Dataset Information

Genetic Contribution of Synthetic Hexaploid Wheat to CIMMYT's Spring Bread Wheat Breeding Germplasm.

ABSTRACT: Synthetic hexaploid (SH) wheat (AABBD'D') is developed by artificially generating a fertile hybrid between tetraploid durum wheat (Triticum turgidum, AABB) and diploid wild goat grass (Aegilops tauschii, D'D'). Over three decades, the International Maize and Wheat Improvement Center (CIMMYT) has developed and utilized SH wheat to bridge gene transfer from Ae. tauschii and durum wheat to hexaploid bread wheat. This is a unique example of success utilizing wild relatives in mainstream breeding at large scale worldwide. Our study aimed to determine the genetic contribution of SH wheat to CIMMYT's global spring bread wheat breeding program. We estimated the theoretical and empirical contribution of D' to synthetic derivative lines using the ancestral pedigree and marker information using over 1,600 advanced lines and their parents. The average marker-estimated D' contribution was 17.5% with difference in genome segments suggesting application of differential selection pressure. The pedigree-based contribution was correlated with marker-based estimates without providing chromosome segment specific variation. Results from international yield trials showed that 20% of the lines were synthetic derived with an average D' contribution of 15.6%. Our results underline the importance of SH wheat in maintaining and enhancing genetic diversity and genetic gain over years and is important for development of a more targeted introgression strategy. The study provides retrospective view into development and utilization of SH in the CIMMYT Global Wheat Program.

SUBMITTER: Rosyara U

PROVIDER: S-EPMC6710277 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genetic Contribution of Synthetic Hexaploid Wheat to CIMMYT's Spring Bread Wheat Breeding Germplasm.

Rosyara Umesh U Kishii Masahiro M Payne Thomas T Sansaloni Carolina Paola CP Singh Ravi Prakash RP Braun Hans-Joachim HJ Dreisigacker Susanne S

Scientific reports 20190826 1

Synthetic hexaploid (SH) wheat (AABBD'D') is developed by artificially generating a fertile hybrid between tetraploid durum wheat (Triticum turgidum, AABB) and diploid wild goat grass (Aegilops tauschii, D'D'). Over three decades, the International Maize and Wheat Improvement Center (CIMMYT) has developed and utilized SH wheat to bridge gene transfer from Ae. tauschii and durum wheat to hexaploid bread wheat. This is a unique example of success utilizing wild relatives in mainstream breeding at ...[more]

PMID: 31451719

Similar Datasets

Project description:BACKGROUND:Synthetic hexaploid wheat (SHW) is a reconstitution of hexaploid wheat from its progenitors (Triticum turgidum ssp. durum L.; AABB x Aegilops tauschii Coss.; DD) and has novel sources of genetic diversity for broadening the genetic base of elite bread wheat (BW) germplasm (T. aestivum L). Understanding the diversity and population structure of SHWs will facilitate their use in wheat breeding programs. Our objectives were to understand the genetic diversity and population structure of SHWs and compare the genetic diversity of SHWs with elite BW cultivars and demonstrate the potential of SHWs to broaden the genetic base of modern wheat germplasm. RESULTS:The genotyping-by-sequencing of SHW provided 35,939 high-quality single nucleotide polymorphisms (SNPs) that were distributed across the A (33%), B (36%), and D (31%) genomes. The percentage of SNPs on the D genome was nearly same as the other two genomes, unlike in BW cultivars where the D genome polymorphism is generally much lower than the A and B genomes. This indicates the presence of high variation in the D genome in the SHWs. The D genome gene diversity of SHWs was 88.2% higher than that found in a sample of elite BW cultivars. Population structure analysis revealed that SHWs could be separated into two subgroups, mainly differentiated by geographical location of durum parents and growth habit of the crop (spring and winter type). Further population structure analysis of durum and Ae. parents separately identified two subgroups, mainly based on type of parents used. Although Ae. tauschii parents were divided into two sub-species: Ae. tauschii ssp. tauschii and ssp. strangulate, they were not clearly distinguished in the diversity analysis outcome. Population differentiation between SHWs (Spring_SHW and Winter_SHW) samples using analysis of molecular variance indicated 17.43% of genetic variance between populations and the remainder within populations. CONCLUSIONS:SHWs were diverse and had a clearly distinguished population structure identified through GBS-derived SNPs. The results of this study will provide valuable information for wheat genetic improvement through inclusion of novel genetic variation and is a prerequisite for association mapping and genomic selection to unravel economically important marker-trait associations and for cultivar development.

Project description:Stem rust caused by Puccinia graminis f. sp. tritici Eriks. is a dangerous disease of common wheat worldwide. Development and cultivation of the varieties with genetic resistance is one of the most effective and environmentally important ways for protection of wheat against fungal pathogens. Field phytopathological screening and genome-wide association study (GWAS) were used for assessment of the genetic diversity of a collection of spring wheat genotypes on stem rust resistance loci. The collection consisting of Russian varieties of spring wheat and introgression lines with alien genetic materials was evaluated over three seasons (2016, 2017 and 2018) for resistance to the native population of stem rust specific to the West Siberian region of Russia. The results indicate that most varieties displayed from moderate to high levels of susceptibility to P. graminis; 16% of genotypes had resistance or immune response. In total, 13,006 single-nucleotide polymorphism (SNP) markers obtained from the Infinium 15K array were used to perform genome-wide association analysis. GWAS detected 35 significant marker-trait associations (MTAs) with SNPs located on chromosomes 1A, 2A, 2B, 3B, 5A, 5B, 6A, 7A and 7B. The most significant associations were found on chromosomes 7A and 6A where known resistance genes Sr25 and Sr6Ai = 2 originated from Thinopyrum ssp. are located. Common wheat lines containing introgressed fragments from Triticum timopheevii and Triticum kiharae were found to carry Sr36 gene on 2B chromosome. It has been suggested that the quantitative trait loci (QTL) mapped to the chromosome 5BL may be new loci inherited from the T. timopheevii. It can be inferred that a number of Russian wheat varieties may contain the Sr17 gene, which does not currently provide effective protection against pathogen. This is the first report describing the results of analysis of the genetic factors conferring resistance of Russian spring wheat varieties to stem rust.

Project description:Our understanding of the mechanisms that govern the cellular process of meiosis is limited in higher plants with polyploid genomes. Bread wheat is an allohexaploid that behaves as a diploid during meiosis. Chromosome pairing is restricted to homologous chromosomes despite the presence of homoeologues in the nucleus. The importance of wheat as a crop and the extensive use of wild wheat relatives in breeding programs has prompted many years of cytogenetic and genetic research to develop an understanding of the control of chromosome pairing and recombination. The rapid advance of biochemical and molecular information on meiosis in model organisms such as yeast provides new opportunities to investigate the molecular basis of chromosome pairing control in wheat. However, building the link between the model and wheat requires points of data contact. We report here a large-scale transcriptomics study using the Affymetrix wheat GeneChipÂ® aimed at providing this link between wheat and model systems and at identifying early meiotic genes. Analysis of the microarray data identified 1,350 transcripts temporally-regulated during the early stages of meiosis. Expression profiles with annotated transcript functions including chromatin condensation, synaptonemal complex formation,recombination and fertility were identified. From the 1,350 transcripts, 30 displayed at least an eight-fold expression change between and including pre-meiosis and telophase II, with more than 50% of these having no similarities to known sequences in NCBI and TIGR databases. This resource is now available to support research into the molecular basis of pairing and recombination control in the complex polyploid, wheat. Experiment Overall Design: The seven stages collected were pre-meiosis (PM), leptotene to pachytene (LP), diplotene to anaphase I (DA), telophase I to telophase II (TT), tetrads (T), immature pollen (IP) and mature anthers (MAN). Immediately after determining the stage, the remaining two anthers from the floret were placed into liquid nitrogen. After collecting at least 25 staged anthers for each time point from several biological samples, anthers from the respective stages were pooled. Leaf material used in this study was collected from glasshouse-grown plants (six weeks) and total RNA isolated using TrizolÂ® (Gibco BRL, Australia) according to the manufacturerâs protocol. Experiment Overall Design: Twenty micrograms of aRNA of from each of the seven samples were fragmented for hybridization to each microarray. In total three technical replicates were conducted for each of the seven stages examined. Affymetrix GeneChipÂ® Wheat Genome Arrays were used for all samples. The arrays were hybridized and processed according to the manufacturerâs specifications. Experiment Overall Design: Normalization of the microarray data was conducted using RMA. The software package Acuity 4 (Molecular Devices, CA, USA) was then used to analyze the microarray data.

Project description:Key messageWe evaluated genomic selection across five breeding cycles of bread wheat breeding. Bias of within-cycle cross-validation and methods for improving the prediction accuracy were assessed. The prospect of genomic selection has been frequently shown by cross-validation studies using the same genetic material across multiple environments, but studies investigating genomic selection across multiple breeding cycles in applied bread wheat breeding are lacking. We estimated the prediction accuracy of grain yield, protein content and protein yield of 659 inbred lines across five independent breeding cycles and assessed the bias of within-cycle cross-validation. We investigated the influence of outliers on the prediction accuracy and predicted protein yield by its components traits. A high average heritability was estimated for protein content, followed by grain yield and protein yield. The bias of the prediction accuracy using populations from individual cycles using fivefold cross-validation was accordingly substantial for protein yield (17-712 %) and less pronounced for protein content (8-86 %). Cross-validation using the cycles as folds aimed to avoid this bias and reached a maximum prediction accuracy of [Formula: see text] = 0.51 for protein content, [Formula: see text] = 0.38 for grain yield and [Formula: see text] = 0.16 for protein yield. Dropping outlier cycles increased the prediction accuracy of grain yield to [Formula: see text] = 0.41 as estimated by cross-validation, while dropping outlier environments did not have a significant effect on the prediction accuracy. Independent validation suggests, on the other hand, that careful consideration is necessary before an outlier correction is undertaken, which removes lines from the training population. Predicting protein yield by multiplying genomic estimated breeding values of grain yield and protein content raised the prediction accuracy to [Formula: see text] = 0.19 for this derived trait.

Dataset Information

Genetic Contribution of Synthetic Hexaploid Wheat to CIMMYT's Spring Bread Wheat Breeding Germplasm.

Publications

Genetic Contribution of Synthetic Hexaploid Wheat to CIMMYT's Spring Bread Wheat Breeding Germplasm.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets