Genomic regions associated with important seed quality traits in food-grade soybeans

Abstract

Background The production of soy-based food products requires specific physical and chemical characteristics of the soybean seed. Identification of quantitative trait loci (QTL) associated with value-added traits, such as seed weight, seed protein and sucrose concentration, could accelerate the development of competitive high-protein soybean cultivars for the food-grade market through marker-assisted selection (MAS). The objectives of this study were to identify and validate QTL associated with these value-added traits in two high-protein recombinant inbred line (RIL) populations. Results The RIL populations were derived from the high-protein cultivar ‘AC X790P’ (49% protein, dry weight basis), and two high-yielding commercial cultivars, ‘S18-R6’ (41% protein) and ‘S23-T5’ (42% protein). Fourteen large-effect QTL (R2 > 10%) were identified associated with seed protein concentration. Of these QTL, seven QTL were detected in both populations, and eight of them were co-localized with QTL associated with either seed sucrose concentration or seed weight. None of the protein-related QTL was found to be associated with seed yield in either population. Sixteen candidate genes with putative roles in protein metabolism were identified within seven of these protein-related regions: qPro_Gm02–3, qPro_Gm04–4, qPro_Gm06–1, qPro_Gm06–3, qPro_Gm06–6, qPro_Gm13–4 and qPro-Gm15–3. Conclusion The use of RIL populations derived from high-protein parents created an opportunity to identify four novel QTL that may have been masked by large-effect QTL segregating in populations developed from diverse parental cultivars. In total, we have identified nine protein QTL that were detected either in both populations in the current study or reported in other studies. These QTL may be useful in the curated selection of new soybean cultivars for optimized soy-based food products.

Identification of Candidate Genes and Genomic Selection for Seed Protein in Soybean Breeding Pipeline

“…Linkage analysis ( Hyten et al, 2004 ; Nichols et al, 2006 ; Pathan et al, 2013 ; Teng et al, 2017 ; Whiting et al, 2020 ) and genome-wide association study (GWAS) are powerful tools to identify markers associated with seed protein content in soybean ( Hwang et al, 2014 ; Leamy et al, 2017 ; Lee et al, 2019 ; Li S. et al, 2019 ); to date, a total of 262 loci have been reported through linkage analysis and 107 loci have been reported through GWAS ( Patil et al, 2017 ; Gangurde et al, 2020 ) per SoyBase. 1 These loci were on all the chromosomes, especially chromosome (Chr.)…”

“…The populations used for mapping protein content in the previous reports included pedigree-based F2 and F4:6 ( Csanádi et al, 2001 ; Chapman et al, 2003 ), recombinant inbred lines (RILs) population ( Qi et al, 2014 ; Hacisalihoglu et al, 2018 ), backcross population ( Sebolt et al, 2000 ; Liang et al, 2010 ), multiline population ( Brummer et al, 1997 ; Wang et al, 2014 ; Whiting et al, 2020 ), nested association mapping population ( Gangurde et al, 2020 ), and natural population ( Hwang et al, 2014 ; Bandillo et al, 2015 ; Li D. et al, 2019 ). Most studies used a single population, but some studies used two populations for QTL verification ( Vaughn et al, 2014 ; Zhang D. et al, 2017 ; Zhang et al, 2019 ); a few studies analyzed QTL using both the linkage mapping and associate mapping methods ( Zhang et al, 2019 ).…”

Identification of major QTLs for soybean seed size and seed weight traits using a RIL population in different environments

“…The qSW-4-1 overlapped with the QTLs associated with the long juvenility ( Yue et al., 2017 ). Researchers sometimes studied the seed weight of soybean for QTLs mapping in collaboration with protein and oil content ( Fasoula et al., 2004 ; Pathan et al., 2013 ; Yesudas et al., 2013 ; Wang X. et al., 2014 ; Whiting et al., 2020 ; He et al., 2021 ). In this study, qHSW-13-1 , qHSW-15-1 , qHSW-17-2 , qHSW-20-2 and the stable QTL qHSW-20-3 had overlap intervals with QTLs related to protein or oil content studied previously ( Panthee et al., 2005 ; Mao et al., 2013 ; Wang X. et al., 2014 ; Han et al., 2015 ; Fliege et al., 2022 ).…”

SoyMAGIC: An Unprecedented Platform for Genetic Studies and Breeding Activities in Soybean

“…Seed samples were placed in a 9 mm diameter clear glass bottle at 4 mm height for the NIR spectrometer. Evaluation of seeds was performed for chemical components concentration as intact (without any treatment) using calibrations provided by Perten Instruments, as reported by Whiting et al (2020) . Three technical replications were applied for each measurement.…”

“…However, one of the main challenges for soybean breeders is the complexity associated with accumulating many of the desired quantitative traits in new cultivars. Many of these traits are regulated by multiple genes, located in different genomic regions, and tend to be dynamically regulated by a range of environmental, molecular, and biochemical factors ( Whiting et al, 2020 ). A crucial step toward overcoming this challenge is deciphering the genetic structure of these quantitative traits, which can provide a prospect for plant breeders on how to select and develop cultivars with accumulated required traits.…”