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

Autor: Stewart-Brown BB; Institute of Plant Breeding, Genetics and Genomics and Dep. of Crop and Soil Sci., University of Georgia, Athens, GA 30602., Song Q; Soybean Genomics and Improvement Lab, USDA-ARS, Beltsville, MD 20705., Vaughn JN; Genomics and Bioinformatics Research Unit, USDA-ARS, Center for Applied Genetic Technologies, Athens, GA 30602., Li Z; Institute of Plant Breeding, Genetics and Genomics and Dep. of Crop and Soil Sci., University of Georgia, Athens, GA 30602 zli@uga.edu.
Jazyk: angličtina
Zdroj: G3 (Bethesda, Md.) [G3 (Bethesda)] 2019 Jul 09; Vol. 9 (7), pp. 2253-2265. Date of Electronic Publication: 2019 Jul 09.
DOI: 10.1534/g3.118.200917
Abstrakt: 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.
(Copyright © 2019 Stewart-Brown et al.)
Databáze: MEDLINE
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