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Genomic Selection Strategies To Predict Grain Yield And Disease Resistance Traits In A Wheat Breeding Program
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Book Synopsis Genomic Selection Strategies to Predict Grain Yield and Disease Resistance Traits in a Wheat Breeding Program by : Virginia Laura Verges
Download or read book Genomic Selection Strategies to Predict Grain Yield and Disease Resistance Traits in a Wheat Breeding Program written by Virginia Laura Verges and published by . This book was released on 2021 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Genomic Selection for Kansas Wheat by : Robert C. Gaynor
Download or read book Genomic Selection for Kansas Wheat written by Robert C. Gaynor and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Genomic Selection in Plants by : Ani A. Elias
Download or read book Genomic Selection in Plants written by Ani A. Elias and published by CRC Press. This book was released on 2022-08-18 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Physiological, Molecular, and Genetic Perspectives of Wheat Improvement by : Shabir H Wani
Download or read book Physiological, Molecular, and Genetic Perspectives of Wheat Improvement written by Shabir H Wani and published by Springer Nature. This book was released on 2020-12-17 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: World population is growing at an alarming rate and may exceed 9.7 billion by 2050, whereas agricultural productivity has been negatively affected due to yield limiting factors such as biotic and abiotic stresses as a result of global climate change. Wheat is a staple crop for ~20% of the world population and its yield needs be augmented correspondingly in order to satisfy the demands of our increasing world population. “Green revolution”, the introduction of semi-dwarf, high yielding wheat varieties along with improved agronomic management practices, gave rise to a substantial increase in wheat production and self-sufficiency in developing countries that include Mexico, India and other south Asian countries. Since the late 1980’s, however, wheat yield is at a standoff with little fluctuation. The current trend is thus insufficient to meet the demands of an increasing world population. Therefore, while conventional breeding has had a great impact on wheat yield, with climate change becoming a reality, newer molecular breeding and management tools are needed to meet the goal of improving wheat yield for the future. With the advance in our understanding of the wheat genome and more importantly, the role of environmental interactions on productivity, the idea of genomic selection has been proposed to select for multi-genic quantitative traits early in the breeding cycle. Accordingly genomic selection may remodel wheat breeding with gain that is predicted to be 3 to 5 times that of crossbreeding. Phenomics (high-throughput phenotyping) is another fairly recent advancement using contemporary sensors for wheat germplasm screening and as a selection tool. Lastly, CRISPR/Cas9 ribonucleoprotein mediated genome editing technology has been successfully utilized for efficient and specific genome editing of hexaploid bread wheat. In summary, there has been exciting progresses in the development of non-GM wheat plants resistant to biotic and abiotic stress and/or wheat with improved nutritional quality. We believe it is important to highlight these novel research accomplishments for a broader audience, with the hope that our readers will ultimately adopt these powerful technologies for crops improvement in order to meet the demands of an expanding world population.
Book Synopsis Improving Breeding Program Efficiency and Genetic Gain Through the Implementation of Genomic Selection in Diverse Wheat Germplasm by : Dylan Larkin
Download or read book Improving Breeding Program Efficiency and Genetic Gain Through the Implementation of Genomic Selection in Diverse Wheat Germplasm written by Dylan Larkin and published by . This book was released on 2020 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic selection (GS) is an important tool for increasing genetic gain for economically important traits in breeding programs. Genomic selection uses molecular markers across the entire genome in order to predict the performance of breeding lines for a trait of interest prior to phenotyping. A training population (TP) of elite germplasm, representative of the University of Arkansas wheat breeding program, was developed in order to predict important agronomic and Fusarium head blight (FHB) resistance traits within the University of Arkansas wheat breeding program through cross-validation and forward prediction. A genome-wide association study (GWAS) was performed on the TP to identify novel FHB resistance loci for deoxynivalenol (DON) accumulation, Fusarium damaged kernels (FDK), incidence (INC), and severity (SEV). Significantly loci were used as fixed effects in a GS model (GS+GWAS) and compared to a naïve GS (NGS) model, where the NGS models had significantly higher prediction accuracies (PA) than the GS+GWAS models for all four FHB traits. The GWAS identified novel loci for all four FHB traits, most notably on chromosomes 3BL and 4BL. Multivariate GS (MVGS) models using correlated traits as covariates were also compared to NGS models and the MVGS models significantly outperformed the NGS models for all four traits. The same TP was also evaluated for five agronomic traits, including grain yield (GY), heading date (HD), maturity date (MD), plant height (PH), and test weight (TW), where MVGS models were compared to NGS models. Again, MVGS models significantly outperformed NGS models for all five agronomic traits, especially when there were strong genetic correlations between predicted traits and covariates. Additionally, MVGS models were tested using GY data for genotypes only grown in some environments to predict said genotypes in missing environments. This method significantly improved PA for GY between 6% and 21% for four of six tested environments. The abovementioned TP was then used for forward prediction to predict GY for untested F4:6 breeding lines and DON, FDK, and SEV for F4:7 breeding lines. The MVGS models were comparable to phenotypic selection and had higher selection accuracies for two of three breeding cycles for GY, both cycles for DON, and at least one cycle for FDK and SEV. The MVGS model also had higher PAs for all four traits compared with the NGS models. These results show that GS, and MVGS, can be successfully implemented in a wheat breeding program over multiple breeding cycles and can be effective alongside phenotypic selection for economically important traits. The MVGS models are particularly effective when predicted traits share strong genetic correlations with covariate traits, and covariate traits have a higher heritability than the predicted traits.
Book Synopsis Genomic Selection: Lessons Learned and Perspectives by : Johannes W. R. Martini
Download or read book Genomic Selection: Lessons Learned and Perspectives written by Johannes W. R. Martini and published by Frontiers Media SA. This book was released on 2022-09-15 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Advances in Wheat Genetics: From Genome to Field by : Yasunari Ogihara
Download or read book Advances in Wheat Genetics: From Genome to Field written by Yasunari Ogihara and published by Springer. This book was released on 2015-09-15 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings is a collection of 46 selected papers that were presented at the 12th International Wheat Genetics Symposium (IWGS). Since the launch of the wheat genome sequencing project in 2005, the arrival of draft genome sequences has marked a new era in wheat genetics and genomics, catalyzing rapid advancement in the field. This book provides a comprehensive review of the forefront of wheat research, across various important topics such as germplasm and genetic diversity, cytogenetics and allopolyploid evolution, genome sequencing, structural and functional genomics, gene function and molecular biology, biotic stress, abiotic stress, grain quality, and classical and molecular breeding. Following an introduction, 9 parts of the book are dedicated to each of these topics. A final, 11th part entitled “Toward Sustainable Wheat Production” contains 7 excellent papers that were presented in the 12th IWGS Special Session supported by the OECD. With rapid population growth and radical climate changes, the world faces a global food crisis and is in need of another Green Revolution to boost yields of wheat and other widely grown staple crops. Although this book focuses on wheat, many of the newly developed techniques and results presented here can be applied to other plant species with large and complex genomes. As such, this volume is highly recommended for all students and researchers in wheat sciences and related plant sciences and for those who are interested in stable food production and food security.
Book Synopsis Enhancing Genetic Gain in a Wheat Breeding Program Using Genomics, Phenomics, Machine and Deep Learning Algorithms by : Karansher Singh Sandhu
Download or read book Enhancing Genetic Gain in a Wheat Breeding Program Using Genomics, Phenomics, Machine and Deep Learning Algorithms written by Karansher Singh Sandhu and published by . This book was released on 2021 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Rice Genomics, Genetics and Breeding by : Takuji Sasaki
Download or read book Rice Genomics, Genetics and Breeding written by Takuji Sasaki and published by Springer. This book was released on 2018-02-14 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest advances in rice genomics, genetics and breeding, with a special focus on their importance for rice biology and how they are breathing new life into traditional genetics. Rice is the main staple food for more than half of the world’s population. Accordingly, sustainable rice production is a crucial issue, particularly in Asia and Africa, where the population continues to grow at an alarming rate. The book’s respective chapters offer new and timely perspectives on the synergistic effects of genomics and genetics in novel rice breeding approaches, which can help address the urgent issue of providing enough food for a global population that is expected to reach 9 billion by 2050.
Book Synopsis Genomic Selection by : Gizachew Haile Gidamo
Download or read book Genomic Selection written by Gizachew Haile Gidamo and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many agronomic traits, such as grain yield, are controlled by polygenes with minor effects and epistatic interaction. Genomic selection (GS) uses genome-wide markers to predict a genomic estimate of breeding value (GEBV) that is used to select favorable individuals. GS involves three essential steps: prediction model training, prediction of breeding value, and selection of favorable individual based on the predicted GEBV. Prediction accuracies were evaluated using either correlation between GEBV (predicted) and empirically estimated (observed) value or cross-validation technique. Factors such as marker diversity and density, size and composition of training population, number of QTL, and heritability affect GS accuracies. GS has got potential applications in hybrid breeding, germplasm enhancement, and yield-related breeding programs. Therefore, GS is promising strategy for rapid improvement of genetic gain per unit time for quantitative traits with low heritability in breeding programs.
Book Synopsis Genomic selection and characterization in cereals by : Muhammad Abdul Rehman Rashid
Download or read book Genomic selection and characterization in cereals written by Muhammad Abdul Rehman Rashid and published by Frontiers Media SA. This book was released on 2023-05-10 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Quantitative Genetics in Maize Breeding by : Arnel R. Hallauer
Download or read book Quantitative Genetics in Maize Breeding written by Arnel R. Hallauer and published by Springer Science & Business Media. This book was released on 2010-09-28 with total page 669 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Association Mapping and Genomic Selection for Yield and Agronomic Traits in Soft Winter Wheat by : Dennis Nicuh Bulusan Lozada
Download or read book Association Mapping and Genomic Selection for Yield and Agronomic Traits in Soft Winter Wheat written by Dennis Nicuh Bulusan Lozada and published by . This book was released on 2018 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tools such as genome-wide association study (GWAS) and genomic selection (GS) have expedited the development of crops with improved genetic potential. While GWAS aims to identify significant markers associated with a trait of interest, the goal of GS is to utilize all marker effects to predict the performance of new breeding lines prior to testing. A GWAS for grain yield (GY), yield components, and agronomic traits was conducted using a diverse panel of 239 soft winter wheat (SWW) lines evaluated in eight site-years in Arkansas and Oklahoma. Broad sense heritability of GY (H2=0.48) was moderate compared to other traits including plant height (H2=0.81) and kernel weight (H2=0.77). Markers associated with multiple traits on chromosomes 1A, 2D, 3B, and 4B serve as potential targets for marker assisted breeding to select for GY improvement. Validation of GY-related loci using spring wheat from the International Maize and Wheat Improvement Center (CIMMYT) in Mexico confirmed the effects of three loci in chromosomes 3A, 4B, and 6B. Lines possessing the favorable allele at all three loci (A-C-G allele combination) had the highest mean GY of possible haplotypes. The same population of 239 lines was used in a GS study as a training population (TP) to determine factors that affect the predictability of GY. The TP size had the greatest effect on predictive ability across the measured traits. Adding covariates in the GS model was more advantageous in increasing prediction accuracies under single population cross validations than in forward predictions. Forward validation of the prediction models on two new populations resulted in a maximum accuracy of 0.43 for GY. Genomic selection was "superior" to marker-assisted selection in terms of response to selection and combining phenotypic selection with GS resulted in the highest response. Results from this study can be used to accelerate the process of GY improvement and increase genetic gains in wheat breeding programs.
Book Synopsis Genetic Data Analysis for Plant and Animal Breeding by : Fikret Isik
Download or read book Genetic Data Analysis for Plant and Animal Breeding written by Fikret Isik and published by Springer. This book was released on 2017-09-09 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book fills the gap between textbooks of quantitative genetic theory, and software manuals that provide details on analytical methods but little context or perspective on which methods may be most appropriate for a particular application. Accordingly this book is composed of two sections. The first section (Chapters 1 to 8) covers topics of classical phenotypic data analysis for prediction of breeding values in animal and plant breeding programs. In the second section (Chapters 9 to 13) we provide the concept and overall review of available tools for using DNA markers for predictions of genetic merits in breeding populations. With advances in DNA sequencing technologies, genomic data, especially single nucleotide polymorphism (SNP) markers, have become available for animal and plant breeding programs in recent years. Analysis of DNA markers for prediction of genetic merit is a relatively new and active research area. The algorithms and software to implement these algorithms are changing rapidly. This section represents state-of-the-art knowledge on the tools and technologies available for genetic analysis of plants and animals. However, readers should be aware that the methods or statistical packages covered here may not be available or they might be out of date in a few years. Ultimately the book is intended for professional breeders interested in utilizing these tools and approaches in their breeding programs. Lastly, we anticipate the usage of this volume for advanced level graduate courses in agricultural and breeding courses.
Download or read book Wheat Blast written by Sudheer Kumar and published by CRC Press. This book was released on 2020-04-09 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wheat Blast provides systematic and practical information on wheat blast pathology, summarises research progress and discusses future perspectives based on current understanding of the existing issues. The book explores advance technologies that may help in deciding the path for future research and development for better strategies and techniques to manage the wheat blast disease. It equips readers with basic and applied understanding on the identification of disease, its distribution and chances of further spread in new areas, its potential to cause yield losses to wheat, the conditions that favour disease development, disease prediction modelling, resistance breeding methods and management strategies against wheat blast. Features: Provides comprehensive information on wheat blast pathogen and its management under a single umbrella Covers disease identification and diagnostics which will be helpful to check introduction in new areas Discusses methods and protocol to study the different aspects of the disease such as diagnostics, variability, resistance screening, epiphytotic creation etc. Gives deep insight on the past, present and future outlook of wheat blast research progress This book’s chapters are contributed by experts and pioneers in their respective fields and it provides comprehensive insight with updated findings on wheat blast research. It serves as a valuable reference for researchers, policy makers, students, teachers, farmers, seed growers, traders, and other stakeholders dealing with wheat.
Book Synopsis Genome wide association studies and genomic selection for crop improvement in the era of big data by : Nunzio D’Agostino
Download or read book Genome wide association studies and genomic selection for crop improvement in the era of big data written by Nunzio D’Agostino and published by Frontiers Media SA. This book was released on 2023-05-05 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Predicting Genetic Value of Breeding Lines Using Genomic Selection in a Winter Wheat Breeding Program by : Elliot Lee Heffner
Download or read book Predicting Genetic Value of Breeding Lines Using Genomic Selection in a Winter Wheat Breeding Program written by Elliot Lee Heffner and published by . This book was released on 2011 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of marker-assisted selection (MAS) to predict genetic value of breeding lines is increasing in private and public plant breeding. MAS is an attractive alternative to phenotypic selection because MAS can be performed on a single plant or seed and decrease selection cycle duration. Advancements in genotyping are rapidly decreasing marker costs so that genotyping is becoming cheaper than phenotyping. Thus, the potential of MAS to achieve greater gains from selection per unit time and cost than phenotypic selection is growing. The ability to achieve genome-wide genotyping, however, may not be best utilized by conventional-MAS methods that have proven to be largely ineffective for improving the complex quantitative traits that dictate the success of new crop varieties. An emerging alternative to MAS is a technique termed genomic selection (GS) that uses a random-effects statistical modeling approach to jointly estimate all marker effects. This method does not require significance testing and has the goal of capturing small-effect QTL that are excluded by significance thresholds used in conventionalMAS. The use of GS is becoming a popular tool in animal breeding and is garnering the attention of plant breeders; however, evidence regarding the performance and the best methodology for applying GS in plant breeding is currently limited. In this research, GS was compared to conventional-MAS and phenotypic selection (PS) by deterministic simulation and empirical evaluations in plant breeding. Performance of these methods was empirically tested in two biparental wheat populations and in an advanced wheat breeding population comprised of multiple families derived from many different crosses. These studies showed that GS was superior to conventional-MAS in predicting the genetic value of breeding lines and that GS was competitive with PS in terms of accuracy. Furthermore, results indicate that GS could significantly reduce the selection cycle duration and achieve prediction accuracies that would enable plant breeders to achieve greater gains per unit time and cost than are possible with current MAS strategies.
