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The synthetic maize population 316PO2 was subjected to genetic correlation analyses between grain yield, yield components and morphological traits. The purpose was to enable estimates to be made of the advantage of using selection indices compared with selection based on grain yield only, and if that advantage was present, to choose enough simple selection indices for practical use. Selection indices were constructed out of four traits highly significantly correlated with grain yield, in addition to yield itself.Grain yield exhibited a highly significant additive genetic correlation with ear diameter (r a =0.588**), kernels row -1 (r a =0.643**), ears plant -1 (r a =0.871**) and ear height (r a =0.427**). The most efficient index was Index No. 14 (R.E.I 12345 = 108.83%), which included all four traits and grain yield. Index No. 3, one of the simplest forms of index, including only ears plant -1 and grain yield, showed slightly less relative efficiency (R.E.I 35 =107.24%) than Index No. 14. Using this simple form of index with two characters (Index No. 3) could improve the efficiency of selection for grain yield. The estimated advantage from its use is 179.6 kg/selection cycle for grain yield over selection based only on grain yield.

One of the objectives of this paper was to determine relationship between grain yield and yield components, in S1 and HS progenies of one early synthetic maize population. Grain yield was in high significant, medium strong and strong association with all studied yield components, in both populations. The strongest correlation was recorded between grain yield and 1000-kernel weight (S1 progenies rg = 0.684; HS progenies rg = 0.633). Between other studied traits, the highest values of genotypic coefficient of correlations were found between 1000-kernel weight and kernel depth in S1 population, and 1000-kernel weight and ear length in HS population. Also, objective of this research was founding the direct and indirect effects of yield components on grain yield. Desirable, high significant influence on grain yield, in path coefficient analysis, was found for 1000-kernel weight and kernel row number, and in S1 and HS progenies, and for ear length in population of S1 progenies. Kernel depth has undesirable direct effect on grain yield, in both populations