Analysis of the Conditional Correlations from Different Genetic Systems Between the Protein Content and the Appearance Quality Traits of Indica Rice

Guoke Ge; Xi Zheng; Jianguo Wu; Zihong Ye; Chunhai Shi

doi:10.1016/S1673-8527(07)60014-9

Volume 34 Issue 2

Feb. 2007

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Article Navigation > Journal of Genetics and Genomics > 2007 > 34(2): 129-137

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Analysis of the Conditional Correlations from Different Genetic Systems Between the Protein Content and the Appearance Quality Traits of Indica Rice

doi: 10.1016/S1673-8527(07)60014-9

Department of Agronomy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310029, China

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Corresponding author: E-mail address: chhshi@zju.edu.cn (Chunhai Shi)
Received Date: 2006-03-24
Accepted Date: 2006-04-12

Available Online: 2007-04-18

Publish Date: 2007-02-20

Abstract

Abstract

A factorial mating design in two environments was conducted using 7 cytoplasmic male sterile lines (A) and 5 restorer lines (R) along with their F₁ (A × R) and F₂ populations. The unconditional and conditional analyses of genetic models and the corresponding statistic methods, including endospermic, cytoplasmic, and maternal plant genetic systems, were used to analyze the genetic relationships between protein content (PC) and the appearance quality traits of indica rice (Oryza sativa L.). The results from unconditional analysis indicated that PC was significantly correlated with the appearance quality traits of rice, except for the brown rice thickness (BRT). Only the genetic covariance between PC and the brown rice width (BRW) was positively correlative, whereas all the other pairwise traits were negatively correlative. The results from conditional analysis revealed that the weight of brown rice (WBR) or the amylose content (AC) could significantly affect the relationships between PC and the appearance quality traits of indica rice. The conditional analysis showed that WBR might negatively affect the relationships between PC and the brown rice length (BRL), BRW, or BRT through the genotype × environmental (GE) interaction effects, but positively affected the relationships between PC and the ratio of brown rice length to width (RLW) or the ratio of brown rice length to thickness (RLT). The amylase content could positively affect the relationships between PC and BRL, RLW, RLT through the cytoplasmic effects and maternal additive effects, but negatively affected the relationships between PC and BRW.
- covariance,
- unconditional and conditional analysis methods,
- protein content,
- amylose content,
- appearance quality

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References(20)

References

[1]	McKenzie, KS, Rutger, et al. Genetic analysis of amylose content, alkali spreading score, and grain dimensions in rice Crop Sci, 23 (1983),pp. 306-319
[2]	Hussain, AA, Maurya, et al. Indian J Genet, 47 (1987),pp. 145-152
[3]	Shi, CH, Zhu, et al. J Zhejiang Agric Univ, 20 (1994),pp. 606-610
[4]	Shi, CH, Zhu, et al. J Biomath, 10 (1995),pp. 15-20
[5]	Shi, CH, Zhu, et al. J Biomath, 11 (1996),pp. 89-96
[6]	Shi, CH, Shen, et al. Acta Agron Sin, 22 (1996),pp. 40-46
[7]	Chen, N, Luo, et al. Correlation between eating quality and physico-chemical properties of high grain quality rice Chinese J Rice Sci, 11 (1997),pp. 70-76
[8]	Yang, LS, Bai, et al. Research on the correlation between rice grain shape and grain quality J Anhui Agric Sci, 29 (2001),pp. 312-316
[9]	Zhu, J Analysis of conditional effects and variance components in developmental genetics Genetics, 141 (1995),pp. 1633-1639
[10]	Wu, JG, Shi, et al. Estimating the amino acid composition in the milled rice powder by near-infrared reflectance spectroscopy Field Crops Res, 75 (2002),pp. 1-7
[11]	Zhu, J, Weir, et al. Analysis of cytoplasmic and maternal effects. II. Genetic models for triploid endosperms Theor Appl Genet (1994),pp. 160-166
[12]	Zhu, J
[13]	Griffing, B Concept of general and specific combining ability in relation to diallel crossing system Austria J Biol Sci (1956),pp. 463-493
[14]	Mo, HD Identification of genetic control for endosperm traits in cereals Acta Genetica Sin, 22 (1995),pp. 126-132
[15]	Zhu, J Mixed model approaches for estimating genetic variances and covariances J Biomath, 7 (1992),pp. 1-11
[16]	Zhu, J Mixed model approaches for estimating covariances between two traits with unequal design matrices J Biomath, 8 (1993),pp. 24-30
[17]	Ye, ZH, Zhu, et al. Advance in development genetic models and analysis methods for quantitative traits Hereditas(Beijing), 23 (2001),pp. 65-68
[18]	Shi, CH, Wu, et al. Acta Genetica Sin, 29 (2002),pp. 56-61
[19]	Wen, YX, Zhu, et al. Multivariable conditional analysis for complex trait and its components Acta Genetica Sin, 32 (2005),pp. 289-296
[20]	Xiao, BG, Zhu, et al. Acta Genetica Sin, 32 (2005),pp. 1089-1093