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Full-Text Articles in Horticulture
Joint Use Of Genome, Pedigree, And Their Interaction With Environment For Predicting The Performance Of Wheat Lines In New Environments, Réka Howard, Daniel Gianola, Osval Montesinos-Lopez, Philomin Juliana, Ravi Singh, Jesse Poland, Sandesh Shrestha, Paulino Pérez-Rodriguez, José Crossa, Diego Jarquin
Joint Use Of Genome, Pedigree, And Their Interaction With Environment For Predicting The Performance Of Wheat Lines In New Environments, Réka Howard, Daniel Gianola, Osval Montesinos-Lopez, Philomin Juliana, Ravi Singh, Jesse Poland, Sandesh Shrestha, Paulino Pérez-Rodriguez, José Crossa, Diego Jarquin
Department of Agronomy and Horticulture: Faculty Publications
Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is highly influenced by environmental stimuli, it is important to accurately model the environment and its interaction with genetic factors in prediction models. Arguably, multi-environmental best linear unbiased prediction (BLUP) may deliver better prediction performance than single-environment genomic BLUP. We evaluated pedigree and genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented …
Predicting Longitudinal Traits Derived From High-Throughput Phenomics In Contrasting Environments Using Genomic Legendre Polynomials And B-Splines, Mehdi Momen, Malachy T. Campbell, Harkamal Walia, Gota Morota
Predicting Longitudinal Traits Derived From High-Throughput Phenomics In Contrasting Environments Using Genomic Legendre Polynomials And B-Splines, Mehdi Momen, Malachy T. Campbell, Harkamal Walia, Gota Morota
Department of Agronomy and Horticulture: Faculty Publications
Recent advancements in phenomics coupled with increased output from sequencing technologies can create the platform needed to rapidly increase abiotic stress tolerance of crops, which increasingly face productivity challenges due to climate change. In particular, high-throughput phenotyping (HTP) enables researchers to generate large-scale data with temporal resolution. Recently, a random regression model (RRM) was used to model a longitudinal rice projected shoot area (PSA) dataset in an optimal growth environment. However, the utility of RRM is still unknown for phenotypic trajectories obtained from stress environments. Here, we sought to apply RRM to forecast the rice PSA in control and water-limited …
Phenotypic Data From Inbred Parents Can Improve Genomic Prediction In Pearl Millet Hybrids, Zhikai Liang, Shashi K. Gupta, Cheng-Ting Yeh, Yang Zhang, Daniel W. Ngu, Ramesh Kumar, Hemant T. Patil, Kanulal D. Mungra, Dev Vart Yadav, Abhishek Rathore, Rakesh K. Srivastava, Rajeev Gupta, Jinliang Yang, Rajeev K. Varshney, Patrick S. Schnable, James C. Schnable
Phenotypic Data From Inbred Parents Can Improve Genomic Prediction In Pearl Millet Hybrids, Zhikai Liang, Shashi K. Gupta, Cheng-Ting Yeh, Yang Zhang, Daniel W. Ngu, Ramesh Kumar, Hemant T. Patil, Kanulal D. Mungra, Dev Vart Yadav, Abhishek Rathore, Rakesh K. Srivastava, Rajeev Gupta, Jinliang Yang, Rajeev K. Varshney, Patrick S. Schnable, James C. Schnable
Department of Agronomy and Horticulture: Faculty Publications
Pearl millet is a non-model grain and fodder crop adapted to extremely hot and dry environments globally. In India, a great deal of public and private sectors’ investment has focused on developing pearl millet single cross hybrids based on the cytoplasmic-genetic male sterility (CMS) system, while in Africa most pearl millet production relies on open pollinated varieties. Pearl millet lines were phenotyped for both the inbred parents and hybrids stage. Many breeding efforts focus on phenotypic selection of inbred parents to generate improved parental lines and hybrids. This study evaluated two genotyping techniques and four genomic selection schemes in pearl …
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, …
Genomic Prediction Of Single Crosses In The Early Stages Of A Maize Hybrid Breeding Pipeline, Dnyaneshwar C. Kadam, Sarah M. Potts, Martin O. Bohn, Alexander E. Lipka, Aaron J. Lorenz
Genomic Prediction Of Single Crosses In The Early Stages Of A Maize Hybrid Breeding Pipeline, Dnyaneshwar C. Kadam, Sarah M. Potts, Martin O. Bohn, Alexander E. Lipka, Aaron J. Lorenz
Department of Agronomy and Horticulture: Faculty Publications
Prediction of single-cross performance has been a major goal of plant breeders since the beginning of hybrid breeding. Recently, genomic prediction has shown to be a promising approach, but only limited studies have examined the accuracy of predicting single-cross performance. Moreover, no studies have examined the potential of predicting single crosses among random inbreds derived from a series of biparental families, which resembles the structure of germplasm comprising the initial stages of a hybrid maize breeding pipeline. The main objectives of this study were to evaluate the potential of genomic prediction for identifying superior single crosses early in the hybrid …
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 …
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 …