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Genome Wide Association Mapping And Genomic Prediction For Enhancing Fhb Resistance In Hard Winter Wheat
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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 Prediction and Genome Wide Association Mapping for Disease Resistance in Wheat by : Philomin Juliana
Download or read book Genomic Prediction and Genome Wide Association Mapping for Disease Resistance in Wheat written by Philomin Juliana and published by . This book was released on 2017 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Wheat (Triticum aestivum L.) is one of the major food crops in the world that is grown on more land area than any other commercial crop. The demand for wheat is expected to increase by 60% by 2050 which cannot be met with the current yield gain of 1%. Hence, it is important to evaluate different strategies for increasing the genetic gain in wheat. With this focus, we evaluated two strategies, genomic prediction and genome-wide association studies (GWAS) for disease resistance in CIMMYT’s international bread wheat screening nurseries (IBWSN). Our objective was to compare different prediction models for resistance to leaf rust (LR), stem rust (SR), stripe rust (STR), Septoria tritici blotch (STB), Stagonospora nodorum blotch (SNB) and tan spot (TS) in the 45th and 46th IBWSN entries. The prediction models tested include: Least-squares (LS), genomic-BLUP (G-BLUP), Bayesian ridge regression (BRR), Bayes A (BA), Bayes B (BB), Bayes C (BC), Bayesian least absolute shrinkage and selection operator (BL), reproducing kernel Hilbert spaces (RKHS) markers (RKHS-M), RKHS pedigree (RKHS-P) and RKHS markers and pedigree (RKHS-MP). The 333 lines in the 45th IBWSN and the 313 lines in the 46th IBWSN were genotyped using genotyping-by-sequencing markers. For the rusts, the mean prediction accuracies were 0.74 for LR seedling, 0.56 for LR APR, 0.65 for SR APR, 0.78 for YR seedling and 0.71 for YR APR. For the leaf spotting diseases, the mean genomic prediction accuracies were 0.45 for STB APR, 0.55 for SNB seedling, 0.66 for TS seedling and 0.48 for TS APR. Using genome-wide marker based models resulted in an average of 42-48% increase in accuracy over LS. Overall, the RKHS-MP model gave the highest accuracies, while LS gave the lowest. GWAS was also performed on these traits and several significant markers and candidate genes were identified. We conclude that implementing GWAS and genomic selection in breeding for these diseases would help to achieve higher accuracies and rapid gains from selection. ...
Book Synopsis Fusarium Head Blight in Canadian Winter Wheat by : Harwinder Sidhu
Download or read book Fusarium Head Blight in Canadian Winter Wheat written by Harwinder Sidhu and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Fusarium Head Blight (FHB) is a devastating disease of Wheat (Triticum aestivum L.) caused primarily by Fusarium graminearum Schwabe in Canada. Wheat FHB results in yield and produce quality losses. The clearest symptoms of FHB are premature bleaching of spikelets in the field, and fusarium damaged kernels in the harvested seed. Insufficient and costly disease control strategies make breeding for FHB resistance in wheat an ideal choice. There is a lack of FHB resistance sources in the Canadian wheat germplasm and Genome Wide Association Studies (GWAS) can be utilized to identify such sources. Furthermore, as no Quantitative Trait Loci (QTL) provides absolute resistance to FHB, identification of novel sources of resistance is desired. Genomic Selection (GS) has great potential in crop improvement, specifically for quantitative traits. A diversity panel that represents the genetic diversity of the Canadian, i.e., high latitude North American winter wheat was assembled for this thesis and the genetic diversity, population structure, and linkage disequilibrium of the germplasm was studied. To identify QTL associated with FHB, a GWAS study was conducted using the phenotypic data collected at three locations over two years. For understanding the role of number of genotypic markers, population structure, and choice of model in trait prediction modelling, a GS study was conducted. The Canadian Winter Wheat Diversity Panel is a diverse collection of winter wheat capturing diversity both in time and geography from Canada. The panel comprises of seven subpopulations with different LD, allele frequencies, and genetic diversity parameters. Multiple QTL associated with FHB related traits were identified on 13 chromosomes which may harbor genes involved in plant defense and stress response mechanisms. For FHB resistance improvement, the GS study demonstrated that the modelling parameters can be determined based upon the genotypic information available. Deoxynivalenol contamination was one of the traits with the highest prediction accuracy. GS can account for minor effect QTL, which is beneficial when breeding for quantitative traits. FHB resistant varieties are needed for an effective and economical disease control strategy in wheat and GS can complement current breeding efforts to develop FHB resistant wheat varieties.
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 Genomic Approaches for Mapping and Predicting Disease Resistance in Wheat (Triticum Aestivum L.) by : Cristiano Lemes Da Silva
Download or read book Genomic Approaches for Mapping and Predicting Disease Resistance in Wheat (Triticum Aestivum L.) written by Cristiano Lemes Da Silva and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Wheat diseases cause significant economic losses every year. To ensure global food security, newly released cultivars must possess increased levels of broadly-effective resistance against wheat pathogens, acceptable end-use quality, and high yield potential. Genetic host resistance stands out from other management strategies as the most viable option for controlling diseases. New genotyping platforms allow whole genome marker discovery at a relatively low cost, favoring the identification of novel loci underlying traits of interest. The work presented here describes genomic approaches for mapping and predicting the resistance to Fusarium head blight (FHB) and wheat rusts. The first study used biparental mapping to identify quantitative trait loci (QTL) associated with Fusarium head blight (FHB) resistance. A doubled haploid population (DH) was originated from a cross of Everest and WB-Cedar, which are widely grown wheat cultivars in Kansas with moderately resistant and moderately susceptible reactions to FHB, respectively. We confirmed that neither of the parents carry known large-effect QTLs, suggesting that FHB resistance is native. Eight small-effect QTLs were identified as associated with multiple mechanisms of FHB resistance. All QTLs had additive effects, providing significant improvements in levels of resistance when they were found in combinations within DH lines. In the second study, a genome-wide association mapping (GWAS) and genomic selection (GS) models were applied for FHB resistance in a panel of 962 elite lines from the K-State Wheat Breeding Program. Significant single nucleotide polymorphisms (SNPs) associated with the percentage of symptomatic spikelets were identified but not reproducible across breeding panels tested in each year. Accuracy of predictions ranged from 0.25 to 0.51 depending on GS model, indicating that it can be a useful tool to increase levels of FHB resistance. GWAS and GS approaches were also applied to a historical dataset to identify loci underlying resistance to leaf and stem rust at seedling stage in a panel of elite winter wheat lines. Infection types of multiple races of wheat rusts from the last sixteen years of the Southern Regional Performance Nursery (SRPN) were used in this study. A total of 533 elite lines originating from several breeding programs were tested in the SRPN during this period of time. GWAS identified significant SNP-trait associations for wheat rusts, confirming the effectiveness of already known genes and revealing potentially novel loci associated with resistance.
Book Synopsis Genome-Wide Association Studies Combined with Genomic Selection as a Tool to Increase Fusarium Head Blight Resistance in Wheat and Its Wild Relatives by : Sampurna Bartaula
Download or read book Genome-Wide Association Studies Combined with Genomic Selection as a Tool to Increase Fusarium Head Blight Resistance in Wheat and Its Wild Relatives written by Sampurna Bartaula and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Fusarium head blight (FHB) is a devastating wheat (Triticum aestivum L.) disease worldwide. Presently, there is insufficient FHB resistance in the Canadian wheat germplasm. Genome-wide association study (GWAS) and genomic selection (GS) can be utilized to identify sources of resistance that could benefit wheat breeding. To define the genetic architecture of FHB resistance, association panels from a spring and a winter collection were evaluated using the Wheat Illumina Infinium 90K array. A total of 206 accessions from the spring panel and 73 from the winter panel were evaluated in field trials for 3-4 years at two locations, namely Morden (Manitoba) and Ottawa (Ontario). These accessions were phenotyped for FHB incidence (INC), severity (SEV), visual rating index (VRI), and deoxynivalenol (DON) content. Significant (p
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 Resistance to Rusts (Puccinia Spp.) in Wheat Germplasm Collections by : Kebede Tadesse Muleta
Download or read book Genome-wide Association Mapping and Genomic Prediction for Resistance to Rusts (Puccinia Spp.) in Wheat Germplasm Collections written by Kebede Tadesse Muleta and published by . This book was released on 2016 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic selection has the potential to enhance the utilization of germplasm collections through prediction of genomic estimated breeding values (GEBV) for as many traits as have been measured. We assessed the effect of different population genetic properties and marker density scenarios on GEBV accuracy in the context of applying GS for wheat germplasm utilization. The results of the cross-validation tests demonstrated that prediction accuracy increased with increase in population size and marker density. The results of the current GS suggests that larger germplasm collections may be more efficiently sampled based on lower-density genotyping methods, while genetic relationships between the training and validation populations remain critical when exploiting GS to select from germplasm collections.
Book Synopsis Improving Marker Assisted Selection in Soft Winter Wheat for Fusarium Head Blight Resistance with QTL Validation, Genome-Wide Association, and Genomic Selection by : Jared Howell Benson
Download or read book Improving Marker Assisted Selection in Soft Winter Wheat for Fusarium Head Blight Resistance with QTL Validation, Genome-Wide Association, and Genomic Selection written by Jared Howell Benson and published by . This book was released on 2011 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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 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.
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 A Genome Wide Association Study for Fusarium Head Blight Resistance in Southern Soft Red Winter Wheat by : Amanda Leigh Holder
Download or read book A Genome Wide Association Study for Fusarium Head Blight Resistance in Southern Soft Red Winter Wheat written by Amanda Leigh Holder and published by . This book was released on 2018 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fusarium head blight (FHB) is a disease of small grains caused by the fungal pathogen Fusarium graminearum. FHB poses potential economic losses and health risks due to the accumulation of the mycotoxin deoxynivalenol (DON) on infected seed heads. The objectives of this study are: 1) evaluate soft red winter wheat (SRWW) lines for resistance to FHB in terms of resistance to initial inoculum (incidence); resistance to spread within the head (severity); resistance to DON accumulation; and resistance to Fusarium damaged kernels (FDK), 2) determine the frequency and effect of known FHB resistance genes and quantitative trait loci (QTL), and 3) identify novel resistance loci using a genome wide association (GWA) approach. From 2014-2017, 360 SRWW breeding lines were evaluated in inoculated misted FHB nurseries in Fayetteville and Newport, AR and Winnsboro, LA (2017 only) in a randomized complete block design. At all locations, lines were sown in two row plots, inoculated with F. graminearum infected corn (Zea mays L.) and overhead misted throughout the months of April and May to provide optimal conditions for FHB infection. In addition to visual ratings and DON analysis, lines were screened with KASP® markers linked to known FHB resistance genes, including Fhb1. The known resistance QTL, Qfhb.nc-2B.1 (Bess), on chromosome 3B was significantly associated with a reduction in incidence, severity, and DON accumulation. Genome wide SNP markers generated through genotype by sequencing (GBS) were used to perform GWA in order to identify marker-trait associations for FHB resistance. The GWA analysis identified 58 highly significant SNPs associated with the four disease traits. The most highly significant SNP was found on chromosome 4A and the minor allele was found to significantly reduce incidence by 1.17%. Results from this study will facilitate the development of SRWW cultivars with improved resistance to FHB.
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 Into the Unknown by : Lance Farley Merrick
Download or read book Into the Unknown written by Lance Farley Merrick and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winter wheat (Triticum aestivum L.) is the most widely grown cereal grain in Washington state. However, it generally takes 12 years to develop a cultivar from crossing to release, which can limit the ability for rapid improvement of traits. Genomic selection (GS) is posed to increase genetic gains by reducing the generation interval, also known as cycle time, while increasing response to selection. To fully utilize GS in breeding programs we reviewed and proposed implementing a two-part breeding strategy by differentiating population improvement and product development to increase the genetic gain of a breeding program. We then compared GS models for various scenarios for two complex traits, seedling emergence and stripe rust (Puccinia striiformis Westend. f. sp. tritici Erikss.) resistance, using two training populations composed of a diverse association mapping panel and breeding lines that can be used as a guideline for the implementation of GS into breeding programs. We compared GS models for seedling emergence and showed the consistent moderate accuracy of the parametric models indicates little advantage of using non-parametric models within individual years, but the non-parametric models show a slight increase in accuracy when combing years for complex traits. We then compared single-trait and multi-trait genome-wide association (GWAS) models with covariates, and were able to identify many small effect markers, while identifying the large effect markers on chromosome 5A. Stripe rust resistance is controlled by both major and minor resistance genes, and it is recommended to combine both major and minor genes for durable resistance. We compared the accuracy of GS models with major gene and GWAS markers as fixed effects and compared them to marker-assisted selection. The major gene and GWAS markers had only a small to zero increase in prediction accuracy over the base GS models but showed a statistical increase in accuracy using major markers when the mean accuracy decreased. Further, stripe rust resistance phenotypes are commonly skewed due to high levels of resistance. We compared classification and regression models and showed that breeders can use linear and non-parametric regression models within their own breeding lines over combined years to accurately predict skewed phenotypes.
