Browsing by Author "Van Sanford, David A."

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    The Accuracy of Genomic Prediction between Environments and Populations for Soft Wheat Traits
    Huang, Mao; Ward, Brian P.; Griffey, Carl A.; Van Sanford, David A.; McKendry, Anne; Brown-Guedira, Gina L.; Tyagi, Priyanka; Sneller, Clay H. (2018-12)
    Genomic selection (GS) uses training population (TP) data to estimate the value of lines in a selection population. In breeding, the TP and selection population are often grown in different environments, which can cause low prediction accuracy when the correlation of genetic effects between the environments is low. Subsets of TP data may be more predictive than using all TP data. Our objectives were (i) to evaluate the effect of using subsets of TP data on GS accuracy between environments, and (ii) to assess the accuracy of models incorporating marker x environment interaction (MEI). Two wheat (Triticum aestivum L.) populations were phenotyped for 11 traits in independent environments and genotyped with single-nucleotide polymorphism markers. Within each population trait combination, environments were clustered. Data from one duster were used as the TP to predict the value of the same lines in the other cluster(s) of environments. Models were built using all TP data or subsets of markers selected for their effect and stability. The GS accuracy using all TP data was >0.25 for 9 of 11 traits. The between-environment accuracy was generally greatest using a subset of stable and significant markers; accuracy increased up to 48% relative to using all TP data. We also assessed accuracy using each population as the TP and the other as the selection population. Using subsets of TP data or the MEI models did not improve accuracy between populations. Using optimized subsets of markers within a population can improve GS accuracy by reducing noise in the prediction data set.
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    Genome-wide association studies for yield-related traits in soft red winter wheat grown in Virginia
    Ward, Brian P.; Brown-Guedira, Gina L.; Kolb, Frederic L.; Van Sanford, David A.; Tyagi, Priyanka; Sneller, Clay H.; Griffey, Carl A. (Public Library of Science, 2019-02-22)
    Grain yield is a trait of paramount importance in the breeding of all cereals. In wheat (Triticum aestivum L.), yield has steadily increased since the Green Revolution, though the current rate of increase is not forecasted to keep pace with demand due to growing world population and increasing affluence. While several genome-wide association studies (GWAS) on yield and related component traits have been performed in wheat, the previous lack of a reference genome has made comparisons between studies difficult. In this study, a GWAS for yield and yield-related traits was carried out on a population of 322 soft red winter wheat lines across a total of four rain-fed environments in the state of Virginia using single-nucleotide polymorphism (SNP) marker data generated by a genotyping-by-sequencing (GBS) protocol. Two separate mixed linear models were used to identify significant marker-trait associations (MTAs). The first was a single-locus model utilizing a leave-one-chromosome-out approach to estimating kinship. The second was a sub-setting kinship estimation multi-locus method (FarmCPU). The single-locus model identified nine significant MTAs for various yield-related traits, while the FarmCPU model identified 74 significant MTAs. The availability of the wheat reference genome allowed for the description of MTAs in terms of both genetic and physical positions, and enabled more extensive post-GWAS characterization of significant MTAs. The results indicate a number of promising candidate genes contributing to grain yield, including an ortholog of the rice aberrant panicle organization (APO1) protein and a gibberellin oxidase protein (GA2ox-A1) affecting the trait grains per square meter, an ortholog of the Arabidopsis thaliana mother of flowering time and terminal flowering 1 (MFT) gene affecting the trait seeds per square meter, and a B2 heat stress response protein affecting the trait seeds per head. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
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    Multienvironment and Multitrait Genomic Selection Models in Unbalanced Early-Generation Wheat Yield Trials
    Ward, Brian P.; Brown-Guedira, Gina L.; Tyagi, Priyanka; Kolb, Frederic L.; Van Sanford, David A.; Sneller, Clay H.; Griffey, Carl A. (Crop Science Society of America, 2019-02-21)
    The majority of studies evaluating genomic selection (GS) for plant breeding have used single-trait, single-site models that ignore genotype x environment interaction (GEI) effects. However, such studies do not accurately reflect the complexities of many applied breeding programs, and previous papers have found that models that incorporate GEI effects and multiple traits can increase the accuracy of genomic estimated breeding values (GEBVs). This study’s goal was to test GS methods for prediction in scenarios that simulate earlygeneration yield testing by correcting for field spatial variation, and fitting multienvironment and multitrait models on data for 14 traits of varying heritability evaluated in unbalanced designs across four environments. Corrections for spatial variation increased across-environment trait heritability by 25%, on average, but had little effect on model predictive ability. Results between all models were generally equivalent when predicting the performance of newly introduced genotypes. However, models incorporating GEI information and multiple traits increased prediction accuracy by up to 9.6% for low-heritability traits when phenotypic data were sparsely collected across environments. The results suggest that GS models using multiple traits and incorporating GEI effects may best be suited to predicting line performance in new environments when phenotypic data have already been collected across a subset of the total testing environments.