Molecular Marker Satt481 is Associated with Iron‐Deficiency Chlorosis Resistance in a Soybean Breeding Population

Charlson, Dirk V.; Bailey, T. B.; Cianzio, Silvia R.; Shoemaker, Randy C.

doi:10.2135/cropsci2004.0510

Cited by 41 publications

(28 citation statements)

References 16 publications

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“…SNPs significantly associated with Fe deficiency were identified on every chromosome except Gm04. The genomic regions on Gm03 and Gm19 reside within previously identified Fe efficiency QTL [28,60,61]. The remaining significant SNPs and the associated 228 genes identified throughout the genome were unique to this study.…”

Section: Gwas Identifies Novel Idc Genes Throughout the Soybean Genomementioning

confidence: 68%

Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches

et al. 2020

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Background: Iron (Fe) is an essential micronutrient for plant growth and development. Iron deficiency chlorosis (IDC), caused by calcareous soils or high soil pH, can limit iron availability, negatively affecting soybean (Glycine max) yield. This study leverages genome-wide association study (GWAS) and a genome-wide epistatic study (GWES) with previous gene expression studies to identify regions of the soybean genome important in iron deficiency tolerance. Results: A GWAS and a GWES were performed using 460 diverse soybean PI lines from 27 countries, in field and hydroponic iron stress conditions, using more than 36,000 single nucleotide polymorphism (SNP) markers. Combining this approach with available RNA-sequencing data identified significant markers, genomic regions, and novel genes associated with or responding to iron deficiency. Sixty-nine genomic regions associated with IDC tolerance were identified across 19 chromosomes via the GWAS, including the major-effect quantitative trait locus (QTL) on chromosome Gm03. Cluster analysis of significant SNPs in this region deconstructed this historically prominent QTL into four distinct linkage blocks, enabling the identification of multiple candidate genes for iron chlorosis tolerance. The complementary GWES identified SNPs in this region interacting with nine other genomic regions, providing the first evidence of epistatic interactions impacting iron deficiency tolerance. Conclusions: This study demonstrates that integrating cutting edge genome wide association (GWA), genome wide epistasis (GWE), and gene expression studies is a powerful strategy to identify novel iron tolerance QTL and candidate loci from diverse germplasm. Crops, unlike model species, have undergone selection for thousands of years, constraining and/or enhancing stress responses. Leveraging genomics-enabled approaches to study these adaptations is essential for future crop improvement.

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Section: Gwas Identifies Novel Idc Genes Throughout the Soybean Genomementioning

confidence: 68%

Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches

et al. 2020

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“…Since soybean has undergone multiple genome duplication events, we also examined the region homeologous to the chromosome 3 QTL. This region on chromosome 19 contains simple sequence repeat marker Satt481 which was associated with IDC resistance by Charlson et al (2005), confirming this region is also important for IDC tolerance. Assuming that a gene shared between these regions was responsible for IDC tolerance, we plotted the expression of all the Gm19 genes that had a homeolog on the chromosome 3 IDC QTL (Online Resource 16b).…”

Section: Leveraging Qtl Mapping Studies and Gene Expression Datamentioning

confidence: 71%

Dynamic gene expression changes in response to micronutrient, macronutrient, and multiple stress exposures in soybean

O’Rourke

McCabe

Graham

2019

Funct Integr Genomics

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Preserving crop yield is critical for US soybean production and the global economy. Crop species have been selected for increased yield for thousands of years with individual lines selected for improved performance in unique environments, constraints not experienced by model species such as Arabidopsis. This selection likely resulted in novel stress adaptations, unique to crop species. Given that iron deficiency is a perennial problem in the soybean growing regions of the USA and phosphate deficiency looms as a limitation to global agricultural production, nutrient stress studies in crop species are critically important. In this study, we directly compared whole-genome expression responses of leaves and roots to iron (Fe) and phosphate (P i) deficiency, representing a micronutrient and macronutrient, respectively. Conducting experiments side by side, we observed soybean responds to both nutrient deficiencies within 24 h. While soybean responds largely to-Fe deficiency, it responds strongly to P i resupply. Though the timing of the responses was different, both nutrient stress signals used the same molecular pathways. Our study is the first to demonstrate the speed and diversity of the soybean stress response to multiple nutrient deficiencies. We also designed the study to examine gene expression changes in response to multiple stress events. We identified 865 and 3375 genes that either altered their direction of expression after a second stress exposure or were only differentially expressed after a second stress event. Understanding the molecular underpinnings of these responses in crop species could have major implications for improving stress tolerance and preserving yield.

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“…In the last decade, AM has been used for mapping various qualitative and quantitative traits in plants, namely, flowering time in maize , disease resistance in rice (Garris et al 2003;Agrama and Eizenga 2008;Zhao et al 2011), iron deficiency chlorosis and protein content in soybean (Charlson et al 2003(Charlson et al , 2005Wang et al 2008), maysin and chlorogenic acid content in corn (Szalma et al 2005;Sun et al 2010), yield traits in barley (Kraakman et al 2004) and earliness in wheat (Gouis et al 2012). It has been identified as a tool to resolve complex trait variations down to the sequence level (Nordborg and Tavare 2002), and to identify novel mutations causing specific phenotypes (Palaisa et al 2004).…”

Section: Introductionmentioning

confidence: 99%

Population structure and association mapping studies for important agronomic traits in soybean

Kumar

Talukdar

Bala

et al. 2014

J Genet

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The present study was carried out with a set of 96 diverse soybean genotypes with the objectives of analysing the population structure and to identify molecular markers associated with important agronomic traits. Large phenotypic variability was observed for the agronomic traits under study indicating suitability of the genotypes for association studies. The maximum values for plant height, pods per plant, seeds per pod, 100-seed weight and seed yield per plant were approximately two and half to three times more than the minimum values for the genotypes. Seed yield per plant was found to be significantly correlated with pods per plant (r = 0.77), 100-seed weight (r = 0.35) and days to maturity (r = 0.23). The population structure studies depicted the presence of seven subpopulations which nearly corresponded with the source of geographical origin of the genotypes. Linkage disequilibrium (LD) between the linked markers decreased with the increased distance, and a substantial drop in LD decay values was observed between 30 and 35 cM. Genomewide marker-traits association analysis carried out using general linear (GLM) and mixed linear models (MLM) identified six genomic regions (two of them were common in both) on chromosomes 6, 7, 8, 13, 15 and 17, which were found to be significantly associated with various important traits viz., plant height, pods per plant, 100-seed weight, plant growth habit, average number of seeds per pod, days to 50% flowering and days to maturity. The phenotypic variation explained by these loci ranged from 6.09 to 13.18% and 4.25 to 9.01% in the GLM and MLM studies, respectively. In conclusion, association mapping (AM) in soybean could be a viable alternative to conventional QTL mapping approach.

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Molecular Marker Satt481 is Associated with Iron‐Deficiency Chlorosis Resistance in a Soybean Breeding Population

Cited by 41 publications

References 16 publications

Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches

Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches

Dynamic gene expression changes in response to micronutrient, macronutrient, and multiple stress exposures in soybean

Population structure and association mapping studies for important agronomic traits in soybean

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