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 t o maize (Zea mays L.) breeders for its importance in selecting appropr iate 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 iso genic lines (NILs). NC264 and B73G are shorter, earlier versions of SC 76 and B73 respectively, developed by introgressing Gaspe Flint and se lecting for early flowering through repeated backcrosses. The NILs wer e screened for introgressed chromosomal regions with restriction fragm ent length polymorphisms (RFLPs). Seven introgressed regions were iden tified in NC264 and two in B73G, with a specific chromosome 8 region m aintained in both NILs. The introgressed regions were tested for their effect on flowering date and plant height in segregating F-2 populati ons and F-3 families developed from crosses between the original inbre d 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 floweri ng 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 ass ociations. Additive gene action predominated at all loci. The most sig nificant effects were constant across environments, generations, and p opulations except for the region on chromosome 8, which was not signif icant in the short-day photoperiod environment. Thus, the maturity QTL on chromosome 8 may represent a photoperiod response element. Selecti ve determination of genotype using only the top and bottom 10% of the phenotypic extremes to identify QTLs was as effective as analysis of t he entire population for detecting the most significant marker trait a ssociations.