Identification of major QTLs and epistatic interactions for seed protein concentration in soybean under multiple environments based on a high-density map

Abstract

The concentration of protein in soybean is an important trait that drives successful soybean quality. A recombinant inbred line derived from a cross between the Charleston and Dongnong594 cultivars was planted in one location across 10 years and two locations across 5 years in China (20 environments in total), and the genetic effects were partitioned into additive main effects, epistatic main effects and their environmental interaction effects using composite interval mapping and inclusive composite interval mapping models based on a high-density genetic map. Ten main-effect quantitative trait loci (QTLs) were identified on chromosomes 3, 6, 7, 13, 15 and 20 and detected in more than three environments, with each of the main-effect QTLs contributing a phenotypic variation of around 10 %.Between the intervals of the main-effect QTLs, 93 candidate genes were screened for their involvement in seed protein storage and/or amino acid biosynthesis and metabolism processes based on gene ontology and annotation information. Furthermore, an analysis of epistatic interactions showed that three epistatic QTL pairs were detected, and could explain approximately 50 % of the phenotypic variation. The additive maineffect QTLs and epistatic QTL pairs contributed to high phenotypic variation under multiple environments, and the results were also validated and corroborated with previous research, indicating that marker-assisted selection can be used to improve soybean protein concentrations and that the candidate genes can also be used as a foundation data set for research on gene function.

Identification of soybean genes related to fatty acid content based on a soybean genome collinearity analysis

“…Glyma.06G168000 and Glyma.06G168100 , which are associated with fatty acid biosynthesis, were annotated. The similarities in the gene annotations between the study of Qi et al () and our study provide evidence that Glyma.06G168000 and Glyma.06G168100 are important for fatty acid synthesis. Our study has also generated new data not included in the report by Qi et al .…”

“…The WRINKLED1 gene encodes a transcription factor that directly or indirectly regulates the expression of genes involved in carbohydrate and lipid metabolism (Baud, Wuillème, To, Rochat, & Lepiniec, ). Meanwhile, Qi et al () used the RIL population generated from a cross between “Charleston” and “Dongnong 594” to identify 12 main‐effect QTLs. Glyma.06G168000 and Glyma.06G168100 , which are associated with fatty acid biosynthesis, were annotated.…”

Agronomic Evaluation of a High Protein Allele from PI407788A on Chromosome 15 across Two Soybean Backgrounds

“…The QTL from PI407788A was mapped by Kim et al (2015), but multiple seed composition QTL have been mapped to this location prior to and following that study (Diers et al, 1992;Sebolt et al, 2000;Fasoula et al, 2004;Pathan et al, 2013;Bandillo et al, 2015;Phansak et al, 2016;Qi et al, 2016). The QTL from PI407788A was mapped by Kim et al (2015), but multiple seed composition QTL have been mapped to this location prior to and following that study (Diers et al, 1992;Sebolt et al, 2000;Fasoula et al, 2004;Pathan et al, 2013;Bandillo et al, 2015;Phansak et al, 2016;Qi et al, 2016).…”

“…The exponential decrease in genotyping costs and improvements in large data analysis capabilities have led to several recent protein and oil QTL mapping studies that used large and diverse population sizes and/or high density genetic markers (Hwang et al, 2014;Bandillo et al, 2015;Vaughn et al, 2014;Phansak et al, 2016;Qi et al, 2016). These studies have mapped additional seed composition QTL, but the chr 15 and chr 20 QTL are still consistently identified as having the largest effects on protein and oil concentration.…”

“…Initial work in QTL mapping for seed composition traits was conducted using biparental populations. Large effect QTL have been mapped repeatedly to these two locations in other biparental populations and also more recently in diverse mapping panels (Lee et al, 1996;Brummer et al, 1997;Sebolt et al, 2000;Chung et al, 2003;Fasoula et al, 2004;Wang et al, 2014;Kim et al, 2015;Bandillo et al, 2015;Warrington et al, 2015;Phansak et al, 2016;Qi et al, 2016). and Zucc.…”