Genomic selection to resistance to Stenocarpella maydis in maize lines using DArTseq markers

Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals

Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.

The Era of Plant Breeding: Conventional Breeding to Genomics-assisted Breeding for Crop Improvement

Plant breeding has made a significant contribution to increasing agricultural production. Conventional breeding based on phenotypic selection is not effective for crop improvement. Because phenotype is considerably influenced by environmental factors, which will affect the selection of breeding materials for crop improvement. The past two decades have seen tremendous progress in plant breeding research. Especially the availability of high-throughput molecular markers followed by genomic-assisted approaches significantly contributed to advancing plant breeding. Integration of speed breeding with genomic and phenomic facilities allowed rapid quantitative trait loci (QTL)/gene identifications and ultimately accelerated crop improvement programs. The advances in sequencing technology helps to understand the genome organization of many crops and helped with genomic selection in crop breeding. Plant breeding has gradually changed from phenotype-to-genotype-based to genotype-to-phenotype-based selection. High-throughput phenomic platforms have played a significant role in the modern breeding program and are considered an essential part of precision breeding. In this review, we discuss the rapid advance in plant breeding technology for efficient crop improvements and provide details on various approaches/platforms that are helpful for crop improvement. This review will help researchers understand the recent developments in crop breeding and improvements.

Plant-Fungi Interactions: Where It Goes?

Fungi live different lifestyles-including pathogenic and symbiotic-by interacting with living plants. Recently, there has been a substantial increase in the study of phytopathogenic fungi and their interactions with plants. Symbiotic relationships with plants appear to be lagging behind, although progressive. Phytopathogenic fungi cause diseases in plants and put pressure on survival. Plants fight back against such pathogens through complicated self-defense mechanisms. However, phytopathogenic fungi develop virulent responses to overcome plant defense reactions, thus continuing their deteriorative impacts. Symbiotic relationships positively influence both plants and fungi. More interestingly, they also help plants protect themselves from pathogens. In light of the nonstop discovery of novel fungi and their strains, it is imperative to pay more attention to plant-fungi interactions. Both plants and fungi are responsive to environmental changes, therefore construction of their interaction effects has emerged as a new field of study. In this review, we first attempt to highlight the evolutionary aspect of plant-fungi interactions, then the mechanism of plants to avoid the negative impact of pathogenic fungi, and fungal strategies to overcome the plant defensive responses once they have been invaded, and finally the changes of such interactions under the different environmental conditions.

Restructuring plant types for developing tailor-made crops

Plants have adapted to different environmental niches by fine-tuning the developmental factors working together to regulate traits. Variations in the developmental factors result in a wide range of quantitative variations in these traits that helped plants survive better. The major developmental pathways affecting plant architecture are also under the control of such pathways. Most notable are the CLAVATA-WUSCHEL pathway regulating shoot apical meristem fate, GID1-DELLA module influencing plant height and tillering, LAZY1-TAC1 module controlling branch/tiller angle and the TFL1-FT determining the floral fate in plants. Allelic variants of these key regulators selected during domestication shaped the crops the way we know them today. There is immense yield potential in the 'ideal plant architecture' of a crop. With the available genome-editing techniques, possibilities are not restricted to naturally occurring variations. Using a transient reprogramming system, one can screen the effect of several developmental gene expressions in novel ecosystems to identify the best targets. We can use the plant's fine-tuning mechanism for customizing crops to specific environments. The process of crop domestication can be accelerated with a proper understanding of these developmental pathways. It is time to step forward towards the next-generation molecular breeding for restructuring plant types in crops ensuring yield stability.

Genetic resources and breeding of maize for Striga resistance: a review

The potential yield of maize (Zea mays L.) and other major crops is curtailed by several biotic, abiotic, and socio-economic constraints. Parasitic weeds, Striga spp., are major constraints to cereal and legume crop production in sub-Saharan Africa (SSA). Yield losses reaching 100% are reported in maize under severe Striga infestation. Breeding for Striga resistance has been shown to be the most economical, feasible, and sustainable approach for resource-poor farmers and for being environmentally friendly. Knowledge of the genetic and genomic resources and components of Striga resistance is vital to guide genetic analysis and precision breeding of maize varieties with desirable product profiles under Striga infestation. This review aims to present the genetic and genomic resources, research progress, and opportunities in the genetic analysis of Striga resistance and yield components in maize for breeding. The paper outlines the vital genetic resources of maize for Striga resistance, including landraces, wild relatives, mutants, and synthetic varieties, followed by breeding technologies and genomic resources. Integrating conventional breeding, mutation breeding, and genomic-assisted breeding [i.e., marker-assisted selection, quantitative trait loci (QTL) analysis, next-generation sequencing, and genome editing] will enhance genetic gains in Striga resistance breeding programs. This review may guide new variety designs for Striga-resistance and desirable product profiles in maize.

Genome-wide association study presents insights into the genetic architecture of drought tolerance in maize seedlings under field water-deficit conditions

Introduction

Drought stress is one of the most serious abiotic stresses leading to crop yield reduction. Due to the wide range of planting areas, the production of maize is particularly affected by global drought stress. The cultivation of drought-resistant maize varieties can achieve relatively high, stable yield in arid and semi-arid zones and in the erratic rainfall or occasional drought areas. Therefore, to a great degree, the adverse impact of drought on maize yield can be mitigated by developing drought-resistant or -tolerant varieties. However, the efficacy of traditional breeding solely relying on phenotypic selection is not adequate for the need of maize drought-resistant varieties. Revealing the genetic basis enables to guide the genetic improvement of maize drought tolerance.

Methods

We utilized a maize association panel of 379 inbred lines with tropical, subtropical and temperate backgrounds to analyze the genetic structure of maize drought tolerance at seedling stage. We obtained the high quality 7837 SNPs from DArT's and 91,003 SNPs from GBS, and a resultant combination of 97,862 SNPs of GBS with DArT's. The maize population presented the lower her-itabilities of the seedling emergence rate (ER), seedling plant height (SPH) and grain yield (GY) under field drought conditions.

Results

GWAS analysis by MLM and BLINK models with the phenotypic data and 97862 SNPs revealed 15 variants that were significantly independent related to drought-resistant traits at the seedling stage above the threshold of P < 1.02 × 10-5. We found 15 candidate genes for drought resistance at the seedling stage that may involve in (1) metabolism (Zm00001d012176, Zm00001d012101, Zm00001d009488); (2) programmed cell death (Zm00001d053952); (3) transcriptional regulation (Zm00001d037771, Zm00001d053859, Zm00001d031861, Zm00001d038930, Zm00001d049400, Zm00001d045128 and Zm00001d043036); (4) autophagy (Zm00001d028417); and (5) cell growth and development (Zm00001d017495). The most of them in B73 maize line were shown to change the expression pattern in response to drought stress. These results provide useful information for understanding the genetic basis of drought stress tolerance of maize at seedling stage.

Wheat end-use quality: State of art, genetics, genomics-assisted improvement, future challenges, and opportunities

Wheat is the most important source of food, feed, and nutrition for humans and livestock around the world. The expanding population has increasing demands for various wheat products with different quality attributes requiring the development of wheat cultivars that fulfills specific demands of end-users including millers and bakers in the international market. Therefore, wheat breeding programs continually strive to meet these quality standards by screening their improved breeding lines every year. However, the direct measurement of various end-use quality traits such as milling and baking qualities requires a large quantity of grain, traits-specific expensive instruments, time, and an expert workforce which limits the screening process. With the advancement of sequencing technologies, the study of the entire plant genome is possible, and genetic mapping techniques such as quantitative trait locus mapping and genome-wide association studies have enabled researchers to identify loci/genes associated with various end-use quality traits in wheat. Modern breeding techniques such as marker-assisted selection and genomic selection allow the utilization of these genomic resources for the prediction of quality attributes with high accuracy and efficiency which speeds up crop improvement and cultivar development endeavors. In addition, the candidate gene approach through functional as well as comparative genomics has facilitated the translation of the genomic information from several crop species including wild relatives to wheat. This review discusses the various end-use quality traits of wheat, their genetic control mechanisms, the use of genetics and genomics approaches for their improvement, and future challenges and opportunities for wheat breeding.

Genomics Enabled Breeding Strategies for Major Biotic Stresses in Pea (Pisum sativum L.)

Pea (Pisum sativum L.) is one of the most important and productive cool season pulse crops grown throughout the world. Biotic stresses are the crucial constraints in harnessing the potential productivity of pea and warrant dedicated research and developmental efforts to utilize omics resources and advanced breeding techniques to assist rapid and timely development of high-yielding multiple stress-tolerant-resistant varieties. Recently, the pea researcher's community has made notable achievements in conventional and molecular breeding to accelerate its genetic gain. Several quantitative trait loci (QTLs) or markers associated with genes controlling resistance for fusarium wilt, fusarium root rot, powdery mildew, ascochyta blight, rust, common root rot, broomrape, pea enation, and pea seed borne mosaic virus are available for the marker-assisted breeding. The advanced genomic tools such as the availability of comprehensive genetic maps and linked reliable DNA markers hold great promise toward the introgression of resistance genes from different sources to speed up the genetic gain in pea. This review provides a brief account of the achievements made in the recent past regarding genetic and genomic resources' development, inheritance of genes controlling various biotic stress responses and genes controlling pathogenesis in disease causing organisms, genes/QTLs mapping, and transcriptomic and proteomic advances. Moreover, the emerging new breeding approaches such as transgenics, genome editing, genomic selection, epigenetic breeding, and speed breeding hold great promise to transform pea breeding. Overall, the judicious amalgamation of conventional and modern omics-enabled breeding strategies will augment the genetic gain and could hasten the development of biotic stress-resistant cultivars to sustain pea production under changing climate. The present review encompasses at one platform the research accomplishment made so far in pea improvement with respect to major biotic stresses and the way forward to enhance pea productivity through advanced genomic tools and technologies.

Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops

Since the inception of the theory and conceptual framework of genomic selection (GS), extensive research has been done on evaluating its efficiency for utilization in crop improvement. Though, the marker-assisted selection has proven its potential for improvement of qualitative traits controlled by one to few genes with large effects. Its role in improving quantitative traits controlled by several genes with small effects is limited. In this regard, GS that utilizes genomic-estimated breeding values of individuals obtained from genome-wide markers to choose candidates for the next breeding cycle is a powerful approach to improve quantitative traits. In the last two decades, GS has been widely adopted in animal breeding programs globally because of its potential to improve selection accuracy, minimize phenotyping, reduce cycle time, and increase genetic gains. In addition, given the promising initial evaluation outcomes of GS for the improvement of yield, biotic and abiotic stress tolerance, and quality in cereal crops like wheat, maize, and rice, prospects of integrating it in breeding crops are also being explored. Improved statistical models that leverage the genomic information to increase the prediction accuracies are critical for the effectiveness of GS-enabled breeding programs. Study on genetic architecture under drought and heat stress helps in developing production markers that can significantly accelerate the development of stress-resilient crop varieties through GS. This review focuses on the transition from traditional selection methods to GS, underlying statistical methods and tools used for this purpose, current status of GS studies in crop plants, and perspectives for its successful implementation in the development of climate-resilient crops.

Back to the wild: mining maize (Zea mays L.) disease resistance using advanced breeding tools

Cultivated modern maize (Zea mays L.) originated through the continuous process of domestication from its wild progenitors. Today, maize is considered as the most important cereal crop which is extensively cultivated in all parts of the world. Maize shows remarkable genotypic and phenotypic diversity which makes it an ideal model species for crop genetic research. However, intensive breeding and artificial selection of desired agronomic traits greatly narrow down the genetic bases of maize. This reduction in genetic diversity among cultivated maize led to increase the chance of more attack of biotic stress as climate changes hampering the maize grain production globally. Maize germplasm requires to integrate both durable multiple-diseases and multiple insect-pathogen resistance through tapping the unexplored resources of maize landraces. Revisiting the landraces seed banks will provide effective opportunities to transfer the resistant genes into the modern cultivars. Here, we describe the maize domestication process and discuss the unique genes from wild progenitors which potentially can be utilized for disease resistant in maize. We also focus on the genetics and disease resistance mechanism of various genes against maize biotic stresses and then considered the different molecular breeding tools for gene transfer and advanced high resolution mapping for gene pyramiding in maize lines. At last, we provide an insight for targeting identified key genes through CRISPR/Cas9 genome editing system to enhance the maize resilience towards biotic stress.

Status and advances in mining for blackleg (Leptosphaeria maculans) quantitative resistance (QR) in oilseed rape (Brassica napus)

KEY MESSAGE

Quantitative resistance (QR) loci discovered through genetic and genomic analyses are abundant in the Brassica napus genome, providing an opportunity for their utilization in enhancing blackleg resistance. Quantitative resistance (QR) has long been utilized to manage blackleg in Brassica napus (canola, oilseed rape), even before major resistance genes (R-genes) were extensively explored in breeding programmes. In contrast to R-gene-mediated qualitative resistance, QR reduces blackleg symptoms rather than completely eliminating the disease. As a polygenic trait, QR is controlled by numerous genes with modest effects, which exerts less pressure on the pathogen to evolve; hence, its effectiveness is more durable compared to R-gene-mediated resistance. Furthermore, combining QR with major R-genes has been shown to enhance resistance against diseases in important crops, including oilseed rape. For these reasons, there has been a renewed interest among breeders in utilizing QR in crop improvement. However, the mechanisms governing QR are largely unknown, limiting its deployment. Advances in genomics are facilitating the dissection of the genetic and molecular underpinnings of QR, resulting in the discovery of several loci and genes that can be potentially deployed to enhance blackleg resistance. Here, we summarize the efforts undertaken to identify blackleg QR loci in oilseed rape using linkage and association analysis. We update the knowledge on the possible mechanisms governing QR and the advances in searching for the underlying genes. Lastly, we lay out strategies to accelerate the genetic improvement of blackleg QR in oilseed rape using improved phenotyping approaches and genomic prediction tools.

Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools

KEY MESSAGE

High genetic variation in two European maize landraces can be harnessed to improve Gibberella ear rot resistance by integrated genomic tools. Fusarium graminearum (Fg) causes Gibberella ear rot (GER) in maize leading to yield reduction and contamination of grains with several mycotoxins. This study aimed to elucidate the molecular basis of GER resistance among 500 doubled haploid lines derived from two European maize landraces, "Kemater Landmais Gelb" (KE) and "Petkuser Ferdinand Rot" (PE). The two landraces were analyzed individually using genome-wide association studies and genomic selection (GS). The lines were genotyped with a 600-k maize array and phenotyped for GER severity, days to silking, plant height, and seed-set in four environments using artificial infection with a highly aggressive Fg isolate. High genotypic variances and broad-sense heritabilities were found for all traits. Genotype-environment interaction was important throughout. The phenotypic (r) and genotypic ([Formula: see text]) correlations between GER severity and three agronomic traits were low (r =  - 0.27 to 0.20; [Formula: see text]=  - 0.32 to 0.22). For GER severity, eight QTLs were detected in KE jointly explaining 34% of the genetic variance. In PE, no significant QTLs for GER severity were detected. No common QTLs were found between GER severity and the three agronomic traits. The mean prediction accuracies ([Formula: see text]) of weighted GS (wRR-BLUP) were higher than [Formula: see text] of marker-assisted selection (MAS) and unweighted GS (RR-BLUP) for GER severity. Using KE as the training set and PE as the validation set resulted in very low [Formula: see text] that could be improved by using fixed marker effects in the GS model.

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.

Non-coding RNAs as emerging targets for crop improvement

Food security is affected by climate change, population growth, as well as abiotic and biotic stresses. Conventional and molecular marker assisted breeding and genetic engineering techniques have been employed extensively for improving resistance to biotic stress in crop plants. Advances in next-generation sequencing technologies have permitted the exploration and identification of parts of the genome that extend beyond the regions with protein coding potential. These non-coding regions of the genome are transcribed to generate many types of non-coding RNAs (ncRNAs). These ncRNAs are involved in the regulation of growth, development, and response to stresses at transcriptional and translational levels. ncRNAs, including long ncRNAs (lncRNAs), small RNAs and circular RNAs have been recognized as important regulators of gene expression in plants and have been suggested to play important roles in plant immunity and adaptation to abiotic and biotic stresses. In this article, we have reviewed the current state of knowledge with respect to lncRNAs and their mechanism(s) of action as well as their regulatory functions, specifically within the context of biotic stresses. Additionally, we have provided insights into how our increased knowledge about lncRNAs may be used to improve crop tolerance to these devastating biotic stresses.

Discovery of thermophilic Bacillales using reduced-representation genotyping for identification

Background

This study demonstrates the use of reduced-representation genotyping to provide preliminary identifications for thermophilic bacterial isolates. The approach combines restriction enzyme digestion and PCR with next-generation sequencing to provide thousands of short-read sequences from across the bacterial genomes. Isolates were obtained from compost, hot water systems, and artesian bores of the Great Artesian Basin. Genomic DNA was double-digested with two combinations of restriction enzymes followed by PCR amplification, using a commercial provider of DArTseq™, Diversity Arrays Technology Pty Ltd. (Canberra, Australia). The resulting fragments which formed a reduced-representation of approximately 2.3% of the genome were sequenced. The sequence tags obtained were aligned against all available RefSeq bacterial genome assemblies by BLASTn to identify the nearest reference genome.

Results

Based on the preliminary identifications, a total of 99 bacterial isolates were identified to species level, from which 8 isolates were selected for whole-genome sequencing to assess the identification results. Novel species and strains were discovered within this set of isolates. The preliminary identifications obtained by reduced-representation genotyping, as well as identifications obtained by BLASTn alignment of the 16S rRNA gene sequence, were compared with those derived from the whole-genome sequence data, using the same RefSeq sequence database for the three methods. Identifications obtained with reduced-representation sequencing agreed with the identifications provided by whole-genome sequencing in 100% of cases. The identifications produced by BLASTn alignment of 16S rRNA gene sequence to the same database differed from those provided by whole-genome sequencing in 37.5% of cases, and produced ambiguous identifications in 50% of cases.

Conclusions

Previously, this method has been successfully demonstrated for use in bacterial identification for medical microbiology. This study demonstrates the first successful use of DArTseq™ for preliminary identification of thermophilic bacterial isolates, providing results in complete agreement with those obtained from whole-genome sequencing of the same isolates. The growing database of bacterial genome sequences provides an excellent resource for alignment of reduced-representation sequence data for identification purposes, and as the available sequenced genomes continue to grow, the technique will become more effective.

Breeding Maize Maternal Haploid Inducers

Maize doubled haploid (DH) lines are usually created in vivo, through crosses with maternal haploid inducers. These inducers have the inherent ability of generating seeds with haploid embryos when used to pollinate other genotypes. The resulting haploid plants are treated with a doubling agent and self-pollinated, producing completely homozygous seeds. This rapid method of inbred line production reduces the length of breeding cycles and, consequently, increases genetic gain. Such advantages explain the wide adoption of this technique by large, well-established maize breeding programs. However, a slower rate of adoption was observed in medium to small-scale breeding programs. The high price and/or lack of environmental adaptation of inducers available for licensing, or the poor performance of those free of cost, might explain why smaller operations did not take full advantage of this technique. The lack of adapted inducers is especially felt in tropical countries, where inducer breeding efforts are more recent. Therefore, defining optimal breeding approaches for inducer development could benefit many breeding programs which are in the process of adopting the DH technique. In this manuscript, we review traits important to maize maternal haploid inducers, explain their genetic basis, listing known genes and quantitative trait loci (QTL), and discuss different breeding approaches for inducer development. The performance of haploid inducers has an important impact on the cost of DH line production.

Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum

The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.

Genomic Prediction of Kernel Zinc Concentration in Multiple Maize Populations Using Genotyping-by-Sequencing and Repeat Amplification Sequencing Markers

Enriching of kernel zinc (Zn) concentration in maize is one of the most effective ways to solve the problem of Zn deficiency in low and middle income countries where maize is the major staple food, and 17% of the global population is affected with Zn deficiency. Genomic selection (GS) has shown to be an effective approach to accelerate genetic gains in plant breeding. In the present study, an association-mapping panel and two maize double-haploid (DH) populations, both genotyped with genotyping-by-sequencing (GBS) and repeat amplification sequencing (rAmpSeq) markers, were used to estimate the genomic prediction accuracy of kernel Zn concentration in maize. Results showed that the prediction accuracy of two DH populations was higher than that of the association mapping population using the same set of markers. The prediction accuracy estimated with the GBS markers was significantly higher than that estimated with the rAmpSeq markers in the same population. The maximum prediction accuracy with minimum standard error was observed when half of the genotypes were included in the training set and 3,000 and 500 markers were used for prediction in the association mapping panel and the DH populations, respectively. Appropriate levels of minor allele frequency and missing rate should be considered and selected to achieve good prediction accuracy and reduce the computation burden by balancing the number of markers and marker quality. Training set development with broad phenotypic variation is possible to improve prediction accuracy. The transferability of the GS models across populations was assessed, the prediction accuracies in a few pairwise populations were above or close to 0.20, which indicates the prediction accuracies across years and populations have to be assessed in a larger breeding dataset with closer relationship between the training and prediction sets in further studies. GS outperformed MAS (marker-assisted-selection) on predicting the kernel Zn concentration in maize, the decision of a breeding strategy to implement GS individually or to implement MAS and GS stepwise for improving kernel Zn concentration in maize requires further research. Results of this study provide valuable information for understanding how to implement GS for improving kernel Zn concentration in maize.

Genomics-assisted breeding for ear rot resistances and reduced mycotoxin contamination in maize: methods, advances and prospects

Genetic mapping, genomic profiling and bioinformatic approaches were used to identify putative resistance genes for ear rots and low mycotoxin contamination in maize. Genomic selection seems to have good perspectives. Maize is globally an indispensable crop for humans and livestock. About 30% of yield is lost by fungal diseases with Gibberella, Fusarium and Aspergillus ear rots (ERs) having a high economic impact in most maize-growing regions of the world. They reduce not only yield, but also contaminate grains with mycotoxins like deoxynivalenol, zearalenone, fumonisins and aflatoxins, respectively. These mycotoxins pose serious health problems to humans and animals. A number of studies have been conducted to dissect the genetic architecture of resistance to these three major ear rots over the past decade. The review concentrates on studies carried out to locate quantitative trait loci (QTL) and candidate genes (CG) on the maize genome as well as the application of genomic selection in maize for resistance against Fusarium graminearum, Fusarium verticillioides and Aspergillus flavus. QTL studies by linkage or genome-wide association mapping, omic technologies (genomics, proteomics, transcriptomics and metabolomics) and bioinformatics are the methods used in the current studies to propose resistance genes against ear rot pathogens. Though a number of QTL and CG are reported, only a few specific genes were found to directly confer ER resistance in maize. A combination of two or more gene identification methods would provide a more powerful and reliable tool. Genomic selection seems to be promising for ER resistance breeding, but there are only a limited number of studies in this area. A strategy that can accurately validate and predict genotypes with major effect QTL and CG for selection will be worthwhile for practical breeding against ERs and mycotoxin contamination in maize.

Genomic Selection with Allele Dosage in Panicum maximum Jacq

Genomic selection is an efficient approach to get shorter breeding cycles in recurrent selection programs and greater genetic gains with selection of superior individuals. Despite advances in genotyping techniques, genetic studies for polyploid species have been limited to a rough approximation of studies in diploid species. The major challenge is to distinguish the different types of heterozygotes present in polyploid populations. In this work, we evaluated different genomic prediction models applied to a recurrent selection population of 530 genotypes of Panicum maximum, an autotetraploid forage grass. We also investigated the effect of the allele dosage in the prediction, i.e., considering tetraploid (GS-TD) or diploid (GS-DD) allele dosage. A longitudinal linear mixed model was fitted for each one of the six phenotypic traits, considering different covariance matrices for genetic and residual effects. A total of 41,424 genotyping-by-sequencing markers were obtained using 96-plex and Pst1 restriction enzyme, and quantitative genotype calling was performed. Six predictive models were generalized to tetraploid species and predictive ability was estimated by a replicated fivefold cross-validation process. GS-TD and GS-DD models were performed considering 1,223 informative markers. Overall, GS-TD data yielded higher predictive abilities than with GS-DD data. However, different predictive models had similar predictive ability performance. In this work, we provide bioinformatic and modeling guidelines to consider tetraploid dosage and observed that genomic selection may lead to additional gains in recurrent selection program of P. maximum.

Enhancing genetic disease control by selecting for lower host infectivity and susceptibility

Infectious diseases have a huge impact on animal health, production and welfare, and human health. Understanding the role of host genetics in disease spread is important for developing disease control strategies that efficiently reduce infection incidence and risk of epidemics. While heritable variation in disease susceptibility has been targeted in livestock breeding, emerging evidence suggests that there is additional genetic variation in host infectivity, but the potential benefits of including infectivity into selection schemes are currently unknown. A Susceptible-Infected-Recovered epidemiological model incorporating polygenic genetic variation in both susceptibility and infectivity was combined with quantitative genetics selection theory to assess the non-linear impact of genetic selection on field measures of epidemic risk and severity. Response to 20 generations of selection was calculated in large simulated populations, exploring schemes differing in accuracy and intensity. Assuming moderate genetic variation in both traits, 50% selection on susceptibility required seven generations to reduce the basic reproductive number R₀ from 7.64 to the critical threshold of <1, below which epidemics die out. Adding infectivity in the selection objective accelerated the decline towards R₀ < 1, to 3 generations. Our results show that although genetic selection on susceptibility reduces disease risk and prevalence, the additional gain from selection on infectivity accelerates disease eradication and reduces more efficiently the risk of new outbreaks, while it alleviates delays generated by unfavourable correlations. In conclusion, host infectivity was found to be an important trait to target in future genetic studies and breeding schemes, to help reducing the occurrence and impact of epidemics.

Identification of bacterial isolates from a public hospital in Australia using complexity-reduced genotyping

Bacterial identification methods used in routine identification of pathogens in medical microbiology include a combination approach of biochemical tests, mass spectrometry or molecular biology techniques. Extensive publicly-available databases of DNA sequence data from pathogenic bacteria have been amassed in recent years; this provides an opportunity for using bacterial genome sequencing for identification purposes. Whole genome sequencing is increasing in popularity, although at present it remains a relatively expensive approach to bacterial identification and typing. Complexity-reduced bacterial genome sequencing provides an alternative. We evaluate genomic complexity-reduction using restriction enzymes and sequencing to identify bacterial isolates. A total of 165 bacterial isolates from hospital patients in the Australian Capital Territory, between 2013 and 2015 were used in this study. They were identified and typed by the Microbiology Department of Canberra Public Hospital, and represented 14 bacterial species. DNA extractions from these samples were processed using a combination of the restriction enzymes PstI with MseI, PstI with HpaII and MseI with HpaII. The resulting sequences (length 30-69 bp) were aligned against publicly available bacterial genome and plasmid sequences. Results of the alignment were processed using a bioinformatics pipeline developed for this project, Currito3.1 DNA Fragment Analysis Software. All 165 samples were correctly identified to genus and species by each of the three combinations of restriction enzymes. A further 35 samples typed to the level of strain identified and compared for consistency with MLST typing data and in silico MLST data derived from the nearest sequenced candidate reference. The high level of agreement between bacterial identification using complexity-reduced genome sequencing and standard hospital identifications indicating that this new approach is a viable alternative for identification of bacterial isolates derived from pathology specimens. The effectiveness of species identification and in particular, strain typing, depends on access to a comprehensive and taxonomically accurate bacterial genome sequence database containing relevant bacterial species and strains.

Bayesian inference for the genetic control of water deficit tolerance in spring wheat by stochastic search variable selection

Drought is the main abiotic stress seriously influencing wheat production. Information about the inheritance of drought tolerance is necessary to determine the most appropriate strategy to develop tolerant cultivars and populations. In this study, generation means analysis to identify the genetic effects controlling grain yield inheritance in water deficit and normal conditions was considered as a model selection problem in a Bayesian framework. Stochastic search variable selection (SSVS) was applied to identify the most important genetic effects and the best fitted models using different generations obtained from two crosses applying two water regimes in two growing seasons. The SSVS is used to evaluate the effect of each variable on the dependent variable via posterior variable inclusion probabilities. The model with the highest posterior probability is selected as the best model. In this study, the grain yield was controlled by the main effects (additive and non-additive effects) and epistatic. The results demonstrate that breeding methods such as recurrent selection and subsequent pedigree method and hybrid production can be useful to improve grain yield.

Assessment of Genetic Variation and Population Structure of Diverse Rice Genotypes Adapted to Lowland and Upland Ecologies in Africa Using SNPs

Using interspecific crosses involving Oryza glaberrima Steud. as donor and O. sativa L. as recurrent parents, rice breeders at the Africa Rice Center developed several 'New Rice for Africa (NERICA)' improved varieties. A smaller number of interspecific and intraspecific varieties have also been released as 'Advanced Rice for Africa (ARICA)'. The objective of the present study was to investigate the genetic variation, relatedness, and population structure of 330 widely used rice genotypes in Africa using DArTseq-based single nucleotide polymorphisms (SNPs). A sample of 11 ARICAs, 85 NERICAs, 62 O. sativa spp. japonica, and 172 O. sativa spp. indica genotypes were genotyped with 27,560 SNPs using diversity array technology (DArT)-based sequencing (DArTseq) platform. Nearly 66% of the SNPs were polymorphic, of which 15,020 SNPs were mapped to the 12 rice chromosomes. Genetic distance between pairs of genotypes that belong to indica, japonica, ARICA, and NERICA varied from 0.016 to 0.623, from 0.020 to 0.692, from 0.075 to 0.763, and from 0.014 to 0.644, respectively. The proportion of pairs of genotypes with genetic distance > 0.400 was the largest within NERICAs (35.1% of the pairs) followed by ARICAs (18.2%), japonica (17.4%), and indica (5.6%). We found one pair of japonica, 11 pairs of indica, and 35 pairs of NERICA genotypes differing by <2% of the total scored alleles, which was due to 26 pairs of genotypes with identical pedigrees. Cluster analysis, principal component analysis, and the model-based population structure analysis all revealed two distinct groups corresponding to the lowland (primarily indica and lowland NERICAs) and upland (japonica and upland NERICAs) growing ecologies. Most of the interspecific lowland NERICAs formed a sub-group, likely caused by differences in the O. glaberrima genome as compared with the indica genotypes. Analysis of molecular variance revealed very great genetic differentiation (F_ST = 0.688) between the lowland and upland ecologies, and 31.2% of variation attributable to differences within cluster groups. About 8% (1,197 of 15,020) of the 15,020 SNPs were significantly (P < 0.05) different between the lowland and upland ecologies and formed contrasting haplotypes that could clearly discriminate lowland from upland genotypes. This is the first study using high density markers that characterized NERICA and ARICA varieties in comparison with indica and japonica varieties widely used in Africa, which could aid rice breeders on parent selection for developing new improved rice germplasm.

Genome-wide association analysis of ear rot resistance caused by Fusarium verticillioides in maize

The identification of causal regions associated with resistance to Fusarium verticillioides can be useful to understand resistance mechanisms and further be used in breeding programs. In this study, a genome-wide association study (GWAS) was conducted to identify candidate markers associated with resistance to the ear rot caused by the fungus F. verticillioides. A total of 242 maize inbred lines were genotyped with 23,153 DArT-seq markers. A total of 12 DArTs were associated with ear rot resistance. Some DArTs were localized close to genes with functions directly related to ear rot resistance, such as a gene responsible for the innate immune response that belongs to the class of NBS-LRR receptors. Some markers were also found to be closely associated with genes that synthesize transcription factors (nactf11 and nactf61), genes responsible for the oxidation-reduction process and peroxidase activity. These results are encouraging since some candidate markers can present functional relationship with ear rot resistance in maize.

Development and characterization of a strawberry MAGIC population derived from crosses with six strawberry cultivars

A strawberry Multi-parent Advanced Generation Intercrosses (MAGIC) population, derived from crosses using six strawberry cultivars was successfully developed. The population was composed of 338 individuals; genome conformation was evaluated by expressed sequence tag-derived simple short repeat (EST-SSR) markers. Cluster analysis and principal component analysis (PCA) based on EST-SSR marker polymorphisms revealed that the MAGIC population was a mosaic of the six founder cultivars and covered the genomic regions of the six founders evenly. Fruit quality related traits, including days to flowering (DTF), fruit weight (FW), fruit firmness (FF), fruit color (FC), soluble solid content (SC), and titratable acidity (TA), of the MAGIC population were evaluated over two years. All traits showed normal transgressive segregation beyond the founder cultivars and most traits, except for DTF, distributed normally. FC exhibited the highest correlation coefficient overall and was distributed normally regardless of differences in DTF, FW, FF, SC, and TA. These facts were supported by PCA using fruit quality related values as explanatory variables, suggesting that major genetic factors, which are not influenced by fluctuations in other fruit traits, could control the distribution of FC. This MAGIC population is a promising resource for genome-wide association studies and genomic selection for efficient strawberry breeding.

Comparative genomics of maize ear rot pathogens reveals expansion of carbohydrate-active enzymes and secondary metabolism backbone genes in Stenocarpella maydis

Stenocarpella maydis is a plant pathogenic fungus that causes Diplodia ear rot, one of the most destructive diseases of maize. To date, little information is available regarding the molecular basis of pathogenesis in this organism, in part due to limited genomic resources. In this study, a 54.8 Mb draft genome assembly of S. maydis was obtained with Illumina and PacBio sequencing technologies, and analyzed. Comparative genomic analyses with the predominant maize ear rot pathogens Aspergillus flavus, Fusarium verticillioides, and Fusarium graminearum revealed an expanded set of carbohydrate-active enzymes for cellulose and hemicellulose degradation in S. maydis. Analyses of predicted genes involved in starch degradation revealed six putative α-amylases, four extracellular and two intracellular, and two putative γ-amylases, one of which appears to have been acquired from bacteria via horizontal transfer. Additionally, 87 backbone genes involved in secondary metabolism were identified, which represents one of the largest known assemblages among Pezizomycotina species. Numerous secondary metabolite gene clusters were identified, including two clusters likely involved in the biosynthesis of diplodiatoxin and chaetoglobosins. The draft genome of S. maydis presented here will serve as a useful resource for molecular genetics, functional genomics, and analyses of population diversity in this organism.

Genomic Selection for Drought Tolerance Using Genome-Wide SNPs in Maize

Traditional breeding strategies for selecting superior genotypes depending on phenotypic traits have proven to be of limited success, as this direct selection is hindered by low heritability, genetic interactions such as epistasis, environmental-genotype interactions, and polygenic effects. With the advent of new genomic tools, breeders have paved a way for selecting superior breeds. Genomic selection (GS) has emerged as one of the most important approaches for predicting genotype performance. Here, we tested the breeding values of 240 maize subtropical lines phenotyped for drought at different environments using 29,619 cured SNPs. Prediction accuracies of seven genomic selection models (ridge regression, LASSO, elastic net, random forest, reproducing kernel Hilbert space, Bayes A and Bayes B) were tested for their agronomic traits. Though prediction accuracies of Bayes B, Bayes A and RKHS were comparable, Bayes B outperformed the other models by predicting highest Pearson correlation coefficient in all three environments. From Bayes B, a set of the top 1053 significant SNPs with higher marker effects was selected across all datasets to validate the genes and QTLs. Out of these 1053 SNPs, 77 SNPs associated with 10 drought-responsive transcription factors. These transcription factors were associated with different physiological and molecular functions (stomatal closure, root development, hormonal signaling and photosynthesis). Of several models, Bayes B has been shown to have the highest level of prediction accuracy for our data sets. Our experiments also highlighted several SNPs based on their performance and relative importance to drought tolerance. The result of our experiments is important for the selection of superior genotypes and candidate genes for breeding drought-tolerant maize hybrids.

Genetic Variation and Population Structure of Oryza glaberrima and Development of a Mini-Core Collection Using DArTseq

The sequence variation present in accessions conserved in genebanks can best be used in plant improvement when it is properly characterized and published. Using low cost and high density single nucleotide polymorphism (SNP) assays, the genetic diversity, population structure, and relatedness between pairs of accessions can be quickly assessed. This information is relevant for different purposes, including creating core and mini-core sets that represent the maximum possible genetic variation contained in the whole collection. Here, we studied the genetic variation and population structure of 2,179 Oryza glaberrima Steud. accessions conserved at the AfricaRice genebank using 27,560 DArTseq-based SNPs. Only 14% (3,834 of 27,560) of the SNPs were polymorphic across the 2,179 accessions, which is much lower than diversity reported in other Oryza species. Genetic distance between pairs of accessions varied from 0.005 to 0.306, with 1.5% of the pairs nearly identical, 8.0% of the pairs similar, 78.1% of the pairs moderately distant, and 12.4% of the pairs very distant. The number of redundant accessions that contribute little or no new genetic variation to the O. glaberrima collection was very low. Using the maximum length sub-tree method, we propose a subset of 1,330 and 350 accessions to represent a core and mini-core collection, respectively. The core and mini-core sets accounted for ~61 and 16%, respectively, of the whole collection, and captured 97-99% of the SNP polymorphism and nearly all allele and genotype frequencies observed in the whole O. glaberrima collection available at the AfricaRice genebank. Cluster, principal component and model-based population structure analyses all divided the 2,179 accessions into five groups, based roughly on country of origin but less so on ecology. The first, third and fourth groups consisted of accessions primarily from Liberia, Nigeria, and Mali, respectively; the second group consisted primarily of accessions from Togo and Nigeria; and the fifth and smallest group was a mixture of accessions from multiple countries. Analysis of molecular variance showed between 10.8 and 28.9% of the variation among groups with the remaining 71.1-89.2% attributable to differences within groups.

Genomic Selection for Drought Tolerance Using Genome-Wide SNPs in Maize

Traditional breeding strategies for selecting superior genotypes depending on phenotypic traits have proven to be of limited success, as this direct selection is hindered by low heritability, genetic interactions such as epistasis, environmental-genotype interactions, and polygenic effects. With the advent of new genomic tools, breeders have paved a way for selecting superior breeds. Genomic selection (GS) has emerged as one of the most important approaches for predicting genotype performance. Here, we tested the breeding values of 240 maize subtropical lines phenotyped for drought at different environments using 29,619 cured SNPs. Prediction accuracies of seven genomic selection models (ridge regression, LASSO, elastic net, random forest, reproducing kernel Hilbert space, Bayes A and Bayes B) were tested for their agronomic traits. Though prediction accuracies of Bayes B, Bayes A and RKHS were comparable, Bayes B outperformed the other models by predicting highest Pearson correlation coefficient in all three environments. From Bayes B, a set of the top 1053 significant SNPs with higher marker effects was selected across all datasets to validate the genes and QTLs. Out of these 1053 SNPs, 77 SNPs associated with 10 drought-responsive transcription factors. These transcription factors were associated with different physiological and molecular functions (stomatal closure, root development, hormonal signaling and photosynthesis). Of several models, Bayes B has been shown to have the highest level of prediction accuracy for our data sets. Our experiments also highlighted several SNPs based on their performance and relative importance to drought tolerance. The result of our experiments is important for the selection of superior genotypes and candidate genes for breeding drought-tolerant maize hybrids.

Genomic Selection in the Era of Next Generation Sequencing for Complex Traits in Plant Breeding

Genomic selection (GS) is a promising approach exploiting molecular genetic markers to design novel breeding programs and to develop new markers-based models for genetic evaluation. In plant breeding, it provides opportunities to increase genetic gain of complex traits per unit time and cost. The cost-benefit balance was an important consideration for GS to work in crop plants. Availability of genome-wide high-throughput, cost-effective and flexible markers, having low ascertainment bias, suitable for large population size as well for both model and non-model crop species with or without the reference genome sequence was the most important factor for its successful and effective implementation in crop species. These factors were the major limitations to earlier marker systems viz., SSR and array-based, and was unimaginable before the availability of next-generation sequencing (NGS) technologies which have provided novel SNP genotyping platforms especially the genotyping by sequencing. These marker technologies have changed the entire scenario of marker applications and made the use of GS a routine work for crop improvement in both model and non-model crop species. The NGS-based genotyping have increased genomic-estimated breeding value prediction accuracies over other established marker platform in cereals and other crop species, and made the dream of GS true in crop breeding. But to harness the true benefits from GS, these marker technologies will be combined with high-throughput phenotyping for achieving the valuable genetic gain from complex traits. Moreover, the continuous decline in sequencing cost will make the WGS feasible and cost effective for GS in near future. Till that time matures the targeted sequencing seems to be more cost-effective option for large scale marker discovery and GS, particularly in case of large and un-decoded genomes.