33

    Nine cytoplasmic male sterile lines (Zhexie 2 A, Xieqingzao A, Zhenan 3 A, Gangzaoyang 1 A, Yinzaoyang 1 A, Erjiuqing A, V20 A, Zuo 5 A, Zhenshan 97 A), and five restorer lines (T49, Cezao 2-2, 26715, 102, 1391) of indica rice were used to analyze genetic effects of cytoplasm and maternal plant for nutrient quality traits of milled rice by using the genetic model for quantitative traits of endosperm in cereal crops (Zhu 1992: Zhu and Weir 1994) in an incomplete diallel cross (9 X 5). In this model, it is assumed that the total genetic effect on an endosperm can be partitioned as endosperm direct additive and dominance effects, cytoplasmic effect, and maternal plant additive and dominance effects. Components of variances and covariances are defined as V_A =endosperm direct additive variance, V_D=endosperm direct dominance variance, Vc =cytoplasmic variance, V_Am=maternal plant additive variance, V_Dm=maternal plant dominance variance, V_e=residual variance, C_AAm=covariance between endosperm and maternal plant additive effects, C_D.Dm=covariance between endosperm and maternal plant dominance effects. The genetic correlation components analyzed for endosperm pairwise traits of milled rice are defined as r_a=endosperm direct additive correlation, r_p =endosperm direct dominance correlation, rc=cytoplasmic correlation, rAm=maternal plant additive correlation, r_Dm=maternal plant dominance correlation, and r_e=residual correlation. The estimate and their standard errors were obtained by using the Jackknife method.
    The seeds were sown on 28 March and single plant per hill was transplanted to the paddy field at Zhejiang Agricultural University on 29 April in 1994. There were 24 plants in plot spaced 20 X20 cm with three replications. Seed samples of parents or F₁s were harvested at maturity from 8 plants in the middle part of the plot. The F₁, seeds were obtained from crossing CMS lines to restorer lines during the same season. Quantitative traits sutdied were protein content (PC, %), protein index (PI, mg), lysine content (LC, %), lysine index (LI, mg) and the ratio of lysine content to protein content (RLP) of milled rice, which were measured with three replications for each sample of parents, F₁s and F₂s.
    It was found that nutrient quality traits were controlled by cytoplasmic and maternal plant effects as well as endosperm direct effects (Table 1). Maternal plant effects for LC, LI and RLP were more important than endosperm direct effects. But PC and PI were mainly affected by endosperm direct effects. Cytoplasmic effects accounted for 2.41-20.80% of total genetic effects for all traits. Additive genetic effects were much more important than dominance effects for all traits studied, so that selection could be applied for these traits in early generations. Significant additive covariance and dominance covariance were not detected and this indicated that there is no relationship between endosperm and maternal plant genetic effects on these nutrient quality traits.
    The results of genetic correlations showed that the genetic correlations of endosperm, cytoplasm and/or maternal plant were responsible for the genetic correlation

Table 1. Estimation of genetic variances and covariances of
nutrient quality traits in indica rice

of pairwise nutrient quality traits (Table 2). rA, rD, rC, rAm and rDm for the most pairwise traits studied were significantly positive. But some of the pairwise traits had negative genetic correlations especially for the traits between RLP and PC or PI. It was suggested that high LC or LI with more RLP was possible by the results of positive rD, rC and rDm between the traits of RLP and LC or LI in hybrid rice. Indirect selection for these traits controlled mainly by additive effects was better than those by dominance effects. The dominance correlations could be effectively used in hybrid rice breeding. The cytoplasmic correlations could be applied in both conventional cross breeding and hybrid rice breeding.

Table 2. Genetic correlation components among nutrient quality traits in
indica rice

Miller, R. G., 1974. The jackknife, a review. Biometrika 61: 1-15. Zhu, J., 1992. Mixed model approaches for
estimating variances and covariances. Chinese J. Biomath., 7(1):
Zhu, J. and B. S. Weir, 1994. Analysis of cytoplasmic and maternal effects. II. Genetic models for triploid
endosperm. Theor. Appl. Genet. 89(2-3): 160-166.

Parameter	PC	PI	LC (x 10^-3)	LI (X10'^-3)	RLP (X10^-3)
V_A	18.171**	0.344**	10.253**	0.363**	0.047**
V_D	2.433**	0.064**	2.920**	0.084**	0.014**
V_C	0.934**	0.121**	3.179**	0.259**	0.035**
V_Am	16.291**	0.363**	13.429**	0.457**	0.048**
v_Dm	0.964**	0.035**	3.204**	0.104**	0.025**
C_A.Am	-9.745	-0.154	-2.858	-0.109	-0.004
C_D.Dm	-0.166	-0.004	-0.245	-0.006	-0.001
Ve	0.063**	0.005**	0.070**	0.003**	0.001**

Traits		r_A	r_d	r_c	r_Am	r_dm	r_e
PC &	PI	0.398**	0.718**	-0.455**	0.473**	0.488**	0.746**
	LC	0.349**	0.442**	-0.523**	0.292**	0.285**	0.117*
	LI	0.055	0.360**	-0.065	0.141**	0.219**	0.211**
	RLP	-0.393**	-0.176**	0.196**	-0.318**	-0.035	-0.586**
PI &	LC	0.382**	0.325**	0.247**	0.337**	0.165**	0.002
	LI	0.157**	0.305**	0.413**	0.268**	0.251**	0.584**
	RLP	-0.235**	-0.203**	0.246**	-0.132*	-0.054	-0.468**
LC &	LI	0.096	0.561**	0.220**	0.090	0.695**	0.731**
	RLP	-0.309**	0.400**	0.097*	-0.323**	0.632**	0.736**
LI &	RLP	-0.103*	0.355**	0.201**	-0.155**	0.594**	0.431**