BERGAMO, ITALY

Istituto Sperimentale per la Cerealicoltura

Wageningen, The Netherlands

Keygene N.V.

Identification of QTLs for grain yield and grain-related traits of maize using an AFLP map, different testers, and cofactor analysis --Ajmone Marsan, P, Redaelli R, van Vijk R, Stam P, Motto M In the last decade the advent of molecular markers has greatly facilitated the systematic dissection of quantitatively inherited traits into their underlying Mendelian factors (QTLs). This has provided the tools to speed up plant improvement for a variety of criteria, including yield, by the generation of fine-scale molecular genetic maps to undertake marker-assisted selection (MAS) and positional cloning (Lee, Adv. Agron. 55: 265-344, 1995). In maize, extensive genome mapping based on DNA restriction fragment length polymorphism (RFLP) markers has been accomplished (Coe et al., Maize Coop. Genet. Newsl. 69: 191-267, 1995). These maps and their associated technology have been used successfully for a number of applications in genetic research and breeding. However, the use of RFLPs in a QTL analysis is an expensive and time-consuming process.

The development of the polymerase chain reaction (PCR) has expanded the repertoire and efficiency of DNA marker systems, which include the AFLP method (Vos et al., Nucleic Acids Res. 23: 4407-4414, 1995). The advantage of AFLP assay over other DNA marker techniques includes the detection of a large number of polymorphisms from a single PCR reaction, within a very short period of time, and the requirement for small amounts of DNA, thus reducing expenses and expediting the construction of high-density linkage maps. Accordingly, as a first step in exploiting AFLPs in a maize genome mapping program, and in the long-term for MAS and positional cloning, we have used AFLPs to identify QTLs for grain yield and grain-related traits in maize F2 lines using different testers and biometrical procedures.

Two-hundred-twenty-nine F3 progenies, each tracing back to an individual F2 plant, derived by crossing the inbred lines B73 and A7 were used. This population has been described previously to construct an RFLP linkage map (Ajmone-Marsan et al., Theor. Appl. Genet. 90: 415-424, 1995). The protocol adopted for the generation of AFLP markers was essentially the same as that described by Vos et al. (Nucleic Acids Res. 23: 4407-4414, 1995) and by Castiglioni et al. (Theor. Appl. Genet. 99: 425-431, 1999).

Basic field experimental procedures were as described earlier (Ajmone-Marsan et al. 1995). Cultural conditions were kept as close as possible to the optimal growth conditions in order to achieve high yield levels. The two series of testcross (TC) progenies were evaluated in field trials for grain yield (t/ha at 15.5% grain moisture), dry matter concentration (% grain dry matter at harvest), and test weight (kg/hl measured at harvest). A linkage map for B73xA7 was assembled by MAPMAKER as previously reported (Castiglioni et al. 1999). Among the 312 RFLP and AFLP markers located on the B73xA7 map, 195 evenly spaced markers belonging to the framework map, and corresponding approximately to a marker every 10 cM, were used for QTL analysis.

QTL analyses were performed on mean values of each trait across environments for each experiment involving the two series of TC progenies and using linkage information. For the analysis of linkage between QTLs and molecular markers the simple interval mapping (SIM) (Lander and Botstein, Genetics 121: 185-199, 1989) and the composite interval mapping (CIM) (Jansen and Stam, Genetics 136: 1447-1455, 1994) statistical methods were used.

The efficiency of generating AFLP markers was substantially higher relative to RFLP markers in the same population, and the speed at which they were generated showed a great potential for application in marker-assisted selection. AFLP markers covered linkage group regions left uncovered by RFLPs; in particular at telomeric regions, previously almost devoid of markers. This increase of genome coverage afforded by the inclusion of the AFLPs revealed new QTL locations for all the traits investigated and permitted mapping of telomeric QTLs with higher precision. The present study has also provided an opportunity to compare SIM and CIM for QTL analysis. Our results indicated that the method of CIM employed in this study has greater power in the detection of QTLs and provided more precise and accurate estimates of QTL positions and effects than SIM.

By the use of selected cofactors, which absorb a major part of the background noise due to other putative QTLs, CIM has allowed the detection of a higher number of QTLs. In some cases also CIM simply increased existing LOD peaks beyond the threshold values. In other situations CIM detected significant QTLs where SIM LOD profiles were almost flat. Furthermore, CIM reduces the significance of QTLs overestimated by SIM. In addition, the R2 values for the simultaneous fit were always higher with CIM and showed a higher value of substitution effects of unfavourable alleles with favourable ones. Hence, advanced statistical methods promise to make an important contribution for improving the prospects of MAS without any additional cost.

The experimental mating design adopted in our experiment was based on two different tester lines. In this study we found that QTLs revealed by one tester may not be detected with the second one. For all traits and both testers we detected a total of 36 QTLs, of which only 2 were in common between testers. These findings indicate that the allelic compositions of a tester line determine whether a QTL segregating in a population will be detected. In fact, the number of QTL associated with grain yield and yield-related traits detected in this study largely depends on the tester under study. In conclusion, our results suggest that the choice of tester for identifying QTL alleles for use in improving an inbred is critical and expression of QTL alleles identified may be tester specific.
 
 


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