Iron deficiency results in iron deficiency chlorosis (IDC) in soybean grown in the north central regions of the United States. Soybean plants display a variety of symptoms, ranging from slight yellowing of the leaves to interveinal chlorosis, and sometimes IDC is followed by stunted growth. In severe cases IDC may even lead to cell death. The objective of this project was to employ a whole genome association mapping approach to uncover the genomic regions associated with the iron deficiency trait in soybean. Golden gate assay technology was applied to expedite the screening of 1,536 single nucleotide polymorphisms in two different sets of soybean populations belonging to the year 2005 and 2006. The two soybean populations were screened for IDC at multiple locations in replicated field trials. The experiment only considered marker loci with a minor allele frequency greater than 0.1. Probability-probability plot helped in selecting the appropriate general linear models, which controlled for only population structure, and mixed linear models, which controlled for both the population structure and the ancestry. For the 2005 population, three statistical approaches (PCA, PCA+K and PCA+K*) identified twelve marker/trait associations, and for the 2006 population, five statistical models (Q, PCA, Q+K, Q+K * and PCA+K*) resulted in the discovery of twenty-two such associations. Although none of the markers significantly associated with JDC was common to both the populations under study, similar regions of significance were observed between the two years. When the phenotypic and the genotypic data of the two populations were combined, 10 markers were significantly (pFDR < 0.01) associated with the IDC trait using the PCA and PCA+K* statistical models. Out of the 10 markers, six selected markers showed a significant phenotypic mean difference for the tolerant and susceptible alleles. A detailed analysis revealed that using a smaller set of combinations from these six markers can effectively identify IDC tolerant genotypes. The next step would be to verify the reproducibility of the selected set of marker combinations in another set of populations.