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Full-Text Articles in Horticulture
Genomic Selection For Yield And Seed Composition Stability In An Applied Soybean Breeding Program, Benjamin Harms
Genomic Selection For Yield And Seed Composition Stability In An Applied Soybean Breeding Program, Benjamin Harms
Department of Agronomy and Horticulture: Dissertations, Theses, and Student Research
Stability traits are of primary importance in plant breeding to ensure consistency in phenotype across a range of environments. However, selection efficiency and accuracy for stability traits can be hindered due to the requirement of obtaining phenotype data across multiple years and environments for proper stability analysis. Genomic selection is a method that allows prediction of a phenotype prior to observation in the field using genome-wide marker data and phenotype data from a training population. To assess prediction of stability traits, two elite-yielding soybean populations developed three years apart in the same breeding program were used. The individuals in each …
Genomic Resources In Plant Breeding For Sustainable Agriculture, Mahendar Thudi, Rajeev K. Varshney, James C. Schnable
Genomic Resources In Plant Breeding For Sustainable Agriculture, Mahendar Thudi, Rajeev K. Varshney, James C. Schnable
Department of Agronomy and Horticulture: Faculty Publications
Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, con- ventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965− 85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In …
Soybean Barcsoysnp6k: An Assay For Soybean Genetics And Breeding Research, Qijian Song, Long Yan, Charles Quigley, Edward Fickus, He Wei, Linfeng Chen, Faming Dong, Susan Araya, Jinlong Liu, David Hyten, Vincent R. Pantalone, Randall L. Nelson
Soybean Barcsoysnp6k: An Assay For Soybean Genetics And Breeding Research, Qijian Song, Long Yan, Charles Quigley, Edward Fickus, He Wei, Linfeng Chen, Faming Dong, Susan Araya, Jinlong Liu, David Hyten, Vincent R. Pantalone, Randall L. Nelson
Department of Agronomy and Horticulture: Faculty Publications
The limited number of recombinant events in recombinant inbred lines suggests that for a biparental population with a limited number of recombinant inbred lines, it is unnecessary to genotype the lines with many markers. For genomic prediction and selection, previous studies have demonstrated that only 1000–2000 genome-wide common markers across all lines/accessions are needed to reach maximum efficiency of genomic prediction in populations. Evaluation of too many markers will not only increase the cost but also generate redundant information. We developed a soybean (Glycine max) assay, BARCSoySNP6K, containing 6000 markers, which were carefully chosen from the SoySNP50K assay based …
Enhancing Hybrid Prediction In Pearl Millet Using Genomic And/Or Multi- Environment Phenotypic Information Of Inbreds, Diego Jarquin, Reka Howard, Zhikai Liang, Shashi K. Gupta, James C. Schnable, Jose Crossa
Enhancing Hybrid Prediction In Pearl Millet Using Genomic And/Or Multi- Environment Phenotypic Information Of Inbreds, Diego Jarquin, Reka Howard, Zhikai Liang, Shashi K. Gupta, James C. Schnable, Jose Crossa
Department of Agronomy and Horticulture: Faculty Publications
Genomic selection (GS) is an emerging methodology that helps select superior lines among experimental cultivars in plant breeding programs. It offers the opportunity to increase the productivity of cultivars by delivering increased genetic gains and reducing the breeding cycles. This methodology requires inexpensive and sufficiently dense marker information to be successful, and with whole genome sequencing, it has become an important tool in many crops. The recent assembly of the pearl millet genome has made it possible to employ GS models to improve the selection procedure in pearl millet breeding programs. Here, three GS models were implemented and compared using …
Deep Kernel And Deep Learning For Genome-Based Prediction Of Single Traits In Multienvironment Breeding Trials, José Crossa, Johannes W.R. Martini, Daniel Gianola, Paulino Pérez-Rodríguez, Diego Jarquin, Philomin Juliana, Osval Antonio Montesinos López, Jaime Cuevas
Deep Kernel And Deep Learning For Genome-Based Prediction Of Single Traits In Multienvironment Breeding Trials, José Crossa, Johannes W.R. Martini, Daniel Gianola, Paulino Pérez-Rodríguez, Diego Jarquin, Philomin Juliana, Osval Antonio Montesinos López, Jaime Cuevas
Department of Agronomy and Horticulture: Faculty Publications
Deep learning (DL) is a promising method for genomic-enabled prediction. However, the implementation of DL is difficult because many hyperparameters (number of hidden layers, number of neurons, learning rate, number of epochs, batch size, etc.) need to be tuned. For this reason, deep kernel methods, which only require defining the number of layers, may be an attractive alternative. Deep kernel methods emulate DL models with a large number of neurons, but are defined by relatively easily computed covariance matrices. In this research, we compared the genome-based prediction of DL to a deep kernel (arc-cosine kernel, AK), to the commonly used …
Genomic Selection In Preliminary Yield Trials In A Winter Wheat Breeding Program, Vikas Belamkar, Mary J. Guttieri, Waseem Hussain, Diego Jarquin, Ibrahim El-Basyoni, Jesse Poland, Aaron J. Lorenz, P. Stephen Baenziger
Genomic Selection In Preliminary Yield Trials In A Winter Wheat Breeding Program, Vikas Belamkar, Mary J. Guttieri, Waseem Hussain, Diego Jarquin, Ibrahim El-Basyoni, Jesse Poland, Aaron J. Lorenz, P. Stephen Baenziger
Department of Agronomy and Horticulture: Faculty Publications
Genomic prediction (GP) is now routinely performed in crop plants to predict unobserved phenotypes. The use of predicted phenotypes to make selections is an active area of research. Here, we evaluate GP for predicting grain yield and compare genomic and phenotypic selection by tracking lines advanced. We examined four independent nurseries of F3:6 and F3:7 lines trialed at 6 to 10 locations each year. Yield was analyzed using mixed models that accounted for experimental design and spatial variations. Genotype-by-sequencing provided nearly 27,000 high-quality SNPs. Average genomic predictive ability, estimated for each year by randomly masking lines as missing …
Genomic Prediction With Pedigree And Genotype X Environment Interaction In Spring Wheat Grown In South And West Asia, North Africa, And Mexico, Sivakumar Sukumaran, José Crossa, Diego Jarquin, Marta Lopes, Matthew P. Reynolds
Genomic Prediction With Pedigree And Genotype X Environment Interaction In Spring Wheat Grown In South And West Asia, North Africa, And Mexico, Sivakumar Sukumaran, José Crossa, Diego Jarquin, Marta Lopes, Matthew P. Reynolds
Department of Agronomy and Horticulture: Faculty Publications
Developing genomic selection (GS) models is an important step in applying GS to accelerate the rate of genetic gain in grain yield in plant breeding. In this study, seven genomic prediction models under two cross-validation (CV) scenarios were tested on 287 advanced elite spring wheat lines phenotyped for grain yield (GY), thousand-grain weight (GW), grain number (GN), and thermal time for flowering (TTF) in 18 international environments (year-location combinations) in major wheat-producing countries in 2010 and 2011. Prediction models with genomic and pedigree information included main effects and interaction with environments. Two random CV schemes were applied to predict a …
Genomic Prediction Of Gene Bank Wheat Landraces, José Crossa, Diego Jarquin, Jorge Franco, Paulino Pérez-Rodríguez, Juan Burgueño, Carolina Saint-Pierre, Prashant Vikram, Carolina Sansaloni, Cesar Petroli, Denis Akdemir, Clay Sneller, Matthew Reynolds, Maria Tattaris, Thomas Payne, Carlos Guzman, Roberto J. Peña, Peter Wenzl, Sukhwinder Singh
Genomic Prediction Of Gene Bank Wheat Landraces, José Crossa, Diego Jarquin, Jorge Franco, Paulino Pérez-Rodríguez, Juan Burgueño, Carolina Saint-Pierre, Prashant Vikram, Carolina Sansaloni, Cesar Petroli, Denis Akdemir, Clay Sneller, Matthew Reynolds, Maria Tattaris, Thomas Payne, Carlos Guzman, Roberto J. Peña, Peter Wenzl, Sukhwinder Singh
Department of Agronomy and Horticulture: Faculty Publications
This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% …
Accuracy Of Genomic Prediction In Switchgrass (Panicum Virgatum L.) Improved By Accounting For Linkage Disequilibrium, Guillaume P. Ramstein, Joseph Evans, Shawn M. Kaeppler, Robert B. Mitchell, Kenneth P. Vogel, C. Robin Buell, Michael D. Casler
Accuracy Of Genomic Prediction In Switchgrass (Panicum Virgatum L.) Improved By Accounting For Linkage Disequilibrium, Guillaume P. Ramstein, Joseph Evans, Shawn M. Kaeppler, Robert B. Mitchell, Kenneth P. Vogel, C. Robin Buell, Michael D. Casler
Department of Agronomy and Horticulture: Faculty Publications
Switchgrass is a relatively high-yielding and environmentally sustainable biomass crop, but further genetic gains in biomass yield must be achieved to make it an economically viable bioenergy feedstock. Genomic selection (GS) is an attractive technology to generate rapid genetic gains in switchgrass, and meet the goals of a substantial displacement of petroleum use with biofuels in the near future. In this study, we empirically assessed prediction procedures for genomic selection in two different populations, consisting of 137 and 110 half-sib families of switchgrass, tested in two locations in the United States for three agronomic traits: dry matter yield, plant height, …
A Genomic Selection Index Applied To Simulated And Real Data, J. Jesus Ceron-Rojas, Jose Crossa, Vivi N. Arief, Kaye Basford, Jessica Rutkoski, Diego Jarquin, Gregorio Alvarado, Yoseph Beyene, Kassa Semagn, Ian Delacy
A Genomic Selection Index Applied To Simulated And Real Data, J. Jesus Ceron-Rojas, Jose Crossa, Vivi N. Arief, Kaye Basford, Jessica Rutkoski, Diego Jarquin, Gregorio Alvarado, Yoseph Beyene, Kassa Semagn, Ian Delacy
Department of Agronomy and Horticulture: Faculty Publications
A genomic selection index (GSI) is a linear combination of genomic estimated breeding values that uses genomic markers to predict the net genetic merit and select parents from a nonphenotyped testing population. Some authors have proposed a GSI; however, they have not used simulated or real data to validate the GSI theory and have not explained how to estimate the GSI selection response and the GSI expected genetic gain per selection cycle for the unobserved traits after the first selection cycle to obtain information about the genetic gains in each subsequent selection cycle. In this paper, we develop the theory …
Selective Genotyping For Marker Assisted Selection Strategies For Soybean Yield Improvement, Benjamin D. Fallen, Fred L. Allen, Dean A. Kopsell, Arnold M. Saxton, Leah Mchale, J. Grover Shannon, Stella K. Kantartzi, Andrea J. Cardinal, P. B. Cregan, D. L. Hyten, Vincent R. Pantalone
Selective Genotyping For Marker Assisted Selection Strategies For Soybean Yield Improvement, Benjamin D. Fallen, Fred L. Allen, Dean A. Kopsell, Arnold M. Saxton, Leah Mchale, J. Grover Shannon, Stella K. Kantartzi, Andrea J. Cardinal, P. B. Cregan, D. L. Hyten, Vincent R. Pantalone
Department of Agronomy and Horticulture: Faculty Publications
Using molecular markers in soybean [Glycine max (L.) Merr.] has lead to the identification of major loci controlling quantitative and qualitative traits that include: disease resistance, insect resistance and tolerance to abiotic stresses. Yield has been considered as one of the most important quantitative traits in soybean breeding. Unfortunately, yield is a very complex trait and most yield quantitative trait loci (QTL) that have been identified have had only limited success for marker assisted selection (MAS). The objective of this study was to identify QTL associated with soybean seed yield in preliminary yield trials grown in different environments and …
Resource Allocation For Maximizing Prediction Accuracy And Genetic Gain Of Genomic Selection In Plant Breeding: A Simulation Experiment, Aaron Lorenz
Department of Agronomy and Horticulture: Faculty Publications
Allocating resources between population size and replication affects both genetic gain through phenotypic selection and quantitative trait loci detection power and effect estimation accuracy for marker-assisted selection (MAS). It is well known that because alleles are replicated across individuals in quantitative trait loci mapping and MAS, more resources should be allocated to increasing population size compared with phenotypic selection. Genomic selection is a form of MAS using all marker information simultaneously to predict individual genetic values for complex traits and has widely been found superior to MAS. No studies have explicitly investigated how resource allocation decisions affect success of genomic …