Computational protocol: Genetic Control of Water Use Efficiency and Leaf Carbon Isotope Discrimination in Sunflower (Helianthus annuus L.) Subjected to Two Drought Scenarios

Similar protocols

Protocol publication

[…] A set of 9832 SNPs were used to produce an Infinium HD iSelect BeadChip (Infinium). These SNPs were selected from either genomic re-sequencing or transcriptomic experiments. The gDNA from the INEDI RILs population obtained from the cross between XRQ and PSC8 lines (210 samples) were genotyped with the Infinium array. All genotyping experiments were performed by Integragen (IntegraGen SA, Genopole Campus 1 - Genavenir 8, 5 rue Henri Desbruères, 91000 Evry, France) and the genotypic data were obtained with the Genome Studio software (Illumina) with automatic and manual calling. From the 9832 SNPs, 7094 were technically functional with more than 200 samples having a genotyping data. From this set of 7094 markers, 2576 were polymorphic between XRQ and PSC8 and 2164 did not show distortion of segregation in the population. We used CarthaGène v1.3 to build the genetic maps. We added the genotypic data of markers from a consensus map to the set of the 2164 SNPs to assign them to the appropriate LG to the group 0.3 8 in CarthaGène. They were ordered using the lkh 1 -1 function in CarthaGène for each group. The genetic map consisted of 2610 markers located on the 17 LG for a total genetic distance of 1863.1 cM and grouped on 999 different loci. All data will be available through the portal. [...] The data were first tested for normal distribution with the Kolmogorov-Smirnov test. These data were subjected to analysis of variance (ANOVA) and phenotypic correlation analysis (Pearson’s correlation) using the software of statistical package PASW statistics 18 (IBM, New York, USA). Means were compared using a Student-Newman-Keuls (SNK) test (P<0.05). The broad sense heritability (h2) was then computed from the estimates of genetic (σ2g) and residual (σ2e) variances derived from the expected mean squares of the analyses of variance as h2 = σ2g/(σ2g+ σ2e/r), where r was the number of replicates.QTL identification was performed using MCQTL, software for QTL analysis ( The MCQTL software package can be used to perform QTL mapping in a multi-cross design. It allows the analysis of the usual populations derived from inbred lines . MCQTL package is comprised of three software applications. The first component, TranslateData reads data from MAPMAKER like files. The second component, ProbaPop computes QTL genotype probabilities given marker information at each chromosome location for each family and stores them in XML formatted files. The last component, Multipop builds the pooled model using the genotype probabilities, computes Fisher tests and estimates the model parameters . The statistical significance of QTLs was assessed using the MCQTL test, which is equal to –log(P-value (F-test)), as described in the MCQTL user guide.Significant thresholds (P<0.05) for QTL detection were calculated for each dataset using 1000 permutations and a genome-wide error rate of 0.01 (Type I error). The corresponding type I error rate at the whole-genome level was calculated as a function of the overall number of markers in the map and the number of markers in each linkage group . In our analysis, the threshold for the Fisher test (–log(P-value (F-test))) was 3.69 for both experiments. This threshold was an average of several thresholds of the traits at a significance level of 5% and was determined after 1000 permutations.In each experiment, the QTL detection was also performed to identify QTL for the phenotypic response (called “response QTL”), calculated as the difference between two different water treatments (WW and WS). This allowed us to detect chromosome regions having quantitative effects on traits, depending on the environment –. […]

Pipeline specifications

Software tools CarthaGene, MAPMAKER, MULTIPOP
Databases Heliagene
Applications Genome annotation, WGS analysis, GWAS
Organisms Helianthus annuus