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Long-Term Recurrent Selection for Increased Seed Protein in Two Genetically Distinct Soybean Populations
Abstract
Soybean [Glycine max (L) Merr.] is the world’s premier source of vegetable protein and oil. Although accounting for a substantial proportion of the economic value, soybean seed protein concentration improvement is plagued by a negative correlation with important agronomic traits like yield and oil concentration. Recurrent selection experiments, many of which are short to intermediate-term in duration, have reported successful improvement in soybean protein concentration. This dissertation evaluated in a multi-environment trial, the response of increased protein concentration from a long-term recurrent selection experiment in two genetically distinct soybean populations. Correlated changes in other plant traits were tracked and both phenotypic and genotypic associations were investigated. Additionally, resultant changes in the quality of protein (amino acid composition) and oil (fatty acid composition) were evaluated. Inter and intra population changes in allele frequencies, and locus-specific effects on agronomic traits were characterized using a set of 1,000 single nucleotide polymorphism markers (SNPs). We report herein, the highest registered protein concentration in soybean genotypes and present evidence of underlying rationale for declared successful and observed continuous response to selection. We reveal the degree of genotypic association of the measured traits and elucidate the genetic mechanisms of their control. Using microscopic techniques, gross morphological changes in mature seed tissue were characterized. Overall, this study presents salient considerations in cultivar improvement and economic value maximization of quality traits in soybean production
Subject Area
Plant sciences|Genetics
Recommended Citation
Sserunkuma, Herbert, "Long-Term Recurrent Selection for Increased Seed Protein in Two Genetically Distinct Soybean Populations" (2022). ETD collection for University of Nebraska-Lincoln. AAI29323593.
https://digitalcommons.unl.edu/dissertations/AAI29323593