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Genotypic main effect and genotype-by-environment interaction effect on seed protein concentration and yield in food-grade soybeans (Glycine max (L.) Merrill)
- Whaley, Rachel, Eskandari, Mehrzad
- Euphytica 2019 v.215 no.2 pp. 33
- Glycine max, crossing, cultivars, environmental factors, genetic background, genotype-environment interaction, inbred lines, inheritance (genetics), phenotypic variation, plant breeding, soybeans, Ontario
- As consumers look for healthier dietary alternatives, many have recognized soybean [Glycine max (L.) Merrill] as a prominent source of high quality protein. The major obstacles to developing commercially competitive high protein food-grade soybean cultivars are the complex inheritance of seed protein concentration and its inverse association with yield. A better understanding of the genetic background and environmental conditions influencing soybean seed protein, and its relationship with yield, can facilitate the development of superior high-protein cultivars. Therefore, the main objective of this research was to study the influence of genotype and genotype-by-environment interaction effects (GGE) on seed protein and yield, and their relationship, using two recombinant inbred line (RIL) populations. The RIL populations were derived from crosses between a high-protein cultivar, AC X790P (486 g kg⁻¹, dry weight basis), and two moderate-protein elite cultivars, S18-R6 (404 g kg⁻¹) and S23-T5 (413 g kg⁻¹), and were evaluated in a multi-environment trial in southwestern Ontario, Canada, in 2015 and 2016. Significant (P < 0.05) phenotypic variation was observed for seed protein and yield in both populations within and across environments. The effects of genotype, environment, and GE interactions on both traits were significant in both populations. Genotypic main effect plus GGE biplot analyses led to the identification of stable high yielding high-protein RILs for the sub-regions and individual testing environments. These results indicated that the association between seed protein and yield can be manipulated using specific genetic backgrounds and environments, and superior genotypes for target regions can be identified using GGE biplots.