Grain yield is the most important and complex trait in maize. In this study, a total of 258 F(9) recombinant inbred lines (RIL), derived from a cross between dent corn inbred Dan232 and popcorn inbred N04, were evaluated for eight grain yield components under four environments. Quantitative trait loci (QTL) and their epistatic interactions were detected for all traits under each environment and in combined analysis. Meta-analysis was used to integrate genetic maps and detected QTL across three generations (RIL, F(2:3) and BC(2)F(2)) derived from the same cross. In total, 103 QTL, 42 pairs of epistatic interactions and 16 meta-QTL (mQTL) were detected. Twelve out of 13 QTL with contributions (R(2)) over 15% were consistently detected in 3-4 environments (or in combined analysis) and integrated in mQTL. Only q100GW-7-1 was detected in all four environments and in combined analysis. 100qGW-1-1 had the largest R(2) (19.3-24.6%) in three environments and in combined analysis. In contrast, 35 QTL for 6 grain yield components were detected in the BC(2)F(2) and F(2:3) generations, no common QTL across three generations were located in the same marker intervals. Only 100 grain weight (100GW) QTL on chromosome 5 were located in adjacent marker intervals. Four common QTL were detected across the RIL and F(2:3) generations, and two between the RIL and BC(2)F(2) generations. Each of five important mQTL (mQTL7-1, mQTL10-2, mQTL4-1, mQTL5-1 and mQTL1-3) included 7-12 QTL associated with 2-6 traits. In conclusion, we found evidence of strong influence of genetic structure and environment on QTL detection, high consistency of major QTL across environments and generations, and remarkable QTL co-location for grain yield components. Fine mapping for five major QTL (q100GW-1-1, q100GW-7-1, qGWP-4-1, qERN-4-1 and qKR-4-1) and construction of single chromosome segment lines for genetic regions of five mQTL merit further studies and could be put into use in marker-assisted breeding.

Despite the well-recognized importance of grain yield in high-oil maize (Zea mays L.) breeding and production, few studies have reported the application of QTL mapping of such traits. An inbred line of high-oil maize designated 'GY220' was crossed with two dent maize inbred lines to generate two connected F 2:3 populations with 284 and 265 F 2:3 families. Our main objective was to evaluate the influence of genetic background on QTL detection of grain yield traits through comparisons between the F 2:3 populations. The field experiments were conducted during the spring in Luoyang and summer in Xuchang, Henan, China. Two genetic linkage maps were constructed with a genetic distance of 2111.7 and 2298.5 cM using 185 and 173 polymorphic SSR markers, respectively. In total, 18 and 15 QTL were detected for six grain yield traits in the two populations. Only one common QTL marker was shared between the two populations. A QTL cluster associated with five traits was identified at bin 1.05-1.06, including the shared QTL for 100GW, which demonstrated the largest effect (16.7%). Among the detected QTL, 12 digenic interactions were identified. Our results reflect the substantial influence of dent maize genetic background on QTL detection of grain yield traits.

High-oil maize (Zea mays L.) has special value in animal feed and human food. Two hundred and eight-four and 265 F 2:3 families developed from two crosses between one high-oil maize inbred and two normal dent maize inbreds were evaluated for grain oil and starch contents under two environments. Using composite interval mapping, 1-6 QTL for each trait were detected under each environment and in combined analysis in both populations. Only one common QTL across two environments in each population and across two populations were found for starch content. Among the detected QTL, nine digenic interactions with small effects were identified. Comparison of single-trait QTL and the results of multiple-trait QTL mapping showed that oil content might be complicatedly correlated with starch content. Although singletrait QTL with the same parental effects for both traits and oil-starch QTL were all detected at the same genetic bin 6.04 as the cloned high-oil QTL (qHO6) with no unfavorable effects on grain weight, our results did reflect the difficulty to realize simultaneous improvement on grain oil and starch contents. Of course, these results should be validated in further experiments under more environments using RILs, NILs and other permanent populations.

Maize endosperm accounts for more than 80% of the grain weight. Cell division and grain filling are the two key stages for endosperm development. Previous studies showed that gene expression during differential stages in endosperm development is greatly different. However, information on systematic identification and characterization of the differentially expressed genes between the two stages are limited. In this study, suppression subtractive hybridization (SSH) was used to generate four subtracted cDNA libraries for the two stages using two maize inbreds with large and small grain. Totally, 4,784 differentially expressed sequence tags (ESTs) were sequenced and 902 were non-redundant, which consisted of 344 unique ESTs. Among them 192 had high sequence similarity to the GenBank entries and represent diverse of functional categories, such as metabolism, cell growth/division, transcription, signal transduction, protein destination/storage, protein synthesis and others. The expression patterns of 75.7% SSH-derived cDNAs were confirmed by reverse Northern blot and semi-quantitative reverse transcription polymerase chain reaction, and exhibited the similar results (75.0%). Genes differentially expressed between two key stages for the two inbreds were involved in diverse physiological process pathway, which might be responsible for the formation of grain weight. 43.8% (70 of the 160 unique ESTs) of the identified ESTs were assigned to 39 chromosome bins distributed over all ten maize chromosomes. Eleven ESTs were found to co-localize with previous detected QTLs for grain weight, which might be considered as the candidate genes of grain weight for further study.

Superior and inferior rice grains have different weights and are located on the upper primary branch and lower secondary branches of the panicle, respectively. To study differences in germination vigour of these two types of grain, a number of factors were investigated from 0 to 48 h of germination. The present study demonstrated that in inferior grains the starch granule structure was looser at 0 h, with full water absorption at 48 h, while in superior grains the structure was tight and dense. Relative water content increased, and dry matter decreased, more rapidly in inferior grains than in superior ones. Abscisic acid and gibberellin levels, as well as α-amylase activity, also changed more rapidly in inferior grains, while soluble sugar content and amylase coding gene expression increased more rapidly in inferior than superior grains during early germination. The expression of OsGAMYB was higher in inferior grains at 24 h but higher in superior grains at 48 h. The phenotypic index of seedlings was higher in seedlings from superior grains at the two-leaf stage. However, the thousand-grain weight and yield per plant in superior and inferior plants showed no significant difference at harvest. The present study indicates that inferior grains germinate faster than superior ones in the early germination stage. Although inferior grains produced weaker seedlings, it is worthwhile using them in rice production due to their comparative yield potential over that of superior grains.