrams []. A population pairwise genetic distance matrix was also calculated and subsequently Principal Coordinates Analysis (PCoA) was performed via distance matrix with data standardization using the previously mentioned software. In order to understand the partition of molecular variance, AMOVA was carried out on the total dataset and for each variety respectively, using 1000 permutations. Population pairwise FST matrix was also calculated to understand both the degree of diversity and the level of evolution [] of the studied material using Arlequin 3.5 [].With the aim of understanding the genetic structure of the material, a Bayesian approach was used to assign membership of each individual (individuals with more than 20% missing loci were not included in the analysis) using both STRUCTURE [] and InStruct []. The number of clusters was initially tested assuming an admixture model for different clusters (K) ranging from 1 to 20. For each tested cluster ten runs were carried out and results were based on a 30,000 burn-in period and a Markov Chain Monte Carlo (MCMC) of 60,000 iterations after burn-in. The effective number of clusters (K) was subsequently assessed using the approach of Evanno et al. [], implemented in STRUCTURE HARVESTER program [].Accordingly, a new analysis was accomplished using InStruct software [] which has been optimized for inferring population structure together with population selfing rates. A single run was performed using a 100,000 burn-in period and 200,000 MCMC iterations. The result was visualized with Distruct software []. A threshold of q > 0.7 was considered to assign an individual to a cluster []. [...] For phenotypic traits, statistics were computed using the programming language and software environment R version 3.3.0 []. In tests, null hypotheses with p-values below the significance level (α) of 0.05 were rejected.Interval and ratio type data (days to flowering, leaf length, stem length, 1000-seed weight, number of seeds per plant) were analyzed with linear mixed effects models using the R package “nlme” [,]. Nesting (main and sub-plots) and pseudo-replication (several plants observed per subplot) were taken into account in the model by setting appropriate random effects. Firstly, the overall effect of variety, version and variety*version interaction was tested in a model including the data over all varieties, where “version” stands for the origin of the population (that is: BZH, LUX or ORI) []. Secondly, the effect of version was specified within each variety by subsetting the data and building the linear mixed effects model with only version as fixed effect. Least square means were computed with the package “lsmeans” [], as well as Tukey’s Honestly Significant Difference (HSD) test for multiple comparisons.For ordinal variables (score data), rank-based ANOVA-type statistic [,] was computed by the “rankFD” [] R package. Nesting and pseudo-replication were taken into account by calculating ANOVA-type statistics in two steps. Relative effects (pd) were computed for appropriate two-way comparisons. […]

Pipeline specifications

Software tools lme4, nlme
Application Mathematical modeling
Organisms Phaseolus vulgaris