PAG-III Plant Genome III Conference

Town & Country Conference Center, San Diego, CA, January, 1995.


PG-III: 26 - QTL DETECTION, CHARACTERIZATION AND MANAGEMENT IN A MAIZE GENETICS AND BREEDING PROJECT

QTL DETECTION, CHARACTERIZATION AND MANAGEMENT IN A MAIZE GENETICS AND BREEDING PROJECT.

M. Causse, A. Charcosset, G. Decoux, L. Moreau, J.L. Prioul *, J.P. Rocher *, S. Santoni, A. Gallais, D. de Vienne. Station de Genetique Vegetale, Ferme du Moulon, 91190 Gif/Yvette, France; *Institut de Biotechnologie des Plantes, UPS, Bat. 630, Orsay, France.

Our laboratory is involved in a long term maize genome mapping project. Three mapping populations (of about 150 RILs each) were derived from the 3 possible crosses between 3 distant lines. With 250 loci, the composite map covers 1800 cM. It is primarily based on expressed sequences of two origins: known function genes, and protein spots revealed by two- dimensional electrophoresis. About a hundred reference anonymous probes are also mapped in order to integrate our results with those of other laboratories. The individual and composite maps are used in various QTL detection projects. The characterization of QTL function is tentatively approached through a candidate gene strategy. QTLs involved in early growth and carbon metabolism (key-enzyme activities or metabolite contents) have been searched. Early growth QTLs were all found located in regions where QTL for enzyme activities were mapped. Furthermore, QTLs involved in the activities of two enzymes responsible of sucrose synthesis (sucrose phosphate synthase) and hydrolysis (sucrose synthase) mapped close to loci of their structural genes. In another experiment, the three RIL populations were used to study the effects of environment, inbreeding level, and genetic background on QTL detection. Each of the RILs have been crossed by the 3 parental lines, and QTL for flowering earliness and grain yield have been searched in 3 environments. This design allowed genetic x environment interactions and epistasis to be found. All these experiments are generating a huge volume of heterogeneous data, to be managed over years, by different people. A local maize genome database (using Oracle and interfaced with World Wide Web) has thus been constructed in order to manage (i) marker (protein and DNA) characteristics, (ii) locus positions on the maps, and (iii) agronomical results and QTLs data. Programs were developed to help for lab management, data acquisition and storage, map visualisation, and interfaces with the current analysis softwares.


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