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Author: Robert C. Gaynor Publisher: ISBN: Category : Languages : en Pages :
Book Description
Wheat breeders are constantly working to develop new wheat varieties with improved performance for agronomically important traits such as yield and disease resistance. Identifying better ways of phenotyping germplasm, developing methods for predicting performance based on genetic information, and identifying novel sources of genetic disease resistance can all improve the efficiency of breeding efforts. Three studies relating to these research interests were conducted. Synthetic hexaploid wheat lines were screened for resistance to root-lesion nematodes, an economically important pest of wheat. This resulted in the identification of three lines resistant to the root-lesion nematode species Pratylenchus thornei. Grain yield data from multi-location yield trials and average yields for counties in Kansas were used to identify wheat production areas in Kansas. Knowledge obtained from this study is useful for both interpreting data from yield trials and deciding where to place them in order to identify new higher yielding varieties. These data also aided the final research study, developing a genomic selection (GS) model for yield in the Kansas State University wheat breeding program. This model was used to assess the accuracy of GS in conditions experienced in a breeding project. Available measurements of GS have been constructed using simulations or using conditions not typical of those experienced in a wheat breeding program. The estimate of accuracy determined in this study was less than many of the reported measurements. This measure of accuracy will aid in determining if GS is a cost efficient tool for use in wheat breeding.
Author: Robert C. Gaynor Publisher: ISBN: Category : Languages : en Pages :
Book Description
Wheat breeders are constantly working to develop new wheat varieties with improved performance for agronomically important traits such as yield and disease resistance. Identifying better ways of phenotyping germplasm, developing methods for predicting performance based on genetic information, and identifying novel sources of genetic disease resistance can all improve the efficiency of breeding efforts. Three studies relating to these research interests were conducted. Synthetic hexaploid wheat lines were screened for resistance to root-lesion nematodes, an economically important pest of wheat. This resulted in the identification of three lines resistant to the root-lesion nematode species Pratylenchus thornei. Grain yield data from multi-location yield trials and average yields for counties in Kansas were used to identify wheat production areas in Kansas. Knowledge obtained from this study is useful for both interpreting data from yield trials and deciding where to place them in order to identify new higher yielding varieties. These data also aided the final research study, developing a genomic selection (GS) model for yield in the Kansas State University wheat breeding program. This model was used to assess the accuracy of GS in conditions experienced in a breeding project. Available measurements of GS have been constructed using simulations or using conditions not typical of those experienced in a wheat breeding program. The estimate of accuracy determined in this study was less than many of the reported measurements. This measure of accuracy will aid in determining if GS is a cost efficient tool for use in wheat breeding.
Author: Megan Calvert Publisher: ISBN: Category : Languages : en Pages :
Book Description
Growing populations and shifting climatic conditions are placing constraints on global food security that have not previously been experienced. Novel technologies are being developed to combat this challenge at many levels including crop germplasm improvement. It is unlikely that all of these technologies will provide a significant benefit to crop breeding programs whilst still being economically viable. The use of high-throughput phenotyping (HTP) in crop breeding programs is becoming more commonplace due in part to the relatively low establishment costs for several of the technologies. Genomic prediction models are also becoming more common in crop breeding programs due to their success in animal breeding schemes and decreases in sequencing costs for genotyping. Here we examine the utility of several HTP technologies and genomic prediction in wheat breeding in Kansas. An uncrewed aerial system (UAS) measuring reflectance values at different bandwidths was used to formulate vegetation indices (VIs) which are known to correlate with economically valuable phenotypes. The genetic architecture of these VIs was examined in an association mapping population of winter wheat (Triticum aestivum) and their use for genomic prediction was examined in the Kansas State University (KSU) winter wheat breeding program. Several economic and population parameters were determined under which genomic prediction would be favored in the breeding program. Based on simulated and empirical observations of model accuracy the KSU breeding program could potentially make larger genetic gains using genomic prediction than traditional phenotypic selection methods.
Author: Ani A. Elias Publisher: CRC Press ISBN: 1000655954 Category : Science Languages : en Pages : 233
Book Description
Genomic selection (GS) is a promising tool in the field of breeding especially in the era where genomic data is becoming cheaper. The potential of this tool has not been realized due to its limited adaptation in various crops. Marker Assisted Selection (MAS) has been the method of choice for plant breeders while using the genomic information in the breeding pipeline. MAS, however, fails to capture vital minor gene effects while focusing only on the major genes, which is not ideal for breeding advancement especially for quantitative traits such as yield. The main aim of statistical methodologies coming under the umbrella of GS on using the whole genome information is to predict potential candidates for breeding advancement while optimizing the use of resources such as land, manpower, and most importantly time. Lack of proper understanding of the methods and their applications is one of the reasons why breeders shy away from this tool. The book is meant for biologists, especially breeders, and provides a comprehensive idea of the statistical methodologies used in GS, guidance on the choice of models, and design of datasets. The book also encourages the readers to adopt GS by demonstrating the current scenarios of these models in some of the important crops among oilseeds, vegetables, legumes, tuber crops, and cereals. For ease of implementation of GS, the book also provides hands-on scripts on GS data design and modeling in a popular open-source statistical program. Additionally, prospective in GS model development and thereby enhancement in crop improvement programs is discussed.
Author: Johannes W. R. Martini Publisher: Frontiers Media SA ISBN: 2889746747 Category : Science Languages : en Pages : 261
Book Description
Genomic selection (GS) has been the most prominent topic in breeding science in the last two decades. The continued interest is promoted by its huge potential impact on the efficiency of breeding. Predicting a breeding value based on molecular markers and phenotypic values of relatives may be used to manipulate three parameters of the breeder's equation. First, the accuracy of the selection may be improved by predicting the genetic value more reliably when considering the records of relatives and the realized genomic relationship. Secondly, genotyping and predicting may be more cost effective than comprehensive phenotyping. Resources can instead be allocated to increasing population sizes and selection intensity. The third, probably most important factor, is time. As shown in dairy cattle breeding, reducing cycle time by crossing selection candidates earlier may have the strongest impact on selection gain. Many different prediction models have been used, and different ways of using predicted values in a breeding program have been explored. We would like to address the questions: i. How did GS change breeding schemes of different crops in the last 20 years? ii. What was the impact on realized selection gain? iii. What would be the best structure of a crop-specific breeding scheme to exploit the full potential of GS? iv. What is the potential of hybrid prediction, epistasis effect models, deep learning methods and other extensions of the standard prediction of additive effects? v. What are the long-term effects of GS? vi. Can predictive breeding approaches also be used to harness genetic resources from germplasm banks in a more efficient way to adapt current germplasm to new environmental challenges? This Research Topic welcomes submissions of Original Research papers, Opinions, Perspectives, Reviews, and Mini-Reviews related to these themes: 1. Genomic selection: statistical methodology 2. The (optimal) use of GS in breeding schemes 3. Practical experiences with GS (selection gain, long-term effects, negative side effects) 4. Predictive approaches to harness genetic resources Concerning point 1): If an original research paper compares different methods empirically without theoretical considerations on when one or the other method should be better, the methods should be compared with at least five different data sets. The data sets should differ either in crop, genotyping method or its source, for instance from a breeding program or gene bank accessions. Concerning point 2): Manuscripts addressing the use of GS in breeding schemes should illustrate breeding schemes that are run in practice. General ideas about schemes that may be run in the future may be considered as 'Perspective' articles. Conflict of Interest statements: - Topic Editor Valentin Wimmer is affiliated to KWS SAAT SE & Co. KGaA, Germany. - Topic Editor Brian Gardunia is affiliated to Bayer Crop Sciences and has a collaboration with AbacusBio, and is an author on patents with Bayer Crop Sciences. The other Topic Editors did not disclose any conflicts of interest. Image credit: CIMMYT, reproduced under the CC BY-NC-SA 2.0 license
Author: Emily Elizabeth Delorean Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Wheat accounts for 20% of daily caloric intake of the world population and has one of the widest cultivation distributions of any crop. With increasing demand for both quantity and quality, wheat yields must increase while also maintaining acceptable end-use quality. However, measuring end-use quality is complex, requires large volumes grain and significant effort. The overarching goal of this dissertation research was to develop genetic and genomic tools to facilitate breeding for end-use quality in wheat. Building on initial work with genomic prediction of wheat quality, we continued application of genomic prediction models to the International Maize and Wheat Improvement Center (CIMMYT) wheat breeding program. For practical application in the breeding program to advance selection, we focused on forward prediction in each cycle of the bread wheat program. Models were built on 12 years of past data including over 18,000 entries with quality data. Predictions for 10,000 yield trial lines were generated each year for selection, with forward prediction accuracies of 0.40 to 0.73, and approached heritability. This is one of the largest scale applications of genomic selection. We also studied the interaction of climate change and the important quality genes, high-molecular weight glutenins (HMW-GS) and low-molecular weight glutenins (HMW-GS). A diverse panel of 54 CIMMYT wheat varieties were grown in 2 levels of drought stress, heat stress and optimal growth conditions. Quality traits, HMW-GS and LMW-GS alleles were measured. We fit a mixed linear model for each quality trait with HMW-GS, LMW-GS, environment, and the interactions of those as predictors. Overall, the superior glutenin alleles either maintained or increased quality in stressful environments. This work confirmed that superior alleles should always be selected for, regardless of target environment. To increase the genetic diversity for wheat quality, we analyzed Glu-D1 gene diversity on the wheat D genome donor, Aegilops tauschii. We constructed Glu-D1 molecular haplotypes from sequence data of 234 Ae. tauschii accessions and found 15 subclades and over 45 haplotypes, representing immense gene diversity. We found evidence that the 5+10 allele originated from a newly described Lineage 3 of Ae. tauschii, further supporting that this unique lineage contributed to modern bread wheat. We also observed rare recombinant haplotypes between the x and y subunits of any HMW-GS locus. This work will facilitate incorporation of Ae. tauschii Glu-D1 alleles into modern wheat. Given that certain HMW-GS alleles are highly desirable, we set out to develop a high-throughput, high resolution genotyping method for HMW-GS alleles that would fit within genotyping already done for genomic prediction models. This 'sequence based genotyping' approach uses diagnostic k-mers developed to predict alleles in skim-sequenced breeding material. Prediction accuracies for Glu-D1 and Glu-A1 were very good, but lower for the Glu-B1 alleles where many alleles are highly related. Overall, SBG offers a high throughput method to call alleles from existing data. These genetic and genomic tools developed and implemented for end-use quality selection in wheat offer promising resources for continued improvement of both yield and quality in wheat breeding.
Author: Arnel R. Hallauer Publisher: Springer Science & Business Media ISBN: 1441907661 Category : Science Languages : en Pages : 669
Book Description
Maize is used in an endless list of products that are directly or indirectly related to human nutrition and food security. Maize is grown in producer farms, farmers depend on genetically improved cultivars, and maize breeders develop improved maize cultivars for farmers. Nikolai I. Vavilov defined plant breeding as plant evolution directed by man. Among crops, maize is one of the most successful examples for breeder-directed evolution. Maize is a cross-pollinated species with unique and separate male and female organs allowing techniques from both self and cross-pollinated crops to be utilized. As a consequence, a diverse set of breeding methods can be utilized for the development of various maize cultivar types for all economic conditions (e.g., improved populations, inbred lines, and their hybrids for different types of markets). Maize breeding is the science of maize cultivar development. Public investment in maize breeding from 1865 to 1996 was $3 billion (Crosbie et al., 2004) and the return on investment was $260 billion as a consequence of applied maize breeding, even without full understanding of the genetic basis of heterosis. The principles of quantitative genetics have been successfully applied by maize breeders worldwide to adapt and improve germplasm sources of cultivars for very simple traits (e.g. maize flowering) and very complex ones (e.g., grain yield). For instance, genomic efforts have isolated early-maturing genes and QTL for potential MAS but very simple and low cost phenotypic efforts have caused significant and fast genetic progress across genotypes moving elite tropical and late temperate maize northward with minimal investment. Quantitative genetics has allowed the integration of pre-breeding with cultivar development by characterizing populations genetically, adapting them to places never thought of (e.g., tropical to short-seasons), improving them by all sorts of intra- and inter-population recurrent selection methods, extracting lines with more probability of success, and exploiting inbreeding and heterosis. Quantitative genetics in maize breeding has improved the odds of developing outstanding maize cultivars from genetically broad based improved populations such as B73. The inbred-hybrid concept in maize was a public sector invention 100 years ago and it is still considered one of the greatest achievements in plant breeding. Maize hybrids grown by farmers today are still produced following this methodology and there is still no limit to genetic improvement when most genes are targeted in the breeding process. Heterotic effects are unique for each hybrid and exotic genetic materials (e.g., tropical, early maturing) carry useful alleles for complex traits not present in the B73 genome just sequenced while increasing the genetic diversity of U.S. hybrids. Breeding programs based on classical quantitative genetics and selection methods will be the basis for proving theoretical approaches on breeding plans based on molecular markers. Mating designs still offer large sample sizes when compared to QTL approaches and there is still a need to successful integration of these methods. There is a need to increase the genetic diversity of maize hybrids available in the market (e.g., there is a need to increase the number of early maturing testers in the northern U.S.). Public programs can still develop new and genetically diverse products not available in industry. However, public U.S. maize breeding programs have either been discontinued or are eroding because of decreasing state and federal funding toward basic science. Future significant genetic gains in maize are dependent on the incorporation of useful and unique genetic diversity not available in industry (e.g., NDSU EarlyGEM lines). The integration of pre-breeding methods with cultivar development should enhance future breeding efforts to maintain active public breeding programs not only adapting and improving genetically broad-based germplasm but also developing unique products and training the next generation of maize breeders producing research dissertations directly linked to breeding programs. This is especially important in areas where commercial hybrids are not locally bred. More than ever public and private institutions are encouraged to cooperate in order to share breeding rights, research goals, winter nurseries, managed stress environments, and latest technology for the benefit of producing the best possible hybrids for farmers with the least cost. We have the opportunity to link both classical and modern technology for the benefit of breeding in close cooperation with industry without the need for investing in academic labs and time (e.g., industry labs take a week vs months/years in academic labs for the same work). This volume, as part of the Handbook of Plant Breeding series, aims to increase awareness of the relative value and impact of maize breeding for food, feed, and fuel security. Without breeding programs continuously developing improved germplasm, no technology can develop improved cultivars. Quantitative Genetics in Maize Breeding presents principles and data that can be applied to maximize genetic improvement of germplasm and develop superior genotypes in different crops. The topics included should be of interest of graduate students and breeders conducting research not only on breeding and selection methods but also developing pure lines and hybrid cultivars in crop species. This volume is a unique and permanent contribution to breeders, geneticists, students, policy makers, and land-grant institutions still promoting quality research in applied plant breeding as opposed to promoting grant monies and indirect costs at any short-term cost. The book is dedicated to those who envision the development of the next generation of cultivars with less need of water and inputs, with better nutrition; and with higher percentages of exotic germplasm as well as those that pursue independent research goals before searching for funding. Scientists are encouraged to use all possible breeding methodologies available (e.g., transgenics, classical breeding, MAS, and all possible combinations could be used with specific sound long and short-term goals on mind) once germplasm is chosen making wise decisions with proven and scientifically sound technologies for assisting current breeding efforts depending on the particular trait under selection. Arnel R. Hallauer is C. F. Curtiss Distinguished Professor in Agriculture (Emeritus) at Iowa State University (ISU). Dr. Hallauer has led maize-breeding research for mid-season maturity at ISU since 1958. His work has had a worldwide impact on plant-breeding programs, industry, and students and was named a member of the National Academy of Sciences. Hallauer is a native of Kansas, USA. José B. Miranda Filho is full-professor in the Department of Genetics, Escola Superior de Agricultura Luiz de Queiroz - University of São Paulo located at Piracicaba, Brazil. His research interests have emphasized development of quantitative genetic theory and its application to maize breeding. Miranda Filho is native of Pirassununga, São Paulo, Brazil. M.J. Carena is professor of plant sciences at North Dakota State University (NDSU). Dr. Carena has led maize-breeding research for short-season maturity at NDSU since 1999. This program is currently one the of the few public U.S. programs left integrating pre-breeding with cultivar development and training in applied maize breeding. He teaches Quantitative Genetics and Crop Breeding Techniques at NDSU. Carena is a native of Buenos Aires, Argentina. http://www.ag.ndsu.nodak.edu/plantsci/faculty/Carena.htm
Author: Meng Lin Publisher: ISBN: Category : Languages : en Pages :
Book Description
Wheat pre-harvest sprouting (PHS), germination of physiologically matured grains in a wheat spike before harvesting, can cause significant reduction in grain yield and end-use quality. Many quantitative trait loci (QTL) for PHS resistance have been reported in different sources. To determine the genetic architecture of PHS resistance and its relationship with grain color (GC) in US hard winter wheat, a genome-wide association study (GWAS) on both PHS resistance and GC was conducted using in a panel of 185 U.S. elite breeding lines and cultivars and 90K wheat SNP arrrays. PHS resistance was assessed by evaluating sprouting rates in wheat spikes harvested from both greenhouse and field experiments. Thirteen QTLs for PHS resistance were identified on 11 chromosomes in at least two experiments, and the effects of these QTLs varied among different environments. The common QTLs for PHS resistance and GC were identified on the long arms of the chromosome 3A and 3D, indicating pleiotropic effect of the two QTLs. Significant QTLs were also detected on chromosome arms 3AS and 4AL, which were not related to GC, suggesting that it is possible to improve PHS resistance in white wheat. To identify markers closely linked to the 4AL QTL, genotyping-by-sequencing (GBS) technology was used to analyze a population of recombinant inbred lines (RILs) developed from a cross between two parents, "Tutoumai A" and "Siyang 936", contrasting in 4AL QTL. Several closely linked GBS SNP markers to the 4AL QTL were identified and some of them were coverted to KASP for marker-assisted breeding. To investigate effects of the two non-GC related QTLs on 3AS and 4AL, both QTLs were transferered from "Tutoumai A" and "AUS1408" into a susceptible US hard winter wheat breeding line, NW97S186, through marker-assisted backcrossing using the gene marker TaPHS1 for 3AS QTL and a tightly linked KASP marker we developed for 4AL QTL. The 3AS QTL (TaPHS1) significantly interacted with environments and genetic backgrounds, whereas 4AL QTL (TaMKK3-A) interacted with environments only. The two QTLs showed additive effects on PHS resistance, indicating pyramiding these two QTLs can increase PHS resistance. To improve breeding selection efficiency, genomic prediction using genome-wide markers and marker-based prediction (MBP) using selected trait-linked markers were conducted in the association panel. Among the four genomic prediction methods evaluated, the ridge regression best linear unbiased prediction (rrBLUP) provides the best prediction among the tested methods (rrBLUP, BayesB, BayesC and BayesC0). However, MBP using 11 significant SNPs identified in the association study provides a better prediction than genomic prediction. Therefore, for traits that are controlled by a few major QTLs, MBP may be more effective than genomic selection.
Author: Osval Antonio Montesinos López Publisher: Springer Nature ISBN: 3030890104 Category : Technology & Engineering Languages : en Pages : 707
Book Description
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Author: Robert C. Gaynor
Author: Robert C. Gaynor
Author: Megan Calvert
Author: Ani A. Elias
Author: Muhammad Abdul Rehman Rashid
Author: Johannes W. R. Martini
Author: Emily Elizabeth Delorean
Author: Diego Jarquin
Author: Arnel R. Hallauer
Author: Meng Lin
Author: Osval Antonio Montesinos López