Enhancing genetic gain in the era of molecular breeding

Integration of ATAC-seq and RNA-seq reveals the dynamics of chromatin accessibility and gene expression in zoysiagrass response to drought

KEY MESSAGE

The 'X4' accession of zoysiagrass demonstrated superior drought tolerance compared to other accessions. Integration analysis of transcriptomics and epigenomics revealed a positive correlation between ATAC-seq peak intensity and gene expression levels. Six motifs involved in regulating drought responses were identified, which are similar to the domains of the ERF and C2H2 transcription factor families. Heterologous expression of Zja11G000860 in yeast enhanced tolerance to drought stress, allowing robust growth even at high PEG6000 concentrations. Zoysiagrass is renowned for its drought tolerance and serves as an exceptional domestic turfgrass in China. However, the changes in chromatin accessibility during drought in zoysiagrass are not well understood. We conducted a preliminary evaluation of the phenotypic changes in drought tolerance for six zoysiagrass cultivars, taking into account their growth characteristics and physiological traits under drought conditions. Additionally, we utilized an integrated multi-omics strategy, encompassing RNA sequencing (RNA-seq), Assay for Transposase Accessible Chromatin using high-throughput sequencing (ATAC-seq), and reverse transcription quantitative PCR (RT-qPCR) verification experiments, to gain deeper understanding of the chromatin accessibility patterns linked to gene expression in response to drought stress in zoysiagrass. Preliminary analysis of the trends in relative water content and proline content suggested that the variety 'X4' exhibited superior drought tolerance compared to the other five accessions. The KEGG pathway enrichment analysis revealed that zoysiagrass responded to environmental stress by regulating stress response and antioxidant defense pathways. Notably, the expression levels of genes Zja03G031540 and Zja11G000860 were significantly increased in the 'X4' zoysiagrass genotype, which exhibited improved drought tolerance, compared to the 'X1' zoysiagrass genotype with reduced drought tolerance. This study suggested that 63 high-confidence genes are related to drought stress, including Zja03G031540 and Zja11G000860. Additionally, six motifs regulating drought responses were unearthed. Furthermore, the heterologous expression of Zja11G000860 in yeast enhanced tolerance to drought stress. The study discovered a positive correlation between ATAC-seq peak intensity and gene expression levels. The expression of high-confidence genes was linked to zoysiagrass resistance evaluation and phenotypic traits, implying that these genes are involved in responding to external drought stress. This study combined ATAC-seq and RNA-seq technologies for the first time to identify drought-related gene expression in zoysiagrass, elucidating the grass adaptation to environmental stress and the regulatory mechanisms underlying stress responses, and laying the groundwork for zoysiagrass improvement and breeding.

Application of machine learning and genomics for orphan crop improvement

Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop improvement technologies have not been widely applied to orphan crops due to the lack of resources available. There are orphan crop representatives across major crop types and the conservation of genes between these related species can be used in crop improvement. Machine learning (ML) has emerged as a promising tool for crop improvement. Transferring knowledge from major crops to orphan crops and using machine learning to improve accuracy and efficiency can be used to improve orphan crops.

Combining genotyping approaches improves resolution for association mapping: a case study in tropical maize under water stress conditions

Genome-wide Association Studies (GWAS) identify genome variations related to specific phenotypes using Single Nucleotide Polymorphism (SNP) markers. Genotyping platforms like SNP-Array or sequencing-based techniques (GBS) can genotype samples with many SNPs. These approaches may bias tropical maize analyses due to reliance on the temperate line B73 as the reference genome. An alternative is a simulated genome called "Mock," adapted to the population using bioinformatics. Recent studies show SNP-Array, GBS, and Mock yield similar results for population structure, heterotic groups definition, tester selection, and genomic hybrid prediction. However, no studies have examined the results generated by these different genotyping approaches for GWAS. This study aims to test the equivalence among the three genotyping scenarios in identifying significant effect genes in GWAS. To achieve this, maize was used as the model species, where SNP-Array genotyped 360 inbred lines from a public panel via the Affymetrix platform and GBS. The GBS data were used to perform SNP calling using the temperate inbred line B73 as the reference genome (GBS-B73) and a simulated genome "Mock" obtained in-silico (GBS-Mock). The study encompassed four above-ground traits with plants grown under two levels of water supply: well-watered (WW) and water-stressed (WS). In total, 46, 34, and 31 SNP were identified in the SNP-Array, GBS-B73, and GBS-Mock scenarios, respectively, across the two water levels, associated with the evaluated traits following the comparative analysis of each genotyping method individually. Overall, the identified candidate genes varied along the various scenarios but had the same functionality. Regarding SNP-Array and GBS-B73, genes with functional similarity were identified even without coincidence in the physical position of the SNPs. These genes and regions are involved in various processes and responses with applications in plant breeding. In terms of accuracy, the combination of genotyping scenarios compared to those isolated is feasible and recommended, as it increased all traits under both water conditions. In this sense, it is worth highlighting the combination of GBS-B73 and GBS-Mock scenarios, not only due to the increase in the resolution of GWAS results but also the reduction of costs associated with genotyping and the possibility of conducting genomic breeding methods.

Transformation of Plant Breeding Using Data Analytics and Information Technology: Innovations, Applications, and Prospective Directions

Our study focused on plant breeding, from traditional methods to the present most advanced genetic and data-driven concepts. Conventional breeding techniques, such as mass selection and cross-breeding, have been instrumental in crop improvement, although they possess inherent limitations in precision and efficiency. Advanced molecular methods allow breeders to improve crops quicker by more accurately targeting specific traits. Data analytics and information technology (IT) are crucial in modern plant breeding, providing tools for data management, analysis, and interpretation of large volumes of data from genomic, phenotypic, and environmental sources. Meanwhile, emerging technologies in machine learning, high-throughput phenotyping, and the Internet of Things (IoT) provide real-time insights into the performance and responses of plants to environmental variables, enabling precision breeding. These tools will allow breeders to select complex traits related to yield, disease resistance, and abiotic stress tolerance more precisely and effectively. Moreover, this data-driven approach will enable breeders to use resources judiciously and make crops resilient, thus contributing to sustainable agriculture. Data analytics integrated into IT will enhance traditional breeding and other key applications in sustainable agriculture, such as crop yield improvement, biofortification, and climate change adaptation. This review aims to highlight the role of interdisciplinary collaboration among breeders, data scientists, and agronomists in absorbing these technologies. Further, this review discusses the future trends that will make plant breeding even more effective with this new wave of artificial intelligence (AI), blockchain, and collaborative platforms, bringing new data transparency, collaboration, and predictability levels. Data and IT-based breeding will greatly contribute to future global food security and sustainable food production. Thus, creating high-performing, resource-efficient crops will be the foundation of a future agricultural vision that balances environmental care. More technological integration in plant breeding is needed for resilient and sustainable food systems to handle the growing population and changing climate challenges.

Univariate and multivariate genomic prediction for agronomic traits in durum wheat under two field conditions

Genomic prediction (GP) has been evaluated in durum wheat breeding programs for several years, but prediction accuracy (PA) remains insufficient for some traits. Recently, multivariate (MV) analysis has gained much attention due to its potential to significantly improve PA. In this study, PA was evaluated for several agronomic traits using a univariate (UV) model in durum wheat, subsequently, different multivariate genomic prediction models were performed to attempt to increase PA. The panel was phenotyped for 10 agronomic traits over two consecutive crop seasons and under two different field conditions: high nitrogen and well-watered (HNW), and low nitrogen and rainfed (LNR). Multivariate GP was implemented using two cross-validation (CV) schemes: MV-CV1, testing the model for each target trait using only the markers, and MV-CV2, testing the model for each target trait using additional phenotypic information. These two MV-CVs were applied in two different analyses: modelling the same trait under both HNW and LNR conditions, and modelling grain yield together with the five most genetically correlated traits. PA for all traits in HNW was higher than LNR for the same trait, except for the trait yellow index. Among all traits, PA ranged from 0.34 (NDVI in LNR) to 0.74 (test weight in HNW). In modelling the same traits in both HNW and LNR, MV-CV1 produced improvements in PA up to 12.45% (NDVI in LNR) compared to the univariate model. By contrast, MV-CV2 increased PA up to 56.72% (thousand kernel weight in LNR). The MV-CV1 scheme did not improve PA for grain yield when it was modelled with the five most genetically correlated traits, whereas MV-CV2 significantly improved PA by up to ~18%. This study demonstrated that increases in prediction accuracy for agronomic traits can be achieved by modelling the same traits in two different field conditions using MV-CV2. In addition, the effectiveness of MV-CV2 was established when grain yield was modelled with additional correlated traits.

Stochastic simulation to optimize rice breeding at IRRI

Introduction

Genetic improvement in rice increased yield potential and improved varieties for farmers over the last decades. However, the demand for rice is growing while its cultivation faces challenges posed by climate change. To address these challenges, rice breeding programs need to adopt efficient breeding strategies to provide a steady increase in the rate of genetic gain for major traits. The International Rice Research Institute (IRRI) breeding program has evolved over time to implement faster and more efficient breeding techniques such as rapid generation advance (RGA) and genomic selection (GS). Simulation experiments support data-driven optimization of the breeding program toward the desired rate of genetic gain for key traits.

Methods

This study used stochastic simulations to compare breeding schemes with different cycle times. The objective was to assess the impact of different genomic selection strategies on medium- and long-term genetic gain. Four genomic selection schemes were simulated, representing the past approaches (5 years recycling), current schemes (3 years recycling), and two options for the future schemes (both with 2 years recycling).

Results

The 2-Year within-cohort prediction scheme showed a significant increase in genetic gain in the medium-term horizon. Specifically, it resulted in a 22%, 24%, and 27% increase over the current scheme in the zero, intermediate, and high genotype-by-environment interaction (GEI) contexts, respectively. On the other hand, the 2-Year scheme based on between-cohort prediction was more efficient in the long term, but only in the absence of GEI. Consistent with our expectations, the shortest breeding schemes showed an increase in genetic gain and faster depletion of genetic variance compared to the current scheme.

Discussion

These results suggest that higher rates of genetic gain are achievable in the breeding program by further reducing the cycle time and adjusting the target population of environments. However, more attention is needed regarding the crossing strategy to use genetic variance optimally.

Insights into the genetic architecture of the reciprocal interspecific hybrids derived from Chrysanthemum dichrum and C. nankingense

Chrysanthemums are versatile ornamental plants, and improving leaf and flower traits is an important breeding objective. Distant hybridization is a powerful method for plant breeding and genetic improvement, whereas the genetic basis in interspecific F1 progeny of chrysanthemums needs to be better understood for breeding purposes. In this study, the leaf and floral traits of the 273 reciprocal interspecific F1 hybrids of diploid C. dichrum (YSJ) and C. nankingense (JHN) were analyzed along with their SNP-derived genetic structure to elucidate the influence of differences in genetic background between the parents on the hybrid performance. We then performed a genome-wide association analysis (GWAS) to reveal the investigated traits' genomic loci and candidate genes. Considerable phenotypic variation (8.81% ~ 55.78%) and heterosis with transgressive segregation in both directions were observed in the reciprocal progenies. We observed a higher level of phenotypic variation in JHN × YSJ rather than in YSJ × JHN. Also, a significant reciprocal effect was observed for most examined traits. Based on the SNP data, we separated the hybrid progenies into three groups (I, II, and III), albeit imperfectly dependent on the cross directions, except for some reciprocal hybrids clustering into group II. Group I from YSJ × JHN and Group III from YSJ × JHN differed with contrasting F ST and π ratios, indicating the genetic changes in the reciprocal populations. The outcome of GWAS via the IIIVmrMLM method detected 339 significant quantitative trait nucleotides (QTNs) and 40 suggestive QTNs, and the phenotypic variation explained by a single QTN ranged from 0.26% to 7.42%. Within 100 kb upstream and downstream of the important QTNs, we discovered 49 known genes and 39 new candidate genes for the investigated leaf and floral traits. Our study provides profound insights into the genetic architecture of reciprocal hybrid progenies of chrysanthemum species, facilitating future breeding activities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01518-0.

Construction of a high-density linkage map and QTL detection for growth traits in South African abalone (Haliotis midae)

Haliotis midae is one of the most important molluscs in South African commercial aquaculture. In this study, a high-resolution integrated linkage map was constructed, and QTL identified using 2b-RADseq for genotyping SNPs in three families. The final integrated linkage map was composed by merging the individual family maps, resulting in 3290 informative SNPs mapping to 18 linkage groups, conforming to the known haploid chromosome number for H. midae. The total map spanned 1798.25 cM with an average marker interval of 0.55 cM, representing a genome coverage of 98.76%. QTL analysis, across all three families, resulted in a total of five QTL identified for growth-related traits, shell width, shell length, and total body weight. For shell width and total body weight, one QTL was identified for each trait respectively, whilst three QTL were identified for shell length. The identified QTL respectively explained between 7.20% and 11.40% of the observed phenotypic variance. All three traits were significantly correlated (r = 0.862-0.970; p < 0.01) and shared overlapping QTL. The QTL for growth traits were mapped back to the H. midae draft genome and BLAST searches revealed the identity of candidate genes, such as egf-1, megf10, megf6, tnx, sevp1, kcp, notch1, and scube2 with possible functional roles in H. midae growth. The constructed high-density linkage map and mapped QTL have given valuable insights regarding the genetic architecture of growth-related traits and will be important genetic resources for marker-assisted selection. It remains, however, important to validate causal variants through linkage disequilibrium fine mapping in future.

Assessment of genetic diversity, inbreeding and collection completeness of Jersey bulls in the US National Animal Germplasm Program

Genomic selection and extensive use of a few elite bulls through artificial insemination are leading to reduced genetic diversity in Jersey cattle. Conservation of genetic diversity through gene banks can protect a breed's genetic diversity and genetic gain, ensuring continued genetic advancement in the future. The availability of genomic information in the US National Animal Germplasm Program (NAGP) facilitates characterization of Jersey bulls in the germplasm collection. Therefore, in this study, we compared the genetic diversity and inbreeding between Jersey bulls in the NAGP and the national cooperator database (NCD). The NCD is maintained and curated by the Council on Dairy Cattle Breeding (CDCB). We found the genetic diversity to be marginally higher in NAGP (Ho = 0.34 ± 0.17) relative to the NCD population (Ho = 0.33 ± 0.16). The average pedigree and genomic inbreeding (FPED, FGRM, FROH > 2Mb) were similar between the groups, with estimates of 7.6% with FPED, 11.07% with FGRM and 20.13% with FROH > 2Mb. An increasing trend in inbreeding was detected, and a significantly higher level of inbreeding was estimated among the older bulls in the NAGP collection, suggesting an overrepresentation of the genetics from elite bulls. Results from principal component analyses (PCA) provided evidence that the NAGP collection is representative of the genetic variation found in the NCD population and a broad majority of the loci segregating (98.2%) in the NCD population were also segregating in the NAGP. Ward's clustering was used to assess collection completeness of Jerseys in the NAGP by comparison with top 1000 sires of bulls, top 1000 sires of cow, and bulls with high Lifetime Net Merit (NM$). All the clusters were represented in the NAGP suggesting that most of the genetic diversity in the US Jersey population is represented in the NAGP and confirmed the PCA results. The decade of birth was the major driver grouping bulls into clusters, suggesting the importance of selection over time. Selection signature analysis between the historic bulls in the NAGP with the newer bulls, born in the decade after implementation of genomic selection, identified selection for milk production, fat and protein yield, fertility, health, and reproductive traits. Cluster analysis revealed that the NAGP has captured allele frequency changes over time associated with selection, validating the strategy of repeated sampling and suggests that the continuation of a repeated sampling policy is essential for the germplasm collection to maintain its future utility. While NAGP should continue to collect bulls that have large influence on the population due to selection, care should be taken to include the entire breadth of bulls, including low merit bulls.

Genomic selection for crop improvement in fruits and vegetables: a systematic scoping review

To ensure the nutritional needs of an expanding global population, it is crucial to optimize the growing capabilities and breeding values of fruit and vegetable crops. While genomic selection, initially implemented in animal breeding, holds tremendous potential, its utilization in fruit and vegetable crops remains underexplored. In this systematic review, we reviewed 63 articles covering genomic selection and its applications across 25 different types of fruit and vegetable crops over the last decade. The traits examined were directly related to the edible parts of the crops and carried significant economic importance. Comparative analysis with WHO/FAO data identified potential economic drivers underlying the study focus of some crops and highlighted crops with potential for further genomic selection research and application. Factors affecting genomic selection accuracy in fruit and vegetable studies are discussed and suggestions made to assist in their implementation into plant breeding schemes. Genetic gain in fruits and vegetables can be improved by utilizing genomic selection to improve selection intensity, accuracy, and integration of genetic variation. However, the reduction of breeding cycle times may not be beneficial in crops with shorter life cycles such as leafy greens as compared to fruit trees. There is an urgent need to integrate genomic selection methods into ongoing breeding programs and assess the actual genomic estimated breeding values of progeny resulting from these breeding programs against the prediction models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-024-01497-2.

Genetic Evaluation of Water Use Efficiency and Nutrient Use Efficiency in Populus deltoides Bartr. ex Marsh. Seedlings in China

Populus deltoides Bartr. ex Marsh. represents a valuable genetic resource for fast-growing plantations in temperate regions. It holds significant cultivation and breeding potential in northern China. To establish an efficient breeding population of poplar, we studied the genetic variation of P. deltoides from different provenances. Our focus was on genotypes exhibiting high growth rates and efficient water and nutrient use efficiency (WUE and NUE). We evaluated 256 one-year-old seedlings from six provenances, measuring height, ground diameter, total biomass, and leaf carbon and nitrogen isotope abundance (δ13C and δ15N). Our analytical methods included variance analysis, multiple comparisons, mixed linear models, correlation analysis, and principal component analysis. The results showed that the coefficient of variation was highest for δ15N and lowest for δ13C among all traits. Except for δ15N, the effects of intra- and inter-provenance were highly significant (p < 0.01). The rates of variation for all traits ranged from 78.36% to 99.49% for intra-provenance and from 0.51% to 21.64% for inter-provenance. The heritability of all traits in AQ provenance was over 0.65, and all exhibited the highest level except for seedling height. All traits were significantly positively correlated with each other (p < 0.05), while ground diameter, total biomass, and WUE were highly significantly negatively correlated with latitude (p < 0.01). After a comprehensive evaluation, two provenances and eight genotypes were selected. The genetic gains for seedling height, ground diameter, total biomass, WUE, and NUE were 27.46 cm (178-2-106), 3.85 mm (178-2-141), 16.40 g (178-2-141), 0.852‱ (LA05-N15), and 3.145‱ (174-1-2), respectively. Overall, we revealed that the abundant genetic variation in P. deltoides populations mainly comes from intra-provenance differences and evaluated provenances and genotypes. The results of this study will contribute to optimizing and enhancing the breeding process of Chinese poplar and improving the productivity of fast-growing plantations.

Genome-wide atlas of rust resistance loci in wheat

Rust diseases, including leaf rust, stripe/yellow rust, and stem rust, significantly impact wheat (Triticum aestivum L.) yields, causing substantial economic losses every year. Breeding and deployment of cultivars with genetic resistance is the most effective and sustainable approach to control these diseases. The genetic toolkit for wheat breeders to select for rust resistance has rapidly expanded with a multitude of genetic loci identified using the latest advances in genomics, mapping and cloning strategies. The goal of this review was to establish a wheat genome atlas that provides a comprehensive summary of reported loci associated with rust resistance. Our atlas provides a summary of mapped quantitative trait loci (QTL) and characterised genes for the three rusts from 170 publications over the past two decades. A total of 920 QTL or resistance genes were positioned across the 21 chromosomes of wheat based on the latest wheat reference genome (IWGSC RefSeq v2.1). Interestingly, 26 genomic regions contained multiple rust loci suggesting they could have pleiotropic effects on two or more rust diseases. We discuss a range of strategies to exploit this wealth of genetic information to efficiently utilise sources of resistance, including genomic information to stack desirable and multiple QTL to develop wheat cultivars with enhanced resistance to rust disease.

GWAS elucidated grain yield genetics in Indian spring wheat under diverse water conditions

KEY MESSAGE

Underpinned natural variations and key genes associated with yield under different water regimes, and identified genomic signatures of genetic gain in the Indian wheat breeding program. A novel KASP marker for TKW under water stress was developed and validated. A comprehensive genome-wide association study was conducted on 300 spring wheat genotypes to elucidate the natural variations associated with grain yield and its eleven contributing traits under fully irrigated, restricted water, and simulated no water conditions. Utilizing the 35K Wheat Breeders' Array, we identified 1155 quantitative trait nucleotides (QTNs), with 207 QTNs exhibiting stability across diverse conditions. These QTNs were further delimited into 539 genomic regions using a genome-wide LD value of 3.0 Mbp, revealing pleiotropic control across traits and conditions. Sub-genome A was significantly associated with traits under irrigated conditions, while sub-genome B showed more QTNs under water stressed conditions. Favourable alleles with significantly associated QTNs were delineated, with a notable pyramiding effect for enhancing trait performance. Additionally, allele of only 921 QTNs significantly affected the population mean. Allele profiling highlighted C-306 as a most potential source of drought tolerance. Moreover, 762 genes overlapping significant QTNs were identified, narrowing down to 27 putative candidate genes overlapping 29 novel and functional SNPs expressing (≥ 0.5 tpm) relevance across various growth conditions. A new KASP assay was developed, targeting a gene TraesCS2A03G1123700 regulating thousand kernel weight under severe drought condition. Genomic selection models (GBLUP, BayesB, MxE, and R-Norm) demonstrated an average prediction accuracy of 0.06-0.58 across environments, indicating potential for trait selection. Retrospective analysis of the Indian wheat breeding program supported a genetic gain in GY at the rate of ca. 0.56% per breeding cycle, since 1960, supporting the identification of genomic signatures driving trait selection and genetic gain. These findings offer insight into improving the rate of genetic gain in wheat breeding programs globally.

Genetic trends in the Kenya Highland Maize Breeding Program between 1999 and 2020

Optimization of a breeding program requires assessing and quantifying empirical genetic trends made through past efforts relative to the current breeding strategies, germplasm, technologies, and policy. To establish the genetic trends in the Kenyan Highland Maize Breeding Program (KHMP), a two-decade (1999-2020) historical dataset from the Preliminary Variety Trials (PVT) and Advanced Variety Trials (AVT) was analyzed. A mixed model analysis was used to compute the genetic gains for traits based on the best linear unbiased estimates in the PVT and AVT evaluation stages. A positive significant genetic gain estimate for grain yield of 88 kg ha-1 year-1 (1.94% year-1) and 26 kg ha-1 year-1 (0.42% year-1) was recorded for PVT and AVT, respectively. Root lodging, an important agronomic trait in the Kenya highlands, had a desired genetic gain of -2.65% year-1 for AVT. Results showed improvement in resistance to Turcicum Leaf Blight (TLB) with -1.19% and -0.27% year-1 for the PVT and AVT, respectively. Similarly, a significant genetic trend of -0.81% was noted for resistance to Gray Leaf Spot (GLS) in AVT. These findings highlight the good progress made by KHMP in developing adapted maize hybrids for Kenya's highland agroecology. Nevertheless, the study identified significant opportunities for the KHMP to make even greater genetic gains for key traits with introgression of favorable alleles for various traits, implementing a continuous improvement plan including marker-assisted forward breeding, sparse testing, and genomic selection, and doubled haploid technology for line development.

High-yield hybrid breeding of Camellia oleifolia based on ISSR molecular markers

BACKGROUND

C. Oleifera is among the world's largest four woody plants known for their edible oil production, yet the contribution rate of improved varieties is less than 20%. The species traditional breeding is lengthy cycle (20-30 years), occupation of land resources, high labor cost, and low accuracy and efficiency, which can be enhanced by molecular marker-assisted selection. However, the lack of high-quality molecular markers hinders the species genetic analysis and molecular breeding.

RESULTS

Through quantitative traits characterization, genetic diversity assessment, and association studies, we generated a selection population with wide genetic diversity, and identified five excellent high-yield parental combinations associated with four reliable high-yield ISSR markers. Early selection criteria were determined based on kernel fresh weight and cultivated 1-year seedling height, aided by the identification of these 4 ISSR markers. Specific assignment of selected individuals as paternal and maternal parents was made to capitalize on their unique attributes.

CONCLUSIONS

Our results indicated that molecular markers-assisted breeding can effectively shorten, enhance selection accuracy and efficiency and facilitate the development of a new breeding system for C. oleifera.

Genetic Gain and Inbreeding in Different Simulated Genomic Selection Schemes for Grain Yield and Oil Content in Safflower

Safflower (Carthamus tinctorius L.) is a multipurpose minor crop consumed by developed and developing nations around the world with limited research funding and genetic resources. Genomic selection (GS) is an effective modern breeding tool that can help to fast-track the genetic diversity preserved in genebank collections to facilitate rapid and efficient germplasm improvement and variety development. In the present study, we simulated four GS strategies to compare genetic gains and inbreeding during breeding cycles in a safflower recurrent selection breeding program targeting grain yield (GY) and seed oil content (OL). We observed positive genetic gains over cycles in all four GS strategies, where the first cycle delivered the largest genetic gain. Single-trait GS strategies had the greatest gain for the target trait but had very limited genetic improvement for the other trait. Simultaneous selection for GY and OL via indices indicated higher gains for both traits than crossing between the two single-trait independent culling strategies. The multi-trait GS strategy with mating relationship control (GS_GY + OL + Rel) resulted in a lower inbreeding coefficeint but a similar gain compared to that of the GS_GY + OL (without inbreeding control) strategy after a few cycles. Our findings lay the foundation for future safflower GS breeding.

Genetic progress in cowpea [Vigna unguiculata (L.) Walp.] stemming from breeding modernization efforts at the International Institute of Tropical Agriculture

Genetic gain has been proposed as a quantifiable key performance indicator that can be used to monitor breeding programs' effectiveness. The cowpea breeding program at the International Institute of Tropical Agriculture (IITA) has developed and released improved varieties in 70 countries globally. To quantify the genetic changes to grain yield and related traits, we exploited IITA cowpea historical multi-environment trials (METs) advanced yield trial (AYT) data from 2010 to 2022. The genetic gain assessment targeted short duration (SD), medium duration (MD), and late duration (LD) breeding pipelines. A linear mixed model was used to calculate the best linear unbiased estimates (BLUE). Regressed BLUE of grain yield by year of genotype origin depicted realized genetic gain of 22.75 kg/ha/year (2.65%), 7.91 kg/ha/year (0.85%), and 22.82 kg/ha/year (2.51%) for SD, MD, and LD, respectively. No significant gain was realized in 100-seed weight (Hsdwt). We predicted, based on 2022 MET data, that recycling the best genotypes at AYT stage would result in grain yield gain of 37.28 kg/ha/year (SD), 28.00 kg/ha/year (MD), and 34.85 kg/ha/year (LD), and Hsdwt gain of 0.48 g/year (SD), 0.68 g/year (MD), and 0.55 g/year (LD). These results demonstrated a positive genetic gain trend for cowpea, indicating that a yield plateau has not yet been reached and that accelerated gain is expected with the recent integration of genomics in the breeding program. Advances in genomics include the development of the reference genome, genotyping platforms, quantitative trait loci mapping of key traits, and active implementation of molecular breeding.

Q&A: Methods for estimating genetic gain in sub-Saharan Africa and achieving improved gains

Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi-environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin. An illustrative example of estimating realized genetic gain was provided by analyzing historical data on fresh cassava (Manihot esculenta Crantz) yield in West Africa (https://github.com/Biometrics-IITA/Estimating-Realized-Genetic-Gain). This approach can serve as a model applicable to other crops and regions. Modernization of breeding programs is necessary to maximize the rate of genetic gain. This can be achieved by adopting genomics to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing. Tracking operational costs, establishing robust, digitalized data management and analytics systems, and developing effective varietal selection processes based on customer insights are also crucial for success. Capacity building and collaboration of breeding programs and institutions also play a significant role in accelerating genetic gains.

Validation of sorghum quality control (QC) markers across African breeding lines

Sorghum [Sorghum bicolor (L.) Moench] is a cereal crop of critical importance in the semi-arid tropics, particularly in Africa where it is second only to maize (Zea mays L.) by area of cultivation. The International Crops Research Institute for the Semi-Arid Tropics sorghum breeding program for Eastern and Southern Africa is the largest in the region and develops improved varieties for target agro-ecologies. Varietal purity and correct confirmation of new crosses are essential for the integrity and efficiency of a breeding program. We used 49 quality control (QC) kompetitive allele-specific PCR single nucleotide polymorphism (SNP) markers to genotype 716 breeding lines. Note that 46 SNPs were polymorphic with the top 10 most informative revealing polymorphism information content (PIC), minor allele frequency (MAF), and observed heterozygosity (Ho) of 0.37, 0.43, and 0.02, respectively, and explaining 45% of genetic variance within the first two principal components (PC). Thirty-nine markers were highly informative across 16 Burkina Faso breeding lines, out of which the top 10 revealed average PIC, MAF, and Ho of 0.36, 0.39, and 0.05, respectively. Discriminant analysis of principal components done using top 30 markers separated the breeding lines into five major clusters, three of which were distinct. Six of the top 10 most informative markers successfully confirmed hybridization of crosses between genotypes IESV240, KARIMTAMA1, F6YQ212, and FRAMIDA. A set of 10, 20, and 30 most informative markers are recommended for routine QC applications. Future effort should focus on the deployment of these markers in breeding programs for enhanced genetic gain.

Exploring genetic variability, heritability, and trait correlations in gari and eba quality from diverse cassava varieties in Nigeria

BACKGROUND

Gari (especially in Nigeria) is an important West African food product made from cassava. It is an affordable, precooked, dry, easy to prepare and store food product. Eba is a stiff dough produced by reconstituting gari in hot water. Gari and eba quality is an important driver of varietal acceptance by farmers, processors, and consumers.

RESULTS

This study characterized the genetic variability, heritability, and correlations among quality-related traits of fresh roots, gari, and eba. Thirty-three diverse genotypes, including landraces and released and advanced breeding genotypes, were used in this study. In total, 40 traits categorized into fresh root quality, colour, functional, and texture properties trait groups were assessed. We observed broad phenotypic variability among the genotypes used in this study. Dry matter content had a positive (P < 0.05) correlation with gari%, bulk density and a negative correlation with eba hardness and gumminess. Broad-sense heritability across all environments varied considerably among the different trait groups: 62% to 79% for fresh root quality, 0% to 96% for colour, 0% to 79% for functional and 0% to 57% for texture properties.

CONCLUSIONS

The stable broad-sense heritability found for gari%, gari and eba colour, bulk density, swelling index, and hardness measured using instrumental texture profile analysis coupled with sufficient variability in the population indicate good potential for genetic improvement of these traits through recurrent selection. Also, it is possible to genetically improve gari%, bulk density, and swelling power by simultaneously improving the dry matter content of fresh roots. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Using agro-ecological zones to improve the representation of a multi-environment trial of soybean varieties

This research introduces a novel framework for enhancing soybean cultivation in North America by categorizing growing environments into distinct ecological and maturity-based zones. Using an integrated analysis of long-term climatic data and records of soybean varietal trials, this research generates a zonal environmental characterization which captures major components of the growing environment which affect the range of adaptation of soybean varieties. These findings have immediate applications for optimizing multi-environment soybean trials. This characterization allows breeders to assess the environmental representation of a multi-environmental trial of soybean varieties, and to strategize the distribution of testing and the placement of test sites accordingly. This application is demonstrated with a historical scenario of a soybean multi-environment trial, using two resource allocation models: one targeted towards improving the general adaptation of soybean varieties, which focuses on widely cultivated areas, and one targeted towards specific adaptation, which captures diverse environmental conditions. Ultimately, the study aims to improve the efficiency and impact of soybean breeding programs, leading to the development of cultivars resilient to variable and changing climates.

Genetic gains in IRRI's rice salinity breeding and elite panel development as a future breeding resource

KEY MESSAGE

Estimating genetic gains and formulating a future salinity elite breeding panel for rice pave the way for developing better high-yielding salinity tolerant lines with enhanced genetic gains. Genetic gain is a crucial parameter to check the breeding program's success and help optimize future breeding strategies for enhanced genetic gains. To estimate the genetic gains in IRRI's salinity breeding program and identify the best genotypes based on high breeding values for grain yield (kg/ha), we analyzed the historical data from the trials conducted in the IRRI, Philippines and Bangladesh. A two-stage mixed-model approach accounting for experimental design factors and a relationship matrix was fitted to obtain the breeding values for grain yield and estimate genetic trends. A positive genetic trend of 0.1% per annum with a yield advantage of 1.52 kg/ha was observed in IRRI, Philippines. In Bangladesh, we observed a genetic gain of 0.31% per annum with a yield advantage of 14.02 kg/ha. In the released varieties, we observed a genetic gain of 0.12% per annum with a 2.2 kg/ha/year yield advantage in the IRRI, Philippines. For the Bangladesh dataset, a genetic gain of 0.14% per annum with a yield advantage of 5.9 kg/ha/year was observed in the released varieties. Based on breeding values for grain yield, a core set of the top 145 genotypes with higher breeding values of > 2400 kg/ha in the IRRI, Philippines, and > 3500 kg/ha in Bangladesh with a reliability of > 0.4 were selected to develop the elite breeding panel. Conclusively, a recurrent selection breeding strategy integrated with novel technologies like genomic selection and speed breeding is highly required to achieve higher genetic gains in IRRI's salinity breeding programs.

GIFTdb: a useful gene database for plant fruit traits improving

Fruit traits are critical determinants of plant fitness, resource diversity, productive and quality. Gene regulatory networks in plants play an essential role in determining fruit traits, such as fruit size, yield, firmness, aroma and other important features. Many research studies have focused on elucidating the associated signaling pathways and gene interaction mechanism to better utilize gene resources for regulating fruit traits. However, the availability of specific database of genes related to fruit traits for use by the plant research community remains limited. To address this limitation, we developed the Gene Improvements for Fruit Trait Database (GIFTdb, http://giftdb.agroda.cn). GIFTdb contains 35 365 genes, including 896 derived from the FR database 1.0, 305 derived from 30 882 articles from 2014 to 2021, 236 derived from the Universal Protein Resource (UniProt) database, and 33 928 identified through homology analysis. The database supports several aided analysis tools, including signal transduction pathways, gene ontology terms, protein-protein interactions, DNAWorks, Basic Local Alignment Search Tool (BLAST), and Protein Subcellular Localization Prediction (WoLF PSORT). To provide information about genes currently unsupported in GIFTdb, potential fruit trait-related genes can be searched based on homology with the supported genes. GIFTdb can provide valuable assistance in determining the function of fruit trait-related genes, such as MYB306-like, by conducting a straightforward search. We believe that GIFTdb will be a valuable resource for researchers working on gene function annotation and molecular breeding to improve fruit traits.

Epidemiology, pathogenesis, and diagnosis of Aleutian disease caused by Aleutian mink disease virus: A literature review with a perspective of genomic breeding for disease control in American mink (Neogale vison)

Aleutian disease (AD) is a multi-systemic infectious disease in American mink (Neogale vison) caused by the Aleutian mink disease virus (AMDV). Commonly referred to as mink plasmacytosis, AD is an economically significant disease in mink-breeding countries. Aleutian disease mainly induces weight loss, lower fertility, and dropped pelt quality in adults and can result in acute interstitial pneumonia with high mortality rates in kits. In this review, we employed the scientific literature on AD over the last 70 years to discuss the historical and contemporary status of AD outbreaks and seroprevalence in mink farming countries. We also explained different forms of AD and the differences between the pathogenicity of the virus in kits and adults. The application of the available AD serological tests in AD control strategies was argued. We explained how selection programs could help AD control and proposed different approaches to selecting animals for building AD-tolerant herds. The advantages of genomic selection for AD tolerance over traditional breeding strategies were discussed in detail. We also explained how genomic selection could help AD control by selecting tolerant animals for the next generation based on genome-wide single nucleotide polymorphisms (SNP) data and the challenges of implementing genomic selection for AD tolerance in the mink industry. This review collected the information required for designing successful breeding programs for AD tolerance. Examples of the application of information are presented, and data gaps are highlighted. We showed that AD tolerance is necessary to be among the traits that animals are selected for in the mink industry.

A comprehensive review on genomic resources in medicinally and industrially important major spices for future breeding programs: Status, utility and challenges

In the global market, spices possess a high-value but low-volume commodities of commerce. The food industry depends largely on spices for taste, flavor, and therapeutic properties in replacement of cheap synthetic ones. The estimated growth rate for spices demand in the world is ∼3.19%. Since spices grow in limited geographical regions, India is one of the leading producer of spices, contributing 25-30 percent of total world trade. Hitherto, there has been no comprehensive review of the genomic resources of industrially important major medicinal spices to overcome major impediments in varietal improvement and management. This review focuses on currently available genomic resources of 24 commercially significant spices, namely, Ajwain, Allspice, Asafoetida, Black pepper, Cardamom large, Cardamom small, Celery, Chillies, Cinnamon, Clove, Coriander, Cumin, Curry leaf, Dill seed, Fennel, Fenugreek, Garlic, Ginger, Mint, Nutmeg, Saffron, Tamarind, Turmeric and Vanilla. The advent of low-cost sequencing machines has contributed immensely to the voluminous data generation of these spices, cracking the complex genomic architecture, marker discovery, and understanding comparative and functional genomics. This review of spice genomics resources concludes the perspective and way forward to provide footprints by uncovering genome assemblies, sequencing and re-sequencing projects, transcriptome-based studies, non-coding RNA-mediated regulation, organelles-based resources, developed molecular markers, web resources, databases and AI-directed resources in candidate spices for enhanced breeding potential in them. Further, their integration with molecular breeding could be of immense use in formulating a strategy to protect and expand the production of the spices due to increased global demand.

Integration of genetic and genomics resources in einkorn wheat enables precision mapping of important traits

Einkorn wheat (Triticum monococcum) is an ancient grain crop and a close relative of the diploid progenitor (T. urartu) of polyploid wheat. It is the only diploid wheat species having both domesticated and wild forms and therefore provides an excellent system to identify domestication genes and genes for traits of interest to utilize in wheat improvement. Here, we leverage genomic advancements for einkorn wheat using an einkorn reference genome assembly combined with skim-sequencing of a large genetic population of 812 recombinant inbred lines (RILs) developed from a cross between a wild and a domesticated T. monococcum accession. We identify 15,919 crossover breakpoints delimited to a median and average interval of 114 Kbp and 219 Kbp, respectively. This high-resolution mapping resource enables us to perform fine-scale mapping of one qualitative (red coleoptile) and one quantitative (spikelet number per spike) trait, resulting in the identification of small physical intervals (400 Kb to 700 Kb) with a limited number of candidate genes. Furthermore, an important domestication locus for brittle rachis is also identified on chromosome 7A. This resource presents an exciting route to perform trait discovery in diploid wheat for agronomically important traits and their further deployment in einkorn as well as tetraploid pasta wheat and hexaploid bread wheat cultivars.

An integrated, systems-wide approach is needed for public-private partnerships to drive genetic innovation in crops

Public-private partnerships are key to successfully translate knowledge to products, but current frameworks do not foster the systems-wide approach required to improve crops to meet the agricultural production challenges of the 21st century.

Allele mining through TILLING and EcoTILLING approaches in vegetable crops

MAIN CONCLUSION

The present review illustrates a comprehensive overview of the allele mining for genetic improvement in vegetable crops, and allele exploration methods and their utilization in various applications related to pre-breeding of economically important traits in vegetable crops. Vegetable crops have numerous wild descendants, ancestors and terrestrial races that could be exploited to develop high-yielding and climate-resilient varieties resistant/tolerant to biotic and abiotic stresses. To further boost the genetic potential of economic traits, the available genomic tools must be targeted and re-opened for exploitation of novel alleles from genetic stocks by the discovery of beneficial alleles from wild relatives and their introgression to cultivated types. This capability would be useful for giving plant breeders direct access to critical alleles that confer higher production, improve bioactive compounds, increase water and nutrient productivity as well as biotic and abiotic stress resilience. Allele mining is a new sophisticated technique for dissecting naturally occurring allelic variants in candidate genes that influence important traits which could be used for genetic improvement of vegetable crops. Target-induced local lesions in genomes (TILLINGs) is a sensitive mutation detection avenue in functional genomics, particularly wherein genome sequence information is limited or not available. Population exposure to chemical mutagens and the absence of selectivity lead to TILLING and EcoTILLING. EcoTILLING may lead to natural induction of SNPs and InDels. It is anticipated that as TILLING is used for vegetable crops improvement in the near future, indirect benefits will become apparent. Therefore, in this review we have highlighted the up-to-date information on allele mining for genetic enhancement in vegetable crops and methods of allele exploration and their use in pre-breeding for improvement of economic traits.

Understanding the genomic selection for crop improvement: current progress and future prospects

Although increased use of modern breeding techniques and technology has resulted in long-term genetic gain, the pace of genetic gain must be sped up to satisfy global agricultural demand. However, marker-assisted selection has proven its potential for improving qualitative traits with large effects regulated by one to few genes. Its contribution to the improvement of the quantitative traits regulated by a number of small-effect genes is modest. In this context, genomic selection (GS) has been regarded as the most promising method for genetically enhancing complicated features that are regulated by several genes, each of which has minor effects. By examining a population's phenotypes and high-density marker scores, genomic selection can forecast the breeding potential of individual lines. The fact that GS uses all marker data in the prediction model prevents skewed marker effect estimations and maximizes the amount of variation caused by small-effect QTL. It has the ability to speed up the breeding cycle and as a consequence of which superior genotypes are selected rapidly. Developing the best GS models while taking into account non-additive effects, genotype-by-environment interaction, and cost-effectiveness will enable the widespread implementation of GS in plants. These steps will also increase heritability estimation and prediction accuracy. This review focuses on the shift from conventional selection methods to GS, underlying statistical tools and methodologies, the state of GS research in agricultural plants, and prospects for its effective use in the creation of climate-resilient crops.

Development of a Quick-Install Rapid Phenotyping System

In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing of the crop canopy with other diverse measurements under varying environmental conditions. The system demonstrates a throughput increase by a factor of 50 when compared to a manual setup, allowing for efficient mapping of crop status across a field with crops grown in rows of any spacing. Tests presented in this paper illustrate the type of experimentation that can be performed with the platform, emphasizing the output from each sensor. The system integration, versatility, and ergonomics are the most significant contributions. The presented system can be used for studying plant responses to different treatments and/or stresses under diverse farming practices in virtually any field environment. It was shown that crop height and several vegetation indices, most of them common indicators of plant physiological status, can be easily paired with corresponding environmental conditions to facilitate data analysis at the fine spatial scale.

Genomic prediction and selection response for grain yield in safflower

In plant breeding programs, multiple traits are recorded in each trial, and the traits are often correlated. Correlated traits can be incorporated into genomic selection models, especially for traits with low heritability, to improve prediction accuracy. In this study, we investigated the genetic correlation between important agronomic traits in safflower. We observed the moderate genetic correlations between grain yield (GY) and plant height (PH, 0.272-0.531), and low correlations between grain yield and days to flowering (DF, -0.157-0.201). A 4%-20% prediction accuracy improvement for grain yield was achieved when plant height was included in both training and validation sets with multivariate models. We further explored the selection responses for grain yield by selecting the top 20% of lines based on different selection indices. Selection responses for grain yield varied across sites. Simultaneous selection for grain yield and seed oil content (OL) showed positive gains across all sites with equal weights for both grain yield and oil content. Combining g×E interaction into genomic selection (GS) led to more balanced selection responses across sites. In conclusion, genomic selection is a valuable breeding tool for breeding high grain yield, oil content, and highly adaptable safflower varieties.

Interaction of genotype-ecological type-plant spacing configuration in sorghum [Sorghum bicolor (L.) Moench] in China

Grain sorghum has been a significant contributor to global food security since the prehistoric period and may contribute even more to the security of both food and energy in the future. Globally, precise management techniques are crucial for increasing grain sorghum productivity. In China, with diverse ecological types, variety introduction occasionally occurs across ecological zones. However, few information is available on the effect of ecological type on genotype performance and how plant spacing configuration influences grain yield in various ecological zones. Hence, a series of two-year field experiments were conducted in 2020 and 2021 in four ecological zones of China, from the northeast to the southwest. The experiments included six widely adapted sorghum varieties under six plant spacing configurations (two row spacing modes: equidistant row spacing (60 cm) mode and wide (80 cm)-narrow (40 cm) row spacing mode; three in-row plant spacings: 10 cm, 15 cm, and 20 cm). Our results indicated that ecological type, variety, and plant spacing configuration had a significant effect on sorghum yield. Ecological type contributed the highest proportion to the yield variance (49.8%), followed by variety (8.3%), while plant spacing configuration contributed 1.8%. Sorghum growth duration was highly influenced by the ecological type, accounting for 87.2% of its total variance, whereas plant height was mainly affected by genotype, which contributed 81.6% of the total variance. All test varieties, developed in the south or north, can reach maturity within 94-108 d, just before fall sowing in central China. Generally, sorghum growth duration becomes longer when a variety is introduced from south to north. A late-maturing variety, developed in the spring sowing and late-maturing regions, possibly could not reach maturity in the early-maturing region. The row spacing modes had no significant affect on sorghum yield, but the equal-row spacing mode consistently caused higher yields with only one exception; this might imply that equal-row spacing mode was more advantageous for boosting sorghum yield potential. In contrast, decreasing in-row plant spacing showed significant positive linear associations with sorghum grain yield in most cases. In addition, these results demonstrated that sorghum is a widely adapted crop and enables success in variety introduction across ecological zones.

Strategies for accommodating gene-edited sires and their descendants in genetic evaluations

Gene editing has the potential to expedite the rate of genetic gain for complex traits. However, changing nucleotides (i.e., QTN) in the genome can affect the additive genetic relationship among individuals and, consequently, impact genetic evaluations. Therefore, the objectives of this study were to estimate the impact of including gene-edited individuals in the genetic evaluation and investigate modeling strategies to mitigate potential errors. For that, a beef cattle population was simulated for nine generations (N = 13,100). Gene-edited sires (1, 25, or 50) were introduced in generation 8. The number of edited QTN was 1, 3, or 13. Genetic evaluations were performed using pedigree, genomic data, or a combination of both. Relationships were weighted based on the effect of the edited QTN. Comparisons were made using the accuracy, average absolute bias, and dispersion of the estimated breeding values (EBV). In general, the EBV of the first generation of progeny of gene-edited sires were associated with greater average absolute bias and overdispersion than the EBV of the progeny of non-gene-edited sires (P ≤ 0.001). Weighting the relationship matrices increased (P ≤ 0.001) the accuracy of EBV when the gene-edited sires were introduced by 3% and decreased (P ≤ 0.001) the average absolute bias and dispersion for the progeny of gene-edited sires. For the second generation of descendants of gene-edited sires, the absolute bias increased as the number of edited alleles increased; however, the rate of increase in absolute bias was 0.007 for each allele edited when the relationship matrices were weighted compared with 0.10 when the relationship matrices were not weighted. Overall, when gene-edited sires are included in genetic evaluations, error is introduced in the EBV, such that the EBV of progeny of gene-edited sires are underestimated. Hence, the progeny of gene-edited sires would be less likely to be selected to be parents of the next generation than what was expected based on their true genetic merit. Therefore, modeling strategies such as weighting the relationship matrices are essential to avoid incorrect selection decisions if animals that have been edited for QTN underlying complex traits are introduced into genetic evaluations.

50 years of rice breeding in Bangladesh: genetic yield trends

To assess the efficiency of genetic improvement programs, it is essential to assess the genetic trend in long-term data. The present study estimates the genetic trends for grain yield of rice varieties released between 1970 and 2020 by the Bangladesh Rice Research Institute. The yield of the varieties was assessed from 2001-2002 to 2020-2021 in multi-locations trials. In such a series of trials, yield may increase over time due to (i) genetic improvement (genetic trend) and (ii) improved management or favorable climate change (agronomic/non-genetic trend). In both the winter and monsoon seasons, we observed positive genetic and non-genetic trends. The annual genetic trend for grain yield in both winter and monsoon rice varieties was 0.01 t ha^-1, while the non-genetic trend for both seasons was 0.02 t ha^-1, corresponding to yearly genetic gains of 0.28% and 0.18% in winter and monsoon seasons, respectively. The overall percentage yield change from 1970 until 2020 for winter rice was 40.96%, of which 13.91% was genetic trend and 27.05% was non-genetic. For the monsoon season, the overall percentage change from 1973 until 2020 was 38.39%, of which genetic and non-genetic increases were 8.36% and 30.03%, respectively. Overall, the contribution of non-genetic trend is larger than genetic trend both for winter and monsoon seasons. These results suggest that limited progress has been made in improving yield in Bangladeshi rice breeding programs over the last 50 years. Breeding programs need to be modernized to deliver sufficient genetic gains in the future to sustain Bangladeshi food security.

Fighting Death for Living: Recent Advances in Molecular and Genetic Mechanisms Underlying Maize Lethal Necrosis Disease Resistance

Maize Lethal Necrosis (MLN) disease, caused by a synergistic co-infection of maize chlorotic mottle virus (MCMV) and any member of the Potyviridae family, was first reported in EasternAfrica (EA) a decade ago. It is one of the most devastating threats to maize production in these regions since it can lead up to 100% crop loss. Conventional counter-measures have yielded some success; however, they are becoming less effective in controlling MLN. In EA, the focus has been on the screening and identification of resistant germplasm, dissecting genetic and the molecular basis of the disease resistance, as well as employing modern breeding technologies to develop novel varieties with improved resistance. CIMMYT and scientists from NARS partner organizations have made tremendous progresses in the screening and identification of the MLN-resistant germplasm. Quantitative trait loci mapping and genome-wide association studies using diverse, yet large, populations and lines were conducted. These remarkable efforts have yielded notable outcomes, such as the successful identification of elite resistant donor lines KS23-5 and KS23-6 and their use in breeding, as well as the identification of multiple MLN-tolerance promising loci clustering on Chr 3 and Chr 6. Furthermore, with marker-assisted selection and genomic selection, the above-identified germplasms and loci have been incorporated into elite maize lines in a maize breeding program, thus generating novel varieties with improved MLN resistance levels. However, the underlying molecular mechanisms for MLN resistance require further elucidation. Due to third generation sequencing technologies as well functional genomics tools such as genome-editing and DH technology, it is expected that the breeding time for MLN resistance in farmer-preferred maize varieties in EA will be efficient and shortened.

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.

Assessing genetic and agronomic gains in rice yield in sub-Saharan Africa: A meta-analysis

Research for development efforts for increasing rice yield in sub-Saharan Africa (SSA) have largely concentrated on genetic improvement and agronomy for more than 50 years. Here we perform the first meta-analysis to quantify genetic gain - yield increase through use of new variety and calculated by yield difference between new variety and variety popularly grown in the target site, and agronomic gain - difference in yield between improved agronomic practices and the control in SSA using 208 paired observations from 40 studies across 12 countries. Among the studies, 41 %, 34 %, and 25 % were from irrigated lowland, rainfed lowland, and rainfed upland rice, respectively. Seventy percent of the studies reported in this paper were conducted on research stations. In agronomic practices, inorganic fertilizer management practices accounted for 78 % of the studies, of which 48 % were nitrogen (N) management. In each study, we identified four types of varieties: check variety (VC), variety with highest yield in the control (VHC), variety with highest yield under improved agronomic practices (VHT), and variety with largest yield difference between improved agronomic practices and control (VHR). VHT was the same as VHC in 35 % of observations, whereas VHR and VHT were the same in 51 %. These indicate that it is possible to develop varieties adapted to different agronomic practices and high-yielding varieties tend to be responsive to improved agronomic practices. On average, total gain in yield with improved agronomic practices and VHT was 1.6 t/ha. Agronomic practice accounted for 75 % of the total variation in total yield gain with variety and agronomic practice by variety interaction responsible for 19 % and 6 %, respectively. Genetic gains in yield with VHC, VHT, and VHR were 0.7, 0.3, and -0.3 t/ha in control, and 0.4, 0.9, and 0.5 t/ha in improved agronomic practices. Agronomic gain in yield averaged 0.5, 0.8, 1.4, and 1.6 t/ha in VHC, VC, VHT, and VHR, respectively. Agronomic gain in yield of VHT was higher than genetic gain under improved agronomic practices in 54 % of observations. Agronomic gain was highest in irrigated lowland rice, followed by rainfed lowland rice. Higher agronomic gain in yield was also associated with larger difference in N application rate between improved agronomic practices and control. Whereas agronomic practices had larger contribution to total gain in yield than genetic improvement in this study, future assessment of agronomic and genetic gains in yield is warranted. Such assessment should focus more on rainfed rice systems, where agronomic gain was small, take into account genetic improvement rate over time and integrated agronomic practices rather than single intervention like nutrient management practice only, and be conducted in farmers' fields.

CRISPR for accelerating genetic gains in under-utilized crops of the drylands: Progress and prospects

Technologies and innovations are critical for addressing the future food system needs where genetic resources are an essential component of the change process. Advanced breeding tools like "genome editing" are vital for modernizing crop breeding to provide game-changing solutions to some of the "must needed" traits in agriculture. CRISPR/Cas-based tools have been rapidly repurposed for editing applications based on their improved efficiency, specificity and reduced off-target effects. Additionally, precise gene-editing tools such as base editing, prime editing, and multiplexing provide precision in stacking of multiple traits in an elite variety, and facilitating specific and targeted crop improvement. This has helped in advancing research and delivery of products in a short time span, thereby enhancing the rate of genetic gains. A special focus has been on food security in the drylands through crops including millets, teff, fonio, quinoa, Bambara groundnut, pigeonpea and cassava. While these crops contribute significantly to the agricultural economy and resilience of the dryland, improvement of several traits including increased stress tolerance, nutritional value, and yields are urgently required. Although CRISPR has potential to deliver disruptive innovations, prioritization of traits should consider breeding product profiles and market segments for designing and accelerating delivery of locally adapted and preferred crop varieties for the drylands. In this context, the scope of regulatory environment has been stated, implying the dire impacts of unreasonable scrutiny of genome-edited plants on the evolution and progress of much-needed technological advances.

Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review

Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.

Evaluation and identification of advanced lentil interspecific derivatives resulted in the development of early maturing, high yielding, and disease-resistant cultivars under Indian agro-ecological conditions

The genetic base revealed by pedigree records of the majority of released cultivars appears to be narrow in major pulse crops, including lentils, because of the frequent use of the same parents and their derivatives in crop improvement programs. Therefore, corrective measures are needed to widen the genetic base by involving the genetic resources of a distinct gene pool. In this direction, rigorous efforts were made to introgress wild Lens taxa, L. culinaris ssp. orientalis, and L. ervoides into the backgrounds of cultivated varieties. Subsequently, genetic materials were advanced through the single seed descent method of breeding along with a rapid generation advancement (normal and off-season) approach. Two F_10:11 interspecific derivatives of lentils were evaluated in augmented block design at two locations, viz. International Centre for Agricultural Research in Dry Areas (ICARDA) and Sher-e-Kashmir University of Agricultural Sciences and Technology (SKUAST), India. The analysis of variance showed remarkable variability for all target characters at both locations. The heritability estimates were high, and correlation analysis exhibited a significant association between the majority of traits assessed at ICARDA and SKUAST, India. Further, SKUAST identified the most promising lines as "Jammu Lentil 144" and "Jammu Lentil 71." These derivatives were further validated separately for their agronomic potential and resistance against major biotic stresses. The results revealed that Jammu Lentil 144 and Jammu Lentil 71 produced 16.65 and 9.40% more seed yield than local and national checks, including earliness, by 25 and 15 days, respectively. These promising interspecific derivatives were also found to be resistant to fusarium wilt, root rot, pod borer, and aphid infestations. The standard agronomy of these cultivars has also been assessed consecutively for 2 years at SKUAST. Overall, the pre-breeding efforts have resulted in the development of early maturing, high-yielding, and disease-resistant lentil cultivars for the Jammu region of India.

Association analysis and expression level of ace polymorphisms with egg-laying trait in Taihang chicken

The number of egg-laying is an important indicator of reproduction performance in poultry breeding. To investigate the relationship between the function of Angiotensin-converting enzyme (ACE) and egg-laying performance of Taihang chicken, the mRNA and protein expression and single nucleotide polymorphism (SNP) of ACE were detected. Analysis of ACE bioinformatics and association analysis of polymorphisms were then performed. The polymorphisms analysis of ACE showed that three SNP loci (g.5066812A>C, g.5080076G>A, and g.5072728A>G) were detected in 800 Taihang chickens with egg-laying records. Association analysis of egg-laying found that ACE g.5066812A>C mutation was significantly associated with the egg-laying performance of Taihang chickens (P < 0.05), and the individuals with the g.5066812A>C mutation showed significantly increasing egg-laying. The mRNA expression was significantly higher in individuals with the AA genotype mutation than those with the AC and CC genotypes (P < 0.01), and the expression of ACE protein levels was consistent with the mRNA expression. Bioinformatics analysis indicated that these mutations affected the secondary and tertiary structure of ACE. This study provides new insights into ACE affecting chicken egg production and some basis for improving the egg production rate of Taihang chickens.

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

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

Agro-Morphological Characterization and Nutritional Profiling of Traditional Himalayan Crop Landraces for Their Promotion Toward Mainstream Agriculture

The northwest Indian Himalayas are often regarded as a biological hotspot for the presence of rich agro-biodiversity harboring locally adapted traditional crop landraces facing utter neglect owing to modern agricultural systems promoting high-yielding varieties. Addressing this challenge requires extricating the potential of such cultivars in terms of agro-morphological and nutritional attributes. In this study, 29 traditional crop landraces of maize (11), paddy (07), finger millet (03), buckwheat (05), and naked barley (03) were characterized and evaluated for target traits of interest. In maize, Chitkanu emerged as an early maturing landrace (107 days) with high concentrations of zinc (Zn), iron (Fe), and potassium (K), and Safed makki showed the highest 100-seed weight (28.20 g). Similarly, Bamkua dhan exhibited high concentrations of K and phosphorus (P), and Lamgudi dhan showed a high protein content (14.86 g/100 g) among paddy landraces. Ogla-I and Phapra-I showed high contents of protein (14.80 g/100 g) and flavonoids (20.50 mg/g) among buckwheat landraces, respectively, followed by Nei-I, which exhibited the highest protein content (15.66 g/100 g) among naked barley landraces. Most of the target traits varied significantly (p < 0.05) among evaluated samples, except those associated with finger millet landraces. The grouping pattern obtained by principal component analysis (PCA) and multidimensional scaling (MDS) was congruent with the geographical relationship among the crop landraces. This study led to the identification of elite crop landraces having useful variations that could be exploited in plant breeding programs and biofortification strategies for future crop improvement. Our endeavor would aid in conserving the depleting Himalayan agro-biodiversity and promoting versatile traditional crops toward mainstream agriculture vis-à-vis future nutritional security.

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

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

Genetic insights in pearl millet breeding in the genomic era: challenges and prospects

Pearl millet, a vital staple food and an important cereal, is emerging as crop having various end-uses as feed, food as well as fodder. Advancement in high-throughput sequencing technology has boosted up pearl millet genomic research in past few years. The available draft genome of pearl millet providing an insight into the advancement of several breeding lines. Comparative and functional genomics have untangled several loci and genes regulating adaptive and agronomic traits in pearl millet. Additionally, the knowledge achieved has far away from being applicable in real breeding practices. We believe that the best path ahead is to adopt genome-based approaches for tailored designing of pearl millet as multi-functional crop with outstanding agronomic traits for various end uses. Presently review highlight several novel concepts and techniques in crop breeding, and summarize the recent advances in pearl millet genomic research, peculiarly genome-wide association dissections of several novel alleles and genes for agronomically important traits.

Root Breeding in the Post-Genomics Era: From Concept to Practice in Apple

The development of rootstocks with a high-quality dwarf-type root system is a popular research topic in the apple industry. However, the precise breeding of rootstocks is still challenging, mainly because the root system is buried deep underground, roots have a complex life cycle, and research on root architecture has progressed slowly. This paper describes ideas for the precise breeding and domestication of wild apple resources and the application of key genes. The primary goal of this research is to combine the existing rootstock resources with molecular breeding and summarize the methods of precision breeding. Here, we reviewed the existing rootstock germplasm, high-quality genome, and genetic resources available to explain how wild resources might be used in modern breeding. In particular, we proposed the 'from genotype to phenotype' theory and summarized the difficulties in future breeding processes. Lastly, the genetics governing root diversity and associated regulatory mechanisms were elaborated on to optimize the precise breeding of rootstocks.

Applications of Genomic Tools in Plant Breeding: Crop Biofortification

Crop breeding has mainly been focused on increasing productivity, either directly or by decreasing the losses caused by biotic and abiotic stresses (that is, incorporating resistance to diseases and enhancing tolerance to adverse conditions, respectively). Quite the opposite, little attention has been paid to improve the nutritional value of crops. It has not been until recently that crop biofortification has become an objective within breeding programs, through either conventional methods or genetic engineering. There are many steps along this long path, from the initial evaluation of germplasm for the content of nutrients and health-promoting compounds to the development of biofortified varieties, with the available and future genomic tools assisting scientists and breeders in reaching their objectives as well as speeding up the process. This review offers a compendium of the genomic technologies used to explore and create biodiversity, to associate the traits of interest to the genome, and to transfer the genomic regions responsible for the desirable characteristics into potential new varieties. Finally, a glimpse of future perspectives and challenges in this emerging area is offered by taking the present scenario and the slow progress of the regulatory framework as the starting point.

Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines

Estimating genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI's rice drought breeding program was used to estimate the genetic trends and assess the breeding program's success. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes. A two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20 and 0.94 under the drought trials and 0.43-0.83 under non-stress trials. Under non-stress conditions, the genetic gain of 0.21% (10.22 kg/ha/year) for genotypes and 0.17% (7.90 kg/ha/year) for checks was observed. The genetic trend under drought conditions exhibited a positive trend with the genetic gain of 0.13% (2.29 kg/ha/year) for genotypes and 0.55% (9.52 kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.27% (8.32 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of > 0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha). A positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI's drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains.

Genetic Trends in Fusarium Head Blight Resistance from 20 Years of Winter Wheat Breeding and Cooperative Testing in the Northern U.S.A

Fusarium head blight (FHB) is a devastating disease of wheat and barley. In the U.S.A., a significant long-term investment in breeding FHB-resistant cultivars began after the 1990s. However, to this date, no study has been performed to understand and monitor the rate of genetic progress in FHB resistance as a result of this investment. Using 20 years of data (1998 to 2018) from the Northern Uniform and Preliminarily Northern Uniform winter wheat scab nurseries that consisted of 1,068 genotypes originating from nine different institutions, we studied the genetic trends in FHB resistance within the northern soft red winter wheat growing region using mixed model analyses. For the FHB resistance traits incidence, severity, Fusarium-damaged kernels, and deoxynivalenol content, the rate of genetic gain in disease resistance was estimated to be 0.30 ± 0.1, 0.60 ± 0.09, and 0.37 ± 0.11 points per year, and 0.11 ± 0.05 parts per million per year, respectively. Among the five FHB-resistance quantitative trait loci assayed for test entries from 2012 to 2018, the frequencies of favorable alleles from Fhb 2DL Wuhan1 W14, Fhb Ernie 3Bc, and Fhb 5A Ning7840 were close to zero across the years. The frequency of the favorable at Fhb1 and Fhb 5A Ernie ranged from 0.08 to 0.33 and 0.06 to 0.20, respectively, across years, and there was no trend in changes in allele frequencies over years. Overall, this study showed that substantial genetic progress has been made toward improving resistance to FHB. It is apparent that today's investment in public wheat breeding for FHB resistance is achieving results and will continue to play a vital role in reducing FHB levels in growers' fields.

Implementation of Epigenetic Variation in Sorghum Selection and Implications for Crop Resilience Breeding

Crop resilience and yield stability are complex traits essential for food security. Sorghum bicolor is an important grain crop that shows promise for its natural resilience to drought and potential for marginal land production. We have developed sorghum lines in the Tx430 genetic background suppressed for MSH1 expression as a means of inducing de novo epigenetic variation, and have used these materials to evaluate changes in plant growth vigor. Plant crossing and selection in two distinct environments revealed features of phenotypic plasticity derived from MSH1 manipulation. Introduction of an epigenetic variation to an isogenic sorghum population, in the absence of selection, resulted in 10% yield increase under ideal field conditions and 20% increase under extreme low nitrogen conditions. However, incorporation of early-stage selection amplified these outcomes to 36% yield increase under ideal conditions and 64% increase under marginal field conditions. Interestingly, the best outcomes were derived by selecting mid-range performance early-generation lines rather than highest performing. Data also suggested that phenotypic plasticity derived from the epigenetic variation was non-uniform in its response to environmental variability but served to reduce genotype × environment interaction. The MSH1-derived growth vigor appeared to be associated with enhanced seedling root growth and altered expression of auxin response pathways, and plants showed evidence of cold tolerance, features consistent with observations made previously in Arabidopsis. These data imply that the MSH1 system is conserved across plant species, pointing to the value of parallel model plant studies to help devise effective plant selection strategies for epigenetic breeding in multiple crops.