The Genome-Wide Association Studies (GWAS) are essential to determine the genetic bases of either ecological or economic phenotypic variation across individuals within populations of model and non-model organisms. For this research question, current practice is the replication of the GWAS testing different parameters and models to validate the reproducibility of results. However, straightforward methodologies that manage both replication and tetraploid data are still missing. To solve this problem, we designed the MultiGWAS, a tool that does GWAS for diploid and tetraploid organisms by executing in parallel four software, two for polyploid data (GWASpoly and SHEsis) and two for diploids data (PLINK and TASSEL). MultiGWAS has several advantages. It runs either in the command line or in an interface. It manages different genotype formats, including VCF. It executes both the full and naive models using several quality filters. Besides, it calculates a score to choose the best gene action model across GWASPoly and TASSEL. Finally, it generates several reports that facilitate the identification of false associations from both the significant and the best-ranked association SNP among the four software. We tested MultiGWAS with tetraploid potato data. The execution demonstrated that the Venn diagram and the other companion reports (i.e., Manhattan and QQ plots, heatmaps for associated SNP profiles, and chord diagrams to trace associated SNP by chromosomes) were useful to identify associated SNP shared among different models and parameters. Therefore, we confirmed that MultiGWAS is a suitable wrapping tool that successfully handles GWAS replication in both diploid and tetraploid organisms.
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Understanding seed germination of native forest species of the Tropical wet forest is essential for ecosystem regeneration and conservation, which have been affected by the exploitation of wood and non-wood forest products. Carapa guianensis Aulb. (Meliaceae) has been exploited due to the high quality of its wood and seeds. Particularly, the latter is a source of a medicinal oil of high value to the pharmaceutical industry. The indiscriminate use of this species has reduced its population size; therefore, scientific knowledge of its sexual propagation in nurseries is vital. This research aimed to evaluate seed germination of C. guianensis under different pre-germination treatments and substrates at the Centro de Investigación El Mira of AGROSAVIA. Pre-germination assays were conducted in a split-plot design with a random distribution of four repetitions. The cumulative germination percentage (CGP) and mean germination time (t50) were calculated and analyzed through an ANOVA, and Tukey’s multiple comparison test using InfoStat V.2016. Significant differences for CGP among treatments (p<0.0001) and interactions (p<0.0096) were found. The highest germination percentage (61%) was achieved in S1+T4; however, the T50 did not show significant differences (p=0.24). This research demonstrates that the highest germination percentage is achieved through a scarification treatment with imbibition in sand substrate under the agroclimatic conditions of Tumaco.
The Genome-Wide Association Studies (GWAS) are essential to determine the genetic bases of either ecological or economic phenotypic variation across individuals within populations of model and non-model organisms. For this research question, current practice is the replication of the GWAS testing different parameters and models to validate the reproducibility of results. However, straightforward methodologies that manage both replication and tetraploid data are still missing. To solve this problem, we designed the MultiGWAS, a tool that does GWAS for diploid and tetraploid organisms by executing in parallel four software, two for polyploid data (GWASpoly and SHEsis) and two for diploids data (PLINK and TASSEL). MultiGWAS has several advantages. It runs either in the command line or in an interface. It manages different genotype formats, including VCF. It executes both the full and naïve models using several quality filters. Besides, it calculates a score to choose the best gene action model across GWASPoly and TASSEL. Finally, it generates several reports that facilitate the identification of false associations from both the significant and the best-ranked association SNP among the four software. We tested MultiGWAS with tetraploid potato data. The execution demonstrated that the Venn diagram and the other companion reports (i.e., Manhattan and QQ plots, heatmaps for associated SNP profiles, and chord diagrams to trace associated SNP by chromosomes) were useful to identify associated SNP shared among different models and parameters. Therefore, we confirmed that MultiGWAS is a suitable wrapping tool that successfully handles GWAS replication in both diploid and tetraploid organisms. Hosted fileMultiGWASv14.pdf available at https://authorea.com/users/358323/articles/480510-multigwas-anintegrative-tool-for-genome-wide-association-studies-gwas-in-tetraploid-organisms
The status of mineral nutrients in the banana crop is commonly determined by foliar and soil analyses, which often do not present a significant relation with its production performance. This study aimed to evaluate whether the root sap analysis determines the nutritional status of plants more accurately in response to fertilization. The experiment was carried out in a completely randomized design, with three treatments (complete fertilization, traditional fertilization and no fertilization), three replicates and four plants per replicate. The contents of macro (N, P, K, Ca and Mg) and micronutrients (B, Zn, Mn, Fe and Cu) were analyzed in the root sap, leaves and soil at the base of the plant. Potassium was the macronutrient found in the highest quantity in the root sap of the fertilized and unfertilized plants, while the predominant micronutrients were Mn in the fertilized plants and Fe in the unfertilized ones. The concentrations of N, P, K, Ca and Mg in the root sap were significantly lower for no fertilization than for complete and traditional fertilization, but did not show significant diferences between the foliar and soil analyses. The root sap analysis was more sensitive than leaf analysis to diagnose the nutritional status of the banana plants.
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