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      Genomic Selection for Yield and Seed Composition Traits Within an Applied Soybean Breeding Program

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          Abstract

          Genomic selection (GS) has become viable for selection of quantitative traits for which marker-assisted selection has often proven less effective. The potential of GS for soybean was characterized using 483 elite breeding lines, genotyped with BARCSoySNP6K iSelect BeadChips. Cross validation was performed using RR-BLUP and predictive abilities ( r MP) of 0.81, 0.71, and 0.26 for protein, oil, and yield, were achieved at the largest tested training set size. Minimal differences were observed when comparing different marker densities and there appeared to be inflation in r MP due to population structure. For comparison purposes, two additional methods to predict breeding values for lines of four bi-parental populations within the GS dataset were tested. The first method predicted within each bi-parental population (WP method) and utilized a training set of full-sibs of the validation set. The second method utilized a training set of all remaining breeding lines except for full-sibs of the validation set to predict across populations (AP method). The AP method is more practical as the WP method would likely delay the breeding cycle and leverage smaller training sets. Averaging across populations for protein and oil content, r MP for the AP method (0.55, 0.30) approached r MP for the WP method (0.60, 0.52). Though comparable, r MP for yield was low for both AP and WP methods (0.12, 0.13). Based on increases in r MP as training sets increased and the effectiveness of WP vs. AP method, the AP method could potentially improve with larger training sets and increased relatedness between training and validation sets.

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          Most cited references29

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          Efficiency and power in genetic association studies.

          We investigated selection and analysis of tag SNPs for genome-wide association studies by specifically examining the relationship between investment in genotyping and statistical power. Do pairwise or multimarker methods maximize efficiency and power? To what extent is power compromised when tags are selected from an incomplete resource such as HapMap? We addressed these questions using genotype data from the HapMap ENCODE project, association studies simulated under a realistic disease model, and empirical correction for multiple hypothesis testing. We demonstrate a haplotype-based tagging method that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency. Examining all observed haplotypes for association, rather than just those that are proxies for known SNPs, increases power to detect rare causal alleles, at the cost of reduced power to detect common causal alleles. Power is robust to the completeness of the reference panel from which tags are selected. These findings have implications for prioritizing tag SNPs and interpreting association studies.
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            Genomic Selection in Wheat Breeding using Genotyping-by-Sequencing

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              Integrating environmental covariates and crop modeling into the genomic selection framework to predict genotype by environment interactions.

              Development of models to predict genotype by environment interactions, in unobserved environments, using environmental covariates, a crop model and genomic selection. Application to a large winter wheat dataset. Genotype by environment interaction (G*E) is one of the key issues when analyzing phenotypes. The use of environment data to model G*E has long been a subject of interest but is limited by the same problems as those addressed by genomic selection methods: a large number of correlated predictors each explaining a small amount of the total variance. In addition, non-linear responses of genotypes to stresses are expected to further complicate the analysis. Using a crop model to derive stress covariates from daily weather data for predicted crop development stages, we propose an extension of the factorial regression model to genomic selection. This model is further extended to the marker level, enabling the modeling of quantitative trait loci (QTL) by environment interaction (Q*E), on a genome-wide scale. A newly developed ensemble method, soft rule fit, was used to improve this model and capture non-linear responses of QTL to stresses. The method is tested using a large winter wheat dataset, representative of the type of data available in a large-scale commercial breeding program. Accuracy in predicting genotype performance in unobserved environments for which weather data were available increased by 11.1% on average and the variability in prediction accuracy decreased by 10.8%. By leveraging agronomic knowledge and the large historical datasets generated by breeding programs, this new model provides insight into the genetic architecture of genotype by environment interactions and could predict genotype performance based on past and future weather scenarios.
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                Author and article information

                Journal
                G3 (Bethesda)
                Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes, Genomes, Genetics
                G3: Genes|Genomes|Genetics
                Genetics Society of America
                2160-1836
                14 May 2019
                July 2019
                : 9
                : 7
                : 2253-2265
                Affiliations
                [* ]Institute of Plant Breeding, Genetics and Genomics and Dep. of Crop and Soil Sci., University of Georgia, Athens, GA 30602
                []Soybean Genomics and Improvement Lab, USDA-ARS, Beltsville, MD 20705
                []Genomics and Bioinformatics Research Unit, USDA-ARS, Center for Applied Genetic Technologies, Athens, GA 30602
                Author notes
                [1 ]Corresponding Author: Institute of Plant Breeding, Genetics and Genomics and Dep. of Crop and Soil Sci., University of Georgia, Athens, GA 30602, E-mail: zli@ 123456uga.edu
                Author information
                http://orcid.org/0000-0001-7551-9833
                http://orcid.org/0000-0003-4114-9509
                Article
                GGG_200917
                10.1534/g3.118.200917
                6643879
                31088906
                7b6cd160-42aa-4727-9afb-8aa891bca46b
                Copyright © 2019 Stewart-Brown et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 22 December 2018
                : 10 May 2019
                Page count
                Figures: 6, Tables: 1, Equations: 0, References: 57, Pages: 13
                Categories
                Genomic Prediction

                Genetics
                genomic selection,rr-blup,seed composition,seed yield,soybean,genomic prediction,genpred,shared data resources

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