Exploring The Genetic Architecture And Improving Genomic Prediction Accuracy For Yield Mineral Concentration And Canning Quality Traits In Common Bean Phaseolus Vulgaris PDF Download

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Exploring the Genetic Architecture and Improving Genomic Prediction Accuracy for Yield, Mineral Concentration, and Canning Quality Traits in Common Bean (Phaseolus Vulgaris)

Exploring the Genetic Architecture and Improving Genomic Prediction Accuracy for Yield, Mineral Concentration, and Canning Quality Traits in Common Bean (Phaseolus Vulgaris)
Author: Paulo Cesar Izquierdo Romero
Publisher:
Total Pages: 0
Release: 2023
Genre: Electronic dissertations
ISBN:

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Dry bean (Phaseolus vulgaris L.) is the most important legumes for human consumption worldwide and is an important source of protein, vitamins, and micronutrients in the human diet. This research aimed to i) uncover the genetic architecture of yield, Fe bioavailability and seed micronutrient concentration, ii) characterize the genetic control of canning quality traits, and ii) assess the accuracy of genomic prediction models for yield and end-use quality traits. The genetic architecture of yield and seed micronutrient concentration was assessed through a combination of meta-QTL analyses integrating published studies over the last two decades in dry bean. A Gaussian mixture model was used to determine the number of distinct QTL in the meta-QTL analyses. Consistent meta-QTL over different genetic backgrounds and environments were identified, reducing the confidence interval compared with initial QTL. Furthermore, a genome-wide association (GWA) study with 295 lines of the yellow bean collection and 82 yellow recombinant inbred lines identified a major QTL for Fe bioavailability related to the ground factor P gene. A black breeding panel with 415 lines was evaluated for yield and canning quality traits in two growing seasons. Consistent associations for color retention, appearance and texture of canned beans were identified across years. Genomic prediction models provided moderate to high accuracy for end-use quality traits on the yellow and black populations. The genomic prediction accuracy was related to the heritability of each trait, and improvement of accuracy was observed for complex traits when secondary traits were included in the model, while for traits with major QTL, the use of associated markers as fixed effects increased prediction ability. The use of meta-QTL analyses and GWA in this study lays a foundation of the genetic control of yield and end-use quality traits and reveals the potential of genomic prediction for these traits in dry beans.


Exploring the Genetic Basis of Seed Coat and Nutritional Traits in Common Bean and Maize

Exploring the Genetic Basis of Seed Coat and Nutritional Traits in Common Bean and Maize
Author: Di Wu
Publisher:
Total Pages: 0
Release: 2021
Genre:
ISBN:

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Common bean (Phaseolus vulgaris L.) and maize (Zea mays L.) are two crops central to indigenous America and of great global agricultural importance. However, the landraces of common bean are largely underrepresented in genebanks, and despite the importance of elements and tocochromanols to plant function and human health, there are still gaps in the understanding of the transport and accumulation of these nutrients in maize grain. Through the array of research tools offered by the field of population genomics and quantitative genetics, this dissertation works towards addressing such gaps. The genomic characterization of ~ 300 accessions of common bean from Native Seeds/SEARCH collected from southwestern US and northwestern Mexico established it as a unique and underrepresented resource that contained important genetic diversity. Five genes encoding MYB transcription factors proximal to the C locus were identified, which is a complex genomic region responsible for the primary control of seed coat patterns. An additional novel association for partial colored seed coats was identified on chromosome 10. Through genome-wide association studies (GWAS) with high density SNP set and the 1500-line Ames panel, I investigated the genetic basis of natural variation for the concentration of 11 elements in grain and identified a total of nine causal genes encoding metal chelator or transporter. Notably, two novel associations were reported between rte2 and irt1 with boron and nickel, respectively, and a potential biofortification target, nas5, was identified for both zinc and iron. Similar moderate predictive abilities (0.33-0.53) were obtained for the 11 grain elemental phenotypes with Bayesian Ridge Regression (BRR) and BayesB. However, BayesB, allowing SNPs to have large effects, had a better fit to the genetic architecture of nickel, molybdenum, and copper, thus outperforming BRR by 4-10%. Finally, through GWAS, transcriptome-wide association studies (TWAS) and expression quantitative trait locus (eQTL) mapping, 13 causal genes that were mostly under strong cis-regulatory control were identified to associate with tocochromanol levels in maize grain. Four genes were pinpointed to be associated with tocochromanol concentrations in maize grain, including vte5, dxs1, vte7, and samt. Overall, this dissertation demonstrates a multidisciplinary approach to characterize a unique common bean collection and the genetic control of its seed coat pattern, and provides a comprehensive assessment of the genetic basis of nutritional qualities in maize grain.


The Common Bean Genome

The Common Bean Genome
Author: Marcelino Pérez de la Vega
Publisher: Springer
Total Pages: 304
Release: 2017-12-20
Genre: Science
ISBN: 3319635263

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This book provides insights into the genetics and the latest advances in genomics research on the common bean, offering a timely overview of topics that are pertinent for future developments in legume genomics. The common bean (Phaseolus vulgaris L.) is the most important grain legume crop for food consumption worldwide, as well as a model for legume research, and the availability of the genome sequence has completely changed the paradigm of the ongoing research on the species. Key topics covered include the numerous genetic and genomic resources, available tools, the identified genes and quantitative trait locus (QTL) identified, and there is a particular emphasis on domestication. It is a valuable resource for students and researchers interested in the genetics and genomics of the common bean and legumes in general.


Genetic and Genomic Resources of Grain Legume Improvement

Genetic and Genomic Resources of Grain Legume Improvement
Author: Lucia Lioi
Publisher: Elsevier Inc. Chapters
Total Pages: 42
Release: 2013-07-18
Genre: Science
ISBN: 0128064366

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In this chapter, we lead the reader through several topics related to common bean germplasm, including crop dissemination across the Old World, landraces developed by farmers, characterization and safeguard of germplasm. These topics are attracting a great deal of attention, especially in recent years, when the international community has become increasingly aware of the relevance of safeguarding plant genetic resources. Early studies were mainly aimed at performing exploratory analyses of agro-morphological traits, with the goal of improving yield and pest resistance. Moreover, phaseolin variation was studied to check the distribution of the two common bean gene pools. The recent development of DNA-based markers has allowed significant improvement in knowledge on genetic variation within the European germplasm, as well as the capacity to trace its divergence from the American germplasm. The different strategies applied to safeguard the European germplasm are described together with their drawbacks.


Enhancing Genetic Gain in a Wheat Breeding Program Using Genomics, Phenomics, Machine and Deep Learning Algorithms

Enhancing Genetic Gain in a Wheat Breeding Program Using Genomics, Phenomics, Machine and Deep Learning Algorithms
Author: Karansher Singh Sandhu
Publisher:
Total Pages: 292
Release: 2021
Genre: Wheat
ISBN:

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Classical plant breeding has evolved considerably during the last century. However, the rate of genetic gain is insufficient to cope with a 2% annual increase in the human population, which is expected to reach 9.8 billion by 2050. Plant breeders and scientists are under pressure to develop new varieties and crops having higher yield, higher nutritional value, climate resilience, and disease and insect resistance. The solution requires the merging of new techniques like next-generation sequencing, genome-wide association studies, genomic selection, high throughput phenotyping, speed breeding, machine and deep learning, and CRISPR mediating gene editing with previously used tools and breeder's skills. The main goal of this research was to explore the potential of genomics, phenomics, machine and deep learning tools in a wheat (Triticum aestivum L.) breeding program. Grain yield and grain protein content (GPC) are two traits very important in hard red spring wheat breeding, yet difficult to select for due to their well-known negative correlation. A nested association mapping population was used to map the regions controlling the stability of grain protein content. This study also demonstrated that genome-wide prediction of GPC with ridge regression best linear unbiased (rrBLUP) estimates reached up to r = 0.69. Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy. We predicted five different quantitative traits with varying genetic architecture using cross-validations, independent validations, and different sets of SNP markers. Deep learning models gave 0 to 5% higher prediction accuracy than rrBLUP model under both cross and independent validations for all five traits used in this study. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where previous years dataset can be used to build the models. Nine different models, including two machine learning and two deep learning models, were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45 - 0.81, 0.29 - 0.55, and 0.27 - 0.50 under cross-validation, forward, and across location predictions. Genomics and phenomics have the potential to revolutionize the field of plant breeding. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. In another study, ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12 for GPC and 20% for grain yield by including secondary traits in the models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.


Genetics and Physiology of Drought Adaptation in Wild and Domesticated Common Bean (Phaseolus Vulgaris L.) of the Mesoamerican Gene Pool

Genetics and Physiology of Drought Adaptation in Wild and Domesticated Common Bean (Phaseolus Vulgaris L.) of the Mesoamerican Gene Pool
Author: Jorge Carlos Berny Mier y Teran
Publisher:
Total Pages:
Release: 2018
Genre:
ISBN: 9780355967524

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Drought is the main constraint in common bean (Phaseolus vulgaris) production. Although breeding efforts to increase drought resistance have shown gains in productivity, their full potential remains untapped. Improvements have been limited by the complexity of the trait involved, limited germplasm recombination, and most importantly, the poorly understood genetic basis of tolerance. To gain information about the genetics and mechanism(s) of drought adaptation, I explored the genetic diversity of wild and domesticated Mesoamerican gene pool of the species through genetic analyses in natural, bi-parental, and multi-parental populations. In the first chapter, I evaluated root and shoot traits in a large panel of wild accessions and reference domesticated cultivars, both from the Mesoamerican distribution of common bean, in a greenhouse tube experiment. The domesticated beans were, in general, more vigorous and deeper-rooted than wild beans. Nevertheless, some wild beans were outstanding in their productivity and rooting capacity. A comparison of the measured phenotypic traits with the climatic and soil characteristics of the location of origin of the individual wild bean accessions showed that most traits were significantly associated with mean annual temperature and aridity. The most deeply rooted and productive accessions originated from the driest regions. Each wild accession was genotyped with both a SNP array and a GBS platform; three populations of wild bean accessions were identified, of which the most productive, deeply rooted, and more drought-tolerant population, originating from Central and North-West Mexico and Oaxaca. Through genome-wide association analyses, genomic regions associated with productivity, root depth, and drought adaptation were identified. Some co-located with regions showing signals of selection suggesting that drought stress is a driver of local adaptation in wild common beans.In the second chapter, I investigated the effects of drought stress on the genetic architecture of photosynthate allocation and remobilization in pods, one of the main mechanisms of drought resistance and overall productivity. A bi-parental recombinant inbred line (RIL) population of the Mesoamerican gene pool was evaluated in field conditions under well-watered conditions and terminal and intermittent drought stress in two years. There was a significant effect of the water regime and year on pod harvest index (PHI), a measure of the partition of seed biomass to pod biomass, its components, phenology and grain yield at the phenotypic and QTL level. QTLs for pod harvest index, including a major, stable QTL on chromosome Pv07 was detected. For grain yield, the QTLs were not stable; however, three were detected for the overall mean across environments. There was differential co-localization of the components of PHI, co-localizing mainly with either the pod wall, seed mass, or both. Three of the eight yield QTLs co-localized with PHI QTLs, underlying the importance of photosynthate remobilization in productivity. Epistasis explained a considerable part of the variation, especially for PHI and yield. In the third chapter, to further test the value of wild variation as compared with the domesticated forms, joint linkage mapping of nested populations was carried out in three newly develop domesticated by wild backcrossed recombinant inbred line populations. The populations were developed by crossing three wild accessions representing the extreme range of rainfall of the Mesoamerican wild bean distribution to an elite drought resistant domesticated parent. Grain yield was evaluated under field conditions in two fully irrigated trials in two seasons and a simulated terminal drought in the second season. The highest yielding populations were the populations from the lower part of the rainfall distribution. The populations were genotyped and a consensus map was developed containing 721 SNP markers. Twenty QTLs were found in 13 unique regions on eight chromosomes. At least one wild allele with a significant positive additive effect was found in five of these regions. The additive effects of all the QTLs ranged from -164 to 277 kg ha−1, with some evidence of allelic series. The variation explained by these QTLs ranged from 0.6 to 5.4 % of the total variation. These results underlie the potential of wild variation for bean crop improvement as well the identification of regions for efficient marker-assisted introgression and candidate genes.


Identification of Genomic Loci Underlying Nutritional Quality Traits in Common Bean (Phaseolus Vulgaris L.) and Participatory Evaluation and Selection of Nutritionally Superior Common Bean Genotypes with Farmers

Identification of Genomic Loci Underlying Nutritional Quality Traits in Common Bean (Phaseolus Vulgaris L.) and Participatory Evaluation and Selection of Nutritionally Superior Common Bean Genotypes with Farmers
Author: Dennis Ndahura Katuuramu
Publisher:
Total Pages: 206
Release: 2017
Genre: Electronic dissertations
ISBN: 9780355547450

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