QTL Analysis of Na+ and K+ Concentrations in Roots and Shoots under Different Levels of NaCl Stress in Rice (Oryza sativa L.)

The key to plant survival under NaCl salt stress is maintaining a low Na⁺ level or Na⁺/K⁺ ratio in the cells. A population of recombinant inbred lines (RILs, F_2∶9) derived from a cross between the salt-tolerant japonica rice variety Jiucaiqing and the salt-sensitive indica variety IR26, was used to determine Na⁺ and K⁺ concentrations in the roots and shoots under three different NaCl stress conditions (0, 100 and 120 mM NaCl). A total of nine additive QTLs were identified by QTL Cartographer program using single-environment phenotypic values, whereas eight additive QTLs were identified by QTL IciMapping program. Among these additive QTLs, five were identified by both programs. Epistatic QTLs and QTL-by-environment interactions were detected by QTLNetwork program in the joint analyses of multi-environment phenotypic values, and one additive QTL and nine epistatic QTLs were identified. There were three epistatic QTLs identified for Na⁺ in roots (RNC), three additive QTLs and two epistatic QTLs identified for Na⁺ in shoots (SNC), four additive QTLs identified for K⁺ in roots (RKC), four additive QTLs and three epistatic QTLs identified for K⁺ in shoots (SKC) and one additive QTL and one epistatic QTL for salt tolerance rating (STR). The phenotypic variation explained by each additive, epistatic QTL and QTL×environment interaction ranged from 8.5 to 18.9%, 0.5 to 5.3% and 0.7 to 7.5%, respectively. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSNC9, qSKC1, qSKC9, qRKC4 and qSTR7, might represent novel salt tolerance loci. The identification of salt tolerance in selected RILs showed that a major QTL qSNC11 played a significant role in rice salt tolerance, and could be used to improve salt tolerance of commercial rice varieties with marker-assisted selection (MAS) approach.     

Genetic analysis of Na+ and K+ concentrations in leaf and stem as physiological components of salt tolerance in Tomato

The sodium and potassium concentrations in leaf and stem have been genetically studied as physiological components of the vegetative and reproductive development in two populations of F₈ lines, derived from a salt sensitive genotype of Solanum lycopersicum cv. Cerasiforme, as female parent, and two salt tolerant lines, as male parents, from S. pimpinellifolium, the P population (142 lines), and S. cheesmaniae, the C population (116 lines). Genetic parameters of ten traits under salinity and five of them under control conditions were studied by ANOVA, correlation, principal component and QTL analysis to understand the global response of the plant. Two linkage maps including some tomato flowering time and salt tolerance candidate genes encoding for SlSOS1, SlSOS2, SlSOS3, LeNHX1, LeNHX3, were used for the QTL detection. Thirteen and 20 QTLs were detected under salinity in the P and C populations, respectively, and four under control conditions. Highly significant and contributing QTLs (over 40%) for the concentrations of Na⁺ and K⁺ in stems and leaves have been detected on chromosome 7 in both the populations. This is the only genomic position where the concentration QTLs for both the cations locate together. The proportion of QTLs significantly affected by salinity was larger in the P population (64.3%, including all QTLs detected under control) than in the C population (21.4%), where the estimated genetic component of variance was larger for most traits. A highly significant association between the leaf area and fruit yield under salinity was found only in the C population, which is supported by the location of QTLs for these traits in a common region of chromososome C1. As far as breeding for salt tolerance is concerned, only two sodium QTLs (lnc1.1 and lnc8.1) map in genomic regions of C1 and C8 where fruit yield QTLs are also located but in both the cases the profitable allele corresponds to the salt sensitive, cultivated species. One of those QTLs, lnc1.1 might involve LeNHX3.     

Dynamic QTL and epistasis analysis on seedling root traits in upland cotton

Roots are involved in acquisition of water and nutrients, as well as in providing structural support to plant. The root system provides a dynamic model for developmental analysis. Here, we investigated quantitative trait loci (QTL), dynamic conditional QTL and epistatic interactions for seedling root traits using an upland cotton F₂ population and a constructed genetic map. Totally, 37 QTLs for root traits, 35 dynamic conditional QTLs based on the net increased amount of root traits (root tips, forks, length, surface area and volume) (i) after transplanting 10 days compared to 5 days, and (ii) after transplanting 15 days to 10 days were detected. Obvious dynamic characteristic of QTL and dynamic conditional QTL existed at different developmental stages of root because QTL and dynamic conditional QTL had not been detected simultaneously. We further confirmed that additive and dominance effects of QTL qRSA-chr1-1 in interval time 5 to 10 DAT (days after transplant) offset the effects in 10 to 15 DAT. Lots of two-locus interactions for root traits were identified unconditionally or dynamically, and a few epistatic interactions were only detected simultaneously in interval time of 5–10 DAT and 10–15 DAT, suggesting different interactive genetic mechanisms on root development at different stages. Dynamic conditional QTL and epistasis effects provide new attempts to understand the dynamics of roots and provide clues for root architecture selection in upland cotton.     

Conditional and unconditional QTL mapping of drought-tolerance-related traits of wheat seedling using two related RIL populations

For discovering the quantitative trait loci (QTLs) contributing to early seedling growth and drought tolerance during germination, conditional and unconditional analyses of 12 traits of wheat seedlings: coleoptile length, seedling height, longest root length, root number, seedling fresh weight, stem and leaves fresh weight, root fresh weight, seedling dry weight, stem and leaves dry weight, root dry weight, root to shoot fresh weight ratio, root-to-shoot dry weight ratio, were conducted under two water conditions using two F_8:9 recombinant inbred line (RIL) populations. The results of unconditional analysis are as follows: 88 QTLs accounting for 3.33–77.01% of the phenotypic variations were detected on chromosomes 1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B and 7D. Among these QTLs, 19 were main-effect QTLs with a contribution rate greater than 10%. The results of the conditional QTL analysis of 12 traits under osmotic stress on normal water conditions were as follows: altogether 22 QTLs concerned with drought tolerance were detected on chromosomes 1B, 2A, 2B, 3B, 4A, 5D, 6A, 6D, 7B, and 7D. Of these QTLs, six were main-effect QTLs. These 22 QTLs were all special loci directly concerned with drought tolerance and most of them could not be detected by unconditional analysis. The finding of these QTLs has an important significance for fine-mapping technique, map-based cloning, and molecular marker-assisted selection of early seedling traits, such as growth and drought tolerance.     

Quantitative trait loci analysis for the developmental behavior of tiller number in rice (Oryza sativa L.)

 A doubled-haploid rice population of 123 lines from Azucena/IR64 was used for analyzing the developmental behavior of tiller number by conditional and unconditional QTL mapping methods. It was indicated that the number of QTLs significantly affecting tiller number was different at different measuring stages. Many QTLs controlling tiller growth identified at the early stages were undetectable at the final stage. Only one QTL could be detected across the whole growth period. By conditional QTL mapping, more QTLs for tiller number could be detected than that by unconditional mapping. The temporal patterns of gene expression for tiller number could be different at different stages. Even an individual gene or genes at the same genomic region might have opposite genetic effects at various growth stages. Received: 7 July 1997 / Accepted: 10 February 1998     

Dynamic QTL analysis of linolenic acid content in different developmental stages of soybean seed

Linolenic acid (LN) in soybean (Glycine max L. Merr.) seed mainly contributes to the undesirable odors and flavors commonly associated with poor oil quality. LN deposition at various stages of soybean seed development had not been reported by 2010. The objects of this study were (1) to identify and measure quantitative trait loci (QTL) underlying LN content and (2) to estimate the QTL effects expressed from earlier seed developmental stages to drying seed of soybean. One hundred and twenty-five F_5:8 and F_5:9 recombinant inbred lines derived from the cross of soybean cultivars ‘Hefeng 25’ and ‘Dongnong L5’ were used for the identification of QTL underlying LN content from the 37 day (D) to 86D stages after flowering, at Harbin in 2008 and 2009. QTL × Environment interactions (QE) effects were evaluated using a mixed genetic model (Zhu in J Zhejiang Univ (Natural Science) 33:327–335, 1999). Twelve unconditional QTL and 12 conditional QTL associated with LN content were identified at different developmental stages. Most of the QTL explained <10% of phenotypic variation of LN content. Unconditional QTL QLNF-1, QLNC2-1, QLND1b-1, QLNA2-1 and QLNH-1 influenced LN content across different development stages and environments. Conditional QTL QLNF-1, QLNC2-1 and QLNH-1 were identified in multiple developmental stages and environments. Conditional and unconditional QTL clustered in neighboring intervals on linkage groups A2, C2 and D1b. Ten QTL with conditional additive main effects (a) and/or conditional additive × environment interaction effects (ae) at specific developmental stage were identified on nine linkage groups. Of them, six QTL only possessed additive main effects and seven QTL had significant ae effects in different developmental stages. A total of 13 epistatic pairwise QTL were identified by conditional mapping in different developmental stages. Two pairs of QTL only showed aa effects and five pairs of QTL only showed aae effects at different developmental stages. QTL with aa effects, as well as their environmental interaction effects, appeared to vary at different developmental stages.     

Dynamic QTL Analysis of Rice Protein Content and Protein Index Using Recombinant Inbred Lines

Protein content (PC) and protein index (PI) play important roles in determining nutritional quality in rice (Oryza sativa L.). We used 71 lines derived from “Asominori/IR24” to analyze the developmental behavior of PC and PI through unconditional and conditional QTL mapping methods. In all, 10 unconditional QTLs and 6 conditional QTLs for PC, and 11 unconditional QTLs and 9 conditional QTLs for PI, were identified at four stages of grain filling. More were identified in the first three stages than at the final stage. Temporal patterns of gene expression for PC and PI differed over time, with several QTLs being expressed across two or three stages but many being expressed at only one stage. Some of these QTLs were closely linked with maturity QTLs reported previously. Many QTLs for PC and PI were co-localized, supporting the significant correlation found between PC and PI. Our results suggest that dynamic QTL mapping might be a valid means for revealing more genetic information about protein accumulations during seed development.     

QTLs mapping of physiological traits related to salt tolerance in young rice seedlings

Oryza sativa L. F₂ population and F_2:3 derived from a cross between salt tolerance cv. Tarommahali and salt sensitive cv. Khazar were used in this study. A linkage map based on F₂ population was constructed (74 SSR markers on 192 individuals), which covered a total of 1231.50 cM with an average two locus interval of 19.83 cM. Two QTLs related to Na⁺/K⁺ ratio were found on chromosome 3 and 6. qDM-3 and qDM-8 (for dry mass of shoot) are major QTLs with very large effects explained 20.90 and 17.72 % of the total phenotypic variance, respectively. Major locus for DM (qDM-3) was bracketed by RM1022 — RM6283 spread over 13.6 cM on chromosome 3. Major part of the variability for standard tolerance ranking (STR) was explained by the qSTR-6 flanked by RM3727 — RM340 on chromosome 6, which exhibited phenotypic variance of 17.25 % and peak likelihood ratio (LR) of 17.51. The length of this QTL is 8.8 cM and identification of any tightly linked markers in this region will serve as a candidate gene for fine-mapping. qSTR-3 overlapped with qNA-3 and qNAK-3. The qSTR-3 may contain a new major gene for salt stress tolerance at seedling stage in rice. Major QTLs identified in this paper, after fine-mapping, could be used for marker assisted selection.     

QTLs for Na<Superscript>+</Superscript> and K<Superscript>+</Superscript> uptake of the shoots and roots controlling rice salt tolerance

An F₂ and an equivalent F₃ population derived from a cross between a high salt-tolerance indica variety, Nona Bokra, and a susceptible elite japonica variety, Koshihikari, were produced. We performed QTL mapping for physiological traits related to rice salt-tolerance. Three QTLs for survival days of seedlings (SDSs) under salt stress were detected on chromosomes 1, 6 and 7, respectively, and explained 13.9% to 18.0% of the total phenotypic variance. Based on the correlations between SDSs and other physiological traits, it was considered that damage of leaves was attributed to accumulation of Na⁺ in the shoot by transport of Na⁺ from the root to the shoot in external high concentration. We found eight QTLs including three for three traits of the shoots, and five for four traits of the roots at five chromosomal regions, controlled complex physiological traits related to rice salt-tolerance under salt stress. Of these QTLs, the two major QTLs with the very large effect, qSNC-7 for shoot Na⁺ concentration and qSKC-1 for shoot K⁺ concentration, explained 48.5% and 40.1% of the total phenotypic variance, respectively. The QTLs detected between the shoots and the roots almost did not share the same map locations, suggesting that the genes controlling the transport of Na⁺ and K⁺ between the shoots and the roots may be different.     

Unconditional and conditional QTL mapping for the developmental behavior of tiller number in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

A single segment substitution population of 26 lines and their recipient parent Hua-jing-xian 74 (HJX74) were selected as experimental materials for analyzing the developmental behavior of tiller number in rice. By the unconditional QTL (quantitative trait locus) mapping method, a total number of 14 SSSLs were detected with QTLs controlling rice tiller number. The number of QTLs significantly affecting tiller number and their effect values estimated differed across measuring stages. More QTLs could be detected based on time-dependent measures of different stages. By the conditional QTL mapping method, it is possible to reveal net expression of gene in a time interval. 14 QTLs on tiller number expressed their effects in dynamic patterns of themselves during whole ontogeny. They exhibited mainly negative effects within 7 days after transplanting. During 7–21 days, QTLs were in active status and expressed larger positive effects. In the mid-period of 21–35 days, they had opposite genetic effects to wither tillers. Since then these QTLs expressed positive effects again to cause the appearance of noneffective tillers. The dynamics of QTL effects was in agreement with the actual change of tillers. Mapping QTL combining unconditional with conditional analysis for time-dependent measures is helpful to understand roundly the genetic bases for the development of quantitative traits.     

Influence of dent corn genetic backgrounds on QTL detection for plant-height traits and their relationships in high-oil maize

QTL mapping for plant-height traits has not been hitherto reported in high-oil maize. A high-oil maize inbred ‘GY220’ was crossed with two dent maize inbreds (‘8984’ and ‘8622’) to generate two connected F_2:3 populations. Four plant-height traits were evaluated in 284 and 265 F_2:3 families. Single-trait QTL mapping and multiple-trait joint QTL mapping was used to detect QTLs for the traits and the genetic relationship between plant height (PH) and two other plant-height traits. A total of 28 QTLs and 12 pairs of digenic interactions among detected QTLs for four traits were detected in the two F_2:3 families. Only one marker was shared between the two populations. Joint analysis of PH with ear height (EH) and PH with top height (TH) detected 32 additional QTLs. Our results showed that QTL detection for PH was dependent on the genetic background of dent corn inbreds. Multiple-trait joint QTL analysis could increase the number of detected QTLs.     

Conditional QTL mapping of oil content in rapeseed with respect to protein content and traits related to plant development and grain yield

Oil content in rapeseed (Brassica napus L.) is generally regarded as a character with high heritability that is negatively correlated with protein content and influenced by plant developmental and yield related traits. To evaluate possible genetic interrelationships between these traits and oil content, QTL for oil content were mapped using data on oil content and on oil content conditioned on the putatively interrelated traits. Phenotypic data were evaluated in a segregating doubled haploid population of 282 lines derived from the F₁ of a cross between the old German cultivar Sollux and the Chinese cultivar Gaoyou. The material was tested at four locations, two each in Germany and in China. QTLMapper version 1.0 was used for mapping unconditional and conditional QTL with additive (a) and locus pairs with additive × additive epistatic (aa) effects. Clear evidence was found for a strong genetic relationship between oil and protein content. Six QTL and nine epistatic locus pairs were found, which had pleiotropic effects on both traits. Nevertheless, two QTL were also identified, which control oil content independent from protein content and which could be used in practical breeding programs to increase oil content without affecting seed protein content. In addition, six additional QTL with small effects were only identified in the conditional mapping. Some evidence was apparent for a genetic interrelationship between oil content and the number of seeds per silique but no evidence was found for a genetic relationship between oil content and flowering time, grain filling period or single seed weight. The results indicate that for closely correlated traits conditional QTL mapping can be used to dissect the genetic interrelationship between two traits at the level of individual QTL. Furthermore, conditional QTL mapping can reveal additional QTL with small effects that are undetectable in unconditional mapping.     

Analyzing quantitative trait loci for yield using a vegetatively replicated F2 population from a cross between the parents of an elite rice hybrid

Although F₂s are the most informative populations for genetic analysis, it has been difficult to use F₂ populations directly for QTL analysis because it is usually difficult to assess the reliability of the data, due to an inability to estimate the experimental errors. In this study, we performed a QTL analysis for yield and yield-component traits of an F₂ population based on data from replicated field trials over 2 years using vegetative shoots of ratooned plants, making use of the ratooning habit of rice. The objective of this study was to explore the possibility of conducting QTL analyses directly based on an F₂ population by means of ratooning plants. The experimental population was from a cross between ’Zhenshan 97’ and ’Minghui 63’, the parents of ’Shanyou 63’, an elite rice hybrid widely grown in China. A genetic linkage map containing 151 molecular markers was constructed for QTL mapping. A total of 20 distinct QTLs were detected; eight of these were detected in both years and remaining 12 in only 1 year. Compared with the results of our previous analysis of the F_2:3 families from the same cross, it was shown that most of the QTLs detected in the ratooned F₂ population were also detected in the F_2:3 population. However, the estimates of both additive and dominant types of genetic effects for many of the QTLs based on F₂ ratoons were substantially larger than those based on F_2:3 families. The results indicate that vegetatively ratooned F₂ populations may have considerable utility in the mapping of QTLs, especially if dominant types of gene actions are of concern, although there were certain technical limitations in making use of such populations in the experiments. Received: 11 November 1999 / Accepted: 24 November 1999     

Identification of Quantitative Trait Loci Controlling Drought Tolerance at Seedling Stage in Chinese Dongxiang Common Wild Rice (Oryza rufipogon Griff.)

Common wild rice (Oryza rufipogon Griff.) is the ancestor of cultivated rice (O. sativa L.), which has a greater genetic diversity and important traits that remain to be employed in cultivated rice. In this study, a set of introgression lines (BC₄F₅ and/or BC₄F₆) carrying various introgressed segments from common wild rice, collected from Dongxiang county, Jiangxi Province, China, in the background of an Indica (O. sativa L. ssp. indica) cultivar, Guichao 2, was used. A total of 12 drought-related quantitative trait loci (QTL) were identified by investigating drought tolerance of introgression lines under 30% PEG treatment at the young seedlings stage. Of these QTLs, the alleles of 4 QTLs on chromosome 2, 6 and 12 from Dongxiang common wild rice were responsible for increased drought tolerance of the introgression lines. In particular, a QTL qSDT12-2, near RM17 on chromosome 12, was consistently detected in different replications, and expressed stably under PEG stress throughout the study. It was also found that the QTLs located on different chromosomes might express at different stages.     

Mapping of Quantitative Trait Loci Underlying Cold Tolerance in Rice Seedlings via High-Throughput Sequencing of Pooled Extremes

Low temperature is a major limiting factor in rice growth and development. Mapping of quantitative trait loci (QTLs) controlling cold tolerance is important for rice breeding. Recent studies have suggested that bulked segregant analysis (BSA) combined with next-generation sequencing (NGS) can be an efficient and cost-effective way for QTL mapping. In this study, we employed NGS-assisted BSA to map QTLs conferring cold tolerance at the seedling stage in rice. By deep sequencing of a pair of large DNA pools acquired from a very large F₃ population (10,800 individuals), we obtained ∼450,000 single nucleotide polymorphisms (SNPs) after strict screening. We employed two statistical methods for QTL analysis based on these SNPs, which yielded consistent results. Six QTLs were mapped on chromosomes 1, 2, 5, 8 and 10. The three most significant QTLs on chromosomes 1, 2 and 8 were validated by comparison with previous studies. Two QTLs on chromosomes 2 and 5 were also identified previously, but at the booting stage rather than the seedling stage, suggesting that some QTLs may function at different developmental stages, which would be useful for cold tolerance breeding in rice. Compared with previously reported QTL mapping studies for cold tolerance in rice based on the traditional approaches, the results of this study demonstrated the advantages of NGS-assisted BSA in both efficiency and statistical power.     

Comparative QTL analysis of salinity tolerance in terms of fruit yield using two solanum populations of F<Subscript>7</Subscript> lines

Salt tolerance has been analysed in two populations of F₇ lines developed from a salt sensitive genotype of Solanum lycopersicum var. cerasiforme, as female parent, and two salt tolerant lines, as male parents, from S. pimpinellifolium, the P population (142 lines), and S. cheesmaniae, the C population (116 lines). Salinity effects on 19 quantitative traits including fruit yield were investigated by correlation, principal component analysis, ANOVA and QTL analysis. A total of 153 and 124 markers were genotyped in the P and C populations, respectively. Some flowering time and salt tolerance candidate genes were included. Since most traits deviated from a normal distribution, results based on the Kruskal–Wallis non-parametric test were preferred. Interval mapping methodology and ANOVA were also used for QTL detection. Eight out of 15 QTLs at each population were detected for the target traits under both control and high salinity conditions, and among them, only average fruit weight (FW) and fruit number (FN) QTLs (fw1.1, fw2.1 and fn1.2) were detected in both populations. The individual contribution of QTLs were, in general, low. After leaf chloride concentration, flowering time is the trait most affected by salinity because different QTLs are detected and some of their QTL×E interactions have been found significant. Also reinforcing the interest on information provided by QTL analysis, it has been found that non-correlated traits may present QTL(s) that are associated with the same marker. A few salinity specific QTLs for fruit yield, not associated with detrimental effects, might be used to increase tomato salt tolerance. The beneficial allele at two of them, fw8.1 (in C) and tw8.1 (for total fruit weight in P) corresponds to the salt sensitive parent, suggesting that the effect of the genetic background is crucial to breed for wide adaptation using wild germplasm.     

Impact of epistasis and QTL×environment interaction on the developmental behavior of plant height in rice (Oryza sativa L.)

QTLs with epistatic effects and environmental interaction effects for the developmental behavior of plant height in rice were studied by conventional and conditional methods for quantitative trait loci (QTLs) by mapping with a doubled-haploid population of 123 lines from IR64/Azucena in three environments. The results showed that epistatic effects were important and most epistasis could be detected only by conditional QTL mapping, while most non–epistatic QTLs could be detected by both conventional and conditional methods. Many modificative QTLs showed only epistatic effects without their own additive effects at some stages. QTL×environment (QE) interaction effects were detected more often than QTL main effects for plant-height behavior, which might indicate that gene expression could be greatly affected by the environment. No QTLs had effects during the whole of ontogeny. Conditional QTL mapping might be a valid way to reveal dynamic gene expression for the development of quantitative traits, especially for epistatic effects. Received: 19 May 2000 / Accepted: 27 October 2000     

Unconditional and conditional QTL analysis of kernel weight related traits in wheat (Triticum aestivum L.) in multiple genetic backgrounds

Wheat thousand kernel weight (TKW) is a complex trait, and is largely controlled by several kernel traits, including kernel length (KL) and kernel width (KW). In order to reveal the genetic relationship between TKW and these kernel traits (KW and KL) as accurate as possible, we applied both unconditional and conditional mapping analyses to three distinct genetic populations, one DH population and two RIL populations. This report describes the identifications of 36 unconditional and conditional additive QTLs and 30 pairs of unconditional and conditional epistatic QTLs, all of which are closely associated with TKW. While the conditional additive locus Qtkw1B, detected in the RIL2 population, exhibited the largest contribution, explaining 14.12 % of TKW variance, the unconditional epistatic QTLs Qtkw3A-2/Qtkw5B.1, detected in the DH population, accounted for 11.95 % of phenotypic variance. This study also showed that, compared with unconditional mapping, conditional mapping resulted in very different numbers and different extent of effects of additive and epistatic QTLs that were associated with TKW when TKW was conditioned on kernel traits (KW and KL). These data strongly suggest that KW and KL indeed play a significant role in determining TKW. Furthermore, we demonstrated that the effects of the 25 additive QTLs for TKW were either entirely or largely determined by KW, while the effects of the other 25 additive QTLs for TKW were either entirely or largely affected by KL. We conclude that the conditional mapping can be useful for a better understanding of the interrelationship between the yield contributing traits at the QTL level.     

Genetic dissection of interactions between wheat flour starch and its components in two populations using two QTL mapping methods

Starch content and its components are important for determining wheat end-use quality and yield. However, little information is available about their interactions at the QTL/gene level in more than one population using different QTL mapping methods. Therefore, to dissect these interactions, two mapping populations from two locations over 2 years were used. The QTLs for the populations were analyzed by unconditional and conditional QTL mapping by two different analysis methods. In the two populations, there were a total of 24 unconditional additive QTLs detected for flour amylose (FAMS), flour amylopectin (FAMP), flour total starch (FTSC), and the ratio of FAMS to FAMP using ICIMapping4.1 methods, but 26 unconditional QTLs were found using QTLNetwork2.0 methods. Of these QTLs, 10 stable major additive QTLs were identified in more than one environment, mainly distributed on chromosomes 3B, 4A, 5A, and 7D. The maximum percentage of phenotypic variance explained (PVE) reached 54.31%. Two new unconditional major additive QTLs on chromosome 3B (Qftsc3B and Qfamp3B) were found. A total of 23 and 19 conditional additive QTLs were identified in the two populations using two different methods, respectively. Of which, eight and six stable major conditional QTLs were detected on chromosomes 3B, 4A, and 7D, respectively. New repressed QTLs were identified, such as Qftsc/fams5B-1 and Qftsc/fams5B-2. There were 20 epistatic unconditional and 15 conditional QTLs detected. In all, important QTLs on chromosomes 3B, 4A, and 7A were found in both populations. However, the number of important QTLs in the special recombinant inbred line (RIL) population was higher than that in the double haploid (DH) population, especially on chromosomes 7D and 5B. Moreover, the QTLs on chromosomes 4A, 7A, and 7D were close to the Wx-1 loci in the RIL population. These indicated better results can be obtained by a special population to target traits than by a common population. The important QTLs on key chromosomes can always be detected no matter what kinds of populations are used, such as the QTLs on chromosome 4A. In addition, QTL clusters were found on chromosomes 4A, 3B, 7A, 7D, and 5A in the two populations, indicating these chromosome regions were very important for starch biosynthesis.     

Genetic bases of appearance quality of rice grains in Shanyou 63, an elite rice hybrid

Appearance quality of the rice grain represents a major problem of rice production in many rice-producing areas of the world, especially in hybrid rice production in China. In this study, we conducted a molecular marker-based genetic analysis of the traits that are determinants of the appearance quality of rice grains, including traits specifying grain shape and endosperm opacity. The materials used in the analysis included an F_2:3 population and an F₁₀ recombinant inbred line population from a cross between the parents of Shanyou 63, the most widely grown rice hybrid in China. Molecular marker-based QTL (quantitative trait locus) analyses revealed that grain length and grain width were each controlled by a major QTL accounting for a very large proportion of the genetic variation, plus one or two minor QTLs each explaining a small proportion of the genetic variation. The major QTLs can be detected in both the F_2:3 and recombinant inbred line population using both paddy rice and brown rice, whereas the minor QTLs were detected only occasionally. The QTL located in the interval of RG393-C1087 on chromosome 3 is the major locus for grain length, and the one in the interval RG360-C734a on chromosome 5 plays a major role in determining grain width. Similarly, white belly, which largely determines the opacity of the endosperm, is almost entirely controlled by a major locus on chromosome 5, located in the same genomic region as the major QTL for grain width. The implications of the results with respect to hybrid rice improvement were discussed. Received: 20 February 2000 / Accepted: 21 March 2000