Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants

Advancing designer crops for climate resilience through an integrated genomics approach

Climate change and exponential population growth are exposing an immediate need for developing future crops that are highly resilient and adaptable to changing environments to maintain global food security in the next decade. Rigorous selection from long domestication history has rendered cultivated crops genetically disadvantaged, raising concerns in their ability to adapt to these new challenges and limiting their usefulness in breeding programmes. As a result, future crop improvement efforts must rely on integrating various genomic strategies ranging from high-throughput sequencing to machine learning, in order to exploit germplasm diversity and overcome bottlenecks created by domestication, expansive multi-dimensional phenotypes, arduous breeding processes, complex traits and big data.

Chile Pepper (Capsicum) Breeding and Improvement in the "Multi-Omics" Era

Chile pepper (Capsicum spp.) is a major culinary, medicinal, and economic crop in most areas of the world. For more than hundreds of years, chile peppers have "defined" the state of New Mexico, USA. The official state question, "Red or Green?" refers to the preference for either red or the green stage of chile pepper, respectively, reflects the value of these important commodities. The presence of major diseases, low yields, decreased acreages, and costs associated with manual labor limit production in all growing regions of the world. The New Mexico State University (NMSU) Chile Pepper Breeding Program continues to serve as a key player in the development of improved chile pepper varieties for growers and in discoveries that assist plant breeders worldwide. Among the traits of interest for genetic improvement include yield, disease resistance, flavor, and mechanical harvestability. While progress has been made, the use of conventional breeding approaches has yet to fully address producer and consumer demand for these traits in available cultivars. Recent developments in "multi-omics," that is, the simultaneous application of multiple omics approaches to study biological systems, have allowed the genetic dissection of important phenotypes. Given the current needs and production constraints, and the availability of multi-omics tools, it would be relevant to examine the application of these approaches in chile pepper breeding and improvement. In this review, we summarize the major developments in chile pepper breeding and present novel tools that can be implemented to facilitate genetic improvement. In the future, chile pepper improvement is anticipated to be more data and multi-omics driven as more advanced genetics, breeding, and phenotyping tools are developed.

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.

Improvement of prediction ability by integrating multi-omic datasets in barley

Background

Genomic prediction (GP) based on single nucleotide polymorphisms (SNP) has become a broadly used tool to increase the gain of selection in plant breeding. However, using predictors that are biologically closer to the phenotypes such as transcriptome and metabolome may increase the prediction ability in GP. The objectives of this study were to (i) assess the prediction ability for three yield-related phenotypic traits using different omic datasets as single predictors compared to a SNP array, where these omic datasets included different types of sequence variants (full-SV, deleterious-dSV, and tolerant-tSV), different types of transcriptome (expression presence/absence variation-ePAV, gene expression-GE, and transcript expression-TE) sampled from two tissues, leaf and seedling, and metabolites (M); (ii) investigate the improvement in prediction ability when combining multiple omic datasets information to predict phenotypic variation in barley breeding programs; (iii) explore the predictive performance when using SV, GE, and ePAV from simulated 3’end mRNA sequencing of different lengths as predictors.

Results

The prediction ability from genomic best linear unbiased prediction (GBLUP) for the three traits using dSV information was higher than when using tSV, all SV information, or the SNP array. Any predictors from the transcriptome (GE, TE, as well as ePAV) and metabolome provided higher prediction abilities compared to the SNP array and SV on average across the three traits. In addition, some (di)-similarity existed between different omic datasets, and therefore provided complementary biological perspectives to phenotypic variation. Optimal combining the information of dSV, TE, ePAV, as well as metabolites into GP models could improve the prediction ability over that of the single predictors alone.

Conclusions

The use of integrated omic datasets in GP model is highly recommended. Furthermore, we evaluated a cost-effective approach generating 3’end mRNA sequencing with transcriptome data extracted from seedling without losing prediction ability in comparison to the full-length mRNA sequencing, paving the path for the use of such prediction methods in commercial breeding programs.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08337-7).

Exploring the correlation between salt tolerance and yield: research advances and perspectives for salt-tolerant forage sorghum selection and genetic improvement

MAIN CONCLUSION

Some salt stress response mechanisms can translate into sorghum forage yield and thus act as targets for genetic improvement. Sorghum is a drought-tolerant cereal that is widely grown in the vast Africa's arid and semi-arid areas. Apart from drought, salinity is a major abiotic factor that, in addition to natural causes, has been exacerbated by increased poor anthropological activities. The importance of sorghum as a forage crop in saline areas has yet to be fully realized. Despite intraspecific variation in salt tolerance, sorghum is generally moderately salt-tolerant, and its productivity in saline soils can be remarkably limited. This is due to the difficulty of replicating optimal field saline conditions due to the great heterogeneity of salt distribution in the soil. As a promising fodder crop for saline areas, classic phenotype-based selection methods can be integrated with modern -omics in breeding programs to simultaneously address salt tolerance and production. To enable future manipulation, selection, and genetic improvement of sorghum with high yield and salt tolerance, here, we explore the potential positive correlations between the reliable indices of sorghum performance under salt stress at the phenotypic and genotypic level. We then explore the potential role of modern selection and genetic improvement programs in incorporating these linked salt tolerance and yield traits and propose a mechanism for future studies.

Genome-Wide Association Study Reveals Genetic Architecture and Candidate Genes for Yield and Related Traits under Terminal Drought, Combined Heat and Drought in Tropical Maize Germplasm

Maize (Zea mays L.) production is constrained by drought and heat stresses. The combination of these two stresses is likely to be more detrimental. To breed for maize cultivars tolerant of these stresses, 162 tropical maize inbred lines were evaluated under combined heat and drought (CHD) and terminal drought (TD) conditions. The mixed linear model was employed for the genome-wide association study using 7834 SNP markers and several phenotypic data including, days to 50% anthesis (AD) and silking (SD), husk cover (HUSKC), and grain yield (GY). In total, 66, 27, and 24 SNPs were associated with the traits evaluated under CHD, TD, and their combined effects, respectively. Of these, four single nucleotide polymorphism (SNP) markers (SNP_161703060 on Chr01, SNP_196800695 on Chr02, SNP_195454836 on Chr05, and SNP_51772182 on Chr07) had pleiotropic effects on both AD and SD under CHD conditions. Four SNPs (SNP_138825271 (Chr03), SNP_244895453 (Chr04), SNP_168561609 (Chr05), and SNP_62970998 (Chr06)) were associated with AD, SD, and HUSKC under TD. Twelve candidate genes containing phytohormone cis-acting regulating elements were implicated in the regulation of plant responses to multiple stress conditions including heat and drought. The SNPs and candidate genes identified in the study will provide invaluable information for breeding climate smart maize varieties under tropical conditions following validation of the SNP markers.

Genotyping-by-sequencing and genomic selection applications in hexaploid triticale

Triticale, a hybrid species between wheat and rye, is one of the newest additions to the plant kingdom with a very short history of improvement. It has very limited genomic resources because of its large and complex genome. Objectives of this study were to generate dense marker data, understand genetic diversity, population structure, linkage disequilibrium (LD), and estimate accuracies of commonly used genomic selection (GS) models on forage yield of triticale. Genotyping-by-sequencing (GBS), using PstI and MspI restriction enzymes for reducing genome complexity, was performed on a triticale diversity panel (n = 289). After filtering for biallelic loci with more than 70% genome coverage, and minor allele frequency (MAF) > 0.05, de novo variant calling identified 16,378 single nucleotide polymorphism (SNP) markers. Sequences of these variants were mapped to wheat and rye reference genomes to infer their homologous groups and chromosome positions. About 45% (7430), and 58% (9500) of the de novo identified SNPs were mapped to the wheat and rye reference genomes, respectively. Interestingly, 28.9% (2151) of the 7430 SNPs were mapped to the D genome of hexaploid wheat, indicating substantial substitution of the R genome with D genome in cultivated triticale. About 27% of marker pairs were in significant LD with an average r² > 0.18 (P < 0.05). Genome-wide LD declined rapidly to r² < 0.1 beyond 10 kb physical distance. The three sub-genomes (A, B, and R) showed comparable LD decay patterns. Genetic diversity and population structure analyses identified five distinct clusters. Genotype grouping did not follow prior winter vs spring-type classification. However, one of the clusters was largely dominated by winter triticale. GS accuracies were estimated for forage yield using three commonly used models with different training population sizes and marker densities. GS accuracy increased with increasing training population size while gain in accuracy tended to plateau with marker densities of 2000 SNPs or more. Average GS accuracy was about 0.52, indicating the potential of using GS in triticale forage yield improvement.

Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple

Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e., genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.

A Study on the Clustering of Extra Virgin Olive Oils Extracted from Cultivars Growing in Four Ionian Islands (Greece) by Multivariate Analysis of Their Phenolic Profile, Antioxidant Activity and Genetic Markers

Background: The phenolic fraction of extra virgin olive oil (EVOO) has disease preventive and health-promoting properties which are supported by numerous studies. As such, EVOO is defined as a functional food. The aim of the present study was to characterize the phenolic profile of olive oil from cultivars farmed in the Ionian Islands (Zakynthos, Kefalonia, Lefkada, and Kerkyra) and to investigate the association of phenols to antioxidant activity, which is central to its functionality. Furthermore, the study investigates whether multivariate analyses on the concentration of individual biophenolic compounds and genetic population diversity could classify the olive oil samples based on their geographic origin. Methods: Phenols were determined in 103 samples from different Ionian Island tree populations by ¹H nuclear magnetic resonance (NMR), and sample antioxidant activity was measured by their capacity to reduce the free radical 2,2-diphenyl-1-picrylhydrazyl) (DPPH). Genetic diversity was measured by estimating Nei’s population genetic distance using 15 reproducible bands from random amplified polymorphic DNA (RAPD) genotyping. Results: Principal component analysis (PCA) of the secoiridoid concentrations clustered samples according to cultivar. Clustering based on genetic distances is not concordant with phenolic clustering. A cultivar effect was also demonstrated in the association between the concentration of individual phenols with DPPH reducing activity. Conclusions: Taken together, the study shows that the olive oil phenolic content defines “cultivar-specific phenolic profiles” and that environmental factors other than agronomic conditions contribute more to phenotype variance than genetics.

Omics-Facilitated Crop Improvement for Climate Resilience and Superior Nutritive Value

Novel crop improvement approaches, including those that facilitate for the exploitation of crop wild relatives and underutilized species harboring the much-needed natural allelic variation are indispensable if we are to develop climate-smart crops with enhanced abiotic and biotic stress tolerance, higher nutritive value, and superior traits of agronomic importance. Top among these approaches are the "omics" technologies, including genomics, transcriptomics, proteomics, metabolomics, phenomics, and their integration, whose deployment has been vital in revealing several key genes, proteins and metabolic pathways underlying numerous traits of agronomic importance, and aiding marker-assisted breeding in major crop species. Here, citing several relevant examples, we appraise our understanding on the recent developments in omics technologies and how they are driving our quest to breed climate resilient crops. Large-scale genome resequencing, pan-genomes and genome-wide association studies are aiding the identification and analysis of species-level genome variations, whilst RNA-sequencing driven transcriptomics has provided unprecedented opportunities for conducting crop abiotic and biotic stress response studies. Meanwhile, single cell transcriptomics is slowly becoming an indispensable tool for decoding cell-specific stress responses, although several technical and experimental design challenges still need to be resolved. Additionally, the refinement of the conventional techniques and advent of modern, high-resolution proteomics technologies necessitated a gradual shift from the general descriptive studies of plant protein abundances to large scale analysis of protein-metabolite interactions. Especially, metabolomics is currently receiving special attention, owing to the role metabolites play as metabolic intermediates and close links to the phenotypic expression. Further, high throughput phenomics applications are driving the targeting of new research domains such as root system architecture analysis, and exploration of plant root-associated microbes for improved crop health and climate resilience. Overall, coupling these multi-omics technologies to modern plant breeding and genetic engineering methods ensures an all-encompassing approach to developing nutritionally-rich and climate-smart crops whose productivity can sustainably and sufficiently meet the current and future food, nutrition and energy demands.

Genome-wide Imputation Using the Practical Haplotype Graph in the Heterozygous Crop Cassava

Genomic applications such as genomic selection and genome-wide association have become increasingly common since the advent of genome sequencing. The cost of sequencing has decreased in the past two decades; however, genotyping costs are still prohibitive to gathering large datasets for these genomic applications, especially in nonmodel species where resources are less abundant. Genotype imputation makes it possible to infer whole-genome information from limited input data, making large sampling for genomic applications more feasible. Imputation becomes increasingly difficult in heterozygous species where haplotypes must be phased. The practical haplotype graph (PHG) is a recently developed tool that can accurately impute genotypes, using a reference panel of haplotypes. We showcase the ability of the PHG to impute genomic information in the highly heterozygous crop cassava (Manihot esculenta). Accurately phased haplotypes were sampled from runs of homozygosity across a diverse panel of individuals to populate PHG, which proved more accurate than relying on computational phasing methods. The PHG achieved high imputation accuracy, using sparse skim-sequencing input, which translated to substantial genomic prediction accuracy in cross-validation testing. The PHG showed improved imputation accuracy, compared to a standard imputation tool Beagle, especially in predicting rare alleles.

Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5-17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.

Genome optimization via virtual simulation to accelerate maize hybrid breeding

The employment of doubled-haploid (DH) technology in maize has vastly accelerated the efficiency of developing inbred lines. The selection of superior lines has to rely on genotypes with genomic selection (GS) model, rather than phenotypes due to the high expense of field phenotyping. In this work, we implemented 'genome optimization via virtual simulation (GOVS)' using the genotype and phenotype data of 1404 maize lines and their F1 progeny. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' or 'advantageous alleles' in a genetic pool. Such a virtually optimized genome, although can never be developed in reality, may help plot the optimal route to direct breeding decisions. GOVS assists in the selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. The assumption is that the more fragments of optimal genotypes a line contributes to the assembly, the higher the likelihood of the line favored in the F1 phenotype, e.g. grain yield. Compared to traditional GS method, GOVS-assisted selection may avoid using an arbitrary threshold for the predicted F1 yield to assist selection. Additionally, the selected lines contributed complementary sets of advantageous alleles to the virtual genome. This feature facilitates plotting the optimal route for DH production, whereby the fewest lines and F1 combinations are needed to pyramid a maximum number of advantageous alleles in the new DH lines. In summary, incorporation of DH production, GS and genome optimization will ultimately improve genomically designed breeding in maize. Short abstract: Doubled-haploid (DH) technology has been widely applied in maize breeding industry, as it greatly shortens the period of developing homozygous inbred lines via bypassing several rounds of self-crossing. The current challenge is how to efficiently screen the large volume of inbred lines based on genotypes. We present the toolbox of genome optimization via virtual simulation (GOVS), which complements the traditional genomic selection model. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' in a breeding population, and then assists in selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. Availability of GOVS (https://govs-pack.github.io/) to the public may ultimately facilitate genomically designed breeding in maize.

Identification and Validation of a QTL for Bacterial Leaf Streak Resistance in Rice (Oryza sativa L.) against Thai Xoc Strains

Rice is one of the most important food crops in the world and is of vital importance to many countries. Various diseases caused by fungi, bacteria and viruses constantly threaten rice plants and cause yield losses. Bacterial leaf streak disease (BLS) caused by Xanthomonas oryzae pv. oryzicola (Xoc) is one of the most devastating rice diseases. However, most modern rice varieties are susceptible to BLS. In this study, we applied the QTL-seq approach using an F2 population derived from the cross between IR62266 and Homcholasit (HSC) to rapidly identify the quantitative trait loci (QTL) that confers resistance to BLS caused by a Thai Xoc isolate, SP7-5. The results showed that a single genomic region at the beginning of chromosome 5 was highly associated with resistance to BLS. The gene xa5 was considered a potential candidate gene in this region since most associated single nucleotide polymorphisms (SNPs) were within this gene. A Kompetitive Allele-Specific PCR (KASP) marker was developed based on two consecutive functional SNPs in xa5 and validated in six F2 populations inoculated with another Thai Xoc isolate, 2NY2-2. The phenotypic variance explained by this marker (PVE) ranged from 59.04% to 70.84% in the six populations. These findings indicate that xa5 is a viable candidate gene for BLS resistance and may help in breeding programs for BLS resistance.

PATRIOT: A Pipeline for Tracing Identity-by-Descent for Chromosome Segments to Improve Genomic Prediction in Self-Pollinating Crop Species

The lowering genotyping cost is ushering in a wider interest and adoption of genomic prediction and selection in plant breeding programs worldwide. However, improper conflation of historical and recent linkage disequilibrium between markers and genes restricts high accuracy of genomic prediction (GP). Multiple ancestors may share a common haplotype surrounding a gene, without sharing the same allele of that gene. This prevents parsing out genetic effects associated with the underlying allele of that gene among the set of ancestral haplotypes. We present "Parental Allele Tracing, Recombination Identification, and Optimal predicTion" (i.e., PATRIOT) approach that utilizes marker data to allow for a rapid identification of lines carrying specific alleles, increases the accuracy of genomic relatedness and diversity estimates, and improves genomic prediction. Leveraging identity-by-descent relationships, PATRIOT showed an improvement in GP accuracy by 16.6% relative to the traditional rrBLUP method. This approach will help to increase the rate of genetic gain and allow available information to be more effectively utilized within breeding programs.

Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview

Genomic selection (GS) is becoming an essential tool in breeding programs due to its role in increasing genetic gain per unit time. The design of the training set (TRS) in GS is one of the key steps in the implementation of GS in plant and animal breeding programs mainly because (i) TRS optimization is critical for the efficiency and effectiveness of GS, (ii) breeders test genotypes in multi-year and multi-location trials to select the best-performing ones. In this framework, TRS optimization can help to decrease the number of genotypes to be tested and, therefore, reduce phenotyping cost and time, and (iii) we can obtain better prediction accuracies from optimally selected TRS than an arbitrary TRS. Here, we concentrate the efforts on reviewing the lessons learned from TRS optimization studies and their impact on crop breeding and discuss important features for the success of TRS optimization under different scenarios. In this article, we review the lessons learned from training population optimization in plants and the major challenges associated with the optimization of GS including population size, the relationship between training and test set (TS), update of TRS, and the use of different packages and algorithms for TRS implementation in GS. Finally, we describe general guidelines to improving the rate of genetic improvement by maximizing the use of the TRS optimization in the GS framework.

Genomic prediction in family bulks using different traits and cross-validations in pine

Genomic prediction integrates statistical, genomic, and computational tools to improve the estimation of breeding values and increase genetic gain. Due to the broad diversity in mating systems, breeding schemes, propagation methods, and unit of selection, no universal genomic prediction approach can be applied in all crops. In a genome-wide family prediction (GWFP) approach, the family is the basic unit of selection. We tested GWFP in two loblolly pine (Pinus taeda L.) datasets: a breeding population composed of 63 full-sib families (5–20 individuals per family), and a simulated population with the same pedigree structure. In both populations, phenotypic and genomic data was pooled at the family level in silico. Marker effects were estimated to compute genomic estimated breeding values (GEBV) at the individual and family (GWFP) levels. Less than six individuals per family produced inaccurate estimates of family phenotypic performance and allele frequency. Tested across different scenarios, GWFP predictive ability was higher than those for GEBV in both populations. Validation sets composed of families with similar phenotypic mean and variance as the training population yielded predictions consistently higher and more accurate than other validation sets. Results revealed potential for applying GWFP in breeding programs whose selection unit are family, and for systems where family can serve as training sets. The GWFP approach is well suited for crops that are routinely genotyped and phenotyped at the plot-level, but it can be extended to other breeding programs. Higher predictive ability obtained with GWFP would motivate the application of genomic prediction in these situations.

Effectiveness of genomic selection for improving provitamin A carotenoid content and associated traits in cassava

Global efforts are underway to develop cassava with enhanced levels of provitamin A carotenoids to sustainably meet increasing demands for food and nutrition where the crop is a major staple. Herein, we tested the effectiveness of genomic selection (GS) for rapid improvement of cassava for total carotenoids content and associated traits. We evaluated 632 clones from Uganda's provitamin A cassava breeding pipeline and 648 West African introductions. At harvest, each clone was assessed for level of total carotenoids, dry matter content, and resistance to cassava brown streak disease (CBSD). All clones were genotyped with diversity array technology and imputed to a set of 23,431 single nucleotide polymorphic markers. We assessed predictive ability of four genomic prediction methods in scenarios of cross-validation, across population prediction, and inclusion of quantitative trait loci markers. Cross-validations produced the highest mean prediction ability for total carotenoids content (0.52) and the lowest for CBSD resistance (0.20), with G-BLUP outperforming other models tested. Across population, predictions showed low ability of Ugandan population to predict the performance of West African clones, with the highest predictive ability recorded for total carotenoids content (0.34) and the lowest for CBSD resistance (0.12) using G-BLUP. By incorporating chromosome 1 markers associated with carotenoids content as independent kernel in the G-BLUP model of a cross-validation scenario, prediction ability slightly improved from 0.52 to 0.58. These results reinforce ongoing efforts aimed at integrating GS into cassava breeding and demonstrate the utility of this tool for rapid genetic improvement.

Evaluation of Genomic Prediction for Fusarium Head Blight Resistance with a Multi-Parental Population

Simple Summary

Genomic selection is a promising approach to select superior wheat lines with better resistance to Fusarium head blight. The accuracy of genomic selection is determined by many factors. In this study, we found a training population with large size, genomic selection models incorporating biological information, and multi-environment modelling led to considerably better predictabilities. A training population designed by the coefficient of determination (CDmean) could increase accuracy of prediction. Relatedness between training population (TP) and testing population is the key for accuracies of genomic selection across populations.

Abstract

Fusarium head blight (FHB) resistance is quantitatively inherited, controlled by multiple minor effect genes, and highly affected by the interaction of genotype and environment. This makes genomic selection (GS) that uses genome-wide molecular marker data to predict the genetic breeding value as a promising approach to select superior lines with better resistance. However, various factors can affect accuracies of GS and better understanding how these factors affect GS accuracies could ensure the success of applying GS to improve FHB resistance in wheat. In this study, we performed a comprehensive evaluation of factors that affect GS accuracies with a multi-parental population designed for FHB resistance. We found larger sample sizes could get better accuracies. Training population designed by CDmean based optimization algorithms significantly increased accuracies than random sampling approach, while mean of predictor error variance (PEVmean) had the poorest performance. Different genomic selection models performed similarly for accuracies. Including prior known large effect quantitative trait loci (QTL) as fixed effect into the GS model considerably improved the predictability. Multi-traits models had almost no effects, while the multi-environment model outperformed the single environment model for prediction across different environments. By comparing within and across family prediction, better accuracies were obtained with the training population more closely related to the testing population. However, achieving good accuracies for GS prediction across populations is still a challenging issue for GS application.

Multi-Locus Genome-Wide Association Study and Genomic Selection of Kernel Moisture Content at the Harvest Stage in Maize

Kernel moisture content at the harvest stage (KMC) is an important trait that affects the mechanical harvesting of maize grain, and the identification of genetic loci for KMC is beneficial for maize molecular breeding. In this study, we performed a multi-locus genome-wide association study (ML-GWAS) to identify quantitative trait nucleotides (QTNs) for KMC using an association mapping panel of 251 maize inbred lines that were genotyped with an Affymetrix CGMB56K SNP Array and phenotypically evaluated in three environments. Ninety-eight QTNs for KMC were detected using six ML-GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, PLARmEB, PKWmEB, and ISIS EM-BLASSO). Eleven of these QTNs were considered to be stable, as they were detected by at least four ML-GWAS models under a uniformed environment or in at least two environments and BLUP using the same ML-GWAS model. With qKMC5.6 removed, the remaining 10 stable QTNs explained <10% of the phenotypic variation, suggesting that KMC is mainly controlled by multiple minor-effect genetic loci. A total of 63 candidate genes were predicted from the 11 stable QTNs, and 10 candidate genes were highly expressed in the kernel at different time points after pollination. High prediction accuracy was achieved when the KMC-associated QTNs were included as fixed effects in genomic selection, and the best strategy was to integrate all KMC QTNs identified by all six ML-GWAS models. These results further our understanding of the genetic architecture of KMC and highlight the potential of genomic selection for KMC in maize breeding.

Sorghum breeding in the genomic era: opportunities and challenges

The importance and potential of the multi-purpose crop sorghum in global food security have not yet been fully exploited, and the integration of the state-of-art genomics and high-throughput technologies into breeding practice is required. Sorghum, a historically vital staple food source and currently the fifth most important major cereal, is emerging as a crop with diverse end-uses as food, feed, fuel and forage and a model for functional genetics and genomics of tropical grasses. Rapid development in high-throughput experimental and data processing technologies has significantly speeded up sorghum genomic researches in the past few years. The genomes of three sorghum lines are available, thousands of genetic stocks accessible and various genetic populations, including NAM, MAGIC, and mutagenised populations released. Functional and comparative genomics have elucidated key genetic loci and genes controlling agronomical and adaptive traits. However, the knowledge gained has far away from being translated into real breeding practices. We argue that the way forward is to take a genome-based approach for tailored designing of sorghum as a multi-functional crop combining excellent agricultural traits for various end uses. In this review, we update the new concepts and innovation systems in crop breeding and summarise recent advances in sorghum genomic researches, especially the genome-wide dissection of variations in genes and alleles for agronomically important traits. Future directions and opportunities for sorghum breeding are highlighted to stimulate discussion amongst sorghum academic and industrial communities.

Dynamic Programming for Resource Allocation in Multi-Allelic Trait Introgression

Trait introgression is a complex process that plant breeders use to introduce desirable alleles from one variety or species to another. Two of the major types of decisions that must be made during this sophisticated and uncertain workflow are: parental selection and resource allocation. We formulated the trait introgression problem as an engineering process and proposed a Markov Decision Processes (MDP) model to optimize the resource allocation procedure. The efficiency of the MDP model was compared with static resource allocation strategies and their trade-offs among budget, deadline, and probability of success are demonstrated. Simulation results suggest that dynamic resource allocation strategies from the MDP model significantly improve the efficiency of the trait introgression by allocating the right amount of resources according to the genetic outcome of previous generations.

An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops

Identifying mechanisms and pathways involved in gene-environment interplay and phenotypic plasticity is a long-standing challenge. It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction. A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments. With extensive field-observed complex traits, environmental profiles, and genome-wide single nucleotide polymorphisms for three major crops (maize, wheat, and oat), we demonstrated that identifying such an environmental index (i.e., a combination of environmental parameter and growth window) enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension. Interestingly, genes identified for two reaction-norm parameters (i.e., intercept and slope) derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels, agreeing with the different diversity levels and genetic constitutions of the panels. In addition, we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments. This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.

Genetic Improvement in Sunflower Breeding—Integrated Omics Approach

Foresight in climate change and the challenges ahead requires a systematic approach to sunflower breeding that will encompass all available technologies. There is a great scarcity of desirable genetic variation, which is in fact undiscovered because it has not been sufficiently researched as detection and designing favorable genetic variation largely depends on thorough genome sequencing through broad and deep resequencing. Basic exploration of genomes is insufficient to find insight about important physiological and molecular mechanisms unique to crops. That is why integrating information from genomics, epigenomics, transcriptomics, proteomics, metabolomics and phenomics enables a comprehensive understanding of the molecular mechanisms in the background of architecture of many important quantitative traits. Omics technologies offer novel possibilities for deciphering the complex pathways and molecular profiling through the level of systems biology and can provide important answers that can be utilized for more efficient breeding of sunflower. In this review, we present omics profiling approaches in order to address their possibilities and usefulness as a potential breeding tools in sunflower genetic improvement.

Toward genomic selection in Pinus taeda: Integrating resources to support array design in a complex conifer genome

PREMISE

An informatics approach was used for the construction of an Axiom genotyping array from heterogeneous, high-throughput sequence data to assess the complex genome of loblolly pine (Pinus taeda).

METHODS

High-throughput sequence data, sourced from exome capture and whole genome reduced-representation approaches from 2698 trees across five sequence populations, were analyzed with the improved genome assembly and annotation for the loblolly pine. A variant detection, filtering, and probe design pipeline was developed to detect true variants across and within populations. From 8.27 million variants, a total of 642,275 were evaluated and 423,695 of those were screened across a range-wide population.

RESULTS

The final informatics and screening approach delivered an Axiom array representing 46,439 high-confidence variants to the forest tree breeding and genetics community. Based on the annotated reference genome, 34% were located in or directly upstream or downstream of genic regions.

DISCUSSION

The Pita50K array represents a genome-wide resource developed from sequence data for an economically important conifer, loblolly pine. It uniquely integrates independent projects that assessed trees sampled across the native range. The challenges associated with the large and repetitive genome are addressed in the development of this resource.

Advances in Cereal Crop Genomics for Resilience under Climate Change

Adapting to climate change, providing sufficient human food and nutritional needs, and securing sufficient energy supplies will call for a radical transformation from the current conventional adaptation approaches to more broad-based and transformative alternatives. This entails diversifying the agricultural system and boosting productivity of major cereal crops through development of climate-resilient cultivars that can sustainably maintain higher yields under climate change conditions, expanding our focus to crop wild relatives, and better exploitation of underutilized crop species. This is facilitated by the recent developments in plant genomics, such as advances in genome sequencing, assembly, and annotation, as well as gene editing technologies, which have increased the availability of high-quality reference genomes for various model and non-model plant species. This has necessitated genomics-assisted breeding of crops, including underutilized species, consequently broadening genetic variation of the available germplasm; improving the discovery of novel alleles controlling important agronomic traits; and enhancing creation of new crop cultivars with improved tolerance to biotic and abiotic stresses and superior nutritive quality. Here, therefore, we summarize these recent developments in plant genomics and their application, with particular reference to cereal crops (including underutilized species). Particularly, we discuss genome sequencing approaches, quantitative trait loci (QTL) mapping and genome-wide association (GWAS) studies, directed mutagenesis, plant non-coding RNAs, precise gene editing technologies such as CRISPR-Cas9, and complementation of crop genotyping by crop phenotyping. We then conclude by providing an outlook that, as we step into the future, high-throughput phenotyping, pan-genomics, transposable elements analysis, and machine learning hold much promise for crop improvements related to climate resilience and nutritional superiority.

Genomic Approaches to Identify Molecular Bases of Crop Resistance to Diseases and to Develop Future Breeding Strategies

Plant diseases are responsible for substantial crop losses each year and affect food security and agricultural sustainability. The improvement of crop resistance to pathogens through breeding represents an environmentally sound method for managing disease and minimizing these losses. The challenge is to breed varieties with a stable and broad-spectrum resistance. Different approaches, from markers to recent genomic and 'post-genomic era' technologies, will be reviewed in order to contribute to a better understanding of the complexity of host-pathogen interactions and genes, including those with small phenotypic effects and mechanisms that underlie resistance. An efficient combination of these approaches is herein proposed as the basis to develop a successful breeding strategy to obtain resistant crop varieties that yield higher in increasing disease scenarios.

Identifying quantitative trait loci for the general combining ability of yield-relevant traits in maize

Maize is the most important staple crop worldwide. Many of its agronomic traits present with a high level of heterosis. Combining ability was proposed to exploit the rule of heterosis, and general combining ability (GCA) is a crucial measure of parental performance. In this study, a recombinant inbred line population was used to construct testcross populations by crossing with four testers based on North Carolina design II. Six yield-relevant traits were investigated as phenotypic data. GCA effects were estimated for three scenarios based on the heterotic group and the number of tester lines. These estimates were then used to identify quantitative trait loci (QTL) and dissect genetic basis of GCA. A higher heritability of GCA was obtained for each trait. Thus, testing in early generation of breeding may effectively select candidate lines with relatively superior GCA performance. The GCA QTL detected in each scenario was slightly different according to the linkage mapping. Most of the GCA-relevant loci were simultaneously detected in all three datasets. Therefore, the genetic basis of GCA was nearly constant although discrepant inbred lines were appointed as testers. In addition, favorable alleles corresponding to GCA could be pyramided via marker-assisted selection and made available for maize hybrid breeding.

Opportunities and Challenges for Improving the Productivity of Swamp Buffaloes in Southeastern Asia

The swamp buffalo is a domesticated animal commonly found in Southeast Asia. It is a highly valued agricultural animal for smallholders, but the production of this species has unfortunately declined in recent decades due to rising farm mechanization. While swamp buffalo still plays a role in farmland cultivation, this species' purposes has shifted from draft power to meat, milk, and hide production. The current status of swamp buffaloes in Southeast Asia is still understudied compared to its counterparts such as the riverine buffaloes and cattle. This review discusses the background of swamp buffalo, with an emphasis on recent work on this species in Southeast Asia, and associated genetics and genomics work such as cytogenetic studies, phylogeny, domestication and migration, genetic sequences and resources. Recent challenges to realize the potential of this species in the agriculture industry are also discussed. Limited genetic resource for swamp buffalo has called for more genomics work to be done on this species including decoding its genome. As the economy progresses and farm mechanization increases, research and development for swamp buffaloes are focused on enhancing its productivity through understanding the genetics of agriculturally important traits. The use of genomic markers is a powerful tool to efficiently utilize the potential of this animal for food security and animal conservation. Understanding its genetics and retaining and maximizing its adaptability to harsher environments are a strategic move for food security in poorer nations in Southeast Asia in the face of climate change.

Omics resources and omics-enabled approaches for achieving high productivity and improved quality in pea (Pisum sativum L.)

Pea (Pisum sativum L.), a cool-season legume crop grown in more than 85 countries, is the second most important grain legume and one of the major green vegetables in the world. While pea was historically studied as the genetic model leading to the discovery of the laws of genetics, pea research has lagged behind that of other major legumes in the genomics era, due to its large and complex genome. The evolving climate change and growing population have posed grand challenges to the objective of feeding the world, making it essential to invest research efforts to develop multi-omics resources and advanced breeding tools to support fast and continuous development of improved pea varieties. Recently, the pea researchers have achieved key milestones in omics and molecular breeding. The present review provides an overview of the recent important progress including the development of genetic resource databases, high-throughput genotyping assays, reference genome, genes/QTLs responsible for important traits, transcriptomic, proteomic, and phenomic atlases of various tissues under different conditions. These multi-faceted resources have enabled the successful implementation of various markers for monitoring early-generation populations as in marker-assisted backcrossing breeding programs. The emerging new breeding approaches such as CRISPR, speed breeding, and genomic selection are starting to change the paradigm of pea breeding. Collectively, the rich omics resources and omics-enable breeding approaches will enhance genetic gain in pea breeding and accelerate the release of novel pea varieties to meet the elevating demands on productivity and quality.

Status and prospects of genome-wide association studies in plants

Genome-wide association studies (GWAS) have developed into a powerful and ubiquitous tool for the investigation of complex traits. In large part, this was fueled by advances in genomic technology, enabling us to examine genome-wide genetic variants across diverse genetic materials. The development of the mixed model framework for GWAS dramatically reduced the number of false positives compared with naïve methods. Building on this foundation, many methods have since been developed to increase computational speed or improve statistical power in GWAS. These methods have allowed the detection of genomic variants associated with either traditional agronomic phenotypes or biochemical and molecular phenotypes. In turn, these associations enable applications in gene cloning and in accelerated crop breeding through marker assisted selection or genetic engineering. Current topics of investigation include rare-variant analysis, synthetic associations, optimizing the choice of GWAS model, and utilizing GWAS results to advance knowledge of biological processes. Ongoing research in these areas will facilitate further advances in GWAS methods and their applications.

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.

Genomic interventions for sustainable agriculture

Agricultural production faces a Herculean challenge to feed the increasing global population. Food production systems need to deliver more with finite land and water resources while exerting the least negative influence on the ecosystem. The unpredictability of climate change and consequent changes in pests/pathogens dynamics aggravate the enormity of the challenge. Crop improvement has made significant contributions towards food security, and breeding climate-smart cultivars are considered the most sustainable way to accelerate food production. However, a fundamental change is needed in the conventional breeding framework in order to respond adequately to the growing food demands. Progress in genomics has provided new concepts and tools that hold promise to make plant breeding procedures more precise and efficient. For instance, reference genome assemblies in combination with germplasm sequencing delineate breeding targets that could contribute to securing future food supply. In this review, we highlight key breakthroughs in plant genome sequencing and explain how the presence of these genome resources in combination with gene editing techniques has revolutionized the procedures of trait discovery and manipulation. Adoption of new approaches such as speed breeding, genomic selection and haplotype-based breeding could overcome several limitations of conventional breeding. We advocate that strengthening varietal release and seed distribution systems will play a more determining role in delivering genetic gains at farmer's field. A holistic approach outlined here would be crucial to deliver steady stream of climate-smart crop cultivars for sustainable agriculture.

Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives

The breeding of forest trees is only a few decades old, and is a much more complicated, longer, and expensive endeavor than the breeding of agricultural crops. One breeding cycle for forest trees can take 20–30 years. Recent advances in genomics and molecular biology have revolutionized traditional plant breeding based on visual phenotype assessment: the development of different types of molecular markers has made genotype selection possible. Marker-assisted breeding can significantly accelerate the breeding process, but this method has not been shown to be effective for selection of complex traits on forest trees. This new method of genomic selection is based on the analysis of all effects of quantitative trait loci (QTLs) using a large number of molecular markers distributed throughout the genome, which makes it possible to assess the genomic estimated breeding value (GEBV) of an individual. This approach is expected to be much more efficient for forest tree improvement than traditional breeding. Here, we review the current state of the art in the application of genomic selection in forest tree breeding and discuss different methods of genotyping and phenotyping. We also compare the accuracies of genomic prediction models and highlight the importance of a prior cost-benefit analysis before implementing genomic selection. Perspectives for the further development of this approach in forest breeding are also discussed: expanding the range of species and the list of valuable traits, the application of high-throughput phenotyping methods, and the possibility of using epigenetic variance to improve of forest trees.

Genome-based trait prediction in multi- environment breeding trials in groundnut

KEY MESSAGE

Comparative assessment identified naïve interaction model, and naïve and informed interaction GS models suitable for achieving higher prediction accuracy in groundnut keeping in mind the high genotype × environment interaction for complex traits. Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K 'Axiom_Arachis' SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400-0.600) were obtained for pods/plant, shelling  %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut.

Application of Genomic Big Data in Plant Breeding:Past, Present, and Future

Plant breeding has a long history of developing new varieties that have ensured the food security of the human population. During this long journey together with humanity, plant breeders have successfully integrated the latest innovations in science and technologies to accelerate the increase in crop production and quality. For the past two decades, since the completion of human genome sequencing, genomic tools and sequencing technologies have advanced remarkably, and adopting these innovations has enabled us to cost down and/or speed up the plant breeding process. Currently, with the growing mass of genomic data and digitalized biological data, interdisciplinary approaches using new technologies could lead to a new paradigm of plant breeding. In this review, we summarize the overall history and advances of plant breeding, which have been aided by plant genomic research. We highlight the key advances in the field of plant genomics that have impacted plant breeding over the past decades and introduce the current status of innovative approaches such as genomic selection, which could overcome limitations of conventional breeding and enhance the rate of genetic gain.

Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches

Genomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain per unit of time and reduce the generation interval by utilizing genome-wide markers in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effects. GS enables the prediction of the breeding value of candidate genotypes for selection. In this work, we address important issues related to GS and its implementation in the plant context with special emphasis on tomato breeding. Genomic constraints and critical parameters affecting the accuracy of prediction such as the number of markers, statistical model, phenotyping and complexity of trait, training population size and composition should be carefully evaluated. The comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding programs is also discussed. GS applied to tomato breeding has already been shown to be feasible. We illustrated how GS can improve the rate of gain in elite line selection, and descendent and backcross schemes. The GS schemes have begun to be delineated and computer science can provide support for future selection strategies. A new promising breeding framework is beginning to emerge for optimizing tomato improvement procedures.

Identification of quantitative trait nucleotides and candidate genes for soybean seed weight by multiple models of genome-wide association study

BACKGROUND

Seed weight is a complex yield-related trait with a lot of quantitative trait loci (QTL) reported through linkage mapping studies. Integration of QTL from linkage mapping into breeding program is challenging due to numerous limitations, therefore, Genome-wide association study (GWAS) provides more precise location of QTL due to higher resolution and diverse genetic diversity in un-related individuals.

RESULTS

The present study utilized 573 breeding lines population with 61,166 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and candidate genes for seed weight in Chinese summer-sowing soybean. GWAS was conducted with two single-locus models (SLMs) and six multi-locus models (MLMs). Thirty-nine SNPs were detected by the two SLMs while 209 SNPs were detected by the six MLMs. In all, two hundred and thirty-one QTNs were found to be associated with seed weight in YHSBLP with various effects. Out of these, seventy SNPs were concurrently detected by both SLMs and MLMs on 8 chromosomes. Ninety-four QTNs co-localized with previously reported QTL/QTN by linkage/association mapping studies. A total of 36 candidate genes were predicted. Out of these candidate genes, four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 and Glyma19g28070) were identified by the integration of co-expression network. Among them, three were relatively expressed higher in the high HSW genotypes at R5 stage compared with low HSW genotypes except Glyma12g33280. Our results show that using more models especially MLMs are effective to find important QTNs, and the identified HSW QTNs/genes could be utilized in molecular breeding work for soybean seed weight and yield.

CONCLUSION

Application of two single-locus plus six multi-locus models of GWAS identified 231 QTNs. Four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 & Glyma19g28070) detected via integration of co-expression network among the predicted candidate genes.

Large-Scale Analysis of Combining Ability and Heterosis for Development of Hybrid Maize Breeding Strategies Using Diverse Germplasm Resources

Understanding combining ability and heterosis among diverse maize germplasm resources is important for breeding hybrid maize (Zea mays L.). Using 28 temperate and 23 tropical maize inbreds that represent different ecotypes and worldwide diversity of maize germplasm, we first developed a large-scale multiple-hybrid population (MHP) with 724 hybrids, which could be divided into three subsets, 325 temperate diallel hybrids and 136 tropical diallel hybrids generated in Griffing IV, and 263 temperate by tropical hybrids generated in NCD II. All the parental lines and hybrids were evaluated for 11 traits in replicated tests across two locations and three years. Several widely used inbreds showed strong general combining ability (GCA), and their derived hybrids showed strong specific combining ability (SCA). Heterosis is a quantifiable, trait-dependent and environment-specific phenotype, and the response of parental lines and their hybrids to environments resulted in various levels of heterosis. For all the tested traits except plant height and hundred grain weight (HGW), NCD II (temperate × tropical) hybrids showed higher average heterosis than the temperate and tropical diallel hybrids, with higher hybrid performance for ear length, ear diameter, and HGW. Tropical maize germplasm can be used to improve the yield potential for temperate lines. Grain number per row and grain number per ear were two most important traits that determined yield heterosis, which can be used as direct selection criteria for yield heterosis. The hybrids from heterotic groups, Reid × SPT, Reid × LRC, SPT × PA, and Lancaster × LRC, contributed highly significant positive SCA effects and strong heterosis to yield-related traits, and the heterotic patterns identified in this study were potentially useful for commercial maize breeding. Heterosis was more significantly and positively correlated with SCA than GCA, indicating that SCA can be used in heterosis prediction to develop potential hybrids in commercial maize breeding. The results of the present study not only contribute to developing breeding strategies, but also improve targeted breeding efficiency by using both temperate and tropical maize to broaden genetic basis. Large sets of parental lines with available genotypic information can be shared and used in worldwide hybrid breeding programs through an open-source breeding strategy. Potential applications of the reported results in developing hybrid maize breeding strategies were also discussed.

Complex Genetic System Involved in Fusarium Ear Rot Resistance in Maize as Revealed by GWAS, Bulked Sample Analysis, and Genomic Prediction

Fusarium ear rot (FER) caused by Fusarium verticillioides is one of the most prevalent maize diseases in China and worldwide. Resistance to FER is a complex trait controlled by multiple genes highly affected by environment. In this paper, genome-wide association study (GWAS), bulked sample analysis (BSA), and genomic prediction were performed for understanding FER resistance using 509 diverse inbred lines, which were genotyped by 37,801 high-quality single-nucleotide polymorphisms (SNPs). Ear rot evaluation was performed using artificial inoculation in four environments in China: Xinxiang, Henan, and Shunyi, Beijing, during 2017 and 2018. Significant phenotypic and genetic variation for FER severity was observed, and FER resistance was significantly correlated among the four environments with a generalized heritability of 0.78. GWAS identified 23 SNPs that were associated with FER resistance, 2 of which (1_226233417 on chromosome 1 and 10_14501044 on chromosome 10) were associated at threshold of 2.65 × 10^-7 [-log(0.01/37,801)]. Using BSA, resistance quantitative trait loci were identified on chromosomes 3, 4, 7, 9, and 10 at the 90% confidence level and on chromosomes 3 and 10 at the 95% confidence level. A key region, bin 10.03, was detected by both GWAS and BSA. Genomic prediction for FER resistance showed that the prediction accuracy by trait-related markers was higher than that by randomly selected markers under different levels of marker density. Marker-assisted selection using genomic prediction could be an efficient strategy for genetic improvement for complex traits like FER resistance.

Toward a "Green Revolution" for Soybean

Soybean (Glycine max), as an economically important food and oilseedcrop, is a major source of plant proteins and oils. Although considerable progress has been made in increasing the yields of rice, wheat, and maize through the "Green Revolution", little improvements have been made for soybean. With the increasing demand of soybean production and the rapid development of crop breeding technologies, time has come for this important crop to undergo a Green Revolution. Here, we briefly summarize the history of crop breeding and Green Revolution in other crops. We then discuss the possible directions and potential approaches toward achieving a Green Revolution for soybean. We provide our views and perspectives on how to breed new soybean varieties with improved yield.

Translational genomics for achieving higher genetic gains in groundnut

KEY MESSAGE

Groundnut has entered now in post-genome era enriched with optimum genomic and genetic resources to facilitate faster trait dissection, gene discovery and accelerated genetic improvement for developing climate-smart varieties. Cultivated groundnut or peanut (Arachis hypogaea), an allopolyploid oilseed crop with a large and complex genome, is one of the most nutritious food. This crop is grown in more than 100 countries, and the low productivity has remained the biggest challenge in the semiarid tropics. Recently, the groundnut research community has witnessed fast progress and achieved several key milestones in genomics research including genome sequence assemblies of wild diploid progenitors, wild tetraploid and both the subspecies of cultivated tetraploids, resequencing of diverse germplasm lines, genome-wide transcriptome atlas and cost-effective high and low-density genotyping assays. These genomic resources have enabled high-resolution trait mapping by using germplasm diversity panels and multi-parent genetic populations leading to precise gene discovery and diagnostic marker development. Furthermore, development and deployment of diagnostic markers have facilitated screening early generation populations as well as marker-assisted backcrossing breeding leading to development and commercialization of some molecular breeding products in groundnut. Several new genomics applications/technologies such as genomic selection, speed breeding, mid-density genotyping assay and genome editing are in pipeline. The integration of these new technologies hold great promise for developing climate-smart, high yielding and more nutritious groundnut varieties in the post-genome era.

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.