To identify the chromosomal regions controlling the eating quality of cooked rice, we performed a quantitative trait locus (QTL) analysis using 93 backcross inbred lines (BILs) and 39 chromosome segment substitution lines (CSSLs) derived from crosses between a japonica rice cultivar Koshihikari (glossier appearance, tasty, sticky and soft eating quality of rice when cooked) and an indica cultivar Kasalath (less glossy appearance, less sticky and hard eating quality of rice when cooked). We evaluated the eating quality of rice including overall evaluation (OE), glossiness (GL), taste (TA), stickiness (ST) and hardness (HA) in each line based on the sensory test of cooked rice. Twenty-one QTLs for eating quality were mapped to eight regions on chromosomes 1, 2, 3 (two regions), 6, 7, 9 and 10. The Koshihikari alleles at 19 out of 21 QTLs increased the eating quality, while the Kasalath alleles at the other two QTLs increased the eating quality. We also mapped the QTLs for chemical properties, such as amylose content (AC) and protein content (PC), which affected the eating quality. Four QTLs in the terminal region of the short arm of chromosome 3 and five QTLs on chromosome 6 for eating quality were mapped to the same region as that of the QTLs for AC. Three QTLs on chromosome 1 for eating quality were also mapped to the same region as that of a QTL for PC. The chromosome positions of the other QTLs for eating quality did not coincide with those of the QTLs for AC and PC. Six out of 21 QTLs for eating quality, qTA3, qOE6, qGL6, qTA6, qST6 and qHA6, were commonly identified by analysis using both BILs and CSSLs. One QTL, qTA3, was not a locus of AC, PC or known eating genes. Thus the QTL was mapped in the interval between the SSR markers RM1332 and RM6676 in the middle region of the short arm of chromosome 3 by fine mapping of three sub-CSSLs. Five QTLs, qOE6, qGL6, qTA6, qST6 and qHA6, seemed to be associated with the Waxy (Wx) gene located on chromosome 6.

Stable carbon isotope ratio (δ13C) in plants has been suggested as a useful indicator for cumulative Ci/Ca signature in a leaf, water use efficiency, and crop productivity, and is known to have genotypic variation in rice (Oryza sativa L.). We conducted a field study to identify quantitative trait loci (QTLs) for δ13C and other related leaf traits, such as leaf N, specific leaf area, and SPAD value, using recombinant inbred lines derived from an indica × japonica cross grown under flooded conditions. We also examined the genetic associations of δ13C with yield, yield components, and biomass productivity. Putative QTLs for δ13C were identified on chromosomes 2, 4, 8, 9, 11, and 12 across plant parts, stages, and years. Differential expression of QTL for δ13C among stages suggests that each QTL had different functions by stages. The QTLs for δ13C were associated with a few colocated QTLs for leaf traits indicating that their physiological and genetic associations with leaf traits may be complex. Values of δ13C at maturity were negatively correlated with harvest index and grain yield. However, genetic association of these traits could not be clarified due to the absence of co‐located QTLs. Further examination would be useful to elucidate the physiological and morphological functions of QTLs for δ13C found in this study.

Our simulation results clarify the areas of applicability of nine prediction methods and suggest the factors that affect their accuracy at predicting empirical traits. Whole-genome prediction is used to predict genetic value from genome-wide markers. The choice of method is important for successful prediction. We compared nine methods using empirical data for eight phenological and morphological traits of Asian rice cultivars (Oryza sativa L.) and data simulated from real marker genotype data. The methods were genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Lasso, elastic net, random forest (RForest), Bayesian lasso (Blasso), extended Bayesian lasso (EBlasso), weighted Bayesian shrinkage regression (wBSR), and the average of all methods (Ave). The objectives were to evaluate the predictive ability of these methods in a cultivar population, to characterize them by exploring the area of applicability of each method using simulation, and to investigate the causes of their different accuracies for empirical traits. GBLUP was the most accurate for one trait, RKHS and Ave for two, and RForest for three traits. In the simulation, Blasso, EBlasso, and Ave showed stable performance across the simulated scenarios, whereas the other methods, except wBSR, had specific areas of applicability; wBSR performed poorly in most scenarios. For each method, the accuracy ranking for the empirical traits was largely consistent with that in one of the simulated scenarios, suggesting that the simulation conditions reflected the factors that affected the method accuracy for the empirical results. This study will be useful for genomic prediction not only in Asian rice, but also in populations from other crops with relatively small training sets and strong linkage disequilibrium structures.

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