Crop Breeding Chips and Genotyping Platforms: Progress, Challenges, and Perspectives

Dissection of the practical soybean breeding pipeline by developing ZDX1, a high-throughput functional array

KEY MESSAGE

We developed the ZDX1 high-throughput functional soybean array for high accuracy evaluation and selection of both parents and progeny, which can greatly accelerate soybean breeding. Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2214 representative soybean accessions, we developed the high-throughput functional array ZDX1, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to important traits. By application of the array, a total of 817 accessions were genotyped, including three subpopulations of candidate parental lines, parental lines and their progeny from practical breeding. The fixed SNPs were identified in progeny, indicating artificial selection during the breeding process. By identifying functional sites of target traits, novel soybean cyst nematode-resistant progeny and maturity-related novel sources were identified by allele combinations, demonstrating that functional sites provide an efficient method for the rapid screening of desirable traits or gene sources. Notably, we found that the breeding index (BI) was a good indicator for progeny selection. Superior progeny were derived from the combination of distantly related parents, with at least one parent having a higher BI. Furthermore, new combinations based on good performance were proposed for further breeding after excluding redundant and closely related parents. Genomic best linear unbiased prediction (GBLUP) analysis was the best analysis method and achieved the highest accuracy in predicting four traits when comparing SNPs in genic regions rather than whole genomic or intergenic SNPs. The prediction accuracy was improved by 32.1% by using progeny to expand the training population. Collectively, a versatile assay demonstrated that the functional ZDX1 array provided efficient information for the design and optimization of a breeding pipeline for accelerated soybean breeding.

Deciphering Haplotypic Variation and Gene Expression Dynamics Associated with Nutritional and Cooking Quality in Rice

Nutritional quality improvement of rice is the key to ensure global food security. Consequently, enormous efforts have been made to develop genomics and transcriptomics resources for rice. The available omics resources along with the molecular understanding of trait development can be utilized for efficient exploration of genetic resources for breeding programs. In the present study, 80 genes known to regulate the nutritional and cooking quality of rice were extensively studied to understand the haplotypic variability and gene expression dynamics. The haplotypic variability of selected genes were defined using whole-genome re-sequencing data of ~4700 diverse genotypes. The analytical workflow identified 133 deleterious single-nucleotide polymorphisms, which are predicted to affect the gene function. Furthermore, 788 haplotype groups were defined for 80 genes, and the distribution and evolution of these haplotype groups in rice were described. The nucleotide diversity for the selected genes was significantly reduced in cultivated rice as compared with that in wild rice. The utility of the approach was successfully demonstrated by revealing the haplotypic association of chalk5 gene with the varying degree of grain chalkiness. The gene expression atlas was developed for these genes by analyzing RNA-Seq transcriptome profiling data from 102 independent sequence libraries. Subsequently, weighted gene co-expression meta-analyses of 11,726 publicly available RNAseq libraries identified 19 genes as the hub of interactions. The comprehensive analyses of genetic polymorphisms, allelic distribution, and gene expression profiling of key quality traits will help in exploring the most desired haplotype for grain quality improvement. Similarly, the information provided here will be helpful to understand the molecular mechanism involved in the development of nutritional and cooking quality traits in rice.

Identification and validation of QTLs for kernel number per spike and spike length in two founder genotypes of wheat

BACKGROUND

Kernel number per spike (KNS) and spike length (SL) are important spike-related traits in wheat variety improvement. Discovering genetic loci controlling these traits is necessary to elucidate the genetic basis of wheat yield traits and is very important for marker-assisted selection breeding.

RESULTS

In the present study, we used a recombinant inbred line population with 248 lines derived from the two founder genotypes of wheat, Bima4 and BainongAK58, to construct a high-density genetic map using wheat 55 K genotyping assay. The final genetic linkage map consists of 2356 bin markers (14,812 SNPs) representing all 21 wheat chromosomes, and the entire map spanned 4141.24 cM. A total of 7 and 18 QTLs were identified for KNS and SL, respectively, and they were distributed on 11 chromosomes. The allele effects of the flanking markers for 12 stable QTLs, including four QTLs for KNS and eight QTLs for SL, were estimated based on phenotyping data collected from 15 environments in a diverse wheat panel including 384 elite cultivars and breeding lines. The positive alleles at seven loci, namely, QKns.his-7D2-1, QKns.his-7D2-2, QSl.his-4A-1, QSl.his-5D1, QSl.his-4D2-2, QSl.his-5B and QSl.his-5A-2, significantly increased KNS or SL in the diverse panel, suggesting they are more universal in their effects and are valuable for gene pyramiding in breeding programs. The transmission of Bima4 allele indicated that the favorite alleles at five loci (QKns.his-7D2-1, QSl.his-5A-2, QSl.his-2D1-1, QSl.his-3A-2 and QSl.his-3B) showed a relatively high frequency or an upward trend following the continuity of generations, suggesting that they underwent rigorous selection during breeding. At two loci (QKns.his-7D2-1 and QSl.his-5A-2) that the positive effects of the Bima4 alleles have been validated in the diverse panel, two and one kompetitive allele-specific PCR (KASP) markers were further developed, respectively, and they are valuable for marker-assisted selection breeding.

CONCLUSION

Important chromosome regions controlling KNS and SL were identified in the founder parents. Our results are useful for knowing the molecular mechanisms of founder parents and future molecular breeding in wheat.

SoySNP618K array: A high-resolution single nucleotide polymorphism platform as a valuable genomic resource for soybean genetics and breeding

Innovations in genomics have enabled the development of low-cost, high-resolution, single nucleotide polymorphism (SNP) genotyping arrays that accelerate breeding progress and support basic research in crop science. Here, we developed and validated the SoySNP618K array (618,888 SNPs) for the important crop soybean. The SNPs were selected from whole-genome resequencing data containing 2,214 diverse soybean accessions; 29.34% of the SNPs mapped to genic regions representing 86.85% of the 56,044 annotated high-confidence genes. Identity-by-state analyses of 318 soybeans revealed 17 redundant accessions, highlighting the potential of the SoySNP618K array in supporting gene bank management. The patterns of population stratification and genomic regions enriched through domestication were highly consistent with previous findings based on resequencing data, suggesting that the ascertainment bias in the SoySNP618K array was largely compensated for. Genome-wide association mapping in combination with reported quantitative trait loci enabled fine-mapping of genes known to influence flowering time, E2 and GmPRR3b, and of a new candidate gene, GmVIP5. Moreover, genomic prediction of flowering and maturity time in 502 recombinant inbred lines was highly accurate (>0.65). Thus, the SoySNP618K array is a valuable genomic tool that can be used to address many questions in applied breeding, germplasm management, and basic crop research.

Chromosome-specific KASP markers for detecting Amblyopyrum muticum segments in wheat introgression lines

Many wild-relative species are being used in prebreeding programs to increase the genetic diversity of wheat (Triticum aestivum L.). Genotyping tools such as single nucleotide polymorphism (SNP)-based arrays and molecular markers have been widely used to characterize wheat-wild relative introgression lines. However, due to the polyploid nature of the recipient wheat genome, it is difficult to develop SNP-based Kompetitive allele-specific polymerase chain reaction (KASP) markers that are codominant to track the introgressions from the wild species. Previous attempts to develop KASP markers have involved both exome- and polymerase chain reaction (PCR)-amplicon-based sequencing of the wild species. But chromosome-specific KASP assays have been hindered by homoeologous SNPs within the wheat genome. This study involved whole genome sequencing of the diploid wheat wild relative Amblyopyrum muticum (Boiss.) Eig and development of a de novo SNP discovery pipeline that generated ∼38,000 SNPs in unique wheat genome sequences. New assays were designed to increase the density of Am. muticum polymorphic KASP markers. With a goal of one marker per 60 Mbp, 335 new KASP assays were validated as diagnostic for Am. muticum in a wheat background. Together with assays validated in previous studies, 498 well distributed chromosome-specific markers were used to recharacterize previously genotyped wheat-Am. muticum doubled haploid (DH) introgression lines. The chromosome-specific nature of the KASP markers allowed clarification of which wheat chromosomes were involved with recombination events or substituted with Am. muticum chromosomes and the higher density of markers allowed detection of new small introgressions in these DH lines.

Identification and DNA Marker Development for a Wheat-Leymus mollis 2Ns (2D) Disomic Chromosome Substitution

Leymus mollis (2n = 4x = 28, NsNsXmXm), a wild relative of common wheat (Triticum aestivum L.), carries numerous loci which could potentially be used in wheat improvement. In this study, line 17DM48 was isolated from the progeny of a wheat and L. mollis hybrid. This line has 42 chromosomes forming 21 bivalents at meiotic metaphase I. Genomic in situ hybridization (GISH) demonstrated the presence of a pair chromosomes from the Ns genome of L. mollis. This pair substituted for wheat chromosome 2D, as shown by fluorescence in situ hybridization (FISH), DNA marker analysis, and hybridization to wheat 55K SNP array. Therefore, 17DM48 is a wheat-L. mollis 2Ns (2D) disomic substitution line. It shows longer spike and a high level of stripe rust resistance. Using specific-locus amplified fragment sequencing (SLAF-seq), 13 DNA markers were developed to identify and trace chromosome 2Ns of L. mollis in wheat background. This line provides a potential bridge germplasm for genetic improvement of wheat stripe rust resistance.

Genome-Wide Association Study on Root System Architecture and Identification of Candidate Genes in Wheat (Triticum aestivum L.)

The root tissues play important roles in water and nutrient acquisition, environmental adaptation, and plant development. In this study, a diversity panel of 388 wheat accessions was collected to investigate nine root system architecture (RSA) traits at the three-leaf stage under two growing environments: outdoor pot culture (OPC) and indoor pot culture (IPC). Phenotypic analysis revealed that root development was faster under OPC than that under IPC and a significant correlation was observed between the nine RSA traits. The 660K single-nucleotide polymorphism (SNP) chip was used for a genome-wide association study (GWAS). Significant SNPs with a threshold of −log10 (p-value) ≥ 4 were considered. Thus, 36 quantitative trait loci (QTLs), including 13 QTL clusters that were associated with more than one trait, were detected, and 31 QTLs were first identified. The QTL clusters on chromosomes 3D and 5B were associated with four and five RSA traits, respectively. Two candidate genes, TraesCS2A01G516200 and TraesCS7B01G036900, were found to be associated with more than one RSA trait using haplotype analysis, and preferentially expressed in the root tissues. These favourable alleles for RSA traits identified in this study may be useful to optimise the root system in wheat.

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.

Breeding for Economically and Environmentally Sustainable Wheat Varieties: An Integrated Approach from Genomics to Selection

There is currently a strong societal demand for sustainability, quality, and safety in bread wheat production. To address these challenges, new and innovative knowledge, resources, tools, and methods to facilitate breeding are needed. This starts with the development of high throughput genomic tools including single nucleotide polymorphism (SNP) arrays, high density molecular marker maps, and full genome sequences. Such powerful tools are essential to perform genome-wide association studies (GWAS), to implement genomic and phenomic selection, and to characterize the worldwide diversity. This is also useful to breeders to broaden the genetic basis of elite varieties through the introduction of novel sources of genetic diversity. Improvement in varieties particularly relies on the detection of genomic regions involved in agronomical traits including tolerance to biotic (diseases and pests) and abiotic (drought, nutrient deficiency, high temperature) stresses. When enough resolution is achieved, this can result in the identification of candidate genes that could further be characterized to identify relevant alleles. Breeding must also now be approached through in silico modeling to simulate plant development, investigate genotype × environment interactions, and introduce marker-trait linkage information in the models to better implement genomic selection. Breeders must be aware of new developments and the information must be made available to the world wheat community to develop new high-yielding varieties that can meet the challenge of higher wheat production in a sustainable and fluctuating agricultural context. In this review, we compiled all knowledge and tools produced during the BREEDWHEAT project to show how they may contribute to face this challenge in the coming years.

Developing and deploying an efficient genotyping workflow for accelerating maize improvement in developing countries

Background: Molecular breeding is an essential tool for accelerating genetic gain in crop improvement, towards meeting the need to feed an ever-growing world population. Establishing low-cost, flexible genotyping platforms in small, public and regional laboratories can stimulate the application of molecular breeding in developing countries. These laboratories can serve plant breeding projects requiring low- to medium-density markers for marker-assisted selection (MAS) and quality control (QC) activities. Methods: We performed two QC and MAS experiments consisting of 637 maize lines, using an optimised genotyping workflow involving an in-house competitive allele-specific PCR (KASP) genotyping system with an optimised sample collection, preparation, and DNA extraction and quantitation process. A smaller volume of leaf-disc size plant samples was collected directly in 96-well plates for DNA extraction, using a slightly modified CTAB-based DArT DNA extraction protocol. DNA quality and quantity analyses were performed using a microplate reader, and the KASP genotyping and data analysis was performed in our laboratory. Results: Applying the optimized genotyping workflow expedited the QC and MAS experiments from over five weeks (when outsourcing) to two weeks and eliminated the shipping cost. Using a set of 28 KASP single nucleotide polymorphisms (SNPs) validated for maize, the QC experiment revealed the genetic identity of four maize varieties taken from five seed sources. Another set of 10 KASP SNPs was sufficient in verifying the parentage of 390 F1 lines. The KASP-based MAS was successfully applied to a maize pro-vitamin A (PVA) breeding program and for introgressing the aflatoxin resistance gene into elite tropical maize lines. Conclusion: This improved workflow has helped accelerate maize improvement activities of IITA's Maize Improvement Program and facilitated DNA fingerprinting for tracking improved crop varieties. National Agricultural Research Systems (NARS) in developing countries can adopt this workflow to fast-track molecular marker-based genotyping for crop improvement.

From Green Super Rice to green agriculture: Reaping the promise of functional genomics research

Producing sufficient food with finite resources to feed the growing global population while having a smaller impact on the environment has always been a great challenge. Here, we review the concept and practices of Green Super Rice (GSR) that have led to a paradigm shift in goals for crop genetic improvement and models of food production for promoting sustainable agriculture. The momentous achievements and global deliveries of GSR have been fueled by the integration of abundant genetic resources, functional gene discoveries, and innovative breeding techniques with precise gene and whole-genome selection and efficient agronomic management to promote resource-saving, environmentally friendly crop production systems. We also provide perspectives on new horizons in genomic breeding technologies geared toward delivering green and nutritious crop varieties to further enhance the development of green agriculture and better nourish the world population.

Genomic Prediction: Progress and Perspectives for Rice Improvement

Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.

High Resolution Genome Wide Association Studies Reveal Rich Genetic Architectures of Grain Zinc and Iron in Common Wheat (Triticum aestivum L.)

Biofortification is a sustainable strategy to alleviate micronutrient deficiency in humans. It is necessary to improve grain zinc (GZnC) and iron concentrations (GFeC) in wheat based on genetic knowledge. However, the precise dissection of the genetic architecture underlying GZnC and GFeC remains challenging. In this study, high-resolution genome-wide association studies were conducted for GZnC and GFeC by three different models using 166 wheat cultivars and 373,106 polymorphic markers from the wheat 660K and 90K single nucleotide polymorphism (SNP) arrays. Totally, 25 and 16 stable loci were detected for GZnC and GFeC, respectively. Among them, 17 loci for GZnC and 8 for GFeC are likely to be new quantitative trait locus/loci (QTL). Based on gene annotations and expression profiles, 28 promising candidate genes were identified for Zn/Fe uptake (8), transport (11), storage (3), and regulations (6). Of them, 11 genes were putative wheat orthologs of known Arabidopsis and rice genes related to Zn/Fe homeostasis. A brief model, such as genes related to Zn/Fe homeostasis from root uptake, xylem transport to the final seed storage was proposed in wheat. Kompetitive allele-specific PCR (KASP) markers were successfully developed for two major QTL of GZnC on chromosome arms 3AL and 7AL, respectively, which were independent of thousand kernel weight and plant height. The 3AL QTL was further validated in a bi-parental population under multi-environments. A wheat multidrug and toxic compound extrusion (MATE) transporter TraesCS3A01G499300, the ortholog of rice gene OsPEZ2, was identified as a potential candidate gene. This study has advanced our knowledge of the genetic basis underlying GZnC and GFeC in wheat and provides valuable markers and candidate genes for wheat biofortification.

Genotyping Platforms for Genome-Wide Association Studies: Options and Practical Considerations

Genome-wide association studies (GWAS) in crops requires genotyping platforms that are capable of producing accurate high density genotyping data on hundreds of plants in a cost-effective manner. Currently there are multiple commercial platforms available that are being effectively used across crops. These platforms include genotyping arrays such as the Illumina Infinium arrays and the Applied Biosystems Axiom Arrays along with a variety of resequencing methods. These methods are being used to genotype tens of thousands of markers up to millions of markers on GWAS panels. They are being used on crops with simple genomes to crops with very complex, large, polyploid genomes. Depending on the crop and the goal of the GWAS, there are several options and practical considerations to take into account when selecting a genotyping technology to ensure that the right coverage, accuracy, and cost for the study is achieved.

Designing a Genome-Wide Association Study: Main Steps and Critical Decisions

In this introductory chapter, we seek to provide the reader with a high-level overview of what goes into designing a genome-wide association study (GWAS) in the context of crop plants. After introducing some general concepts regarding GWAS, we divide the contents of this overview into four main sections that reflect the key components of a GWAS: assembly and phenotyping of an association panel, genotyping, association analysis and candidate gene identification. These sections largely reflect the structure of the chapters which follow later in the book, and which provide detailed discussions of these various steps. In each section, in addition to providing external references from the literature, we also often refer the reader to the appropriate chapters in this book in which they can further explore a topic. We close by summarizing some of the key questions that a prospective user of GWAS should answer prior to undertaking this type of experiment.

Exploring Phylogenetic Relationships and Divergence Times of Bioluminescent Species Using Genomic and Transcriptomic Data

Next-generation sequencing (NGS) has dominated the scene of genomics and evolutionary biology as a great amount of genomic data have been accumulated for a diverse set of species. At the same time, phylogenetic approaches and programs are in development to allow better use of such large-size datasets. Phylogenomics appears as a promising field to accommodate and explore all the information of NGS data in phylogenetic methods, being an important approach to investigate the evolution of bioluminescence in different organisms. To guarantee accurate results in phylogenomic studies, it is mandatory to correctly identify orthologous genes in phylogenetic reconstruction. Here, we show a simplified step-by-step framework to perform phylogenetic analysis along with divergence time estimation, beginning with an orthologous search. As empirical data, we exemplify transcriptome sequences of six species of the Elateroidea superfamily (Coleoptera). We introduce several bioinformatics tools for handling genomic data, especially those available in the software OrthoFinder, IQTREE, BEAST2, and TreePL.

Enhanced stripe rust resistance obtained by combining Yr30 with a widely dispersed, consistent QTL on chromosome arm 4BL

YrFDC12 and PbcFDC, co-segregated in chromosome 4BL, and significantly interacted with Yr30/Pbc1 to enhance stripe rust resistance and to promote pseudo-black chaff development. Cultivars with durable resistance are the most popular means to control wheat stripe rust. Durable resistance can be achieved by stacking multiple adult plant resistance (APR) genes that individually have relatively small effect. Chinese wheat cultivars Ruihua 520 (RH520) and Fengdecun 12 (FDC12) confer partial APR to stripe rust across environments. One hundred and seventy recombinant inbred lines from the cross RH520 × FDC12 were used to determine the genetic basis of resistance and identify genomic regions associated with stripe rust resistance. Genotyping was carried out using 55 K SNP array, and eight quantitative trait loci (QTL) were detected on chromosome arms 2AL, 2DS, 3BS, 4BL, 5BL (2), and 7BL (2) by inclusive composite interval mapping. Only QYr.nwafu-3BS from RH520 and QYr.nwafu-4BL.2 (named YrFDC12 for convenience) from FDC12 were consistent across the four testing environments. QYr.nwafu-3BS is likely the pleiotropic resistance gene Sr2/Yr30. YrFDC12 was mapped in a 2.1-cM interval corresponding to 12 Mb and flanked by SNP markers AX-111121224 and AX-89518393. Lines harboring both Yr30 and YrFDC12 displayed higher resistance than the parents and expressed pseudo-black chaff (PBC) controlled by loci Pbc1 and PbcFDC12, which co-segregated with Yr30 and YrFDC12, respectively. Both marker-based and pedigree-based kinship analyses revealed that YrFDC12 was inherited from founder parent Zhou 8425B. Fifty-four other wheat cultivars shared the YrFDC12 haplotype. These results suggest an effective pyramiding strategy to acquire highly effective, durable stripe rust resistance in breeding.

Novel Design of Imputation-Enabled SNP Arrays for Breeding and Research Applications Supporting Multi-Species Hybridization

Array-based single nucleotide polymorphism (SNP) genotyping platforms have low genotype error and missing data rates compared to genotyping-by-sequencing technologies. However, design decisions used to create array-based SNP genotyping assays for both research and breeding applications are critical to their success. We describe a novel approach applicable to any animal or plant species for the design of cost-effective imputation-enabled SNP genotyping arrays with broad utility and demonstrate its application through the development of the Illumina Infinium Wheat Barley 40K SNP array Version 1.0. We show that the approach delivers high quality and high resolution data for wheat and barley, including when samples are jointly hybridised. The new array aims to maximally capture haplotypic diversity in globally diverse wheat and barley germplasm while minimizing ascertainment bias. Comprising mostly biallelic markers that were designed to be species-specific and single-copy, the array permits highly accurate imputation in diverse germplasm to improve the statistical power of genome-wide association studies (GWAS) and genomic selection. The SNP content captures tetraploid wheat (A- and B-genome) and Aegilops tauschii Coss. (D-genome) diversity and delineates synthetic and tetraploid wheat from other wheat, as well as tetraploid species and subgroups. The content includes SNP tagging key trait loci in wheat and barley, as well as direct connections to other genotyping platforms and legacy datasets. The utility of the array is enhanced through the web-based tool, Pretzel (https://plantinformatics.io/) which enables the content of the array to be visualized and interrogated interactively in the context of numerous genetic and genomic resources to be connected more seamlessly to research and breeding. The array is available for use by the international wheat and barley community.

Delineating the elusive BaMMV resistance gene rym15 in barley by medium-resolution mapping

Barley mild mosaic virus (BaMMV), transmitted by the soil-borne protist Polymyxa graminis, has a serious impact on winter barley production. Previously, the BaMMV resistance gene rym15 was mapped on chromosome 6HS, but the order of flanking markers was non-collinear between different maps. To resolve the position of the flanking markers and to enable map-based cloning of rym15, two medium-resolution mapping populations Igri (susceptible) × Chikurin Ibaraki 1 (resistant) (I × C) and Chikurin Ibaraki 1 × Uschi (susceptible) (C × U), consisting of 342 and 180 F2 plants, respectively, were developed. Efficiency of the mechanical inoculation of susceptible standards varied from 87.5 to 100% and in F2 populations from 90.56 to 93.23%. Phenotyping of F2 plants and corresponding F3 families revealed segregation ratios of 250 s:92r (I × C, χ2 = 0.659) and 140 s:40r (C × U, χ2 = 0.741), suggesting the presence of a single recessive resistance gene. After screening the parents with the 50 K Infinium chip and anchoring corresponding SNPs to the barley reference genome, 8 KASP assays were developed and used to remap the gene. Newly constructed maps revealed a collinear order of markers, thereby allowing the identification of high throughput flanking markers. This study demonstrates how construction of medium-resolution mapping populations in combination with robust phenotyping can efficiently resolve conflicting marker ordering and reduce the size of the target interval. In the reference genome era and genome-wide genotyping era, medium-resolution mapping will help accelerate candidate gene identification for traits where phenotyping is difficult.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01270-9.

Population-tailored mock genome enables genomic studies in species without a reference genome

Based on molecular markers, genomic prediction enables us to speed up breeding schemes and increase the response to selection. There are several high-throughput genotyping platforms able to deliver thousands of molecular markers for genomic study purposes. However, even though its widely applied in plant breeding, species without a reference genome cannot fully benefit from genomic tools and modern breeding schemes. We used a method to assemble a population-tailored mock genome to call single-nucleotide polymorphism (SNP) markers without an available reference genome, and for the first time, we compared the results with standard genotyping platforms (array and genotyping-by-sequencing (GBS) using a reference genome) for performance in genomic prediction models. Our results indicate that using a population-tailored mock genome to call SNP delivers reliable estimates for the genomic relationship between genotypes. Furthermore, genomic prediction estimates were comparable to standard approaches, especially when considering only additive effects. However, mock genomes were slightly worse than arrays at predicting traits influenced by dominance effects, but still performed as well as standard GBS methods that use a reference genome. Nevertheless, the array-based SNP markers methods achieved the best predictive ability and reliability to estimate variance components. Overall, the mock genomes can be a worthy alternative for genomic selection studies, especially for those species where the reference genome is not available.

Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip

Genotyping platforms, as critical supports for genomics, genetics, and molecular breeding, have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput, automatic, large-scale, and shared facilities. In this study, we integrated an improved genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology to develop a multiple single-nucleotide polymorphism (mSNP) approach in which mSNPs can be captured from a single amplicon. From one 40K maize mSNP panel, we developed three types of markers (40K mSNPs, 251K SNPs, and 690K haplotypes), and generated multiple panels with various marker densities (1K-40K mSNPs) by sequencing at different depths. Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs. Compared with the one-amplicon-one-SNP system, mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection, linkage disequilibrium decay analysis, and genome-wide association studies. The technologies, protocols, and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals, plants, and microorganisms.

Features and applications of haplotypes in crop breeding

Climate change with altered pest-disease dynamics and rising abiotic stresses threatens resource-constrained agricultural production systems worldwide. Genomics-assisted breeding (GAB) approaches have greatly contributed to enhancing crop breeding efficiency and delivering better varieties. Fast-growing capacity and affordability of DNA sequencing has motivated large-scale germplasm sequencing projects, thus opening exciting avenues for mining haplotypes for breeding applications. This review article highlights ways to mine haplotypes and apply them for complex trait dissection and in GAB approaches including haplotype-GWAS, haplotype-based breeding, haplotype-assisted genomic selection. Improvement strategies that efficiently deploy superior haplotypes to hasten breeding progress will be key to safeguarding global food security.

Genomic Variation in Korean japonica Rice Varieties

Next-generation sequencing technologies have enabled the discovery of numerous sequence variations among closely related crop varieties. We analyzed genome resequencing data from 24 Korean temperate japonica rice varieties and discovered 954,233 sequence variations, including 791,121 single nucleotide polymorphisms (SNPs) and 163,112 insertions/deletions (InDels). On average, there was one variant per 391 base-pairs (bp), a variant density of 2.6 per 1 kbp. Of the InDels, 10,860 were longer than 20 bp, which enabled conversion to markers resolvable on an agarose gel. The effect of each variant on gene function was predicted using the SnpEff program. The variants were categorized into four groups according to their impact: high, moderate, low, and modifier. These groups contained 3524 (0.4%), 27,656 (2.9%), 24,875 (2.6%), and 898,178 (94.1%) variants, respectively. To test the accuracy of these data, eight InDels from a pre-harvest sprouting resistance QTL (qPHS11) target region, four highly polymorphic InDels, and four functional sequence variations in known agronomically important genes were selected and successfully developed into markers. These results will be useful to develop markers for marker-assisted selection, to select candidate genes in map-based cloning, and to produce efficient high-throughput genome-wide genotyping systems for Korean temperate japonica rice varieties.

Breeding Canola (Brassica napus L.) for Protein in Feed and Food

Interest in canola (Brassica napus L.). In response to this interest, scientists have been tasked with altering and optimizing the protein production chain to ensure canola proteins are safe for consumption and economical to produce. Specifically, the role of plant breeders in developing suitable varieties with the necessary protein profiles is crucial to this interdisciplinary endeavour. In this article, we aim to provide an overarching review of the canola protein chain from the perspective of a plant breeder, spanning from the genetic regulation of seed storage proteins in the crop to advancements of novel breeding technologies and their application in improving protein quality in canola. A review on the current uses of canola meal in animal husbandry is presented to underscore potential limitations for the consumption of canola meal in mammals. General discussions on the allergenic potential of canola proteins and the regulation of novel food products are provided to highlight some of the challenges that will be encountered on the road to commercialization and general acceptance of canola protein as a dietary protein source.

Development and application of the Faba_bean_130K targeted next-generation sequencing SNP genotyping platform based on transcriptome sequencing

KEY MESSAGE

Large-scale faba bean transcriptome data are available, and the first genotyping platform based on liquid-phase probe targeted capture technology was developed for genetic and molecular breeding studies. Faba bean (Vicia faba L., 2n = 12) is an important food legume crop that is widely grown for multiple uses worldwide. However, no reference genome is currently available due to its very large genome size (approximately 13 Gb) and limited single nucleotide polymorphism (SNP) markers as well as highly efficient genotyping tools have been reported for faba bean. In this study, 16.7 billion clean reads were obtained from transcriptome libraries of flowers and leaves of 102 global faba bean accessions. A total of 243,120 unigenes were de novo assembled and functionally annotated. Moreover, a total of 1,579,411 SNPs were identified and further filtered according to a selection pipeline to develop a high-throughput, flexible, low-cost Faba_bean_130K targeted next-generation sequencing (TNGS) genotyping platform. A set of 69 Chinese faba bean accessions were genotyped with the TNGS genotyping platform, and the average mapping rate of captured reads to reference transcripts was 93.14%, of which 53.23% were located in the targeted regions. The TNGS genotyping results were validated by Sanger sequencing and the average consistency rate reached 93.6%. Comprehensive population genetic analysis was performed on the 69 Chinese faba bean accessions and identified four genetic subgroups correlated with the geographic distribution. This study provides valuable genomic resources and a reliable genotyping tool that could be implemented in genetic and molecular breeding studies to accelerate new cultivar development and improvement in faba bean.

The Bnapus50K array: a quick and versatile genotyping tool for Brassica napus genomic breeding and research

Rapeseed is a globally cultivated commercial crop, primarily grown for its oil. High-density single nucleotide polymorphism (SNP) arrays are widely used as a standard genotyping tool for rapeseed research, including for gene mapping, genome-wide association studies, germplasm resource analysis, and cluster analysis. Although considerable rapeseed genome sequencing data have been released, DNA arrays are still an attractive choice for providing additional genetic data in an era of high-throughput whole-genome sequencing. Here, we integrated re-sequencing DNA array data (32,216, 304 SNPs) from 505 inbred rapeseed lines, allowing us to develop a sensitive and efficient genotyping DNA array, Bnapus50K, with a more consistent genetic and physical distribution of probes. A total of 42,090 high-quality probes were filtered and synthesized, with an average distance between adjacent SNPs of 8 kb. To improve the practical application potential of this array in rapeseed breeding, we also added 1,618 functional probes related to important agronomic traits such as oil content, disease resistance, male sterility, and flowering time. The additional probes also included those specifically for detecting genetically modified material. These probes show a good detection efficiency and are therefore useful for gene mapping, along with crop variety improvement and identification. The novel Bnapus50K DNA array developed in this study could prove to be a quick and versatile genotyping tool for B. napus genomic breeding and research.

Fast-forward breeding for a food-secure world

Crop production systems need to expand their outputs sustainably to feed a burgeoning human population. Advances in genome sequencing technologies combined with efficient trait mapping procedures accelerate the availability of beneficial alleles for breeding and research. Enhanced interoperability between different omics and phenotyping platforms, leveraged by evolving machine learning tools, will help provide mechanistic explanations for complex plant traits. Targeted and rapid assembly of beneficial alleles using optimized breeding strategies and precise genome editing techniques could deliver ideal crops for the future. Realizing desired productivity gains in the field is imperative for securing an adequate future food supply for 10 billion people.

Genome-wide association study for grain mineral content in a Brazilian common bean diversity panel

QTNs significantly associated to nine mineral content in grains of common bean were identified. The accumulation of favorable alleles was associated with a gradually increasing nutrient content in the grain. Biofortification is one of the strategies developed to address malnutrition in developing countries, the aim of which is to improve the nutritional content of crops. The common bean (Phaseolus vulgaris L.), a staple food in several African and Latin American countries, has excellent nutritional attributes and is considered a strong candidate for biofortification. The objective of this study was to identify genomic regions associated with nutritional content in common bean grains using 178 Mesoamerican accessions belonging to a Brazilian Diversity Panel (BDP) and 25,011 good-quality single nucleotide polymorphisms. The BDP was phenotyped in three environments for nine nutrients (phosphorus, potassium, calcium, magnesium, copper, manganese, sulfur, zinc, and iron) using four genome-wide association multi-locus methods. To obtain more accurate results, only quantitative trait nucleotides (QTNs) that showed repeatability (i.e., those detected at least twice using different methods or environments) were considered. Forty-eight QTNs detected for the nine minerals showed repeatability and were considered reliable. Pleiotropic QTNs and overlapping genomic regions surrounding the QTNs were identified, demonstrating the possible association between the deposition mechanisms of different nutrients in grains. The accumulation of favorable alleles in the same accession was associated with a gradually increasing nutrient content in the grain. The BDP proved to be a valuable source for association studies. The investigation of different methods and environments showed the reliability of markers associated with minerals. The loci identified in this study will potentially contribute to the improvement of Mesoamerican common beans, particularly carioca and black beans, the main groups consumed in Brazil.

Targeted genome modifications in cereal crops

The recent advent of customizable endonucleases has led to remarkable advances in genetic engineering, as these molecular scissors allow for the targeted introduction of mutations or even precisely predefined genetic modifications into virtually any genomic target site of choice. Thanks to its unprecedented precision, efficiency, and functional versatility, this technology, commonly referred to as genome editing, has become an effective force not only in basic research devoted to the elucidation of gene function, but also for knowledge-based improvement of crop traits. Among the different platforms currently available for site-directed genome modifications, RNA-guided clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) endonucleases have proven to be the most powerful. This review provides an application-oriented overview of the development of customizable endonucleases, current approaches to cereal crop breeding, and future opportunities in this field.

Breeding custom-designed crops for improved drought adaptation

The current pace of crop improvement is inadequate to feed the burgeoning human population by 2050. Higher, more stable, and sustainable crop production is required against a backdrop of drought stress, which causes significant losses in crop yields. Tailoring crops for drought adaptation may hold the key to address these challenges and provide resilient production systems for future harvests. Understanding the genetic and molecular landscape of the functionality of alleles associated with adaptive traits will make designer crop breeding the prospective approach for crop improvement. Here, we highlight the potential of genomics technologies combined with crop physiology for high-throughput identification of the genetic architecture of key drought-adaptive traits and explore innovative genomic breeding strategies for designing future crops.

Next-Generation Breeding Strategies for Climate-Ready Crops

Climate change is a threat to global food security due to the reduction of crop productivity around the globe. Food security is a matter of concern for stakeholders and policymakers as the global population is predicted to bypass 10 billion in the coming years. Crop improvement via modern breeding techniques along with efficient agronomic practices innovations in microbiome applications, and exploiting the natural variations in underutilized crops is an excellent way forward to fulfill future food requirements. In this review, we describe the next-generation breeding tools that can be used to increase crop production by developing climate-resilient superior genotypes to cope with the future challenges of global food security. Recent innovations in genomic-assisted breeding (GAB) strategies allow the construction of highly annotated crop pan-genomes to give a snapshot of the full landscape of genetic diversity (GD) and recapture the lost gene repertoire of a species. Pan-genomes provide new platforms to exploit these unique genes or genetic variation for optimizing breeding programs. The advent of next-generation clustered regularly interspaced short palindromic repeat/CRISPR-associated (CRISPR/Cas) systems, such as prime editing, base editing, and de nova domestication, has institutionalized the idea that genome editing is revamped for crop improvement. Also, the availability of versatile Cas orthologs, including Cas9, Cas12, Cas13, and Cas14, improved the editing efficiency. Now, the CRISPR/Cas systems have numerous applications in crop research and successfully edit the major crop to develop resistance against abiotic and biotic stress. By adopting high-throughput phenotyping approaches and big data analytics tools like artificial intelligence (AI) and machine learning (ML), agriculture is heading toward automation or digitalization. The integration of speed breeding with genomic and phenomic tools can allow rapid gene identifications and ultimately accelerate crop improvement programs. In addition, the integration of next-generation multidisciplinary breeding platforms can open exciting avenues to develop climate-ready crops toward global food security.

Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review

The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype-environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.

Candidate Rlm6 resistance genes against Leptosphaeria. maculans identified through a genome-wide association study in Brassica juncea (L.) Czern

One hundred and sixty-seven B. juncea varieties were genotyped on the 90K Brassica assay (42,914 SNPs), which led to the identification of sixteen candidate genes for Rlm6. Brassica species are at high risk of severe crop loss due to pathogens, especially Leptosphaeria maculans (the causal agent of blackleg). Brassica juncea (L.) Czern is an important germplasm resource for canola improvement, due to its good agronomic traits, such as heat and drought tolerance and high blackleg resistance. The present study is the first using genome-wide association studies to identify candidate genes for blackleg resistance in B. juncea based on genome-wide SNPs obtained from the Illumina Infinium 90 K Brassica SNP array. The verification of Rlm6 in B. juncea was performed through a cotyledon infection test. Genotyping 42,914 single nucleotide polymorphisms (SNPs) in a panel of 167 B. juncea lines revealed a total of seven SNPs significantly associated with Rlm6 on chromosomes A07 and B04 in B. juncea. Furthermore, 16 candidate Rlm6 genes were found in these regions, defined as nucleotide binding site leucine-rich-repeat (NLR), leucine-rich repeat RLK (LRR-RLK) and LRR-RLP genes. This study will give insights into the blackleg resistance in B. juncea and facilitate identification of functional blackleg resistance genes which can be used in Brassica breeding.

Development and Exploitation of KASP Assays for Genes Underpinning Drought Tolerance Among Wheat Cultivars From Pakistan

High-throughput genotyping for functional markers offers an excellent opportunity to effectively practice marker-assisted selection (MAS) while breeding cultivars. We developed kompetitive allele-specific PCR (KASP) assays for genes conferring drought tolerance in common wheat (Triticum aestivum L.). In total, 11 KASP assays developed in this study and five already reported assays were used for their application in wheat breeding. We investigated alleles at 16 loci associated with drought tolerance among 153 Pakistani hexaploid wheat cultivars released during 1953-2016; 28 diploid wheat accessions (16 for AA and 12 for BB) and 19 tetraploid wheat (AABB) were used to study the evolutionary history of the studied genes. Superior allelic variations of the studied genes were significantly associated with higher grain yield. Favored haplotypes of TaSnRK2.3-1A, TaSnRK2.3-1B, TaSnRK2.9-5A, TaSAP-7B, and TaLTPs-1A predominated in Pakistani wheat germplasm indicating unconscious pyramiding and selection pressure on favorable haplotypes during selection breeding. TaSnRK2.8-5A, TaDreb-B1, 1-feh w3, TaPPH-7A, TaMOC-7A, and TaPARG-2A had moderate to low frequencies of favorable haplotype among Pakistani wheat germplasm pointing toward introgression of favorable haplotypes by deploying functional markers in marker-assisted breeding. The KASP assays were compared with gel-based markers for reliability and phenotypically validated among 62 Pakistani wheat cultivars. Association analyses showed that the favorable allelic variations were significantly associated with grain yield-contributing traits. The developed molecular marker toolkit of the genes can be instrumental for the wheat breeding in Pakistan.

Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits

KEY MESSAGE

Integrating genomics technologies and breeding methods to tweak core parameters of the breeder's equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security. Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers' fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder's equation. A prerequisite to enhancing genetic variance (σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder's equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.

Introgressing the Aegilops tauschii genome into wheat as a basis for cereal improvement

Increasing crop production is necessary to feed the world's expanding population, and crop breeders often utilize genetic variations to improve crop yield and quality. However, the narrow diversity of the wheat D genome seriously restricts its selective breeding. A practical solution is to exploit the genomic variations of Aegilops tauschii via introgression. Here, we established a rapid introgression platform for transferring the overall genetic variations of A. tauschii to elite wheats, thereby enriching the wheat germplasm pool. To accelerate the process, we assembled four new reference genomes, resequenced 278 accessions of A. tauschii and constructed the variation landscape of this wheat progenitor species. Genome comparisons highlighted diverse functional genes or novel haplotypes with potential applications in wheat improvement. We constructed the core germplasm of A. tauschii, including 85 accessions covering more than 99% of the species' overall genetic variations. This was crossed with elite wheat cultivars to generate an A. tauschii-wheat synthetic octoploid wheat (A-WSOW) pool. Laboratory and field analysis with two examples of the introgression lines confirmed its great potential for wheat breeding. Our high-quality reference genomes, genomic variation landscape of A. tauschii and the A-WSOW pool provide valuable resources to facilitate gene discovery and breeding in wheat.

Characterization of effects of genetic variants via genome-scale metabolic modelling

Genome-scale metabolic networks for model plants and crops in combination with approaches from the constraint-based modelling framework have been used to predict metabolic traits and design metabolic engineering strategies for their manipulation. With the advances in technologies to generate large-scale genotyping data from natural diversity panels and other populations, genome-wide association and genomic selection have emerged as statistical approaches to determine genetic variants associated with and predictive of traits. Here, we review recent advances in constraint-based approaches that integrate genetic variants in genome-scale metabolic models to characterize their effects on reaction fluxes. Since some of these approaches have been applied in organisms other than plants, we provide a critical assessment of their applicability particularly in crops. In addition, we further dissect the inferred effects of genetic variants with respect to reaction rate constants, abundances of enzymes, and concentrations of metabolites, as main determinants of reaction fluxes and relate them with their combined effects on complex traits, like growth. Through this systematic review, we also provide a roadmap for future research to increase the predictive power of statistical approaches by coupling them with mechanistic models of metabolism.

Diversifying maize genomic selection models

Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.

Integration of Infinium and Axiom SNP array data in the outcrossing species Malus × domestica and causes for seemingly incompatible calls

Background

Single nucleotide polymorphism (SNP) array technology has been increasingly used to generate large quantities of SNP data for use in genetic studies. As new arrays are developed to take advantage of new technology and of improved probe design using new genome sequence and panel data, a need to integrate data from different arrays and array platforms has arisen. This study was undertaken in view of our need for an integrated high-quality dataset of Illumina Infinium® 20 K and Affymetrix Axiom® 480 K SNP array data in apple (Malus × domestica). In this study, we qualify and quantify the compatibility of SNP calling, defined as SNP calls that are both accurate and concordant, across both arrays by two approaches. First, the concordance of SNP calls was evaluated using a set of 417 duplicate individuals genotyped on both arrays starting from a set of 10,295 robust SNPs on the Infinium array. Next, the accuracy of the SNP calls was evaluated on additional germplasm (n = 3141) from both arrays using Mendelian inconsistent and consistent errors across thousands of pedigree links. While performing this work, we took the opportunity to evaluate reasons for probe failure and observed discordant SNP calls.

Results

Concordance among the duplicate individuals was on average of 97.1% across 10,295 SNPs. Of these SNPs, 35% had discordant call(s) that were further curated, leading to a final set of 8412 (81.7%) SNPs that were deemed compatible. Compatibility was highly influenced by the presence of alternate probe binding locations and secondary polymorphisms. The impact of the latter was highly influenced by their number and proximity to the 3′ end of the probe.

Conclusions

The Infinium and Axiom SNP array data were mostly compatible. However, data integration required intense data filtering and curation. This work resulted in a workflow and information that may be of use in other data integration efforts. Such an in-depth analysis of array concordance and accuracy as ours has not been previously described in the literature and will be useful in future work on SNP array data integration and interpretation, and in probe/platform development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07565-7.

Linkage disequilibrium patterns, population structure and diversity analysis in a worldwide durum wheat collection including Argentinian genotypes

Background

Durum wheat (Triticum turgidum L. ssp. durum Desf. Husn) is the main staple crop used to make pasta products worldwide. Under the current climate change scenarios, genetic variability within a crop plays a crucial role in the successful release of new varieties with high yields and wide crop adaptation. In this study we evaluated a durum wheat collection consisting of 197 genotypes that mainly comprised a historical set of Argentinian germplasm but also included worldwide accessions.

Results

We assessed the genetic diversity, population structure and linkage disequilibrium (LD) patterns in this collection using a 35 K SNP array. The level of polymorphism was considered, taking account of the frequent and rare allelic variants. A total of 1547 polymorphic SNPs was located within annotated genes. Genetic diversity in the germplasm collection increased slightly from 1915 to 2010. However, a reduction in genetic diversity using SNPs with rare allelic variants was observed after 1979. However, larger numbers of rare private alleles were observed in the 2000–2009 period, indicating that a high reservoir of rare alleles is still present among the recent germplasm in a very low frequency. The percentage of pairwise loci in LD in the durum genome was low (13.4%) in our collection. Overall LD and the high (r² > 0.7) or complete (r² = 1) LD presented different patterns in the chromosomes. The LD increased over three main breeding periods (1915–1979, 1980–1999 and 2000–2020).

Conclusions

Our results suggest that breeding and selection have impacted differently on the A and B genomes, particularly on chromosome 6A and 2A. The collection was structured in five sub-populations and modern Argentinian accessions (cluster Q4) which were clearly differentiated. Our study contributes to the understanding of the complexity of Argentinian durum wheat germplasm and to derive future breeding strategies enhancing the use of genetic diversity in a more efficient and targeted way.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07519-z.

[NGS sequencing in barley breeding and genetic studies]

Barley (Hordeum vulgare L.) is the one of the most important cereal species used as food and feed crops, as well as for malting and alcohol production. At the end of the last century, traditional breeding techniques were complemented by the use of DNA markers. Molecular markers have also been used extensively for molecular genetic mapping and QTL analysis. In 2012, the barley genome sequencing was completed, which provided a broad range of new opportunities - from a more efficient search for candidate genes controlling economically important traits to genomic selection. The review summarizes the results of the studies performed after barley genome sequencing, which discovered new areas of barley genetics and breeding with high throughput screening and genotyping methods. During this period, intensive studies aimed at identification of barley genomic loci associated with economically important traits have been carried out; online databases and tools for working with barley genomic data and their deposition have appeared and are being replenished. In recent years, GWAS analysis has been used for large-scale phenotypegenotype association studies, which has been widely used in barley since 2010 due to the developed SNP-arrays, as well as genotyping methods based on direct NGS sequencing of selected fractions of the genome. To date, more than 80 papers have been published that describe the results of the GWAS analysis in barley. SNP identification associated with economically important traits and their transformation into CAPS or KASP markers convenient for screening selection material significantly expands the possibilities of marker-assisted selection of barley. In addition, the currently available information on potential target genes and the quality of the whole barley genome sequence provides a good base for applying genome editing technologies to create material for the creation of varieties with desired properties.

Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.

Genetic Mapping by Integration of 55K SNP Array and KASP Markers Reveals Candidate Genes for Important Agronomic Traits in Hexaploid Wheat

Agronomic traits such as heading date (HD), plant height (PH), thousand grain weight (TGW), and spike length (SL) are important factors affecting wheat yield. In this study, we constructed a high-density genetic linkage map using the Wheat55K SNP Array to map quantitative trait loci (QTLs) for these traits in 207 recombinant inbred lines (RILs). A total of 37 QTLs were identified, including 9 QTLs for HD, 7 QTLs for PH, 12 QTLs for TGW, and 9 QTLs for SL, which explained 3.0-48.8% of the phenotypic variation. Kompetitive Allele Specific PCR (KASP) markers were developed based on sequencing data and used for validation of the stably detected QTLs on chromosomes 3A, 4B and 6A using 400 RILs. A QTL cluster on chromosome 4B for PH and TGW was delimited to a 0.8 Mb physical interval explaining 12.2-22.8% of the phenotypic variation. Gene annotations and analyses of SNP effects suggested that a gene encoding protein Photosynthesis Affected Mutant 68, which is essential for photosystem II assembly, is a candidate gene affecting PH and TGW. In addition, the QTL for HD on chromosome 3A was narrowed down to a 2.5 Mb interval, and a gene encoding an R3H domain-containing protein was speculated to be the causal gene influencing HD. The linked KASP markers developed in this study will be useful for marker-assisted selection in wheat breeding, and the candidate genes provide new insight into genetic study for those traits in wheat.

Accurate Imputation of Untyped Variants from Deep Sequencing Data

The quality, statistical power, and resolution of genome-wide association studies (GWAS) are largely dependent on the comprehensiveness of genotypic data. Over the last few years, despite the constant decrease in the price of sequencing, whole-genome sequencing (WGS) of association panels comprising a large number of samples remains cost-prohibitive. Therefore, most GWAS populations are still genotyped using low-coverage genotyping methods resulting in incomplete datasets. Imputation of untyped variants is a powerful method to maximize the number of SNPs identified in study samples, it increases the power and resolution of GWAS and allows to integrate genotyping datasets obtained from various sources. Here, we describe the key concepts underlying imputation of untyped variants, including the architecture of reference panels, and review some of the associated challenges and how these can be addressed. We also discuss the need and available methods to rigorously assess the accuracy of imputed data prior to their use in any genetic study.

Integrated genomic selection for rapid improvement of crops

An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.

Genetic characterization of an almond germplasm collection and volatilome profiling of raw and roasted kernels

Almond is appreciated for its nutraceutical value and for the aromatic profile of the kernels. In this work, an almond collection composed of 96 Sicilian accessions complemented with 10 widely cultivated cultivars was phenotyped for the production of volatile organic compounds using a proton-transfer time-of-flight mass spectrometer and genotyped using the Illumina Infinium^®18 K Peach SNP array. The profiling of the aroma was carried out on fresh and roasted kernels enabling the detection of 150 mass peaks. Sixty eight, for the most related with sulfur compounds, furan containing compounds, and aldehydes formed by Strecker degradation, significantly increased during roasting, while the concentration of fifty-four mass peaks, for the most belonging to alcohols and terpenes, significantly decreased. Four hundred and seventy-one robust SNPs were selected and employed for population genetic studies. Structure analysis detected three subpopulations with the Sicilian accessions characterized by a different genetic stratification compared to those collected in Apulia (South Italy) and the International cultivars. The linkage-disequilibrium (LD) decay across the genome was equal to r² = 0.083. Furthermore, a high level of collinearity (r² = 0.96) between almond and peach was registered confirming the high synteny between the two genomes. A preliminary application of a genome-wide association analysis allowed the detection of significant marker-trait associations for 31 fresh and 33 roasted almond mass peaks respectively. An accurate genetic and phenotypic characterization of novel germplasm can represent a valuable tool for the set-up of marker-assisted selection of novel cultivars with an enhanced aromatic profile.

New resources for genetic studies in maize (Zea mays L.): a genome-wide Maize6H-60K single nucleotide polymorphism array and its application

Despite the availability of numerous molecular markers in maize, effective evaluation of all types of germplasm resources, accurate identification of varieties and analysis of a large number of materials in a timely, low-cost manner is challenging. Here, we present Maize6H-60K, a genome-wide single nucleotide polymorphism (SNP) array to facilitate maize genotyping. We first identified 160 million variants by sequencing data of 388 representative inbreds and then tiled 200 000 high-quality variants on a screening array. These variants were further narrowed down to 61 282 using stringent filtering criteria. Among the 60 000 markers, 21 460 SNPs (35%) were within genic regions and 12 835 (21%) were located in coding regions. To assess their effectiveness, 329 inbreds, 221 hybrids, 34 parent-offspring sets and six breeding samples were genotyped. Overall, 48 972 SNPs (80%) were categorized into the highest quality class, that of 'poly high resolution'. A total of 54 658 (89.29%) and 53 091 (86.73%) SNPs had minor allele frequency values ≥ 0.20 in inbreds and hybrids respectively. A linkage disequilibrium (LD) analysis revealed that LD decline was in equilibrium when r² was between 0.10 and 0.15, which corresponds to a physical distance of 400-600 kb. UPGMA clustering analysis divided the 329 inbred lines into nine groups that were consistent with known pedigrees. A background analysis of breeding materials indicated that the 60 000 markers were suitable for evaluation of breeding populations constructed by materials between or within heterotic groups. The developed Maize6H-60K array should be an important tool in maize genetic studies, variety identification and molecular breeding.

Genomic resources in plant breeding for sustainable agriculture

Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.

The soybean (Glycine max L.) cytokinin oxidase/dehydrogenase multigene family; Identification of natural variations for altered cytokinin content and seed yield

Cytokinins (CKs) play a fundamental role in regulating dynamics of organ source/sink relationships during plant development, including flowering and seed formation stages. As a result, CKs are key drivers of seed yield. The cytokinin oxidase/dehydrogenase (CKX) is one of the critical enzymes responsible for regulating plant CK levels by causing their irreversible degradation. Variation of CKX activity is significantly correlated with seed yield in many crop species while in soybean (Glycine max L.), the possible associations between CKX gene family members (GFMs) and yield parameters have not yet been assessed. In this study, 17 GmCKX GFMs were identified, and natural variations among GmCKX genes were probed among soybean cultivars with varying yield characteristics. The key CKX genes responsible for regulating CK content during seed filling stages of reproductive development were highlighted using comparative phylogenetics, gene expression analysis and CK metabolite profiling. Five of the seventeen identified GmCKX GFMs, showed natural variations in the form of single nucleotide polymorphisms (SNPs). The gene GmCKX7-1, with high expression during critical seed filling stages, was found to have a non-synonymous mutation (H105Q), on one of the active site residues, Histidine 105, previously reported to be essential for co-factor binding to maintain structural integrity of the enzyme. Soybean lines with this mutation had higher CK content and desired yield characteristics. The potential for marker-assisted selection based on the identified natural variation within GmCKX7-1, is discussed in the context of hormonal control that can result in higher soybean yield.