Exome association analysis sheds light onto leaf rust (Puccinia triticina) resistance genes currently used in wheat breeding (Triticum aestivum L.)

A catalogue of resistance gene homologs and a chromosome-scale reference sequence support resistance gene mapping in winter wheat

A resistance gene atlas is an integral component of the breeder's arsenal in the fight against evolving pathogens. Thanks to high-throughput sequencing, catalogues of resistance genes can be assembled even in crop species with large and polyploid genomes. Here, we report on capture sequencing and assembly of resistance gene homologs in a diversity panel of 907 winter wheat genotypes comprising ex situ genebank accessions and current elite cultivars. In addition, we use accurate long-read sequencing and chromosome conformation capture sequencing to construct a chromosome-scale genome sequence assembly of cv. Attraktion, an elite variety representative of European winter wheat. We illustrate the value of our resource for breeders and geneticists by (i) comparing the resistance gene complements in plant genetic resources and elite varieties and (ii) conducting genome-wide associations scans (GWAS) for the fungal diseases yellow rust and leaf rust using reference-based and reference-free GWAS approaches. The gene content under GWAS peaks was scrutinized in the assembly of cv. Attraktion.

Dissection of the Genetic Basis of Yield Traits in Line per se and Testcross Populations and Identification of Candidate Genes for Hybrid Performance in Maize

Dissecting the genetic basis of yield traits in hybrid populations and identifying the candidate genes are important for molecular crop breeding. In this study, a BC1F3:4 population, the line per se (LPS) population, was constructed by using elite inbred lines Zheng58 and PH4CV as the parental lines. The population was genotyped with 55,000 SNPs and testcrossed to Chang7-2 and PH6WC (two testers) to construct two testcross (TC) populations. The three populations were evaluated for hundred kernel weight (HKW) and yield per plant (YPP) in multiple environments. Marker-trait association analysis (MTA) identified 24 to 151 significant SNPs in the three populations. Comparison of the significant SNPs identified common and specific quantitative trait locus/loci (QTL) in the LPS and TC populations. Genetic feature analysis of these significant SNPs proved that these SNPs were associated with the tested traits and could be used to predict trait performance of both LPS and TC populations. RNA-seq analysis was performed using maize hybrid varieties and their parental lines, and differentially expressed genes (DEGs) between hybrid varieties and parental lines were identified. Comparison of the chromosome positions of DEGs with those of significant SNPs detected in the TC population identified potential candidate genes that might be related to hybrid performance. Combining RNA-seq analysis and MTA results identified candidate genes for hybrid performance, providing information that could be useful for maize hybrid breeding.

Broadening the horizon of crop research: a decade of advancements in plant molecular genetics to divulge phenotype governing genes

Advancements in sequencing, genotyping, and computational technologies during the last decade (2011-2020) enabled new forward-genetic approaches, which subdue the impediments of precise gene mapping in varied crops. The modern crop improvement programs rely heavily on two major steps-trait-associated QTL/gene/marker's identification and molecular breeding. Thus, it is vital for basic and translational crop research to identify genomic regions that govern the phenotype of interest. Until the advent of next-generation sequencing, the forward-genetic techniques were laborious and time-consuming. Over the last 10 years, advancements in the area of genome assembly, genotyping, large-scale data analysis, and statistical algorithms have led faster identification of genomic variations regulating the complex agronomic traits and pathogen resistance. In this review, we describe the latest developments in genome sequencing and genotyping along with a comprehensive evaluation of the last 10-year headways in forward-genetic techniques that have shifted the focus of plant research from model plants to diverse crops. We have classified the available molecular genetic methods under bulk-segregant analysis-based (QTL-seq, GradedPool-Seq, QTG-Seq, Exome QTL-seq, and RapMap), target sequence enrichment-based (RenSeq, AgRenSeq, and TACCA), and mutation-based groups (MutMap, NIKS algorithm, MutRenSeq, MutChromSeq), alongside improvements in classical mapping and genome-wide association analyses. Newer methods for outcrossing, heterozygous, and polyploid plant genetics have also been discussed. The use of k-mers has enriched the nature of genetic variants which can be utilized to identify the phenotype-causing genes, independent of reference genomes. We envisage that the recent methods discussed herein will expand the repertoire of useful alleles and help in developing high-yielding and climate-resilient crops.

Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions

Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 120 female and 15 male wheat lines. Twenty superior female lines were selected for initiation of the reciprocal recurrent genomic selection program. Focusing on the female pool, one cycle was performed with genomic selection steps at the F₂ (60 out of 629 plants) and the F₅ stage (49 out of 382 plants). Selection gain for grain yield was evaluated at six locations. Analyses of the phenotypic data showed pronounced genotype-by-environment interactions with two environments that formed an outgroup compared to the environments used for the genome-wide prediction equations. Removing these two environments for further analysis resulted in a selection gain of 1.0 dt ha^-1 compared to the hybrids of the original 20 parental lines. This underscores the potential of reciprocal recurrent genomic selection to promote hybrid wheat breeding, but also highlights the need to develop robust genome-wide predictive equations.

Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat

The potential of big data to support businesses has been demonstrated in financial services, manufacturing, and telecommunications. Here, we report on efforts to enter a new data era in plant breeding by collecting genomic and phenotypic information from 12,858 wheat genotypes representing 6575 single-cross hybrids and 6283 inbred lines that were evaluated in six experimental series for yield in field trials encompassing ~125,000 plots. Integrating data resulted in twofold higher prediction ability compared with cases in which hybrid performance was predicted across individual experimental series. Our results suggest that combining data across breeding programs is a particularly appropriate strategy to exploit the potential of big data for predictive plant breeding. This paradigm shift can contribute to increasing yield and resilience, which is needed to feed the growing world population.

A large-scale genomic association analysis identifies the candidate causal genes conferring stripe rust resistance under multiple field environments

The incorporation of resistance genes into wheat commercial varieties is the ideal strategy to combat stripe or yellow rust (YR). In a search for novel resistance genes, we performed a large-scale genomic association analysis with high-density 660K single nucleotide polymorphism (SNP) arrays to determine the genetic components of YR resistance in 411 spring wheat lines. Following quality control, 371 972 SNPs were screened, covering over 50% of the high-confidence annotated gene space. Nineteen stable genomic regions harbouring 292 significant SNPs were associated with adult-plant YR resistance across nine environments. Of these, 14 SNPs were localized in the proximity of known loci widely used in breeding. Obvious candidate SNP variants were identified in certain confidence intervals, such as the cloned gene Yr18 and the major locus on chromosome 2BL, despite a large extent of linkage disequilibrium. The number of causal SNP variants was refined using an independent validation panel and consideration of the estimated functional importance of each nucleotide polymorphism. Interestingly, four natural polymorphisms causing amino acid changes in the gene TraesCS2B01G513100 that encodes a serine/threonine protein kinase (STPK) were significantly involved in YR responses. Gene expression and mutation analysis confirmed that STPK played an important role in YR resistance. PCR markers were developed to identify the favourable TraesCS2B01G513100 haplotype for marker-assisted breeding. These results demonstrate that high-resolution SNP-based GWAS enables the rapid identification of putative resistance genes and can be used to improve the efficiency of marker-assisted selection in wheat disease resistance breeding.

Haplotype-based genome-wide association increases the predictability of leaf rust (Puccinia triticina) resistance in wheat

Resistance breeding is crucial for sustainable control of wheat leaf rust and single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) are widely used to dissect leaf rust resistance. Unfortunately, GWAS based on SNPs often explained only a small proportion of the genetic variation. We compared SNP-based GWAS with a method based on functional haplotypes (FH) considering epistasis in a comprehensive hybrid wheat mapping population composed of 133 parents plus their 1574 hybrids and characterized with 626 245 high-quality SNPs. In total, 2408 and 1 139 828 significant associations were detected in the mapping population by using SNP-based and FH-based GWAS, respectively. These associations mapped to 25 and 69 candidate regions, correspondingly. SNP-based GWAS highlighted two already-known resistance genes, Lr22a and Lr34-B, while FH-based GWAS detected associations not only on these genes but also on two additional genes, Lr10 and Lr1. As revealed by a second hybrid wheat population for independent validation, the use of detected associations from SNP-based and FH-based GWAS reached predictabilities of 11.72% and 22.86%, respectively. Therefore, FH-based GWAS is not only more powerful for detecting associations, but also improves the accuracy of marker-assisted selection compared with the SNP-based approach.

Comparing the Potential of Marker-Assisted Selection and Genomic Prediction for Improving Rust Resistance in Hybrid Wheat

Improving leaf rust and stripe rust resistance is a central goal in wheat breeding. The objectives of this study were to (1) elucidate the genetic basis of leaf rust and stripe rust resistance in a hybrid wheat population, (2) compare the findings using a previously published hybrid wheat data set, and (3) contrast the prediction accuracy with those of genome-wide prediction. The hybrid wheat population included 1,744 single crosses from 236 parental lines. The genotypes were fingerprinted using a 15k SNP array and evaluated for leaf rust and stripe rust resistance in multi-location field trials. We observed a high congruency of putative quantitative trait loci (QTL) for leaf rust resistance between both populations. This was not the case for stripe rust resistance. Accordingly, prediction accuracy of the detected QTL was moderate for leaf rust but low for stripe rust resistance. Genome-wide selection increased the prediction accuracy slightly for stripe rust albeit at a low level but not for leaf rust. Thus, our findings suggest that marker-assisted selection seems to be a robust and efficient tool to improve leaf rust resistance in European wheat hybrids.