340 Li, Zhang, Wu, Guo, Liu, Wan, Jin, Hanh, Thoa, Chen, Liu, Chen, Liu, Wang, Zhai and Wan AC was determined with the simplified method developed by Fujita et al. (2007). RVA analysis was conducted using a Series 4 Rapid Visco Analyser (RVA) (Newport Sci. Co., Australia) and analyzed with Thermal Cycle for Windows software according to the American Association of Cereal Chemists Standard Method AACC 61-02. Rapid viscosity analyzer (RVA) profiles were characterized by six parame- ters including peak paste viscosity (PKV), hot paste viscosi- ty (HPV), cool paste viscosity (CPV), breakdown viscosity (BDV = PKV-HPV), consistency viscosity (CSV = CPV- HPV), and setback viscosity (SBV = CPV-PKV) (Brabender 1998). All the viscosity parameters were expressed in rapid visco unit (RVU). QTL analysis Identifications of QTLs for AC and RVA traits were per- formed in two steps. Firstly, at each marker locus, pheno- typic data for lines homozygous for Asominori (AA) or IR24 (II) alleles were grouped, and then the QTLs were detected based on significant differences between the phenotypic means for the contrasting AA and II genotypic groups by t-tests (Tan et al. 2000). P ≤ 0.001 was used as the threshold for the presence of putative QTLs. The additive effect at a QTL was estimated by (II-AA)/2 (Monforte and Tanksley 2000). Secondly, Paterson et al. (1990) and Wissuwa et al. (2002) proposed a method of substitution mapping to local- ize small chromosomal fragments involved in segregation distortions. Studies of subsequent backcross generations in our material were performed to fine map the region associat- ed with deviations. If one QTL could be detected in multiple overlapping CSSLs containing substituted segments, the QTL was mapped to those segments. If a QTL was detected in only one CSSL and not detected in other CSSLs in an overlapping group, the QTL was mapped to the particular non-overlapping interval of the substituted segments in the CSSL. QTL nomenclature followed the method described by McCouch et al. (1997). Inclusive composite interval mapping (ICIM) (Wang 2009a) was used to confirm QTLs and estimate the pheno- typic variation explained (PVE), as implemented in the inte- grated software QTL IciMapping for building genetic link- age maps and mapping quantitative trait genes (available from http://www.isbreeding.net). qRT-PCR analysis The distance between the auricle of the flag-leaf (last leaf) and that of the penultimate leaf, or so-called auricle dis- tance (AD) was used as a nondestructive measurement to gauge the rice flowering stage. The parents (Asominori and CSSL50-1) and seven sub-CSSL (denoted as Lines 1, 3, 6, 11, 12, 13 and 14) started to flower when their AD reached 17 cm (time 0); periods after that were described as days af- ter flowering (DAF). Grain endosperms of the batches that flowered simultaneously with CSSL50-1 and Asominori were collected at 6, 9, 12, 15 and 18 DAF. The samples were immediately frozen in liquid nitrogen and stored at −80°C for RNA extraction and qRT-PCR analysis. Total RNA were extracted, treated with RNase-free DNaseI, and used for cDNA synthesis. qRT-PCR was performed using the SYBR Green Realtime PCR Premix method (Takara DRR041A). The rice β-actin gene was amplified (with primers 5′-TCGTCTGCGATAATGGAA CTG-3′ and 5′-CCGACAATGCTGGGGAAG-3′) and used as a positive internal control. The Os08g0534900 gene was amplified with primers R1 (5′-CATTGGGTGAAGTAGT TGG-3′) and R2 (5′-ATGTTGCCGTCTTGCTTA-3′). The Os08g0536000 gene was amplified with primers R1 (5′- CAGTGTTATGCGGCTTGG-3′) and R2 (5′-ATCCCTGA TTGCTGCTTT-3′). Results Phenotypic and starch properties of rice grains from Asominori and CSSL50-1 The phenotypic values for the 7 quality traits (parame- ters) of Asominori and CSSL50-1 are shown in Table 1. There was a significant difference of 2% in AC between Asominori and CSSL50-1. For RVA profiles, CSSL50-1 had a significantly higher SBV value than Asominori. For other RVA parameters, CSS50-1 showed higher PKV and BDV values, and lower HPV, CPV and CSV values, than Asominori. No difference was observed in the agronomic traits between Asominori and CSSL50-1, including grain shape, plant height and heading date (Guo et al. 2011). Graphical representation of sub-CSSLs genotypes The genotypes of the developed sub-CSSLs are shown in Fig. 2. Each sub-CSSL contained a single defined Table 1. Measurements of various gtraits in Asominori and CSSL50-1 determined in two environments Location Parent Nanjing Hainan Asominori CSSL50-1 Asominori CSSL50-1 16.65 ± 0.23 274.84 ± 16.93 163.60 ± 14.59 262.20 ± 15.92 111.24 ± 9.43 18.68 ± 0.41**b 279.30 ± 8.41 161.70 ± 9.04 258.30 ± 8.35 117.60 ± 3.65 12.00 ± 0.31 284.05 ± 17.17 169.39 ± 16.20 278.52 ± 18.70 114.68 ± 12.92 14.21 ± 0.25** 295.84 ± 10.05 166.14 ± 4.28 274.92 ± 5.78 129.70 ± 7.83 AC PKV HPV BDV Traita CPV SBV CSV −12.64 ± 8.42 98.60 ± 4.01 −21.00 ± 5.44** 96.60 ± 3.68 −5.53 ± 11.38 109.13 ± 5.99 −20.92 ± 8.47** 108.78 ± 5.90 a AC, amylose content; PKV, peak paste viscosity; HPV, hot paste viscosity; CPV, cool paste viscosity; BDV, breakdown viscosity; CSV, consis- tency viscosity; SBV, setback viscosity. Data are presented as means ± SD from three replicates. b ** represent the significant differences between Asominori and CSSL50-1 in the same environments at P = 0.01.