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Context‐Specific Genomic Selection Strategies Outperform Phenotypic Selection for Soybean Quantitative Traits in the Progeny Row Stage
Author(s) -
Smallwood Christopher J.,
Saxton Arnold M.,
Gillman Jason D.,
Bhandari Hem S.,
Wadl Phillip A.,
Fallen Benjamin D.,
Hyten David L.,
Song Qijian,
Pantalone Vincent R.
Publication year - 2019
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2018.03.0197
Subject(s) - biology , best linear unbiased prediction , inbred strain , quantitative trait locus , genetic gain , selection (genetic algorithm) , trait , context (archaeology) , marker assisted selection , population , genetics , single nucleotide polymorphism , microbiology and biotechnology , plant breeding , genetic variation , genotype , agronomy , gene , paleontology , demography , sociology , computer science , programming language , artificial intelligence
Evaluating different breeding selection strategies for relative utility is necessary to choose those that maximize efficiency. Soybean [ Glycine max (L.) Merr.] seed yield and fatty acid, protein, and oil contents are all commercially important traits that display complex quantitative inheritance. A soybean population consisting of 860 F 5 –derived recombinant inbred lines (RILs), genotyped with 4867 polymorphic single nucleotide polymorphism (SNPs) was used to compare phenotypic and context specific genomic selection (GS) strategies. To simulate progeny rows, each RIL was grown in a single plot in 2010 in Knoxville, TN, and phenotype was recorded. A subset of 276 RILs with similar maturity was then grown in multilocation, replicated field trials in 2013 to compare the performance of each selection method in field conditions. Notably, the preferred method for each trait was GS. Of the GS approaches evaluated, Epistacy performed best for yield, and BayesB and/or genomic best linear unbiased prediction (G‐BLUP) were preferred for each of the other traits. Yield was the only trait for which the predictions had a large change when the number of SNPs and the number of RILs were randomly reduced for the G‐BLUP model, with the best predictions occurring when RILs with different maturity that were not grown in 2013 were removed from the training set. These findings provide important information on how soybean breeders can maximize selections from the progeny row stage for yield and fatty acid, protein, and oil contents by using appropriate selection strategies.