IDENTIFICATION OF QUANTITATIVE TRAIT LOCI CONTROLLING DAYS TO FLOWERING AND PLANT HEIGHT IN 2 NEAR-ISOGENIC LINES OF MAIZE

The number of days from planting to flowering is a trait of interest to maize (Zea mays L.) breeders for its importance in selecting appropriate hybrid parents, and for its role in the utilization of unadapted germplasm. Using molecular marker technology, we were able to identify quantitative trait loci (QTLs) controlling days to flowering and two correlated traits, plant height and total leaf number, in two near isogenic lines (NILs). NC264 and B73G are shorter, earlier versions of SC76 and B73 respectively, developed by introgressing Gaspe Flint and selecting for early flowering through repeated backcrosses. The NILs were screened for introgressed chromosomal regions with restriction fragment length polymorphisms (RFLPs). Seven introgressed regions were identified in NC264 and two in B73G, with a specific chromosome 8 region maintained in both NILs. The introgressed regions were tested for their effect on flowering date and plant height in segregating F-2 populations and F-3 families developed from crosses between the original inbred and the NIL. The NC264 x SC76 F-2 population was tested in both long and short-day photoperiod environments. The RFLP analysis of the F-2 individuals and F-3 families identified major QTLs for days to flowering and plant height on chromosomes 1, 8, and 10. Major QTLs for total leaf number were found on chromosomes 1 and 8. Single-factor analysis of variance techniques were employed for all pairwise marker trait associations. Additive gene action predominated at all loci. The most significant effects were constant across environments, generations, and populations except for the region on chromosome 8, which was not significant in the short-day photoperiod environment. Thus, the maturity QTL on chromosome 8 may represent a photoperiod response element. Selective determination of genotype using only the top and bottom 10% of the phenotypic extremes to identify QTLs was as effective as analysis of the entire population for detecting the most significant marker trait associations.