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Predicting Genetic Value Of Breeding Lines Using Genomic Selection In A Winter Wheat Breeding Program
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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.
Book Synopsis Evolution and Selection of Quantitative Traits by : Bruce Walsh
Download or read book Evolution and Selection of Quantitative Traits written by Bruce Walsh and published by Oxford University Press. This book was released on 2018-06-21 with total page 1504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative traits-be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene-usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences.
Book Synopsis Accuracy of Genomic Selection in a Soft Winter Wheat (Triticum Aestivum L.) Breeding Program by : Mao Huang
Download or read book Accuracy of Genomic Selection in a Soft Winter Wheat (Triticum Aestivum L.) Breeding Program written by Mao Huang and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic selection (GS) is a new marker assisted selection tool that utilizes data from lines in a training population (TP) to predict performance of other related lines by generating their genetic estimated breeding values. The selection process is complicated by genotype by environment interaction (GEI), as the performance of lines in one environment may not predict their performance in other environments. It is critical to evaluate and optimize GS accuracy with the existence of GEI. The GS accuracy can be evaluated by testing the GS model on different validation populations (VP). This study utilized subset of soft winter wheat lines from TP as the VP, and also utilized a VP composed of lines not included in, yet were genetically related to the TP. Our objectives were: 1) to assess GEI patterns and generate trait stability indices; 2) to evaluate GS accuracy for traits and trait stability indices for within and between-population predictions; 3) to assess the effects of optimization approaches on GS accuracy for between-environment predictions within population; 4) to assess GS accuracy from different optimization approaches for between-environment predictions across populations. An elite population (EP) of 273 lines and a yield population (YP) of 294 lines were phenotyped in independent sets of environments. A total of up to 24 different environments, representing four years across locations in five different states were assessed. The EP and YP were both phenotyped for yield (YLD), test weight (TW), plant height (HGT), and heading date (HD), and were genotyped with a common set of 3,537 single nucleotide polymorphism (SNP) markers. The EP was additionally phenotyped for seven quality traits. We produced useful GS prediction accuracy for within-population predictions for all traits (r ranging from 0.33 to 0.74) and most trait stability indices. We observed that ridge regression Best Linear Unbiased Prediction model was as predictive as other GS models, including the ones incorporating GEI term. The best approach to optimize the TP for between-environment accuracy was to subset markers that had the significant and stable effects coupled with eliminating least predictive lines in the TP. The between-population prediction for TW, HGT and HD were useful (r exceeded 0.29) though the between-population prediction for YLD was not within useful range (r ranged from -0.28 to 0.17). The EP and YP environments were separated in two distinct clusters based on the marker effects of YLD, and further supported the hypothesis that the low GS accuracy for YLD was mainly due to the marker effects by population interaction. This suggests that in order to obtain maximum GS accuracy for complex traits such as yield, the population to be predicted could consist of the same lines as in TP, but would be grown under different environments, or the new population to be predicted may be directly derived from TP. Our findings are directly applicable for wheat breeders in North-Eastern U.S. to best design GS schemes, and to implement GS in wheat breeding programs to achieve higher genetic gains with reduced costs and time than conventional breeding methods.
Book Synopsis Assessing Genome Wide Breeding Strategies for Economic Traits in Soft Winter Wheat and Their Impact on Genetic Architecture by : Amber L. Hoffstetter
Download or read book Assessing Genome Wide Breeding Strategies for Economic Traits in Soft Winter Wheat and Their Impact on Genetic Architecture written by Amber L. Hoffstetter and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With next generation sequencing technology, such as genotyping-by-sequencing (GBS), breeders can now genotype large populations with thousands of markers. This technology can be coupled with statistical methods such as genome-wide association studies (GWAS) and genomic selection (GS) to identify marker-trait associations and estimate marker effects. Where GWAS studies estimate each marker separately and use a p-value to determine significance, GS ignores significant thresholds and uses a training population (TP) with phenotypic and genotypic data to estimate all markers simultaneously. These effects are then used to predict the genomic estimated breeding values (GEBV) of other individuals. We performed a GWAS analysis using an elite population of soft red winter wheat lines and identified 14 QTL for grain yield (GY), four for Fusarium Head Blight (FHB) index, four for flour yield (FY), and five for softness equivalence (SE) Across all traits the R2 values ranged from 1.8 to 3.5%. We also determined the prediction accuracy GS for these four traits. Using all markers and lines we found the prediction accuracies ranged from 0.35 (FHB) to 0.57 (GY, Wooster, Ohio). In general using only data from TP lines with low GEI or marker subsets increased the GS accuracy. When using the TP to predict the performance of the 23 parental lines, accuracies using weighted correlations based on the parent’s contribution to the TP produced the highest prediction accuracies (r = 0.08 to 0.85). The accuracy of the TP model for predicting the phenotypes of the validation population was low (r = -0.25 to 0.22), especially for GY, but improved when using a subset of VP lines more related to the TP (r = 0.01 to 0.71). When analyzing the impact of GS on diversity and linkage disequilibrium (LD) we found that there was a loss of diversity across the two cycles of GS and that the second cycle of GS (GC1) is more inbred than the TP. LD for most marker pairs remains low across all three populations. The correlation of R2 values across the three populations ranged from 0.46 to 0.65. As LD between markers in the TP increases, a similar or higher LD is found with the F2 individuals comprising the two cycles of GS (GC0 and GC1). The frequency of the 1 allele for majority (46%) of markers associated with GY in Wooster, Ohio decreases while the remaining markers have either the 1 allele increasing or remaining unchanged. The preferred allele for these two trends is increasing for 95% and 80% of the markers respectively. The frequency of the 1 allele for individuals in the top 10% (best) and bottom 10% (worst) of the GC0 and GC1 individuals relative to the TP indicates that in the first cycle the majority (53%) of markers show signs of genetic drift while in the second cycle the majority (60%) show signs of direction selection. The results of this work show that these two breeding strategies could be useful for the SRWW program here of Ohio State. And indicates that GS impacts genetic diversity, LD, and allele frequencies.
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 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 Wheat Landraces by : Nusret Zencirci
Download or read book Wheat Landraces written by Nusret Zencirci and published by Springer Nature. This book was released on 2021-09-15 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Landraces possess a very large genetic base in population structure and are dynamic populations of cultivated plants with historical origin, distinct identity, and without any formal crop improvement. They are often genetically diverse, locally adapted, and associated with traditional farming systems. Resistance genes to biotic and abiotic stress factors, which are especially diversified in landraces, are of great interest to plant breeders, faced with global climate challenge. In addition, gene pools made of different landraces grown in different ecological conditions can be used for wheat breeding to enhance quality; yield and other desirable agricultural parameters. An estimated 75% of the genetic diversity of crop plants was lost in the last century due to the replacement of high yielding modern varieties. There is, thus, an urgent need to preserve existing species, not only for posterity but also as a means to secure food supply for a rising world population. In this book, we provide an overview of wheat landraces with special attention to genetic diversities, conservation, and utilization.
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 Quantitative Genetics and Selection in Plant Breeding by : Günter Wricke
Download or read book Quantitative Genetics and Selection in Plant Breeding written by Günter Wricke and published by Walter de Gruyter. This book was released on 2010-10-06 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative Genetics and Selection in Plant Breeding.
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 Molecular Plant Breeding by : Yunbi Xu
Download or read book Molecular Plant Breeding written by Yunbi Xu and published by CABI. This book was released on 2010 with total page 756 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in plant genomics and molecular biology have revolutionized our understanding of plant genetics, providing new opportunities for more efficient and controllable plant breeding. Successful techniques require a solid understanding of the underlying molecular biology as well as experience in applied plant breeding. Bridging the gap between developments in biotechnology and its applications in plant improvement, Molecular Plant Breeding provides an integrative overview of issues from basic theories to their applications to crop improvement including molecular marker technology, gene mapping, genetic transformation, quantitative genetics, and breeding methodology.
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.
Book Synopsis Plant Breeding: Past, Present and Future by : John E. Bradshaw
Download or read book Plant Breeding: Past, Present and Future written by John E. Bradshaw and published by Springer. This book was released on 2016-03-08 with total page 710 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to help plant breeders by reviewing past achievements, currently successful practices, and emerging methods and techniques. Theoretical considerations are also presented to strike the right balance between being as simple as possible but as complex as necessary. The United Nations predicts that the global human population will continue rising to 9.0 billion by 2050. World food production will need to increase between 70-100 per cent in just 40 years. First generation bio-fuels are also using crops and cropland to produce energy rather than food. In addition, land area used for agriculture may remain static or even decrease as a result of degradation and climate change, despite more land being theoretically available, unless crops can be bred which tolerate associated abiotic stresses. Lastly, it is unlikely that steps can be taken to mitigate all of the climate change predicted to occur by 2050, and beyond, and hence adaptation of farming systems and crop production will be required to reduce predicted negative effects on yields that will occur without crop adaptation. Substantial progress will therefore be required in bridging the yield gap between what is currently achieved per unit of land and what should be possible in future, with the best farming methods and best storage and transportation of food, given the availability of suitably adapted cultivars, including adaptation to climate change. My book is divided into four parts: Part I is an historical introduction; Part II deals with the origin of genetic variation by mutation and recombination of DNA; Part III explains how the mating system of a crop species determines the genetic structure of its landraces; Part IV considers the three complementary options for future progress: use of sexual reproduction in further conventional breeding, base broadening and introgression; mutation breeding; and genetically modified crops.
Book Synopsis The Effect of Cycles of Genomic Selection on Wheat (Triticum Aestivum L.) Traits and on the Wheat Genome by : Maria Nelly Arguello Blanco
Download or read book The Effect of Cycles of Genomic Selection on Wheat (Triticum Aestivum L.) Traits and on the Wheat Genome written by Maria Nelly Arguello Blanco and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Breeders use genomic selection (GS) for rapid cycling of parents for crop improvement. GS increases efficiency of breeding by shortening the duration of breeding cycles. The effect of rapid cycling on the wheat genome is unknown. The first objective of this dissertation was to assess the effect of five rapid cycles on the wheat genome. The OSU wheat breeding program began GS with a training population (YTP) phenotyped for yield, quality, and fusarium head blight (FHB) resistance traits. This YTP was genotyped with 3972 single nucleotide polymorphism (SNP) markers. The phenotypes and genotypes were used to build a GS model to obtain genomic estimated breeding values (GEBV). The GEBVs of F2 plants were then used to advance plants through five cycles of GS (YC1, YC2, YC3, YC4, YC5). We assessed the impact of GS on allele frequencies, the forces driving these changes, genetic distance (GD), population differentiation (FST), and linkage disequilibrium (LD). Relative to the YTP, we found that 27% of SNP had a significant change in allele frequency, 18% SNPs were under selection, 13% changed due to genetic drift, 9.3% were undiscernible for drift or selection, and 18.5% were fixed by YC5. Genetic distance narrowed within cycle while the GD between cycles increased at a 0.02 units per cycle. The cycles differentiated from the YTP at a rate of 0.046 FST units per cycle. The YC5 was highly differentiated from the YTP with an FST value of 0.224. We found the correlation between LD matrices decreasing at -0.057 units per cycle. Overall, we found reduction in genetic diversity, increased genetic differentiation of cycles from the YTP, and changes in LD patterns over cycles. The change in the genome is not desired when implementing GS because the changes will lower the prediction accuracy as the number of cycles increases. The second objective of this dissertation was to estimate the accuracy of GS at predicting the phenotypes of lines derived from cycles of GS. Prediction accuracy is the correlation between GEVBs and observed phenotypes. We compared the prediction accuracies using three training (YTP, TP1721 and TP21) and two prediction populations (GSELPP and GDERPP). We found moderate prediction accuracies (range of -0.25 to 0.25) that varied by trait, training population, and prediction population. While the accuracies were moderate to low, there were significant differences between the top and bottom groups of GSELPP for yield and FHB resistance when they were classified by their GEBVs. This is important as breeders select lines with the best values and discard those with the worst values. Classifying the GSELPP lines using GEBVs based on the TP1721 and TP21 produced significant positive differences for yield suggesting that GS impacts yield even when accuracies are modest. An important role for GS may lie in discarding the worst lines prior to expensive field testing, rather than its ability to identify the very best lines. Breeders should use the prediction accuracies to modify their program to maximize the benefits of GS. This would include the selection of parents, crossing schemes, and field testing so program resources are used to create TPs based on past data have the greatest potential to accurately predict the future.
Download or read book Stripe Rust written by Xianming Chen and published by Springer. This book was released on 2017-07-11 with total page 723 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively introduces stripe rust disease, its development and its integral control. Covering the biology, genetics, genome, and functional genomics of the pathogen, it also discusses host and non-host resistance, their interactions and the epidemiology of the disease. It is intended for scientists, postgraduates and undergraduate studying stripe rust, plant pathology, crop breeding, crop protection and agricultural science, but is also a valuable reference book for consultants and administrators in agricultural businesses and education.
Book Synopsis Genomic Selection Can Replace Phenotypic Selection in Early Generation Wheat Breeding by : Daniel Terry Borrenpohl
Download or read book Genomic Selection Can Replace Phenotypic Selection in Early Generation Wheat Breeding written by Daniel Terry Borrenpohl and published by . This book was released on 2019 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic selection (GS) enables plant breeders to estimate the value of unphenotyped lines using phenotypic and genotypic data from phenotyped lines. Plant breeders are investigating if GS can replace phenotypic selection (PS) in Stage-1 trials (also known as preliminary trials) where lines are phenotyped with minimal replication in one or few locations. We genotyped 1769 lines from the OSU winter wheat breeding program and used historical data from 2013-2018 to test various GS methods and compare them to PS. Our objectives were to: 1) test the prediction accuracy of GS as compared to PS; 2) test the impact of Stage-1 phenotypic data on GS prediction accuracy; and 3) to compare selections from PS and GS in Stage-1 trials. GS prediction accuracy for grain yield and fusarium head blight (FHB) index was comparable to PS regardless of Stage-1 phenotypic data used. GS methods using no Stage-1 phenotypic data (NST1) had marginally lower rank equivalence and selection coincidence for grain yield and FHB index compared to GS methods using Stage-1 phenotypic data and PS. Economic analysis suggests that using NST1 for Stage-1 selections could triple Stage-1 trial size compared to PS using equivalent resources. Given a fixed number of lines are selected for Stage-2 trials, higher selection intensity is observed for NST1 than PS. As a result, higher estimates of indirect gain from selection were observed for NST1 than PS for grain yield and FHB index. Our results suggest that GS can potentially replace PS in making selections from Stage-1 trials.
Book Synopsis Genetic and Genomic Tools for Improving End-use Quality in Wheat by : Emily Elizabeth Delorean
Download or read book Genetic and Genomic Tools for Improving End-use Quality in Wheat written by Emily Elizabeth Delorean and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
