2014
Iron deficiency chlorosis: Getting to the root of the problem
Category:
Sustainable Production
Keywords:
Abiotic stressAgricultureLand Use Water supply
Lead Principal Investigator:
Carroll Vance, USDA/ARS-University of Minnesota
Co-Principal Investigators:
Robert Stupar, University of Minnesota
Project Code:
Contributing Organization (Checkoff):
Institution Funded:
Brief Project Summary:

The purpose of this continuing project is to address iron deficiency chlorosis in the upper Midwest using a combination of molecular genetics and gene cloning and manipulation. This is a coordinated project that combines the results of two distinct, but complementary approaches to solving this significant production constraint.

Soybean cultivars or breeding materials that combine complete tolerance to the IDC and acceptable yields are not available. The confounding factor is that the conditions necessary for screening are not found in all disease-prone ("hot") fields each year. To address this concern, molecular markers have been identified that are being used to test breeding materials....

Unique Keywords:
#environmental stress, #iron deficiency chlorosis (idc)
Information And Results
Project Deliverables

Final Project Results

Molecular Assisted Breeding (MAS) is a method where markers are used for selection instead of phenotyping. We had previously discovered 69 markers from 2 large scale genome-wide association studies (GWAS). Our goal recently was to analyze the data and determine their utility in three additional populations of unique breeding lines. We worked with these populations previously, and they were very useful confirmation populations for marker discovery. Analysis of variance showed the three populations have significant genotype, location and line × location interaction effects. This confirms that both genetic and environmental factors influence IDC response in soybean. The IDC score range for 2008 and 2009 populations is from 1.5 to 4.1 with a mean of 3.1 and 3 respectively, while the score for 2010 population is in the range of 1.6 to 3.1 with a mean of 2.3. Using a statistical model that accounted for population structure and line relatedness, we found nine, five and six markers significant at p < 0.05 in the 2008, 2009 and 2010 populations. The r-square is up to 18% for the 2008 and 2010 populations but up to 10% for the 2009 population. The allelic mean differences were 0.37, 0.27 and 0.29 for the 2008, 2009 and 2010 populations.

The significance of the markers varied between populations. The QTL on Gm03 at 45 Mbp was significant in all the three populations. This QTL region was noted to be significant in multiple other experiments us and others have performed. The best marker will be one that is in the causative gene. Therefore we searched for candidate genes. We discovered three in the QTL region. FRE1 is gene involved in increased reductase activity and provides higher tolerance in high pH soils, NRT1:2 is involved in increasing nitrate concentration in plants that leads to increased growth and root expansion. NAS3 is involved in the synthesis of iron chelator, nicotiamine. Overexpression of NAS genes increases Fe uptake (Lee et al. 2009, Masuda et al. 2009). The QTL on Gm19 was also found to be an important previously.

The genes in the QTL region include metal transporters (OPT5) and HMA that sequesters metals (Cd, Zn, and Ni) in vacuoles under Fe deficient conditions to maintain metal balance and to detoxify heavy metals. The QTL on Gm13 (27.14 Mbp) is includes the candidate iron gene FRD3 that facilitates citrate efflux into xylem. A mutation in this transporter leads to a failure to transport this metal to aerial parts (Green and Rogers 2004).

A stepwise regression that controls for correlations and interactions among different QTL was performed for each of these populations. Only the Gm03QTL was included in all the three years, making it the highly influenced QTL in multiple environments. The other QTL regions on Gm02, Gm06 and Gm19 are significant in only two of the three populations tested because of the varied effect on different QTL in different populations and their interactions with soils and environments. The genes in the QTL region on Gm06 include bHLH105 which has a role in intracellular iron trafficking and storage. This gene also interacts with PYE and brutus (BTS) and is known to be involved in Fe sensing. QTL on Gm07 that was not included in any of the populations could simply be interacting with other QTL making it least important to be included in the stepwise regression.

A second confirmation of a set of markers found to be associated within iron deficiency chlorosis (IDC), a yield limiting problem in soybean (Glycine max (L.) Merr) production regions with calcareous soils, was completed.

Genome-wide association study was performed using a high density SNP map to discover significant markers, QTL and candidate genes associated with IDC trait variation. A stepwise regression model included eight markers after considering LD between markers, and identified seven major effect QTL on seven chromosomes. Twelve candidate genes were found known to be associated with iron metabolism mapped near these QTL supporting the polygenic nature of IDC. A non-synonymous substitution with the highest significance in a major QTL region suggests soybean orthologs of FRE1 on Gm03 is a major gene responsible for trait variation. NAS3, a gene that encodes the enzyme nicotianamine synthase which synthesizes the iron chelator nicotianamine also maps to the same QTL region. Disease resistant genes also map to the major QTL, supporting the hypothesis that pathogens compete with the plant for Fe and increase iron deficiency. The markers and the allelic combinations identified here can be further used for marker assisted selection.

The United Soybean Research Retention policy will display final reports with the project once completed but working files will be purged after three years. And financial information after seven years. All pertinent information is in the final report or if you want more information, please contact the project lead at your state soybean organization or principal investigator listed on the project.