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Genomic Science Program

Systems Biology for Energy and the Environment

Department of Energy Office of Science. Click to visit main DOE SC site.

Genomic Science Program

2006 Awardee

Using Association Mapping to Identify Markers for Cell Wall Constituents and Biomass Yield in Alfalfa

INVESTIGATOR(S): Brummer, E. C.; Doyle, J.J.; Moore, K. J.

INSTITUTION: University of Georgia

NON-TECHNICAL SUMMARY: Alfalfa (Medicago sativa) is a potential biofuel crop because it produces high yield, its leaves can be used as a high value, high protein coproduct, it fixes atmospheric nitrogen, and it has beneficial effects on the environment. Improving alfalfa as a biofuel crop will entail breeding for increased biomass yield and altered cell wall composition. While traditional phenotypic selection can be successful,the perennial nature of alfalfa requires that a selection cycle lasts for several years. Decreasing the cycle time would increase genetic gain for all traits. This could be achieved using marker assisted selection for the traits of interest, but marker identification research conducted previously has not focused on representative alfalfa breeding populations nor has it examined wild germplasm as a source of new alleles to improve agronomically important traits. Our experiment will address these issues by studying both wild germplasm not typically used in alfalfa breeding programs and also a cultivated breeding population currently under selection. We will evaluate biomass yield and cell wall composition in the field. Concurrently, we will evaluate the genotype of each plant using genetic markers selected throughout the genome. We will also develop markers based on DNA sequence variation in genes of possible involvement in cell wall synthesis. Ultimately, this project will improve the efficiency of selection for enhanced bioenergy characteristics in alfalfa, produce numerous new markers at important candidate genes, and identify potentially useful alleles in wild germplasm.

OBJECTIVES: Our objectives are to use genomics approaches to identify chromosomal regions, and ultimately genes, controlling the two most important bioenergy traits, biomass yield and composition, and to develop genetic markers that can be used directly in applied plant breeding programs to improve the bioenergy qualities of alfalfa. We will pursue two complementary objectives to attain our goals: 1. Identify loci, and specific alleles, that control the concentration of alfalfa stem cell wall constituents and that are associated with biomass production using whole genome and candidate gene association mapping across a diverse set of natural diploid alfalfa accessions, and 2. Extend the analysis and methods used in the first objective to a tetraploid alfalfa breeding population currently under selection. As a result of this project, we (a) will have identified novel alleles in wild alfalfa germplasm that may be useful to improve cultivated alfalfa; (b) will have developed and used SNP markers in genes known to be involved in the biosynthesis of cell wall composition; (c) will be able to select individuals within a breeding population on the basis of these markers, and (d) will identify new alleles from wild germplasm useful for improving cultivated alfalfa. This experiment will provide the first estimate of linkage disequilibrium (LD) in alfalfa, both in a broad cross-section of wild diploid germplasm and in a practically important cultivated breeding population, both on a genome-wide and on an individual gene basis. Additionally, we will have applied association mapping to this important crop legume for the first time.

APPROACH: We will use association mapping to identify genome regions and candidate genes that are associated with biomass production and cell wall composition in both diploid and tetraploid alfalfa populations. We are proposing to begin by screening a broad diversity of diploid germplasm (three individuals from each of 96 plant introductions) in order to identify new genetic variation for these traits that could be useful in alfalfa improvement. We will begin by analyzing diploid genotypes because they likely harbor a reservoir of unexploited genetic diversity and are more tractable for association mapping experiments than tetraploid genotypes. Subsequently we will extend the results to tetraploids. The tetraploid population we will examine is a breeding population currently under clonal selection at four locations, with 200 individuals being evaluated. As a breeding population, markers associated with traits could be immediately used in a recurrent selection program leading to the development of improved cultivars. Phenotypic analysis will be conducted based on field grown plant material clonally replicated to enable assessment of individual genotypes. In addition to biomass production and plant height measurements, we will conduct a through analysis of the stem cell wall composition of all entries. All plants will be genotyped throughout the genome with simple sequence repeat (SSR) markers, some of which will be selected based on their association with quantitative trait loci (QTL) for biomass yield, stem cell wall cellulose, hemicellulose, and lignin concentration, or agronomic traits that we have identified in other experiments. Concurrently, we will sequence portions of up to 100 genes that are candidate loci involved with cell wall biosynthesis. The sequencing will lead to the identification of single nucleotide polymorphisms (SNP), which we will develop into markers for those specific genes. All plants (288 diploid and 200 tetraploid) will be genotyped with the SNP markers. Association mapping will be conducted using the recently described mixed-model method that will account for underlying population structure within our two groups of genotypes (diploid and tetraploid), which will be analyzed separately. We will test for associations based on both genome-wide SSR molecular markers, as well as on SNP markers for 20 candidate genes, which will be developed from sequence data on 96 diploid and 20 tetraploid individuals.


Name: Brummer, E. C.


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