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Full-Text Articles in Life Sciences
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 …