Genetic Architecture and Genomic Prediction of Cooking Time in Common Bean (Phaseolus vulgaris L.)

Genome-wide association studies dissect the genetic architecture of seed and yield component traits in cowpea (Vigna unguiculata L. Walp)

The identification of loci related to seed and yield component traits in cowpea constitutes a key step for improvement through marker-assisted selection (MAS). Furthermore, seed morphology has an impact on industrial processing and influences consumer and farmer preferences. In this study, we performed genome-wide association studies (GWAS) on a mini-core collection of cowpea to dissect the genetic architecture and detect genomic regions associated with seed morphological traits and yield components. Phenotypic data were measured both manually and by high-throughput image-based approaches to test associations with 41,533 single nucleotide polymorphism markers using the FarmCPU model. From genome-associated regions, we also investigated putative candidate genes involved in the variation of the phenotypic traits. We detected 42 marker-trait associations for pod length and 100-seed weight, length, width, perimeter, and area of the seed. Candidate genes encoding leucine-rich repeat-containing (LRR) and F-box proteins, known to be associated with seed size, were identified; in addition, we identified candidate genes encoding PPR (pentatricopeptide repeat) proteins, recognized to have an important role in seed development in several crops. Our findings provide insights into natural variation in cowpea for yield-related traits and valuable information for MAS breeding strategies in this and other closely related crops.

GWAS-assisted and multitrait genomic prediction for improvement of seed yield and canning quality traits in a black bean breeding panel

In recent years, black beans (Phaseolus vulgaris L.) have gained popularity in the United States, with improved seed yield and canning quality being critical traits for new cultivars. Achieving genetic gains in these traits is often challenging due to negative trait associations and the need for specialized equipment and trained sensory panels for evaluation. This study investigates the integration of genomics and phenomics to enhance selection accuracy for these complex traits. We evaluated the prediction accuracy of single-trait (ST) and multitrait (MT) genomic prediction (GP) models, incorporating near-infrared spectroscopy (NIRS) data and markers identified through genome-wide association studies (GWAS). The models demonstrated moderate prediction accuracies for yield and canning appearance (App) and high accuracies for color retention. No significant differences were found between ST and MT models within the same breeding cycle. However, across breeding cycles, MT models outperformed ST models by up to 45 and 63% for canning App and seed yield, respectively. Interestingly, incorporating significant SNP markers identified by GWAS and NIRS data into the models tended to decrease prediction accuracy both within and between breeding cycles. As genotypes from the new breeding cycle were included, the models' prediction accuracy generally increased. Our findings underscore the potential of MT models to enhance the prediction of complex traits such as seed yield and canning quality in dry beans and highlight the importance of continually updating the training dataset for effective GP implementation in dry bean breeding.

Dissecting genomic regions and underlying candidate genes in groundnut MAGIC population for drought tolerance

BACKGROUND

Groundnut is mainly grown in the semi-arid tropic (SAT) regions worldwide, where abiotic stress like drought is persistent. However, a major research gap exists regarding exploring the genetic and genomic underpinnings of tolerance to drought. In this study, a multi-parent advanced generation inter-cross (MAGIC) population was developed and evaluated for five seasons at two locations for three consecutive years (2018-19, 2019-20 and 2020-21) under drought stress and normal environments.

RESULTS

Phenotyping data of drought tolerance related traits, combined with the high-quality 10,556 polymorphic SNPs, were used to perform multi-locus model genome-wide association study (GWAS) analysis. We identified 37 significant marker-trait associations (MTAs) (Bonferroni-corrected) accounting, 0.91- 9.82% of the phenotypic variance. Intriguingly, 26 significant MTAs overlap on four chromosomes (Ah03, Ah07, Ah10 and Ah18) (harboring 70% of MTAs), indicating genomic hotspot regions governing drought tolerance traits. Furthermore, important candidate genes associated with leaf senescence (NAC transcription factor), flowering (B3 domain-containing transcription factor, Ulp1 protease family, and Ankyrin repeat-containing protein), involved in chlorophyll biosynthesis (FAR1 DNA-binding domain protein), stomatal regulation (Rop guanine nucleotide exchange factor; Galacturonosyltransferases), and associated with yield traits (Fasciclin-like arabinogalactan protein 11 and Fasciclin-like arabinogalactan protein 21) were found in the vicinity of significant MTAs genomic regions.

CONCLUSION

The findings of our investigation have the potential to provide a basis for significant MTAs validation, gene discovery and development of functional markers, which could be employed in genomics-assisted breeding to develop climate-resilient groundnut varieties.

Stacked generalization as a computational method for the genomic selection

As genomic selection emerges as a promising breeding method for both plants and animals, numerous methods have been introduced and applied to various real and simulated data sets. Research suggests that no single method is universally better than others; rather, performance is highly dependent on the characteristics of the data and the nature of the prediction task. This implies that each method has its strengths and weaknesses. In this study, we exploit this notion and propose a different approach. Rather than comparing multiple methods to determine the best one for a particular study, we advocate combining multiple methods to achieve better performance than each method in isolation. In pursuit of this goal, we introduce and develop a computational method of the stacked generalization within ensemble methods. In this method, the meta-model merges predictions from multiple base models to achieve improved performance. We applied this method to plant and animal data and compared its performance with currently available methods using standard performance metrics. We found that the proposed method yielded a lower or comparable mean squared error in predicting phenotypes compared to the current methods. In addition, the proposed method showed greater resistance to overfitting compared to the current methods. Further analysis included statistical hypothesis testing, which showed that the proposed method outperformed or matched the current methods. In summary, the proposed stacked generalization integrates currently available methods to achieve stable and better performance. In this context, our study provides general recommendations for effective practices in genomic selection.

Tools and Techniques to Accelerate Crop Breeding

As climate changes and a growing global population continue to escalate the need for greater production capabilities of food crops, technological advances in agricultural and crop research will remain a necessity. While great advances in crop improvement over the past century have contributed to massive increases in yield, classic breeding schemes lack the rate of genetic gain needed to meet future demands. In the past decade, new breeding techniques and tools have been developed to aid in crop improvement. One such advancement is the use of speed breeding. Speed breeding is known as the application of methods that significantly reduce the time between crop generations, thereby streamlining breeding and research efforts. These rapid-generation advancement tactics help to accelerate the pace of crop improvement efforts to sustain food security and meet the food, feed, and fiber demands of the world's growing population. Speed breeding may be achieved through a variety of techniques, including environmental optimization, genomic selection, CRISPR-Cas9 technology, and epigenomic tools. This review aims to discuss these prominent advances in crop breeding technologies and techniques that have the potential to greatly improve plant breeders' ability to rapidly produce vital cultivars.

Identification of consistent QTL and candidate genes associated with seed traits in common bean by combining GWAS and RNA-Seq

KEY MESSAGE

Association analysis, colocation study with previously reported QTL, and differential expression analyses allowed the identification of the consistent QTLs and main candidate genes controlling seed traits. Common beans show wide seed variations in shape, size, water uptake, and coat proportion. This study aimed to identify consistent genomic regions and candidate genes involved in the genetic control of seed traits by combining association and differential expression analyses. In total, 298 lines from the Spanish Diversity Panel were genotyped with 4,658 SNP and phenotyped for seven seed traits in three seasons. Thirty-eight significant SNP-trait associations were detected, which were grouped into 23 QTL genomic regions with 1,605 predicted genes. The positions of the five QTL regions associated with seed weight were consistent with previously reported QTL. HCPC analysis using the SNP that tagged these five QTL regions revealed three main clusters with significantly different seed weights. This analysis also separated groups that corresponded well with the two gene pools described: Andean and Mesoamerican. Expression analysis was performed on the seeds of the cultivar 'Xana' in three seed development stages, and 1,992 differentially expressed genes (DEGs) were detected, mainly when comparing the early and late seed development stages (1,934 DEGs). Overall, 91 DEGs related to cell growth, signaling pathways, and transcriptomic factors underlying these 23 QTL were identified. Twenty-two DEGs were located in the five QTL regions associated with seed weight, suggesting that they are the main set of candidate genes controlling this character. The results confirmed that seed weight is the sum of the effects of a complex network of loci, and contributed to the understanding of seed phenotype control.

Genomic selection for target traits in the Australian lentil breeding program

Genomic selection (GS) uses associations between markers and phenotypes to predict the breeding values of individuals. It can be applied early in the breeding cycle to reduce the cross-to-cross generation interval and thereby increase genetic gain per unit of time. The development of cost-effective, high-throughput genotyping platforms has revolutionized plant breeding programs by enabling the implementation of GS at the scale required to achieve impact. As a result, GS is becoming routine in plant breeding, even in minor crops such as pulses. Here we examined 2,081 breeding lines from Agriculture Victoria's national lentil breeding program for a range of target traits including grain yield, ascochyta blight resistance, botrytis grey mould resistance, salinity and boron stress tolerance, 100-grain weight, seed size index and protein content. A broad range of narrow-sense heritabilities was observed across these traits (0.24-0.66). Genomic prediction models were developed based on 64,781 genome-wide SNPs using Bayesian methodology and genomic estimated breeding values (GEBVs) were calculated. Forward cross-validation was applied to examine the prediction accuracy of GS for these targeted traits. The accuracy of GEBVs was consistently higher (0.34-0.83) than BLUP estimated breeding values (EBVs) (0.22-0.54), indicating a higher expected rate of genetic gain with GS. GS-led parental selection using early generation breeding materials also resulted in higher genetic gain compared to BLUP-based selection performed using later generation breeding lines. Our results show that implementing GS in lentil breeding will fast track the development of high-yielding cultivars with increased resistance to biotic and abiotic stresses, as well as improved seed quality traits.

Plant breeding for harmony between sustainable agriculture, the environment, and global food security: an era of genomics-assisted breeding

MAIN CONCLUSION

Genomics-assisted breeding represents a crucial frontier in enhancing the balance between sustainable agriculture, environmental preservation, and global food security. Its precision and efficiency hold the promise of developing resilient crops, reducing resource utilization, and safeguarding biodiversity, ultimately fostering a more sustainable and secure food production system. Agriculture has been seriously threatened over the last 40 years by climate changes that menace global nutrition and food security. Changes in environmental factors like drought, salt concentration, heavy rainfalls, and extremely low or high temperatures can have a detrimental effects on plant development, growth, and yield. Extreme poverty and increasing food demand necessitate the need to break the existing production barriers in several crops. The first decade of twenty-first century marks the rapid development in the discovery of new plant breeding technologies. In contrast, in the second decade, the focus turned to extracting information from massive genomic frameworks, speculating gene-to-phenotype associations, and producing resilient crops. In this review, we will encompass the causes, effects of abiotic stresses and how they can be addressed using plant breeding technologies. Both conventional and modern breeding technologies will be highlighted. Moreover, the challenges like the commercialization of biotechnological products faced by proponents and developers will also be accentuated. The crux of this review is to mention the available breeding technologies that can deliver crops with high nutrition and climate resilience for sustainable agriculture.

Gene expression profiling of soaked dry beans (Phaseolus vulgaris L.) reveals cell wall modification plays a role in cooking time

Dry beans (Phaseolus vulgaris L.) are a nutritious food, but their lengthy cooking requirements are barriers to consumption. Presoaking is one strategy to reduce cooking time. Soaking allows hydration to occur prior to cooking, and enzymatic changes to pectic polysaccharides also occur during soaking that shorten the cooking time of beans. Little is known about how gene expression during soaking influences cooking times. The objectives of this study were to (1) identify gene expression patterns that are altered by soaking and (2) compare gene expression in fast-cooking and slow-cooking bean genotypes. RNA was extracted from four bean genotypes at five soaking time points (0, 3, 6, 12, and 18 h) and expression abundances were detected using Quant-seq. Differential gene expression analysis and weighted gene coexpression network analysis were used to identify candidate genes within quantitative trait loci for water uptake and cooking time. Genes related to cell wall growth and development as well as hypoxic stress were differentially expressed between the fast- and slow-cooking beans due to soaking. Candidate genes identified in the slow-cooking beans included enzymes that increase intracellular calcium concentrations and cell wall modification enzymes. The expression of cell wall-strengthening enzymes in the slow-cooking beans may increase their cooking time and ability to resist osmotic stress by preventing cell separation and water uptake in the cotyledon.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Biosorption of potassium ion using bean seeds and its energy saving application

Ca and Mg have been implicated in causing hardness in beans resulting in relatively long cooking time. This study used potassium to replace the cations and determined the adsorption of potassium solution to bean seeds. Then, plantain peel, a natural source of potassium, was used to cook beans and its impact on the cooking time of beans was investigated. The adsorption experiments were performed using batch technique, while metal compositions of the bean seeds and plantain peel were determined by spectroscopy. Optimum removal conditions of potassium ion biosorption using bean seeds were observed at pH 10.2, 2 g bean seed dosage, 180 min agitation time, with 75 ppm as initial metal concentration. The kinetic model correlate with pseudo-second order reaction and the Langmuir adsorption model best fitted the adsorption. After cooking the beans with plantain peel, the concentration of Mg reduced in the bean seeds by about 48%, while the concentration of Ca reduced by about 22%, but the concentration of K increased by over 200% in the cooked bean seeds. Beans treated with plantain peel cooked earlier than the control experiment. This may be affected by pH, adsorbent dosage, metal concentration and contact time.

Genetic analysis of resistance to bean leaf crumple virus identifies a candidate LRR-RLK gene

Bean leaf crumple virus (BLCrV) is a novel begomovirus (family Geminiviridae, genus Begomovirus) infecting common bean (Phaseolus vulgaris L.), threatening bean production in Latin America. Genetic resistance is required to ensure yield stability and reduce the use of insecticides, yet the available resistance sources are limited. In this study, three common bean populations containing a total of 558 genotypes were evaluated in different yield and BLCrV resistance trials under natural infection in the field. A genome-wide association study identified the locus BLC7.1 on chromosome Pv07 at 3.31 Mbp, explaining 8 to 16% of the phenotypic variation for BLCrV resistance. In comparison, whole-genome regression models explained 51 to 78% of the variation and identified the same region on Pv07 to confer resistance. The most significantly associated markers were located within the gene model Phvul.007G040400, which encodes a leucine-rich repeat receptor-like kinase subfamily III member and is likely to be involved in the innate immune response against the virus. The allelic diversity within this gene revealed five different haplotype groups, one of which was significantly associated with BLCrV resistance. As the same genome region was previously reported to be associated with resistance against other geminiviruses affecting common bean, our study highlights the role of previous breeding efforts for virus resistance in the accumulation of positive alleles against newly emerging viruses. In addition, we provide novel diagnostic single-nucleotide polymorphism markers for marker-assisted selection to exploit BLC7.1 for breeding against geminivirus diseases in one of the most important food crops worldwide.

Impact of Web Blight on Photosynthetic Performance of an Elite Common Bean Line in the Western Amazon Region of Colombia

Disease stress caused by plant pathogens impacts the functioning of the photosynthetic apparatus, and the symptoms caused by the degree of severity of the disease can generally be observed in different plant parts. The accurate assessment of plant symptoms can be used as a proxy indicator for managing disease incidence, estimating yield loss, and developing genotypes with disease resistance. The objective of this work was to determine the response of the photosynthetic apparatus to the increased disease severity caused by web blight Thanatephorus cucumeris (Frank) Donk on the common bean (Phaseolus vulgaris L.) leaves under acidic soil and the humid tropical conditions of the Colombian Amazon. Differences in chlorophyll fluorescence parameters, including F_v/F_m, Y(II), Y(NPQ), Y(NO), ETR, qP, and qN in leaves with different levels of severity of web blight in an elite line (BFS 10) of common bean were evaluated under field conditions. A significant effect of web blight on the photosynthetic apparatus was found. A reduction of up to 50% of energy use dedicated to the photosynthetic machinery was observed, even at the severity scale score of 2 (5% surface incidence). The results from this study indicate that the use of fluorescence imaging not only allows for the quantifying of the impact of web blight on photosynthetic performance, but also for detecting the incidence of disease earlier, before severe symptoms occur on the leaves.

Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and 'Omics' Tools

Grain legumes are a rich source of dietary protein for millions of people globally and thus a key driver for securing global food security. Legume plant-based 'dietary protein' biofortification is an economic strategy for alleviating the menace of rising malnutrition-related problems and hidden hunger. Malnutrition from protein deficiency is predominant in human populations with an insufficient daily intake of animal protein/dietary protein due to economic limitations, especially in developing countries. Therefore, enhancing grain legume protein content will help eradicate protein-related malnutrition problems in low-income and underprivileged countries. Here, we review the exploitable genetic variability for grain protein content in various major grain legumes for improving the protein content of high-yielding, low-protein genotypes. We highlight classical genetics-based inheritance of protein content in various legumes and discuss advances in molecular marker technology that have enabled us to underpin various quantitative trait loci controlling seed protein content (SPC) in biparental-based mapping populations and genome-wide association studies. We also review the progress of functional genomics in deciphering the underlying candidate gene(s) controlling SPC in various grain legumes and the role of proteomics and metabolomics in shedding light on the accumulation of various novel proteins and metabolites in high-protein legume genotypes. Lastly, we detail the scope of genomic selection, high-throughput phenotyping, emerging genome editing tools, and speed breeding protocols for enhancing SPC in grain legumes to achieve legume-based dietary protein security and thus reduce the global hunger risk.

Beyond a reference genome: pangenomes and population genomics of underutilized and orphan crops for future food and nutrition security

Underutilized crops are, by definition, under-researched compared to staple crops yet come with traits that may be especially important given climate change and the need to feed a globally increasing population. These crops are often stress-tolerant, and this combined with unique and beneficial nutritional profiles. Whilst progress is being made by generating reference genome sequences, in this Tansley Review, we show how this is only the very first step. We advocate that going 'beyond a reference genome' should be a priority, as it is only at this stage one can identify the specific genes and the adaptive alleles that underpin the valuable traits. We sum up how population genomic and pangenomic approaches have led to the identification of stress- and disease-tolerant alleles in staple crops and compare this to the small number of examples from underutilized crops. We also demonstrate how previously underutilized crops have benefitted from genomic advances and that many breeding targets in underutilized crops are often well studied in staple crops. This cross-crop population-level resequencing could lead to an understanding of the genetic basis of adaptive traits in underutilized crops. This level of investment may be crucial for fully understanding the value of these crops before they are lost.

Evaluation of 93 Accessions of African Yam Bean (Sphenostylis stenocarpa) Grown in Ethiopia for Physical, Nutritional, Antinutritional, and Cooking Properties

African yam bean has immense food and nutrition potential and is resilient to adverse environmental conditions. Despite its potential, the crop is underutilized, which could be attributed to seed hardness (requiring about 6–24 hours of cooking time); and the abundance of antinutrient factors (tannin, phytate, and oxalate). This study evaluated the physical (seed hardness, cooking time) and chemical compositions (crude protein, tannin, phytate, and oxalate) of 93 AYB accessions grown in Ethiopia. The seed hardness of each accession was determined by the compression force and compression time using Texture Analyzer, whereas cooking time was ascertained using Mattson Bean Cooker. The accession’s crude protein level, tannin, oxalate, and phytate were investigated from flour samples using standard laboratory procedures. Highly significant ( $P<0.01$ ) differences were observed for cluster means of compression force, cooking time, and oxalate. The accessions were grouped into three clusters: cluster-II was prominent with 42 accessions, while cluster-I had the least (25). The mean values for compression force ranged from 50.05 N ± 10.25 (TSs-423) to 278.05 N ± 13.42 (TSs-378) whereas compression time varied from 0.35 secs ± 0.02 (TSs-334) to 5.57 secs ± 6.12 (TSs-62B). Cooking time ranged from 127.50 mins ± 2.12 (TSs-82A) to 199.50 mins ± 10.61 (TSs-138B); crude protein ranged from 15.41% ± 0.11 (TSs-269) to 24.51% ± 0.22 (TSs-446). Tannin ranged from 0.61 mg/g  ± 0.02 (TSs-47) to 9.62 mg/g ± 0.03 (TSs-334) likewise, phytate ranged from 0.28 ± 0.01 (TSs-137) to 7.01 ± 0.10 (TSs-3). Accessions TSs-55; TSs-82 showed the lowest oxalate content of 0.21% ± 0.01; 0.21% ± 0.00, respectively. Similarly, TSs-352; TSs-47 revealed the most abundant tannin content of 0.70 ± 0.00 and 0.70 ± 0.07. The correlation analysis revealed a low positive and significant ( $P<0.05$ ) association (r = 0.24) between protein and phytate content.

Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure

Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59-0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources.

Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results

We evaluated the performances of three BLUP and five Bayesian methods for genomic prediction by using nine actual and 54 simulated datasets. The genomic prediction accuracy was measured using Pearson's correlation coefficient between the genomic estimated breeding value (GEBV) and the observed phenotypic data using a fivefold cross-validation approach with 100 replications. The Bayesian alphabets performed better for the traits governed by a few genes/QTLs with relatively larger effects. On the contrary, the BLUP alphabets (GBLUP and CBLUP) exhibited higher genomic prediction accuracy for the traits controlled by several small-effect QTLs. Additionally, Bayesian methods performed better for the highly heritable traits and, for other traits, performed at par with the BLUP methods. Further, genomic BLUP (GBLUP) was identified as the least biased method for the GEBV estimation. Among the Bayesian methods, the Bayesian ridge regression and Bayesian LASSO were less biased than other Bayesian alphabets. Nonetheless, genomic prediction accuracy increased with an increase in trait heritability, irrespective of the sample size, marker density, and the QTL type (major/minor effect). In sum, this study provides valuable information regarding the choice of the selection method for genomic prediction in different breeding programs.

Improving Association Studies and Genomic Predictions for Climbing Beans With Data From Bush Bean Populations

Common bean (Phaseolus vulgaris L.) has two major origins of domestication, Andean and Mesoamerican, which contribute to the high diversity of growth type, pod and seed characteristics. The climbing growth habit is associated with increased days to flowering (DF), seed iron concentration (SdFe), nitrogen fixation, and yield. However, breeding efforts in climbing beans have been limited and independent from bush type beans. To advance climbing bean breeding, we carried out genome-wide association studies and genomic predictions using 1,869 common bean lines belonging to five breeding panels representing both gene pools and all growth types. The phenotypic data were collected from 17 field trials and were complemented with 16 previously published trials. Overall, 38 significant marker-trait associations were identified for growth habit, 14 for DF, 13 for 100 seed weight, three for SdFe, and one for yield. Except for DF, the results suggest a common genetic basis for traits across all panels and growth types. Seven QTL associated with growth habits were confirmed from earlier studies and four plausible candidate genes for SdFe and 100 seed weight were newly identified. Furthermore, the genomic prediction accuracy for SdFe and yield in climbing beans improved up to 8.8% when bush-type bean lines were included in the training population. In conclusion, a large population from different gene pools and growth types across multiple breeding panels increased the power of genomic analyses and provides a solid and diverse germplasm base for genetic improvement of common bean.

Genome-wide association studies dissect the genetic architecture of seed shape and size in common bean

Seed weight and size are important yield components. Thus, selecting for large seeds has been a key objective in crop domestication and breeding. In common bean, seed shape is also important since it influences industrial processing and plays a vital role in determining the choices of consumers and farmers. In this study, we performed genome-wide association studies on a core collection of common bean accessions to dissect the genetic architecture and identify genomic regions associated with seed morphological traits related to weight, size, and shape. Phenotypic data were collected by high-throughput image-based approaches, and utilized to test associations with 10,362 single-nucleotide polymorphism markers using multilocus mixed models. We searched within genome-associated regions for candidate genes putatively involved in seed phenotypic variation. The collection exhibited high variability for the entire set of seed traits, and the Andean gene pool was found to produce larger, heavier seeds than the Mesoamerican gene pool. Strong pairwise correlations were verified for most seed traits. Genome-wide association studies identified marker-trait associations accounting for a considerable amount of phenotypic variation in length, width, projected area, perimeter, and circularity in 4 distinct genomic regions. Promising candidate genes were identified, e.g. those encoding an AT-hook motif nuclear-localized protein 8, type 2C protein phosphatases, and a protein Mei2-like 4 isoform, known to be associated with seed size and weight regulation. Moreover, the genes that were pinpointed are also good candidates for functional analysis to validate their influence on seed shape and size in common bean and other related crops.

Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs

The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.

Genetic Correlation Between Fe and Zn Biofortification and Yield Components in a Common Bean (Phaseolus vulgaris L.)

Common bean (Phaseolus vulgaris L.) is the most important legume for direct human consumption worldwide. It is a rich and relatively inexpensive source of proteins and micronutrients, especially iron and zinc. Bean is a target for biofortification to develop new cultivars with high Fe/Zn levels that help to ameliorate malnutrition mainly in developing countries. A strong negative phenotypic correlation between Fe/Zn concentration and yield is usually reported, posing a significant challenge for breeders. The objective of this study was to investigate the genetic relationship between Fe/Zn. We used Quantitative Trait Loci (QTLs) mapping and Genome-Wide Association Studies (GWAS) analysis in three bi-parental populations that included biofortified parents, identifying genomic regions associated with yield and micromineral accumulation. Significant negative correlations were observed between agronomic traits (pod harvest index, PHI; pod number, PdN; seed number, SdN; 100 seed weight, 100SdW; and seed per pod, Sd/Pd) and micronutrient concentration traits (SdFe and SdZn), especially between pod harvest index (PHI) and SdFe and SdZn. PHI presented a higher correlation with SdN than PdN. Seventy-nine QTLs were identified for the three populations: 14 for SdFe, 12 for SdZn, 13 for PHI, 11 for SdN, 14 for PdN, 6 for 100SdW, and 9 for Sd/Pd. Twenty-three hotspot regions were identified in which several QTLs were co-located, of which 13 hotpots displayed QTL of opposite effect for yield components and Fe/Zn accumulation. In contrast, eight QTLs for SdFe and six QTLs for SdZn were observed that segregated independently of QTL of yield components. The selection of these QTLs will enable enhanced levels of Fe/Zn and will not affect the yield performance of new cultivars focused on biofortification.

Development of Selection Indices for Improvement of Seed Yield and Lipid Composition in Bambara Groundnut (Vigna subterranea (L.) Verdc.)

The underutilised grain legume bambara groundnut (Vigna subterranea) has the potential to contribute significantly to nutritional security. However, the lack of commercial cultivars has hindered its wider adoption and utilisation as a food source. The development of competitive cultivars is impeded by (1) lack of systematic data describing variation in nutritional composition within the gene pool, and (2) a poor understanding of how concentrations of different nutritional components interact. In this study, we analysed seed lipid and protein concentration and lipid composition within a collection of 100 lines representing the global gene pool. Seed protein and lipid varied over twofold with a normal distribution, but no significant statistical correlation was detected between the two components. Seed lipid concentration (4.2–8.8 g/100 g) is primarily determined by the proportion of oleic acid (r² = 0.45). Yield and composition data for a subset of 40 lines were then used to test selection parameters for high yielding, high lipid breeding lines. From five selection indices tested using 15 scenarios, an index based on the seed number, seed weight, and oleic acid yielded a >50% expected increase in each of the mean values of seed number, pod dry weight, seed dry weight, and seed size, as well as an expected 7% increase in seed lipid concentration.

Multivariate genomic analysis and optimal contributions selection predicts high genetic gains in cooking time, iron, zinc, and grain yield in common beans in East Africa

Common bean (Phaseolus vulgaris L.) is important in African diets for protein, iron (Fe), and zinc (Zn), but traditional cultivars have long cooking time (CKT), which increases the time, energy, and health costs of cooking. Genomic selection was used to predict genomic estimated breeding values (GEBV) for grain yield (GY), CKT, Fe, and Zn in an African bean panel of 358 genotypes in a two-stage analysis. In Stage 1, best linear unbiased estimates (BLUE) for each trait were obtained from 898 genotypes across 33 field trials in East Africa. In Stage 2, BLUE in a training population of 141 genotypes were used in a multivariate genomic analysis with genome-wide single nucleotide polymorphism data from the African bean panel. Moderate to high genomic heritability was found for GY (0.45 ± 0.10), CKT (0.50 ± 0.15), Fe (0.57 ± 0.12), and Zn (0.61 ± 0.13). There were significant favorable genetic correlations between Fe and Zn (0.91 ± 0.06), GY and Fe (0.66 ± 0.17), GY and Zn (0.44 ± 0.19), CKT and Fe (-0.57 ± 0.21), and CKT and Zn (-0.67 ± 0.20). Optimal contributions selection (OCS), based on economic index of weighted GEBV for each trait, was used to design crossing within four market groups relevant to East Africa. Progeny were predicted by OCS to increase in mean GY by 12.4%, decrease in mean CKT by 9.3%, and increase in mean Fe and Zn content by 6.9 and 4.6%, respectively, with low achieved coancestry of 0.032. Genomic selection with OCS will accelerate breeding of high-yielding, biofortified, and rapid cooking African common bean cultivars.

Associated SNPs, Heritabilities, Trait Correlations, and Genomic Breeding Values for Resistance in Snap Beans (Phaseolus vulgaris L.) to Root Rot Caused by Fusarium solani (Mart.) f. sp. phaseoli (Burkholder)

Root rot is a major constraint to snap bean (Phaseolus vulgaris) production in the United States and around the world. Genetic resistance is needed to effectively control root rot disease because cultural control methods are ineffective, and the pathogen will be present at the end of one season of production on previously clean land. A diversity panel of 149 snap bean pure lines was evaluated for resistance to Fusarium root rot in Oregon. Morphological traits potentially associated with root rot resistance, such as aboveground biomass, adventitious roots, taproot diameter, basal root diameter, deepest root angle, shallowest root angle, root angle average, root angle difference, and root angle geometric mean were evaluated and correlated to disease severity. A genome wide association study (GWAS) using the Fixed and random model Circulating Probability Unification (FarmCPU) statistical method, identified five associated single nucleotide polymorphisms (SNPs) for disease severity and two SNPs for biomass. The SNPs were found on Pv03, Pv07, Pv08, Pv10, and Pv11. One candidate gene for disease reaction near a SNP on Pv03 codes for a peroxidase, and two candidates associated with biomass SNPs were a 2-alkenal reductase gene cluster on Pv10 and a Pentatricopeptide repeat domain on Pv11. Bean lines utilized in the study were ranked by genomic estimated breeding values (GEBV) for disease severity, biomass, and the root architecture traits, and the observed and predicted values had high to moderate correlations. Cross validation of genomic predictions showed slightly lower correlational accuracy. Bean lines with the highest GEBV were among the most resistant, but did not necessarily rank at the very top numerically. This study provides information on the relationship of root architecture traits to root rot disease reaction. Snap bean lines with genetic merit for genomic selection were identified and may be utilized in future breeding efforts.