Genetic control of soybean seed isoflavone content: importance of statistical model and epistasis in complex traits

Abstract

A major objective for geneticists is to decipher genetic architecture of traits associated with agronomic importance. However, a majority of such traits are complex, and their genetic dissection has been traditionally hampered not only by the number of minor-effect quantitative trait loci (QTL) but also by genome-wide interacting loci with little or no individual effect. Soybean (Glycine max [L.] Merr.) seed isoflavonoids display a broad range of variation, even in genetically stabilized lines that grow in a fixed environment, because their synthesis and accumulation are affected by many biotic and abiotic factors. Due to this complexity, isoflavone QTL mapping has often produced conflicting results especially with variable growing conditions. Herein, we comparatively mapped soybean seed isoflavones genistein, daidzein, and glycitein by using several of the most commonly used mapping approaches: interval mapping, composite interval mapping, multiple interval mapping and a mixed-model based composite interval mapping. In total, 26 QTLs, including many novel regions, were found bearing additive main effects in a population of RILs derived from the cross between Essex and PI 437654. Our comparative approach demonstrates that statistical mapping methodologies are crucial for QTL discovery in complex traits. Despite a previous understanding of the influence of additive QTL on isoflavone production, the role of epistasis is not well established. Results indicate that epistasis, although largely dependent on the environment, is a very important genetic component underlying seed isoflavone content, and suggest epistasis as a key factor causing the observed phenotypic variability of these traits in diverse environments.

Additional Quantitative Trait Loci and Candidate Genes for Seed Isoflavone Content in Soybean

“…Other studies identified QTL for total and individual isoflavone on the same Chr or LG. The QTL for daidgein (qDAID001-2010 on Chr_7/LG-M) reported in this study is 16 cM apart from the QTL (dai-M) identified on same LG in the 'Essex' by 'Forrest' RIL population (n=100) by Gutierrez-Gonzalez et al (2009). They identified two QTL for glycitein (gly-D2_1 and gly-D2_2) on LG-D2, on the same LG we reported a second QTL for daidgein (qDAID002-2010).…”

“…This might due to desire well-watered and temperature condition. Temperature, water, and soil nutrient conditions are known to play significant role in isoflavone production in soybean (Gutierrez-Gonzalez et al, 2009) and these findings could explain the difference we reported here in this study for accumulation of isoflavones in two different seasons. But also the variability observed for the seed isoflavones among RILs was attributable not only by environment but also for genetic and also genetic and environment (GXE) interactions.…”

“…However, it is likely that the genetic map locations of all quantitative trait loci (QTL) associated with isoflavone content have not yet been determined. The control of isoflavone content in soybean seeds is largely genetic rather than the environmental interactions (Gutierrez-Gonzalez et al, 2009;Hoeck, Fehr, Murphy, & Welke, 2000;Murphy et al, 2009), and several minor effects QTL have been found which determined amounts of isoflavonein soybean. Numerous studies have shown soybean isoflavones to be a quantitatively inherited trait (Kassem et al, 2004;Meksem et al, 2001;Kassem et al, 2006;Zeng et al, 2009).…”

“…Another high density genetic linkage maps was constructed using the 1,536 Universal Soy Linkage Panel 1.0 SNP markers in the 'PI 438489B' by 'Hamilton' populations and 31 linkage groups were obtained and used in this study . Up to date several isoflavones QTL were identified from the linkage map constructed by different markers (Kassem et al, 2004;Gutierrez-Gonzalez et al, 2009;Zeng et al, 2009;Gutierrez-Gonzalez et al, 2010). SNP markers allow the anchoring of genetic maps and QTL on the soybean genome sequence so identifying large genomic regions each containing several hundred genes.…”

Challenges for Metabolomics as a Tool in Safety Assessments

“…To illustrate, as far back as 1983, it was noted that "The concentration of the isoflavones vary from [soybean] variety to variety, and there are also differences when the same variety is grown in different locations" (Eldridge and Kwolek, 1983). Given the extensive scientific literature on isoflavone variability, it was unsurprising that Gutierrez-Gonzalez et al (2009) recently concluded that "The range of values of isoflavones is overwhelming, even for homozygous genotypes growing in the same year and location, which greatly complicates genetic studies." This is true for almost all crop compositional components as evidenced by challenges in enhancing nutritional quality in staple crops through conventional approaches.…”