Genomics Approaches to Dissect the Genetic Basis of Drought Resistance in Durum Wheat

A better knowledge of the genetic basis of the mechanisms underlying the adaptive response to drought will be instrumental to more effectively deploy marker-assisted selection (MAS) to improve yield potential while optimizing water-use efficiency. Genomics approaches allow us to identify and clone the genes and QTLs that underlie the adaptive response of durum wheat to drought. Linkage and association mapping have allowed us to identify QTLs for traits that influence drought resistance and yield in durum and bread wheat. Once major genes and QTLs that affect yield under drought conditions are identified, their cloning provides a more direct path for mining and manipulating beneficial alleles. While QTL analysis and cloning addressing natural variation will increasingly shed light on mechanisms of adaptation to drought and other adverse conditions, more emphasis on approaches relying on resequencing, candidate gene identification, 'omics' platforms and reverse genetics will accelerate the pace of gene/QTL discovery. Genomic selection provides a valuable option to improve wheat performance under drought conditions without prior knowledge of the relevant QTLs. Modeling crop growth and yield based on the effects of major QTLs offers an additional opportunity to leverage genomics information. Although it is expected that genomics-assisted breeding will enhance the pace of durum wheat improvement, major limiting factors are how to (i) phenotype genetic materials in an accurate, relevant and high-throughput fashion and (ii) more effectively translate the deluge of molecular and phenotypic data into improved cultivars. A multidisciplinary effort will be instrumental to meet these challenges

Sequence-Based Marker Assisted Selection in Wheat

Wheat improvement has traditionally been conducted by relying on artificial crossing of suitable parental lines followed by selection of the best genetic combinations. At the same time wheat genetic resources have been characterized and exploited with the aim of continuously improving target traits. Over this solid framework, innovations from emerging research disciplines have been progressively added over time: cytogenetics, quantitative genetics, chromosome engineering, mutagenesis, molecular biology and, most recently, comparative, structural, and functional genomics with all the related -omics platforms. Nowadays, the integration of these disciplines coupled with their spectacular technical advances made possible by the sequencing of the entire wheat genome, has ushered us in a new breeding paradigm on how to best leverage the functional variability of genetic stocks and germplasm collections. Molecular techniques first impacted wheat genetics and breeding in the 1980s with the development of restriction fragment length polymorphism (RFLP)-based approaches. Since then, steady progress in sequence-based, marker-assisted selection now allows for an unprecedently accurate ‘breeding by design’ of wheat, progressing further up to the pangenome-based level. This chapter provides an overview of the technologies of the ‘circular genomics era’ which allow breeders to better characterize and more effectively leverage the huge and largely untapped natural variability present in the Triticeae gene pool, particularly at the tetraploid level, and its closest diploid and polyploid ancestors and relatives

Genome-wide association mapping for grain shape and color traits in Ethiopian durum wheat (Triticum turgidum ssp. durum)

Abstract Grain shape and color strongly influence yield and quality of durum wheat. Identifying QTL for these traits is essential for transferring favorable alleles based on selection strategies and breeding objectives. In the present study, 192 Ethiopian durum wheat accessions comprising 167 landraces and 25 cultivars were genotyped with a high-density Illumina iSelect 90 K single-nucleotide polymorphism (SNP) wheat array to conduct a genome-wide association analysis for grain width (GW), grain length (GL), CIE (Commission Internationale l'Eclairage) L* (brightness), CIE a* (redness), and CIE b* (yellowness) traits. The accessions were planted at Sinana Agricultural Research Center, Ethiopia in the 2015/2016 cropping season in a complete randomized block design with three replications. Twenty homogeneous and healthy seeds per replicate were used for trait measurement. Digital image analysis of seeds with GrainScan software package was used to generate the phenotypic data. Analysis of variance revealed highly significant differences between accessions for all traits. A total of 46 quantitative trait loci (QTL) were identified for all traits across all chromosomes. One novel major candidate QTL (−lg P ≥ 4) with pleiotropic effects for grain CIE L* (brightness) and CIE a* (redness) was identified on the long arm of chromosome 2A. Eighteen nominal QTL (−lg P ≥ 3) and 26 suggestive QTL (−lg P ≥ 2.5) were identified. Pleiotropic QTL influencing both grain shape and color were identified

Genomics of plant genetic resources: a gateway to a new era of global food security

The special issue focuses on how genomics of plant genetic resources (PGRs) provides key information and materials to meet the challenges that agriculture will face in the next few decades to meet the fast growing demand for plant derived products. Sessions at the 3rd International Symposium on Genomics of Plant Genetic Resources (GPGR3) held in Jeju, Korea, from 16 to 19 April 2013 covered topics including basic plant genome diversity and its applications..

Genetic dissection of maize phenology using an intraspecific introgression library

Background: Collections of nearly isogenic lines where each line carries a delimited portion of a donor source genome into a common recipient genetic background are known as introgression libraries and have already shown to be instrumental for the dissection of quantitative traits. By means of marker-assisted backcrossing, we have produced an introgression library using the extremely early-flowering maize (Zea mays L.) variety Gasp\ue9 Flint and the elite line B73 as donor and recipient genotypes, respectively, and utilized this collection to investigate the genetic basis of flowering time and related traits of adaptive and agronomic importance in maize.Results: The collection includes 75 lines with an average Gasp\ue9 Flint introgression length of 43.1 cM. The collection was evaluated for flowering time, internode length, number of ears, number of nodes (phytomeres), number of nodes above the ear, number and proportion of nodes below the ear and plant height. Five QTLs for flowering time were mapped, all corresponding to major QTLs for number of nodes. Three additional QTLs for number of nodes were mapped. Besides flowering time, the QTLs for number of nodes drove phenotypic variation for plant height and number of nodes below and above the top ear, but not for internode length. A number of apparently Mendelian-inherited phenotypes were also observed.Conclusions: While the inheritance of flowering time was dominated by the well-known QTL Vgt1, a number of other important flowering time QTLs were identified and, thanks to the type of plant material here utilized, immediately isogenized and made available for fine mapping. At each flowering time QTL, early flowering correlated with fewer vegetative phytomeres, indicating the latter as a key developmental strategy to adapt the maize crop from the original tropical environment to the northern border of the temperate zone (southern Canada), where Gasp\ue9 Flint was originally cultivated. Because of the trait differences between the two parental genotypes, this collection will serve as a permanent source of nearly isogenic materials for multiple studies of QTL analysis and cloning. \ua9 2011 Salvi et al; licensee BioMed Central Ltd

Root system architecture phenotyping of durum wheat reveals differential selection for a major QTL in contrasting environments

This study reports the characterization of 183 elite durum wheat (Triticum turgidum ssp. durum Desf.) for RSA and shoot developmental traits. Plants were grown in controlled conditions up to the 7th leaf appearance (late tillering) using the phenotyping platform GROWSCREEN-Rhizo at the Institut f\ufcr Bio und Geowissenschaften Pflanzenwissenschaften

Prioritizing quantitative trait loci for root system architecture in tetraploid wheat

Optimization of root system architecture (RSA) traits is an important objective for modern wheat breeding. Linkage and association mapping for RSA in two recombinant inbred line populations and one association mapping panel of 183 elite durum wheat (Triticum turgidum L. var. durum Desf.) accessions evaluated as seedlings grown on filter paper/polycarbonate screening plates revealed 20 clusters of quantitative trait loci (QTLs) for root length and number, as well as 30 QTLs for root growth angle (RGA). Divergent RGA phenotypes observed by seminal root screening were validated by root phenotyping of field-grown adult plants. QTLs were mapped on a high-density tetraploid consensus map based on transcript-Associated Illumina 90K single nucleotide polymorphisms (SNPs) developed for bread and durum wheat, thus allowing for an accurate cross-referencing of RSA QTLs between durum and bread wheat. Among the main QTL clusters for root length and number highlighted in this study, 15 overlapped with QTLs for multiple RSA traits reported in bread wheat, while out of 30 QTLs for RGA, only six showed co-location with previously reported QTLs in wheat. Based on their relative additive effects/significance, allelic distribution in the association mapping panel, and co-location with QTLs for grain weight and grain yield, the RSA QTLs have been prioritized in terms of breeding value. Three major QTL clusters for root length and number (RSA-QTL-cluster-5#, RSA-QTL-cluster-6#, and RSA-QTL-cluster-12#) and nine RGA QTL clusters (QRGA.ubo-2A.1, QRGA.ubo-2A.3, QRGA.ubo-2B.2/2B.3, QRGA.ubo-4B.4, QRGA.ubo-6A.1, QRGA.ubo-6A.2, QRGA.ubo-7A.1, QRGA.ubo-7A.2, and QRGA.ubo-7B) appear particularly valuable for further characterization towards a possible implementation of breeding applications in marker-Assisted selection and/or cloning of the causal genes underlying the QTLs

Multi-Trait, Multi-Environment Genomic Prediction of Durum Wheat With Genomic Best Linear Unbiased Predictor and Deep Learning Methods

Although durum wheat (Triticum turgidum var. durum Desf.) is a minor cereal crop representing just 5\u20137% of the world\u2019s total wheat crop, it is a staple food in Mediterranean countries, where it is used to produce pasta, couscous, bulgur and bread. In this paper, we cover multi-trait prediction of grain yield (GY), days to heading (DH) and plant height (PH) of 270 durum wheat lines that were evaluated in 43 environments (country\u2013location\u2013year combinations) across a broad range of water regimes in the Mediterranean Basin and other locations. Multi-trait prediction analyses were performed by implementing a multi-trait deep learning model (MTDL) with a feed-forward network topology and a rectified linear unit activation function with a grid search approach for the selection of hyper-parameters. The results of the multi-trait deep learning method were also compared with univariate predictions of the genomic best linear unbiased predictor (GBLUP) method and the univariate counterpart of the multi-trait deep learning method (UDL). All models were implemented with and without the genotype 7 environment interaction term. We found that the best predictions were observed without the genotype 7 environment interaction term in the UDL and MTDL methods. However, under the GBLUP method, the best predictions were observed when the genotype 7 environment interaction term was taken into account. We also found that in general the best predictions were observed under the GBLUP model; however, the predictions of the MTDL were very similar to those of the GBLUP model. This result provides more evidence that the GBLUP model is a powerful approach for genomic prediction, but also that the deep learning method is a practical approach for predicting univariate and multivariate traits in the context of genomic selection

Cloning the barley nec3 disease lesion mimic mutant using complementation by sequencing

Disease lesion mimic (DLM) or necrotic mutants display necrotic lesions in the absence of pathogen infections. They can show improved resistance to some pathogens and their molecular dissection can contribute to revealing components of plant defense pathways. Although forward-genetics strategies to find genes causal to mutant phenotypes are available in crops, these strategies require the production of experimental cross populations, mutagenesis, or gene editing and are time- and resource-consuming or may have to deal with regulated plant materials. In this study, we described a collection of 34 DLM mutants in barley (Hordeum vulgare L.) and applied a novel method called complementation by sequencing (CBS), which enables the identification of the gene responsible for a mutant phenotype given the availability of two or more chemically mutagenized individuals showing the same phenotype. Complementation by sequencing relies on the feasibility to obtain all induced mutations present in chemical mutants and on the low probability that different individuals share the same mutated genes. By CBS, we identified a cytochrome P450 CYP71P1 gene as responsible for orange blotch DLM mutants, including the historical barley nec3 locus. By comparative phylogenetic analysis we showed that CYP71P1 gene family emerged early in angiosperm evolution but has been recurrently lost in some lineages including Arabidopsis thaliana (L.) Heynh. Complementation by sequencing is a straightforward cost-effective approach to clone genes controlling phenotypes in a chemically mutagenized collection. The TILLMore (TM) collection will be instrumental for understanding the molecular basis of DLM phenotypes and to contribute knowledge about mechanisms of host-pathogen interaction