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Full-Text Articles in Life Sciences
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 …