Comparative QTL Mapping of Resistance to Gray Leaf Spot in Maize Based on Bioinformatics

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SHI Li-yu et al.
tance is expected to be the most cost-effective, efficient and acceptable control (Gevers et al. 1994; Saghai Maroof et al. 1996; Ward et al. 1999). The development of resistant germplasm canssing resistance factors from donors into elite maize germplasm (Gordon et al. 2004). Most of the identified sources of resistance to GLS which have been used in maize have genes for resistance inherited in a quantitative manner (Manh 1977; Thompson et al. 1987; Huff et al. 1988; Ulrich et al. 1990; Gevers et al. 1994). The identification of molecular markers linked to disease resistance loci will aid breeding efforts by complementing traditional phenotypic selection with marker assisted selection (MAS) (Simmonds 1985). Since the first publication of a quantitative trait locus (QTL) using molecular markers (Paterson et al. 1988), a large number of QTLs have been detected in different populations and environments. Having data concerning different populations, it would be interesting to know whether QTLs identified for a given trait in one population correspond to those detected in other populations, or whether QTLs identified in one species correspond to QTLs or other types of loci detected in the corresponding regions of other species (Goffinet and Gerber 2000). Comparative analysis of QTLs between species reveals the existence of homologous QTLs for plant height and maturity within the Poaceae (Lin et al. 1995). Comparing species is also a means to find new QTLs, increasing their potential use for plant breeding, as in the tomato (Fulton et al. 1997). These results will be helpful to map, isolate, and clone QTLs in large genome crops, such as maize, by using rice genome information, as well as to understand the evolutionary forces that structured the organization of grass genomes. Meta-analysis is a method to combine QTL results from different independent analyses. Its application in genetics and evolution is illustrated in recent publications (Allison and Heo 1998; Goffinet and Gerber 2000; Chardon et al. 2004), and the usefulness of these kinds of methods for pooling information when raw data are not available is emphasized (Lander and Kruglyak 1995; Allison and Heo 1998). This method provides a modified Akaike criterion that can be used to decide how many QTLs are actually represented by the QTLs detected in different experiments. This criterion is computed to choose between models with one, two, three,
lodging and early senescence (Latterell and Rossi 1983; Stromberg and Donahue 1986; McGee 1988). The causal agent of GLS, Cercospora zeae-maydis (Tehon and Daniels 1925), predominantly overwinters in previously diseased plant debris that remains unincorporated on the soil surface between seasons (Payne et al. 1987). Warm and humid conditions favor the spread of the disease (Rupe et al. 1982; Lipps et al. 1996). Methods to control GLS include the discontinuation of conservation tillage, the use of crop rotation, the application of foliar fungicides, and growing hybrids with resistance (Latterell and Rossi 1983; Ward et al. 1997; Coates and White 1998). In comparison with others, host resis-
SHI Li-yu1, 2, LI Xin-hai2, HAO Zhuan-fang2, XIE Chuan-xiao2, JI Hai-lian2, LÜ Xiang-ling2, ZHANG Shihuang2 and PAN Guang-tang1
1 2
Sichuan Agricultural University, Ya’an 625014, P.R.China Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/National Key Facilities for Crop Genetic Resources and Improvement, Beijing 100081, P.R.China
INTRODUCTION
Since gray leaf spot (GLS) was initially described in 1925 (Tehon and Daniel 1925), the disease has become an economic problem in the maize growing areas worldwide (Turner 1982; Latterell and Rossi 1983; Ayers et al . 1984; Hawk et al. 1985; Carson et al . 1998; Wang et al. 1998; Ward et al. 1999; Clements et al. 2000; Liu et al. 2005). Documented yield losses of maize attributed to GLS vary from 11 to 69% (Lipps et al . 1996; Ward et al. 1999) to as high as 100% when severe epidemics contributed to increased stalk
Received 13 April, 2007 Accepted 27 June, 2007 SHI Li-yu, MSc, E-mail: shiliyu321@; Correspondence PAN Guang-tang, Tel: +86-835-2882714; LI Xin-hai, Tel: +86-10-68918598
Abstract
The integration QTL map for gray leaf spot resistance in maize was constructed by compiling a total of 57 QTLs available with genetic map IBM2 2005 neighbors as reference. Twenty-six “real QTLs” and seven consensus QTLs were identified by refining these 57 QTLs using overview and meta-analysis approaches. Seven consensus QTLs were found on chromosomes 1.06, 2.06, 3.04, 4.06, 4.08, 5.03, and 8.06, and the map coordinates were 552.53, 425.72, 279.20, 368.97, 583.21, 308.68 and 446.14 cM, respectively. Using a synteny conservation approach based on comparative mapping between the maize genetic map and rice physical map, a total of 69 rice and maize resistance genes collected from websites Gramene and MaizeGDB were projected onto the maize genetic map IBM2 2005 neighbors, and 2 (Rgene32, ht1), 4 (Rgene5, rp3, scmv2, wsm2), and 4 (ht 2, Rgene6, Rgene8 and Rgene7) positional candidate genes were found in three consensus QTLs on chromosomes 2.06, 3.04, and 8.06, respectively. The results suggested that the combination of meta-analysis of gray leaf spot in maize and sequence homologous comparison between maize and rice could be an efficient strategy for identifying major QTLs and corresponding candidate genes for the gray leaf spot. Ｋｅｙｓ ｗｏｒｄｓ: maize (Zea mays L.), gray leaf spot, quantitative trait loci, positional candidate gene, meta-analysis