Unraveling the genetic architecture for carbon and nitrogen related traits and leaf hydraulic conductance in soybean using genome-wide association analyses

Abstract

BackgroundDrought stress is a major limiting factor of soybean [Glycine max (L.) Merr.] production around the world. Soybean plants can ameliorate this stress with improved water-saving, sustained N2 fixation during water deficits, and/or limited leaf hydraulic conductance. In this study, carbon isotope composition (δ13C), which can relate to variation in water-saving capability, was measured. Additionally, nitrogen isotope composition (δ15N) and nitrogen concentration that relate to nitrogen fixation were evaluated. Decrease in transpiration rate (DTR) of de-rooted soybean shoots in a silver nitrate (AgNO3) solution compared to deionized water under high vapor pressure deficit (VPD) conditions was used as a surrogate measurement for limited leaf hydraulic conductance. A panel of over 200 genetically diverse soybean accessions genotyped with the SoySNP50K iSelect BeadChips was evaluated for the carbon and nitrogen related traits in two field environments (Athens, GA in 2015 and 2016) and for transpiration response to AgNO3 in a growth chamber. A multiple loci linear mixed model was implemented in FarmCPU to perform genome-wide association analyses for these traits.ResultsThirty two, 23, 26, and nine loci for δ13C, δ15N, nitrogen concentration, and transpiration response to AgNO3, respectively, were significantly associated with these traits. Candidate genes that relate to drought stress tolerance enhancement or response were identified near certain loci that could be targets for improving and understanding these traits. Soybean accessions with favorable breeding values were also identified. Low correlations were observed between many of the traits and the genetic loci associated with each trait were largely unique, indicating that these drought tolerance related traits are governed by different genetic loci.ConclusionsThe genomic regions and germplasm identified in this study can be used by breeders to understand the genetic architecture for these traits and to improve soybean drought tolerance. Phenotyping resources needed, trait heritability, and relationship to the target environment should be considered before deciding which of these traits to ultimately employ in a specific breeding program. Potential marker-assisted selection efforts could focus on loci which explain the greatest amount of phenotypic variation for each trait, but may be challenging due to the quantitative nature of these traits.

How does nitrogen shape plant architecture?

“…Principal component analysis in genome-wide association studies is an effective means of extracting key information from phenotypically complex traits, and has been performed for analyzing rice architecture ( Yano et al , 2019 ). This method has been broadly used for the analysis of N-related phenotyping in some other crops ( Zhang et al , 2015 ; Monostori et al , 2017 ; Nigro et al , 2019 ; Steketee et al , 2019 ) and it can be applied to isolate the key genes involved in the N regulation of plant architecture.…”

Genome-Wide Association Analyses Reveal Genomic Regions Controlling Canopy Wilting in Soybean

“…Population structure All population structure analyses were conducted using the full panel of 209 genotypes initially selected from MG III to IX for other drought tolerance related studies conducted in Steketee et al (2019), which include the 162 genotypes evaluated for canopy wilting in this study. The first two principal coordinates were visualized and colored by continent of origin ( Figure 2A).…”

Association mapping identifies and confirms loci for soybean seed weight

“…A number of recent reports indicate that Bonferroni correction is too conservative for multilocus models, resulting in false negatives (Kaler et al., 2020a, 2020b; Kaler & Purcell, 2019; Steketee et al, 2019; Zhang, Jia, & Dunwell, 2019). Zhang et al.…”

“…The selection of an appropriate threshold value can change with the use of different software packages, the heritability of the trait in question, the LD of the species, and the population structure of the association panel (Kaler & Purcell, 2019), among other factors. The threshold value we have used is based on a formula that uses marker‐based heritability (Kaler & Purcell, 2019) and is similar to the value used in other association mapping studies in soybean that use multi‐locus models (Kaler et al., 2020b; Steketee et al, 2019; Zhang et al., 2019).…”

“…The advantages of association mapping over linkage mapping include higher resolution, the lack of a need to create inbred populations, reduced research time, and greater allele numbers (Yu et al., 2006). Association mapping in soybean has been used to identify QTLs associated with numerous traits, including canopy temperature under drought (Kaler et al., 2018a), slow wilting (Kaler, Ray, King, Schapaugh, & Purcell, 2017a; Steketee, Sinclair, Mandeep, Schapaugh, & Li, 2019), carbon and oxygen isotopic ratios (Kaler et al., 2017b), oil and protein concentrations (Bandillo et al., 2015), shoot nitrogen concentration (Dhanapal et al., 2015), ureide concentration (Ray et al., 2015), and SW (Yan et al., 2017; Zhang et al., 2016).…”