Dataset Information

Environmental and Genetic Variation for Yield-Related Traits of Durum Wheat as Affected by Development.

ABSTRACT: Phenology has a profound effect on adaptation and productivity of crops. The impact of phenology on tillering and fertility traits of durum wheat (Triticum turgidum L. subsp. durum Desf.) was evaluated with the aim of specifying which group of flowering genes (Vrn, Ppd, or eps) was involved in their control. A recombinant inbred line population was grown under four contrasting conditions of vernalization and daylength. Phenotyping was carried out according to robust phenological models dissecting both phenological and yield related traits. Whole-genome profiling was performed using the DArT-Seq technology. The genetic variability for tillering was mainly related to the genetic variability for vernalization sensitivity, as shown by the many quantitative trait loci (QTLs) identified in non-vernalized plants associated to both tillering and phenological traits. No effects of photoperiod sensitivity on spikelet number were detected in short-day-grown plants, apparently because of limited genetic variability in photoperiod sensitivity of the population. Earliness per se was involved in control of spikelet number via final leaf number, with these traits genetically correlated and sharing some QTLs. Chaff weight and number of kernels per g chaff were negatively associated and related to anthesis date under most conditions. QTL mapping uncovered novel loci involved in phenological control of tillering and fertility traits, and confirmed the presence of several well-established loci. Phenotyping of both phenology and kernel number according to a robust physiological model amplified the possibility of identifying genetic factors underlying their variations. Also, isolating known flowering gene cues by manipulation of environmental conditions provided the opportunity for each group of genes to be expressed without confounding effects of the others. This information helps to predict the consequences of either genetic manipulation of flowering genes and changes in environmental conditions on the potential yield of durum wheat.

SUBMITTER: Giunta F

PROVIDER: S-EPMC5778143 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Environmental and Genetic Variation for Yield-Related Traits of Durum Wheat as Affected by Development.

Giunta Francesco F De Vita Pasquale P Mastrangelo Anna M AM Sanna Gavino G Motzo Rosella R

Frontiers in plant science 20180118

Phenology has a profound effect on adaptation and productivity of crops. The impact of phenology on tillering and fertility traits of durum wheat (Triticum turgidum L. subsp. durum Desf.) was evaluated with the aim of specifying which group of flowering genes (Vrn, Ppd, or eps) was involved in their control. A recombinant inbred line population was grown under four contrasting conditions of vernalization and daylength. Phenotyping was carried out according to robust p ...[more]

PMID: 29403518

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Project description:Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.

Dataset Information

Environmental and Genetic Variation for Yield-Related Traits of Durum Wheat as Affected by Development.

Publications

Environmental and Genetic Variation for Yield-Related Traits of Durum Wheat as Affected by Development.

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