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Genome Wide Prediction Of Hybrid Performance And Yield Stability Analysis In Winter Wheat
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Book Synopsis Genome-wide Prediction of Hybrid Performance and Yield Stability Analysis in Winter Wheat by : Guozheng Liu
Download or read book Genome-wide Prediction of Hybrid Performance and Yield Stability Analysis in Winter Wheat written by Guozheng Liu and published by . This book was released on 2018* with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Hybrid wheat; Yield stability; Quality trait; Disease resistance; Genomic selection; Marker-assisted selection
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 Plant Breeding Reviews, Volume 22 by : Jules Janick
Download or read book Plant Breeding Reviews, Volume 22 written by Jules Janick and published by John Wiley & Sons. This book was released on 2003-03-19 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: Plant Breeding Reviews, Volume 22 presents state-of-the-art reviews on plant genetics and the breeding of all types of crops by both traditional means and molecular methods. The emphasis of the series is on methodology, a practical understanding of crop genetics, and applications to major crops.
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 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 Genome-wide Association Mapping and Genomic Prediction for Enhancing FHB Resistance in Hard Winter Wheat by : Jinfeng Zhang
Download or read book Genome-wide Association Mapping and Genomic Prediction for Enhancing FHB Resistance in Hard Winter Wheat written by Jinfeng Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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 Plant Breeding Reviews, Part 2: Long-term Selection by : Jules Janick
Download or read book Plant Breeding Reviews, Part 2: Long-term Selection written by Jules Janick and published by Wiley. This book was released on 2011-02-25 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Plant Breeding Reviews, Volume 24, Part 2 presents state-of-the-art reviews on plant genetics and the breeding of all types of crops by both traditional means and molecular methods. The emphasis of the series is on methodology, a practical understanding of crop genetics, and applications to major crops.
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 Association Analysis and Genome-wide Selection for Early Maturity in Wheat by : Nafeti Titus Mheni
Download or read book Association Analysis and Genome-wide Selection for Early Maturity in Wheat written by Nafeti Titus Mheni and published by . This book was released on 2014 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Crop phenology is an important component of wheat adaptation to climate change. A few major genes and QTLs, along with minor genes have been reported to control variation for flowering and maturity in wheat. This study aimed to 1) identify QTLs for heading date (HD) 2) evaluate the accuracy and relative efficiency of genomic selection (GS) versus phenotypic selection for HD 3) assess the stability of alleles and genomic selection models for HD in spring and winter wheat. We used a soft winter wheat (SWW) panel phenotyped for HD in North America, and a hard spring wheat (HSW) panel phenotyped in the United States and Arusha Tanzania. The panels were genotyped with SNP markers. The analysis of genotype by environmental interaction produced two clusters of environments for each population with one cluster consisting of environments that produced a large range of HD and the other a narrow range. In both winter and spring wheat we detected seven very significant (p
Book Synopsis Genetics and Genomics to Enhance Crop Production, Towards Food Security by : Ajay Kumar
Download or read book Genetics and Genomics to Enhance Crop Production, Towards Food Security written by Ajay Kumar and published by Frontiers Media SA. This book was released on 2022-01-17 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Crop Breeding written by Delphine Fleury and published by . This book was released on 2014 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
Author :Food and Agriculture Organization of the United Nations Publisher :Food & Agriculture Organization of the UN (FAO) ISBN 13 : Total Pages :576 pages Book Rating :4.3/5 (91 download)
Book Synopsis Bread Wheat by : Food and Agriculture Organization of the United Nations
Download or read book Bread Wheat written by Food and Agriculture Organization of the United Nations and published by Food & Agriculture Organization of the UN (FAO). This book was released on 2002 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wheat is the most widely grown cereal crop in the world, with an ever increasing demand. It plays a fundamental role in food security, and a major challenge is to meet the additional requirements with new cultivars and improved cropping technologies. This book covers information from a number of international experts on the current status of research on wheat improvement and production (wheat breeding programs and genetics), as well as on other aspects (including wheat diseases), from its evolutionary origins to seed production technologies. [from the FAO website]
Book Synopsis The Applications of New Multi-Locus GWAS Methodologies in the Genetic Dissection of Complex Traits by : Yuan-Ming Zhang
Download or read book The Applications of New Multi-Locus GWAS Methodologies in the Genetic Dissection of Complex Traits written by Yuan-Ming Zhang and published by Frontiers Media SA. This book was released on 2019-06-19 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genome-Wide Association Studies (GWAS) are widely used in the genetic dissection of complex traits. Most existing methods are based on single-marker association in genome-wide scans with population structure and polygenic background controls. To control the false positive rate, the Bonferroni correction for multiple tests is frequently adopted. This stringent correction results in the exclusion of important loci, especially for GWAS in crop genetics. To address this issue, multi-locus GWAS methodologies have been recommended, i.e., FASTmrEMMA, ISIS EM-BLASSO, mrMLM, FASTmrMLM, pLARmEB, pKWmEB and FarmCPU. In this Research Topic, our purpose is to clarify some important issues in the application of multi-locus GWAS methods. Here we discuss the following subjects: First, we discuss the advantages of new multi-locus GWAS methods over the widely-used single-locus GWAS methods in the genetic dissection of complex traits, metabolites and gene expression levels. Secondly, large experiment error in the field measurement of phenotypic values for complex traits in crop genetics results in relatively large P-values in GWAS, indicating the existence of small number of significantly associated SNPs. To solve this issue, a less stringent P-value critical value is often adopted, i.e., 0.001, 0.0001 and 1/m (m is the number of markers). Although lowering the stringency with which an association is made could identify more hits, confidence in these hits would significantly drop. In this Research Topic we propose a new threshold of significant QTN (LOD=3.0 or P-value=2.0e-4) in multi-locus GWAS to balance high power and low false positive rate. Thirdly, heritability missing in GWAS is a common phenomenon, and a series of scientists have explained the reasons why the heritability is missing. In this Research Topic, we also add one additional reason and propose the joint use of several GWAS methodologies to capture more QTNs. Thus, overall estimated heritability would be increased. Finally, we discuss how to select and use these multi-locus GWAS methods.
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
