Computational protocol: An Atypical Kinase under Balancing Selection Confers Broad-Spectrum Disease Resistance in Arabidopsis

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Protocol publication

[…] For QTL mapping, data were analyzed for each block and each experiment. Adjusted means of disease scores (LSmeans) of RILs in blocks were estimated from variance analysis (ANOVA). Broad sense heritabilities (H 2) were estimated from the mean square (MS) of ANOVA using the formula adapted from Gallais . Variance analysis of in planta bacterial growth data was performed using PROC GLM of SAS with random effects. QTL analysis was done using the R-qtl package .For GWA mapping, the following general linear model was used to analyze disease index (GLM procedure in SAS9.1, SAS Institute Inc., Cary, North Carolina, USA):Where ‘μ’ is the overall mean; ‘block’ accounts for differences among the four experimental blocks; ‘accession’ corresponds to the 384 natural accessions; covCol-5 and covKas-1 are covariates accounting for mini-greenhouse effects; and ‘ε’ is the residual term. Normality of the residuals was not improved by transformation of the data. Least-square mean (LSmean) was obtained for each natural accession and was subsequently used for GWA mapping analyses. All the 384 accessions have been genotyped for 214,051 SNPs. In order to fine-map genomic regions associated with natural disease index variation, we ran a Wilcoxon rank-sum test and a mixed-model approach implemented in the software EMMAX (Efficient Mixed-Model Association eXpedited . The latter model includes a genetic kinship matrix as a covariate to control for population structure. The percentage of quantitative disease resistance explained by the three allelic groups detected by nested GWA mapping was estimated in two polymorphic natural populations MIB and TOU (). […]

Pipeline specifications

Software tools R/qtl, EMMAX
Applications WGS analysis, GWAS
Organisms Xanthomonas campestris, Arabidopsis thaliana, Caenorhabditis elegans