Computational protocol: Genetic Diversity and Linkage Disequilibrium in Chinese Bread Wheat (Triticum aestivum L.) Revealed by SSR Markers

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Protocol publication

[…] Population structure analysis for the 250 Chinese wheat accessions was performed using the molecular datasets of 512 whole-genome SSR markers with STRUCTURE v2.2 software . We adopted the “admixture model”, burn-in period equal to 50,000 iterations and a run of 100,000 replications of Markov Chain Monte Carlo (MCMC) after burn in. For each run, 5 independent runs of STRUCTURE were performed with the number of clusters (K) varying from 1 to 11, leading to 55 Structure outputs. We then estimated the number of subpopulations and the best output on the basis of the Evanno criterion .Genetic dissimilarities between accessions were calculated using the simple matching coefficient in DARwin software . Cluster analysis and dendrogram tree construction were performed based on dissimilarity matrices with the un-weighted pair-group method using arithmetic averages (UPGMA). Principal coordinate analysis was also used to reveal the relationships among the 250 accessions based on the above dissimilarity matrices, with the help of NTSYS-pc version2.1 software .Basic statistics of genetic diversity including total number of alleles, and polymorphism information content (PIC) at each SSR locus according to the formula PIC = 1-∑pi2 where pi is the frequency of the ith allele, were carried out with PowerMarker v3.25 . Genetic differentiation between landraces and modern varieties on a genome basis was detected with POPGENE software using coefficients gene flow (Nm), genetic distance (GD), genetic identity (GI), Shannon's information index (I) and coefficient of gene differentiation (Fst). The genetic variation within and among populations of wheat accessions for different genomes was evaluated using analysis of molecular variance (AMOVA) implemented in Arlequin v3.11 software . Due to the different sample sizes of the two sub-groups, an allele rarefaction method was used to standardize the allelic richness of samples .Linkage disequilibrium (LD) between markers, including the pairwise estimated squared allele-frequency correlations (r2) and significance of each pair of loci , was calculated with the dedicated procedure of the TASSEL software . In the process of LD estimation, SSR datasets were filtered for rare alleles with frequencies of less than 5% in the whole collection and computed using 100,000 permutations. […]

Pipeline specifications

Software tools PowerMarker, POPGENE, Arlequin, TASSEL
Applications Phylogenetics, Population genetic analysis
Organisms Triticum aestivum