## 1 Introduction  
Rice (_Oryza sativa_ L.) is one of the most important cereal crops in the world, playing a key role in meeting the demand of food for a growing global population. During the last 60 years, grain yield has progressively increased in Yangtze River basin, the main _indica_ rice-producing area in China, and such an increase is mainly attributed to the expanded sink size as a result of more number of spikelets per panicle (NSP), especially for the case of super rice [1]. [PERSON] et al. analyzed the data of national rice regional trial in southern China from 1986 to 2002 and found that number of grains per panicle (NGP) played an important role in improving both the yield and rice quality based on suited number of effective panicles and grain weight [2]. Therefore, it is essential to understand the molecular mechanism of grain number in rice.  
All three yield components--panicle number, grain number, and grain weight--are typical quantitative traits that are controlled by polygenes referred to as quantitative trait loci (QTLs) [3]. QTL mapping is an efficient strategy to dissect the molecular mechanisms of rice yield traits such as NGP [4]. Along with molecular marker technology development, hundreds of QTLs distributed on rice 12 chromosomes had been identified for NGP and NSP (www.gramene.org/archive/QTL data). Most of them were detected in different mapping populations such as F\({}_{2}\), doubled haploid lines, and recombinant inbred lines (RILs) [5; 6; 7; 8; 9; 10]. Several dozens of QTLs for NGP and NSP have been fine-mapped [11; 12; 13; 14; 15; 16; 17]. So far, eight QTLs have been cloned. _Gn1a_ and _NOG1_ regulate grain numbers [18; 19]. _DEPI_, _IPA1/WFP_, _APO1_, and _GNP1_ control panic architecture and meristems [20; 21; 22; 23; 24; 25]. _NALI_ mainly controls panic size and plant architecture [26; 27]. _Ghd7_ presents large pleiotropic effect on NGP, heading date and plant height [28; 29]. These findings have greatly promoted the dissection of genetic bases of rice grain number.  
However, these studies mainly focused on major-effect QTLs, few studies investigated minor-effect QTLs. Recently, studies have shown that QTLs with minor effect also play a role in the regulation of important agronomic traits in rice [30]. Hence, identification of minor-effect QTLs would enrich our knowledge of genetic and molecular network regulating rice grain number. Usually, minor-effect QTLs are more sensitive to genetic background and environment than major-effect QTLs [31; 32]. Identification of minor-effect QTLs would be facilitated by eliminating the effect of major-effect QTLs. The use of residual heterozygote (RH) to construct near-isogenic lines (NILs) or secondary segregation populations is an effective strategy for identification and verification of minor-effect QTLs [33; 34]. Based on this approach, several QTLs for yield-related trait have been fine-mapped [35; 36].  
In the present study, QTL analysis for NGP, NSP, and spikelet fertility (SF) was performed using three RIL populations. Three minor-effect QTLs on chromosome 5, 9 and 12 were identified using one RH-derived population fixing the major-effect QTLs and further validated in the new secondary segregation population under more homozygous genetic background, respectively.