Computational protocol: Genetics of phenotypic plasticity and biomass traits in hybrid willows across contrasting environments and years

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Protocol publication

[…] The linkage map developed for the S1 pedigree population (; ) was used in QTL mapping analyses. For this study, 41 new markers were added to the map with the JoinMap software (). Nineteen markers were positioned on linkage group (LG) XV, one on LG XII, 19 on LG XIX and two on LG XIII. The new markers were developed and genotyped as described in . In total, the linkage map contains 696 markers with a mean distance of 4.4 cM between the markers. QTL mapping was performed using the clone predictors for each trait in each environment and also on the plasticity traits. The program MapQTL 6.0 () with interval mapping and a regression model was used, where the genome was scanned at 1-cM intervals to determine putative QTL associated with the variation of each trait. In order to determine significant QTL, significance threshold values, estimated as the logarithm of the odds ratio (LOD), were determined with a permutation test of 1000 repetitions. A genome-wide threshold for a significant QTL was set at 0·05. One and two LOD confidence intervals for each QTL were estimated using the LOD drop-off method () based on the LOD value at the peak position of the QTL. The proportion of the clone predictor variation explained by each significant QTL was estimated (PVE %). The difference between maternal alleles was estimated as the absolute effect of: (3)Am=μac+μad-μbc+μbd2 the differences between paternal alleles were estimated as the absolute effect of: (4)Ap=μac+μbc-μad+μbd2 and the paternal–maternal interaction effect was estimated as the absolute effect of: (5)Ai=μac+μbd-μad+μbc2 where μac, μbc, μad and μbd are the estimated phenotypic means of the four genotypic classes ac, bc, ad and bd obtained from an ab ×cd cross (). For comparison between traits, the effects were calculated as the percentage of the trait mean value. However, for the plasticity QTL the absolute raw effect estimates were retained since the plasticity of traits was based on standardized predictor values. Non-parametric Kruskal–Wallis analyses were performed to verify significant differences between marker genotypic classes close to the peak positions of the QTL. Furthermore, in order to determine phenotypic effects of S. schwerinii alleles, haplotypes were constructed for QTL clusters based on phased data from the linkage map and genotypic data from grandparents. Linkage map and QTL positions were depicted using MapChart (. […]

Pipeline specifications

Software tools JoinMap, MapQTL
Application WGS analysis