Dataset for raw data of the publication of Desaint, Hereil et al (2023) " Integration of QTL and transcriptome approaches for the identification of genes involved in tomato response to N deficiency "

[Lien vers la fiche source] The dataset contains raw data associated to the publication " Integration of QTL and transcriptome approaches for the identification of genes involved in tomato response to N deficiency", Desaint, Hereil et al, 2023. This study aimed at identifying quantitative trait loci (QTL) and differential expressed genes (DEGs) between two Nitrogen treatments in order to list candidate genes related to nitrogen-related contrasting traits in tomato varieties. We characterised a genetic diversity core-collection (CC) and a multi-parental advanced generation intercross (MAGIC) tomato population grown in greenhouse under two nitrogen levels and assessed several N-related traits and mapped QTLs. Transcriptome response under the two N conditions was also investigated through RNA sequencing of fruit and leaves in four parents of the MAGIC population. Significant differences in response to N input reduction were observed at the phenotypic level for biomass and N-related traits. Twenty-seven (27) QTLs were detected for three target traits (Leaf N content, leaf Nitrogen Balance Index and petiole NO3- content), ten and six at low and high N condition, respectively; while 19 QTLs were identified for plasticity traits. At the transcriptome level, 4,752 and 2,405 DEGs were detected between the two N conditions in leaves and fruits, respectively, among which 3,628 (50.6%) in leaves and 1,717 (71.4%) in fruit were genotype specific. When considering all the genotypes, 1,677 DEGs were shared between organs or tissues. Finally, we integrated DEGs and QTLs analyses to identify the most promising candidate genes. The results highlighted a complex genetic architecture of N homeostasis in tomato and novel putative genes useful for breeding improved-NUE tomato. It contains 14 files : DV1: VCF file used for GWAS analysis (CC_GWAS_filtered_imputed.vcf.gz) DV2: Read counts in each sample in Leaf (RNAseq_Leaf_COUNTS.txt) DV3: Read counts in each sample in fruit (RNAseq_Fruit_COUNTS.txt) DV4 : Statistic of genes differentially expressed in the contrast Control_Stress in fruit over all samples (DEG (C-S) fruit.txt) DV5 : Statistic of genes differentially expressed in the contrast Control_Stress in leaf over all samples (DEG (C-S) leaf.txt) DV6 : Statistic of genes differentially expressed in the contrast Control_Stress in fruit in Cervil (DEG (Ce_C- Ce_S) fruit.txt) DV7 : Statistic of genes differentially expressed in the contrast Control_Stress in leaf in Cervil (DEG (Ce_C- Ce_S) leaf.txt) DV8 : Statistic of genes differentially expressed in the contrast Control_Stress in fruit in Ferum (DEG (Fe_C-Fe_S) fruit.txt) DV9 : Statistic of genes differentially expressed in the contrast Control_Stress in leaf in Ferum (DEG (Fe_C-Fe_S) leaf.txt) DV10 : Statistic of genes differentially expressed in the contrast Control_Stress in fruit in LA1420 (DEG (LA_C-LA_S) fruit.txt) DV11 : Statistic of genes differentially expressed in the contrast Control_Stress in leaf in LA1420 (DEG (LA_C-LA_S) leaf.txt) DV12 : Statistic of genes differentially expressed in the contrast Control_Stress in fruit in Levovil (DEG (Le_C-Le_S) fruit.txt) DV13 : Statistic of genes differentially expressed in the contrast Control_Stress in leaf in Levovil (DEG (Le_C-Le_S) leaf.txt) DV14: 1_transcriptome_analysis_12072022_leaf.Rmd : R script corresponding to the analysis of DEG in leaf