To address the issue of genetic relatedness and variability between the inbred lines developed in Italy by the Bergamo Maize Station, an AFLP analysis (Vos, P et al., Ac. Res. 23:4407-4414, 1995) was performed on a series of 71 inbred lines, considered representative of optimized breeding material. Twenty reference lines, encompassing the major heterotic groups available in the U.S. Corn Belt were included in the analyses in order to maximize genetic variability across the data set. Hence, the reference lines supplied a basis of genetic diversity to which the Italian inbred lines were related in the evaluation of their relative genetic relationships.

AFLP analysis of the Italian and reference inbred lines produced stable and repeatable profiles, which allowed us to unequivocally fingerprint each inbred line analyzed. A total of 682 polymorphic bands were revealed by the use of 5 PstI/MseI and 9 EcoRI/MseI primer combinations (PCs). E/M PCs displayed between 29 and 65 polymorphic bands resulting in an average of 47.56 12.68 markers. P/M PCs appeared less variable, disclosing between 41 and 54 polymorphic bands with an average of 50.8 5.63 markers. E/M PCs displayed an average Polymorphism Information Content (PICav) value of 0.33 0.027, while an average PICav of 0.35 0.014 was obtained with P/M PCs. In general, PICav values ranged from 0.28 to 0.36, demonstrating the good discriminatory power of the markers identified.

Scoring of the markers allowed the construction of a 682x91 binary array, which was consequently utilized to compute genetic distance (GD) values (Nei, M and Li, WH, Proc. Natl. Acad. Sci. USA 76:5269-5273, 1979) for all pairs of inbred lines considered. GD values ranged from 0.124 for inbred lines Lo876 and Lo1064, both derived from Lo876o2, to 0.62 for inbreds Lo3 and Lo903. An average GD of 0.437 0.012 was calculated for the entire data set.

Distance measures were subsequently used to construct a hierarchical tree using the UPGMA method using an NTSYS-PC program. Table 1 summarizes the grouping of inbred lines, presenting the major groups identified as well as their disclosed subgroups. Cluster analysis largely agreed with pedigree information as can be established by comparing the pedigree with clustering information.

Table 1. Principal groups of inbred lines as identified by cluster analysis.
 

	BSSS1 (22)a			BSSS2 (9)	References (12)
	Lo950	Lo903	Lo999	Lo876	Lo986
	Lo951	Lo904	Lo1055	Lo1016	A71
	Lo960	Lo1086	Lo1137	Lo1064	A69Y
	Lo964	Lo1101	Lo1141	Lo1066	B57
	Lo1054	Lo1106	B37	Lo1067	CI187-2
	Lo1053	Lo1127		Lo1123	FR5
	Lo1087	Lo1167		Lo1169	H55
	Lo1094	B73		Lo1170	N6
	Lo1173			A632	Oh07
					Os420
					W64A
					Wf9
Aveb		0.32		0.37	0.47
Min		0.13		0.12	0.27
Max		0.48		0.49	0.54
	LSC1 (21)		LSC2 (18)		Lo932 (4)
	Lo1059	Lo863	Lo881	Lo902	Lo932
	Lo1061	Lo1077	Lo1038	Lo924	Lo937
	Lo1063	Lo1095	Lo1056	Lo976	Lo944
	Lo1076	Lo1096	Lo1035	Lo1124	W153
	Lo1156	Lo1125	Lo1090	Lo1126
	Lo1157	Lo1128	Lo1140	Lo1142
	Lo1158	Lo1172	C103	Lo1166
	Lo1159	Lo1176	Va59	Mo17
	Lo1160	Lo1182	T8	Oh43
	Lo1162
	Lo1168
	Lo1171
Ave	0.37		0.40		0.33
Min	0.13		0.15		0.19
Max	0.51		0.54		0.47

anumbers in parentheses represent the number of inbreds in each group;

^bAve: average GD, Min: lowest GD, Max: highest GD within groups.

BSSS: Iowa Stiff Stalk Synthetic; LSC: Lancaster Sure Crop

For data with a hierarchical structure, analysis of molecular variance (AMOVA) allows the study of patterns of genetic variation within and between groups through the examination of variance. This assay can be extended to evaluate molecular marker data even in the absence of replicated values for samples (Law, JR et al., Euphytica 102:335-342, 1998). An AMOVA of the AFLP data based on the grouping obtained in cluster analysis of the inbred lines considered is presented in Table 2. Clusters were used to recompose, in broad terms, BSSS, LSC, and unrelated heterotic groups. The amalgamation into heterotic groups was performed using both a small number of larger clusters, as well as a larger number of clusters of reduced size. In both cases, the within-population (clusters) components of variance dominated the AMOVA, accounting for 73% to 79% of the variation, with less than five percent representing variation between heterotic groups. Changes in the grouping pattern applied had no significant effect on the distribution of variation. Furthermore, the genetic distance between clusters (Fst values) exceeded both the degree of inbreeding within clusters (Fsc values) and the degree of relatedness between genes within inbred lines (Fct values) in all cases.

Table 2. Summary of AMOVA for AFLP data from Italian and U.S. Corn Belt Inbred Lines.
 

Nested AMOVA Variance Components
	Between Groups	Between Populations Within Groups	Within Populations
Populations	V(A)%	V(B)%	V(C)%	Fst	Fsc	Fct
Large	2.61	18.31	79.1	0.209	0.188	0.026
Small	4.27	22.38	73.4	0.266	0.234	0.043

Heterogeneity within breeding groups was further analyzed by computing a series of diversity statistical indices from the AFLP data (Table 3). Estimates of q, which is the product of population gene number and mutation rate, were computed based on the number of polymorphic sites (�q_s) and on the mean number of pairwise differences (�q_p). Both estimates hold under the assumption of random mating, population equilibrium and neutral mutations. If these assumptions are valid, �q_s has smaller stochastic variance than �q_p. However, since �q_p is independent of sample size while �q_s is not, �q_p may be a more reliable estimate of gene diversity within heterotic groups. None of the breeding groups analyzed showed significant variation in �q_s and �q_p values. Subsequently, the average gene diversity per site was computed. This parameter of diversity, as determined on the entire population of inbreds analyzed, equaled 0.33 ± 0.16 and varied within a narrow range (0.24 � 0.33) when determined for the heterotic groups identified. Expansion or contraction of the heterotic groups was assayed by statistics developed by Tajima (Tajima, F, Genetics 123:585-595, 1989). Tajima�s D statistics, computed on the heterotic groups listed in Table 3, did not reveal any expansion or contraction of groups, since none of the values obtained reached statistical significance using coalescent simulation (Hudson, R.R., Oxford Surways in Evalutionary Biology, Oxford Univ. Press, 1-44, 1990), or parametric approximation assuming a beta-distribution (Tajima, 1989).

Table 3. Summary of diversity statistics for Italian and U.S. Corn Belt Inbred Lines.
 

		Groups
	All Inbreds	Referencea	BSSS1	BSS2	LSC1	LSC2
No. inbreds	91	20	22	9	21	18
No. polymorphic sites	682	475	442	412	235	329
Theta(S)	0.16	0.33	0.27	0.36	0.28	0.29
S.D.b Theta(S)	0.04	0.13	0.09	0.15	0.09	0.10
Theta(p)	0.25	0.38	0.36	0.40	0.36	0.35
S.D. Theta(p)	0.12	0.20	0.17	0.22	0.18	0.18
Average gene diversity	0.33	0.33	0.24	0.26	0.27	0.30
+/- average diversity	0.16	0.17	0.12	0.14	0.14	0.15
Tajima�s D	2.24	0.68	0.92	0.45	1.15	0.91

^aAll Reference lines; ^bS.D. Standard Deviation

In this study, AMOVA, performed on genetic structures obtained by means of two different amalgamation schemes showed that the within cluster component of variance largely exceeds the variance between heterotic groups, regardless of the amalgamation scheme selected. Moreover, additional statistical indices of diversity show that no significant differences occur between the variability encountered within identified heterotic groups and the overall level of genetic diversity among the inbred lines taken into consideration. It can therefore be concluded that breeding activity has by no means caused a decline of genetic variability within heterotic groups. On the contrary, levels of genetic diversity have remained substantially unchanged over time and hence, plant breeding has resulted in a qualitative rather than a quantitative shift in diversity. In conclusion, this study has shown that a large genetic variability occurs among maize germplasm available in Italy. This variability can be exploited in hybrid and line development for further yield improvement.
 
 

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