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Title: Identification of quantitative trait loci associated with seed iron in the legumes Lotus japonicus and Medicago truncatula

item Grusak, Michael

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 3/31/2008
Publication Date: 10/11/2008
Citation: Klein, M., Sankaran, R., Grusak, M.A. 2008. Identification of quantitative trait loci associated with seed iron in the legumes Lotus japonicus and Medicago truncatula [abstract]. XIV International Symposium on Iron Nutrition and Interactions in Plants, October 11-16, 2008, Beijing, China. p. 135.

Interpretive Summary:

Technical Abstract: Iron deficiency is one of the leading micronuntrient deficiencies in humans, and increasing the amount of bioavailable iron in commonly consumed plant foods has been proposed as a means to ameliorate this deficiency. This approach seems especially beneficial in developing countries where plant foods comprise a significant portion of the diet. Legumes comprise one of the largest and most economically important plant families. Legume seeds are a rich source of proteins and can provide a number of essential nutrients required for human health. However, the amount and bioavailability of seed micronutrients, such as iron, in these legume crops are limited when compared to animal food products. As such, our research program focuses on enhancing the mineral concentration and availability of plant-based protein sources in order to improve human nutrition. To improve seed mineral concentrations, our lab is focused on examining the genetic and molecular components involved in mineral distribution to developing seeds. Genetic tools such as recombinant inbred lines (RILs) can be used to identify quantitative trait loci (QTLs) associated with seed mineral traits. The use of QTLs to identify genes of interest for studying seed mineral homeostasis has been successfully used in a number of other model species. By identifying loci and the underlying genes involved in the deposition of minerals in seeds, we can then find homologs in related crop species and develop molecular markers that will assist breeders in the improvement of agronomically important legumes. To that end, we are working to identify QTLs linked to a number of nutritive qualities, including improved seed Fe content and concentration in the model legumes Lotus japonicus and Medicago truncatula. Both L. japonicus and M. truncatula are the focus of international genome sequencing efforts, and a number of scientific resources have been created and released to the scientific community in order to further study this family. To identify the loci influencing seed mineral concentrations, 120 lines of L. japonicus RIL population (Gifu B-129 x Miyakojia MG-20; obtained from the Biological Resource Center, University of Miyazaki, Japan) and 95 lines of M. truncatula RIL population (Jemalong x DZA315.16; obtained from INRA-SGAP, Montpellier, France) were grown in two different population sets for seed harvest and nutrient analysis using ICP-OES. MapQTL software (v. 5.0; Kyazma, Wageningen, Netherlands) was used to identify QTLs linked to 112 previously mapped molecular markers in L. japonicus using interval mapping. QTL Cartographer software (v. 2.5; N.C. State University, Bioinformatics Research Center) was used to identify QTLs linked to 93 previously mapped molecular markers in M. truncatula using composite interval mapping. Results from these RIL populations have identified transgressive segregation with regard to seed Fe concentration. In both Lotus and Medicago, significant Fe QTLs were identified and some of these QTLs overlapped with other mineral QTLs. Transgressive segregation for seed mineral concentrations demonstrates that new allelic combinations can be used to increase seed Fe levels. For loci where multiple mineral QTLs co-localize, it is possible that a common whole plant phenotypic trait might be contributing to the seed mineral levels; thus, analyses using both seed Fe concentration and seed Fe content will be presented. In loci with extensive genomic data, we will also present and discuss possible candidate genes that might be contributing to the mineral phenotype.