The Experts below are selected from a list of 110289 Experts worldwide ranked by ideXlab platform
Jean-luc Jannink - One of the best experts on this subject based on the ideXlab platform.
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Genomic Selection in Plant Breeding: A Comparison of Models
Crop Science, 2012Co-Authors: Nicolas Heslot, Hsiao-pei Yang, Mark E Sorrells, Jean-luc JanninkAbstract:Simulation and empirical studies of genomic selection (GS) show accuracies sufficient to generate rapid genetic gains. However, with the increased popularity of GS approaches, numerous models have been proposed and no comparative analysis is available to identify the most promising ones. Using eight wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), Arabidopsis thaliana (L.) Heynh., and maize (Zea mays L.) datasets, the predictive ability of currently available GS models along with several machine learning methods was evaluated by comparing accuracies, the genomic estimated Breeding values (GEBVs), and the marker effects for each model. While a similar level of accuracy was observed for many models, the level of overfitting varied widely as did the computation time and the distribution of marker effect estimates. Our comparisons suggested that GS in Plant Breeding programs could be based on a reduced set of models such as the Bayesian Lasso, weighted Bayesian shrinkage regression (wBSR, a fast version of BayesB), and random forest (RF) (a machine learning method that could capture nonadditive effects). Linear combinations of different models were tested as well as bagging and boosting methods, but they did not improve accuracy. This study also showed large differences in accuracy between subpopulations within a dataset that could not always be explained by differences in phenotypic variance and size. The broad diversity of empirical datasets tested here adds evidence that GS could increase genetic gain per unit of time and cost.
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genomic selection in Plant Breeding knowledge and prospects
Advances in Agronomy, 2011Co-Authors: Aaron J Lorenz, Mark E Sorrells, Elliot Lee Heffner, Shiaoman Chao, Franco G Asoro, Takeshi Hayashi, Hiroyoshi Iwata, Kevin P Smith, Jean-luc JanninkAbstract:Abstract “Genomic selection,” the ability to select for even complex, quantitative traits based on marker data alone, has arisen from the conjunction of new high-throughput marker technologies and new statistical methods needed to analyze the data. This review surveys what is known about these technologies, with sections on population and quantitative genetic background, DNA marker development, statistical methods, reported accuracies of genomic selection (GS) predictions, prediction of nonadditive genetic effects, prediction in the presence of subpopulation structure, and impacts of GS on long-term gain. GS works by estimating the effects of many loci spread across the genome. Marker and observation numbers therefore need to scale with the genetic map length in Morgans and with the effective population size of the population under GS. For typical crops, the requirements range from at least 200 to at most 10,000 markers and observations. With that baseline, GS can greatly accelerate the Breeding cycle while also using marker information to maintain genetic diversity and potentially prolong gain beyond what is possible with phenotypic selection. With the costs of marker technologies continuing to decline and the statistical methods becoming more routine, the results reviewed here suggest that GS will play a large role in the Plant Breeding of the future. Our summary and interpretation should prove useful to breeders as they assess the value of GS in the context of their populations and resources.
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Plant Breeding with genomic selection gain per unit time and cost
Crop Science, 2010Co-Authors: Elliot Lee Heffner, Jean-luc Jannink, Aaron J Lorenz, Mark E SorrellsAbstract:ABSTRACTAdvancements in Lorenz, USDA-ARS, R.W. Holley Center for Agriculture and Health, genotyping are rapidly decreasing Cornell Univ., Ithaca, NY 14853. Received 9 Nov. 2009. *Corremarker costs and increasing genome coverage. This is facilitating the use of marker-assisted selection (MAS) in Plant Breeding. Commonly employed MAS strategies, however, are not well suited for agronomically important complex traits, requiring extra time for !eld-based phenotyping to identify agro-nomically superior lines. Genomic selection (GS) is an emerging alternative to MAS that uses all marker information to calculate genomic esti-mated Breeding values (GEBVs) for complex traits. Selections are made directly on GEBV without further phenotyping. We developed an analytical framework to (i) compare gains from MAS and GS for complex traits and (ii) provide a Plant Breeding context for interpreting results from studies on GEBV accuracy. We designed MAS and GS Breeding strategies with equal budgets for a high-investment maize (Zea mays L.) program and a low-investment winter wheat (Triticum aestivum L.) program. Results indicate that GS can outperform MAS on a per-year basis even at low GEBV accuracies. Using a previ-ously reported GEBV accuracy of 0.53 for net merit in dairy cattle, expected annual gain from GS exceeded that of MAS by about threefold for maize and twofold for winter wheat. We con-clude that if moderate selection accuracies can be achieved, GS could dramatically accelerate genetic gain through its shorter Breeding"cycle.E.L. He-ner and M.E. Sorrells, Cornell Univ., Dep. of Plant Breeding and Genetics, Emerson Hall, Ithaca, NY 14853; J.-L. Jannink and A.J. -sponding author (mes12@cornell.edu).
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Plant Breeding with genomic selection gain per unit time and cost
Crop Science, 2010Co-Authors: Elliot Lee Heffner, Jean-luc Jannink, Aaron J Lorenz, Mark E SorrellsAbstract:ABSTRACTAdvancements in Lorenz, USDA-ARS, R.W. Holley Center for Agriculture and Health, genotyping are rapidly decreasing Cornell Univ., Ithaca, NY 14853. Received 9 Nov. 2009. *Corremarker costs and increasing genome coverage. This is facilitating the use of marker-assisted selection (MAS) in Plant Breeding. Commonly employed MAS strategies, however, are not well suited for agronomically important complex traits, requiring extra time for !eld-based phenotyping to identify agro-nomically superior lines. Genomic selection (GS) is an emerging alternative to MAS that uses all marker information to calculate genomic esti-mated Breeding values (GEBVs) for complex traits. Selections are made directly on GEBV without further phenotyping. We developed an analytical framework to (i) compare gains from MAS and GS for complex traits and (ii) provide a Plant Breeding context for interpreting results from studies on GEBV accuracy. We designed MAS and GS Breeding strategies with equal budgets for a high-investment maize (Zea mays L.) program and a low-investment winter wheat (Triticum aestivum L.) program. Results indicate that GS can outperform MAS on a per-year basis even at low GEBV accuracies. Using a previ-ously reported GEBV accuracy of 0.53 for net merit in dairy cattle, expected annual gain from GS exceeded that of MAS by about threefold for maize and twofold for winter wheat. We con-clude that if moderate selection accuracies can be achieved, GS could dramatically accelerate genetic gain through its shorter Breeding"cycle.E.L. He-ner and M.E. Sorrells, Cornell Univ., Dep. of Plant Breeding and Genetics, Emerson Hall, Ithaca, NY 14853; J.-L. Jannink and A.J. -sponding author (mes12@cornell.edu).
Tetsuya Ishii - One of the best experts on this subject based on the ideXlab platform.
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towards social acceptance of Plant Breeding by genome editing
Trends in Plant Science, 2015Co-Authors: Motoko Araki, Tetsuya IshiiAbstract:Although genome-editing technologies facilitate efficient Plant Breeding without introducing a transgene, it is creating indistinct boundaries in the regulation of genetically modified organisms (GMOs). Rapid advances in Plant Breeding by genome-editing require the establishment of a new global policy for the new biotechnology, while filling the gap between process-based and product-based GMO regulations. In this Opinion article we review recent developments in producing major crops using genome-editing, and we propose a regulatory model that takes into account the various methodologies to achieve genetic modifications as well as the resulting types of mutation. Moreover, we discuss the future integration of genome-editing crops into society, specifically a possible response to the ‘Right to Know' movement which demands labeling of food that contains genetically engineered ingredients.
Jens Mohring - One of the best experts on this subject based on the ideXlab platform.
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comparison of weighting in two stage analysis of Plant Breeding trials
Crop Science, 2009Co-Authors: Jens Mohring, Hans-peter PiephoAbstract:Series of Plant Breeding trials are often unbalanced and have a complex genetic structure. To reduce computing cost, it is common practice to employ a two-stage approach, where adjusted means per location are estimated and then a mixed model analysis of these adjusted means is performed. An important question is how means from the first step should be weighted in the second step. Our objective therefore was the comparison of different weighting methods in the analysis of four typical series of Plant Breeding trials using mixed models with fixed or random genetic effects. We used four published weighting methods and proposed three new methods. Four evaluation criteria were computed to compare methods, using one-stage analysis as benchmark. We found that the two-stage analysis gave acceptable results with fixed genetic effects. When genetic effects were taken as random in stage two, in three of four datasets the two-stage analysis gave acceptable results. In both cases differences between weighting methods were small and the best weighting method depended on the dataset but not on the evaluation criteria. A two-stage analysis without weighting also produced acceptable results, but weighting mostly performed better. In the fourth dataset the missing data pattern was informative, resulting in violation of the missing-at-random (MAR) assumption in one- and two-stage analysis. In this case both analyses were not strictly valid.
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blup for phenotypic selection in Plant Breeding and variety testing
Euphytica, 2008Co-Authors: Hans-peter Piepho, Jens Mohring, Albrecht E Melchinger, A BuchseAbstract:Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method was originally developed in animal Breeding for estimation of Breeding values and is now widely used in many areas of research. It does not, however, seem to have gained the same popularity in Plant Breeding and variety testing as it has in animal Breeding. In Plants, application of mixed models with random genetic effects has up until recently been mainly restricted to the estimation of genetic and non- genetic components of variance, whereas estimation of genotypic values is mostly based on a model with fixed effects. This paper reviews recent developments in the application of BLUP in Plant Breeding and variety testing. These include the use of pedigree information to model and exploit genetic correlation among relatives and the use of flexible variance-covariance structures for genotype-by-environment interaction. We demonstrate that BLUP has good predictive accuracy compared to other procedures. While pedi- gree information is often included via the so-called numerator relationship matrix ðAÞ, we stress that it is frequently straightforward to exploit the same infor- mation by a simple mixed model without explicit reference to the A-matrix.
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computing heritability and selection response from unbalanced Plant Breeding trials
Genetics, 2007Co-Authors: Hans-peter Piepho, Jens MohringAbstract:Heritability is often used by Plant breeders and geneticists as a measure of precision of a trial or a series of trials. Its main use is for computing the response to selection. Most formulas proposed for calculating heritability implicitly assume balanced data and independent genotypic effects. Both of these assumptions are often violated in Plant Breeding trials. This article proposes a simulation-based approach to tackle the problem. The key idea is to directly simulate the quantity of interest, e.g., response to selection, rather than trying to approximate it using some ad hoc measure of heritability. The approach is illustrated by three examples.
Sachiko N Isobe - One of the best experts on this subject based on the ideXlab platform.
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will genomic selection be a practical method for Plant Breeding
Annals of Botany, 2012Co-Authors: Akihiro Nakaya, Sachiko N IsobeAbstract:Background Genomic selection or genome-wide selection (GS) has been highlighted as a new approach for marker-assisted selection (MAS) in recent years. GS is a form of MAS that selects favourable individuals based on genomic estimated Breeding values. Previous studies have suggested the utility of GS, especially for capturing small-effect quantitative trait loci, but GS has not become a popular methodology in the field of Plant Breeding, possibly because there is insufficient information available on GS for practical use.
Birgit Arnholdtschmitt - One of the best experts on this subject based on the ideXlab platform.
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functional markers and a systemic strategy convergency between Plant Breeding Plant nutrition and molecular biology
Plant Physiology and Biochemistry, 2005Co-Authors: Birgit ArnholdtschmittAbstract:Research perspectives towards sustainable Plant production through Plant Breeding on abiotic stress tolerance require interdisciplinary competence. The present paper wants to strengthen awareness of important activities or current insights in Plant Breeding, Plant nutrition and molecular biology to facilitate approximation of the disciplines and to direct efforts in molecular biology early in development towards application. As a consequence of recent discussion on appropriate approaches in systems biology towards crop improvement, a global 'systemic strategy' is presented to identify functional marker candidates for abiotic stress tolerance more efficiently.
