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Full-Text Articles in Life Sciences
Evaluating Metabolic And Genomic Data For Predicting Grain Traits Under High Night Temperature Stress In Rice, Ye Bi, Rafael Massahiro Yassue, Puneet Paul, Balpreet Kaur Dhatt, Jaspreet Sandhu, Phuc Thi Do, Harkamal Walia, Toshihiro Obata, Gota Morota
Evaluating Metabolic And Genomic Data For Predicting Grain Traits Under High Night Temperature Stress In Rice, Ye Bi, Rafael Massahiro Yassue, Puneet Paul, Balpreet Kaur Dhatt, Jaspreet Sandhu, Phuc Thi Do, Harkamal Walia, Toshihiro Obata, Gota Morota
Department of Agronomy and Horticulture: Faculty Publications
The asymmetric increase in average nighttime temperatures relative to increase in average daytime temperatures due to climate change is decreasing grain yield and quality in rice. Therefore, a better genome-level understanding of the impact of higher night temperature stress on the weight of individual grains is essential for future development of more resilient rice. We investigated the utility of metabolites obtained from grains to classify high night temperature (HNT) conditions of genotypes, and metabolites and single-nucleotide polymorphisms (SNPs) to predict grain length, width, and perimeter phenotypes using a rice diversity panel. We found that the metabolic profiles of rice genotypes …
Sorghum Association Panel Whole-Genome Sequencing Establishes Cornerstone Resource For Dissecting Genomic Diversity, J. Lucas Boatwright, Sirjan Sapkota, Hongyu Jin, James C. Schnable, Zachary Brenton, Richard Boyles, Stephen Kresovich
Sorghum Association Panel Whole-Genome Sequencing Establishes Cornerstone Resource For Dissecting Genomic Diversity, J. Lucas Boatwright, Sirjan Sapkota, Hongyu Jin, James C. Schnable, Zachary Brenton, Richard Boyles, Stephen Kresovich
Department of Agronomy and Horticulture: Faculty Publications
Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions. We report the whole-genome sequencing (WGS) of 400 sorghum (Sorghum bicolor (L.) Moench) accessions from the Sorghum Association Panel (SAP) at an average coverage of 38× (25–72×), enabling the development of a high-density genomic marker set of 43 983 694 variants including single-nucleotide polymorphisms (approximately 38 million), insertions/deletions (indels) (approximately 5 million), and copy number variants (CNVs) (approximately 170 000). We observe slightly more deletions among indels and a much higher prevalence …
Utility Of Climatic Information Via Combining Ability Models To Improve Genomic Prediction For Yield Within The Genomes To Fields Maize Project, Diego Jarquin, Natalia De Leon, Cinta Romay, Martin Bohn, Edward S. Buckler, Ignacio Ciampitti, Jode Edwards, David Ertl, Sherry Flint-Garcia, Michael A. Gore, Christopher Graham, Candice N. Hirsch, James B. Holland, David Hooker, Shawn M. Kaeppler, Joseph Knoll, Elizabeth C. Lee, Carolyn J. Lawrence-Dill, Jonathan P. Lynch, Stephen P. Moose, Seth C. Murray, Rebecca Nelson, Torbert Rocheford, James C. Schnable, Patrick S. Schnable, Margaret Smith, Nathan Springer, Peter Thomison, Mitch Tuinstra, Randall J. Wisser, Wenwei Xu, Jianming Yu, Aaron Lorenz
Utility Of Climatic Information Via Combining Ability Models To Improve Genomic Prediction For Yield Within The Genomes To Fields Maize Project, Diego Jarquin, Natalia De Leon, Cinta Romay, Martin Bohn, Edward S. Buckler, Ignacio Ciampitti, Jode Edwards, David Ertl, Sherry Flint-Garcia, Michael A. Gore, Christopher Graham, Candice N. Hirsch, James B. Holland, David Hooker, Shawn M. Kaeppler, Joseph Knoll, Elizabeth C. Lee, Carolyn J. Lawrence-Dill, Jonathan P. Lynch, Stephen P. Moose, Seth C. Murray, Rebecca Nelson, Torbert Rocheford, James C. Schnable, Patrick S. Schnable, Margaret Smith, Nathan Springer, Peter Thomison, Mitch Tuinstra, Randall J. Wisser, Wenwei Xu, Jianming Yu, Aaron Lorenz
Department of Agronomy and Horticulture: Faculty Publications
Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables …
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 …
Prediction Strategies For Leveraging Information Of Associated Traits Under Single- And Multi-Trait Approaches In Soybeans, Reyna Persa, Arthur Bernardeli, Diego Jarquin
Prediction Strategies For Leveraging Information Of Associated Traits Under Single- And Multi-Trait Approaches In Soybeans, Reyna Persa, Arthur Bernardeli, Diego Jarquin
Department of Agronomy and Horticulture: Faculty Publications
The availability of molecular markers has revolutionized conventional ways to improve genotypes in plant and animal breeding through genome-based predictions. Several models and methods have been developed to leverage the genomic information in the prediction context to allow more efficient ways to screen and select superior genotypes. In plant breeding, usually, grain yield (yield) is the main trait to drive the selection of superior genotypes; however, in many cases, the information of associated traits is also routinely collected and it can potentially be used to enhance the selection. In this research, we considered different prediction strategies to leverage the information …
Response Surface Analysis Of Genomic Prediction Accuracy Values Using Quality Control Covariates In Soybean, Diego Jarquin, Reka Howard, George L. Graef, Aaron Lorenz
Response Surface Analysis Of Genomic Prediction Accuracy Values Using Quality Control Covariates In Soybean, Diego Jarquin, Reka Howard, George L. Graef, Aaron Lorenz
Department of Agronomy and Horticulture: Faculty Publications
An important and broadly used tool for selection purposes and to increase yield and genetic gain in plant breeding programs is genomic prediction (GP). Genomic prediction is a technique where molecular marker information and phenotypic data are used to predict the phenotype (eg, yield) of individuals for which only marker data are available. Higher prediction accuracy can be achieved not only by using efficient models but also by using quality molecular marker and phenotypic data. The steps of a typical quality control (QC) of marker data include the elimination of markers with certain level of minor allele frequency (MAF) and …
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 …
Utilizing Random Regression Models For Genomic Prediction Of A Longitudinal Trait Derived From High‐Throughput Phenotyping, Malachy T. Campbell, Harkamal Walia, Gota Morota
Utilizing Random Regression Models For Genomic Prediction Of A Longitudinal Trait Derived From High‐Throughput Phenotyping, Malachy T. Campbell, Harkamal Walia, Gota Morota
Department of Agronomy and Horticulture: Faculty Publications
The accessibility of high‐throughput phenotyping platforms in both the greenhouse and field, as well as the relatively low cost of unmanned aerial vehicles, has provided researchers with an effective means to characterize large populations throughout the growing season. These longitudinal phenotypes can provide important insight into plant development and responses to the environment. Despite the growing use of these new phenotyping approaches in plant breeding, the use of genomic prediction models for longitudinal phenotypes is limited in major crop species. The objective of this study was to demonstrate the utility of random regression (RR) models using Legendre polynomials for genomic …
Increasing Predictive Ability By Modeling Interactions Between Environments, Genotype And Canopy Coverage Image Data For Soybeans, Diego Jarquin, Reka Howard, Alencar Xavier, Sruti Das Choudhury
Increasing Predictive Ability By Modeling Interactions Between Environments, Genotype And Canopy Coverage Image Data For Soybeans, Diego Jarquin, Reka Howard, Alencar Xavier, Sruti Das Choudhury
Department of Agronomy and Horticulture: Faculty Publications
Phenomics is a new area that offers numerous opportunities for its applicability in plant breeding. One possibility is to exploit this type of information obtained from early stages of the growing season by combining it with genomic data. This opens an avenue that can be capitalized by improving the predictive ability of the common prediction models used for genomic prediction. Imagery (canopy coverage) data recorded between days 14–71 using two collection methods (ground information in 2013 and 2014; aerial information in 2014 and 2015) on a soybean nested association mapping population (SoyNAM) was used to calibrate the prediction models together …
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% …
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 …
Prospects Of Genomic Prediction In The Usda Soybean Germplasm Collection: Historical Data Creates Robust Models For Enhancing Selection Of Accessions, Diego Jarquin, James Specht, Aaron Lorenz
Prospects Of Genomic Prediction In The Usda Soybean Germplasm Collection: Historical Data Creates Robust Models For Enhancing Selection Of Accessions, Diego Jarquin, James Specht, Aaron Lorenz
Department of Agronomy and Horticulture: Faculty Publications
The identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K SNP data collected on 18,480 domesticated soybean (G. max) accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained …