Location: Plant Science ResearchTitle: Use of Mutant-Assisted Gene Identification and Characterization (MAGIC) to identify novel genetic loci that modify the maize hypersensitive response) Author
Submitted to: Theoretical and Applied Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/13/2011
Publication Date: 7/27/2011
Citation: Chaikam, V., Negeri, A., Dhawan, R., Puchaka, B., Chintamanani, S., Gachomo, E.W., Zillmer, A., Doran, T., Weil, C., Balint Kurti, P.J., Johal, G. 2011. Use of Mutant-Assisted Gene Identification and Characterization (MAGIC) to identify novel genetic loci that modify the maize hypersensitive response. Theoretical and Applied Genetics. 123(6):985-997. Interpretive Summary: In this paper we identify several regions of the genome from several different maize lines which are important in controlling the disease resistance response. We have done this by using a gene that causes a very obvious “autoimmune” response and identifying loci which suppress or enhance it.
Technical Abstract: The partially-dominant, autoactive maize disease resistance gene Rp1-D21 causes hypersensitive response (HR) lesions to form spontaneously on the leaves and stem in the absence of pathogen recognition. The maize nested association mapping (NAM) population consists of 25 200-line subpopulations each derived from a cross between the maize line B73 and one of 25 diverse inbred lines. By crossing a line carrying the Rp1-D21 gene with lines from three of these subpopulations and assessing the F1 progeny, we were able to map several novel loci that modify the maize HR, using both single-population Quantitative trait locus (QTL) and joint analysis of all three populations. Joint analysis detected QTL in greater number and with greater confidence and precision than did single population analysis. In particular, QTL were detected in bins 1.02, 4.04, 9.03 and 10.03. We have previously termed this technique, where a mutant phenotype is used as a “reporter” for a trait of interest, Mutant-Assisted Gene Identification and Characterization (MAGIC).