Validation of qGSIO, a quantitative trait locus for grain size on the long arm of chromosome 10 in rice (Oryza sativa L.)

Grain size is a major determinant of grain weight and a trait having important impact on grain quality in rice. The objective of this study is to detect QTLs for grain size in rice and identify important QTLs that have not been well characterized before. The QTL mapping was first performed using three recombinant inbred line populations derived from indica rice crosses Teqing/IRBB lines, Zhenshan 97/Milyang 46, Xieqingzao/Milyang 46. Fourteen QTLs for grain length and 10 QTLs for grain width were detected, including seven shared by two populations and 17 found in one population. Three of the seven com- mon QTLs were found to coincide in position with those that have been cloned and the four others remained to be clarified. One of them, qGSIO located in the interval RM6100-RM228 on the long arm of chromosome 10, was validated using F2：3 populations and near isogenic lines derived from residual heterozygotes for the interval RM6100-RM228. The QTL was found to have a considerable effect on grain size and grain weight, and a small effect on grain number. This region was also previously detected for quality traits in rice in a number of studies, providing a good candidate for functional analysis and breeding utilization.

Fine-Mapping of qTGW1.2a, a Quantitative Trait Locus for 1000-Grain Weight in Rice

Thousand-grain weight (TGW) is a key component of grain yield in rice. This study was conducted to validate and fine-map qTGW1.2a, a quantitative trait locus for grain weight and grain size previously located in a 933.6-kb region on the long arm of rice chromosome 1. Firstly, three residual heterozygotes (RHs) were selected from a BC2F11 population of the indica rice cross Zhenshan 97 (ZS97)///ZS97//ZS97/Milyang 46. The heterozygous segments in these RHs were arranged successively in physical positions, forming one set of sequential residual heterozygotes (SeqRHs). In each of the populations derived, non-recombinant homozygotes were identified to produce near isogenic lines (NILs) comprising the two homozygous genotypes. The NILs were tested for grain weight, grain length and grain width. QTL analyses for the three traits were performed. Then, the updated QTL location was followed for a new run of SeqRHs identification-NIL development-QTL mapping. Altogether, 11 NIL populations derived from four sets of SeqRHs were developed and used. qTGW1.2a was finally delimitated into a 77.5-kb region containing 13 annotated genes. In the six populations segregating this QTL, which were in four generations and were tested across four years, the allelic direction of qTGW1.2a remained consistent and the genetic effects were stable. For TGW, the additive effects ranged from 0.23 to 0.38 g and the proportions of phenotypic variance explained ranged from 26.15% to 41.65%. These results provide a good foundation for the cloning and functional analysis of qTGW1.2a.

Quantitative Trait Locus Analysis for Rice Yield Traits under Two Nitrogen Levels

A recombinant inbred line population derived from a super hybrid rice Xieyou 9308（Xieqingzao B/Zhonghui 9308） and its genetic linkage map were used to detect quantitative trait loci（QTLs） for rice yield traits under the low and normal nitrogen（N） levels. A total of 52 QTLs for yield traits distributed in 27 regions on 9 chromosomes were detected, with each QTL explaining 4.93%–26.73% of the phenotypic variation. Eleven QTLs were simultaneously detected under the two levels, and 30 different QTLs were detected under the two N levels, thereby suggesting that the genetic bases controlling rice growth under the low and normal N levels were different. QTLs for number of panicles per plant, number of spikelets per panicle, number of filled grains per panicle, and grain density per panicle under the two N levels were detected in the RM135–RM168 interval on chromosome 3. QTLs for number of spikelets per panicle and number of filled grains per panicle under the two N levels, as well as number of panicles per plant and grain density per panicle, under the low N level, were detected in the RM5556–RM310 interval on chromosome 8. The above described QTLs shared similar regions with previously reported QTLs for rice N recycling.

A major quantitative trait locus controlling phosphorus utilization efficiency under different phytate-P conditions at vegetative stage in barley

Organic phosphorus（P） is an important component of the soil P pool, and it has been proven to be a potential source of P for plants. The phosphorus utilization efficiency（PUE） and PUE related traits（tiller number（TN）, shoot dry weight（DW）, and root dry weight） under different phytate-P conditions（low phytate-P, 0.05 mmol L^-1 and normal phytate-P, 0.5 mmol L^-1） were investigated using a population consisting of 128 recombinant inbred lines（RILs） at the vegetative stage in barley. The population was derived from a cross between a P-inefficient genotype（Baudin） and a P-efficient genotype（CN4027, a Hordeum spontaneum accession）. A major locus（designated Qpue.sau-3 H） conferring PUE was detected in shoots and roots from the RIL population. The quantitative trait locus（QTL） was mapped on chromosome 3 H and the allele from CN4027 confers high PUE. This locus explained up to 30.3 and 28.4% of the phenotypic variance in shoots under low and normal phytate-P conditions, respectively. It also explains 28.3 and 30.7% of the phenotypic variation in root under the low and normal phytate-P conditions, respectively. Results from this study also showed that TN was not correlated with PUE, and a QTL controlling TN was detected on chromosome 5 H. However, dry weight（DW） was significantly and positively correlated with PUE, and a QTL controlling DW was detected near the Qpue.sau-3 H locus. Based on a covariance analysis, existing data indicated that, although DW may affect PUE, different genes at this locus are likely involved in controlling these two traits.

Mapping of qTGW1.1,a Quantitative Trait Locus for 1000-Grain Weight in Rice (Oryza sativa L.)

1000-grain weight （ TGW） is one ot the three component traits ot the grain yiela in rice （Oryza sativa L）. This study was conducted to validate and fine-map qTGWl. 1, a minor QTL for TGW which was previously located in a 3.7-Mb region on the long arm of rice chromosome 1. Five sets of near isogenic lines （NILs） were developed from two BC2F4 populations of the indica rice cross Zhenshan 973/Milyang 46 The NIL sets consisted of two homozygous genotypic groups differing in the regions RM11448-RM11522, RM11448-RM11549, RM1232-RM11615, RM11543-RM11554 and RM11569-RM11621, respectively. Four traits, including TGW, grain length, grain width and heading date, were measured. Phenotypic difference between the two genotypic groups in each NIL population was analyzed using SAS procedure GLM. Significant QTL effects were detected on TGW with the Zhenshan 97 allele increasing grain weight by 0.12 g to 0.14 g and explaining 8.30% to 15.19% of the phenotypic variance. Significant effects were also observed for grain length and width, whereas no significant effect was found for heading date. Based on comparison among the five NILs on the segregating regions and the results of QTL analysis, qTGWl. 1 was delimited to a 376.9-kb region flanked by DNA markers Wn28382 and RMl1554. Our results indicate that the effects of minor QTLs could be steadily detected in a highly isogenic background and suggest that such QTLs could be utilized in the breeding of high-yielding rice varieties.

QTL analysis for some quantitative traits in bread wheat

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) sug- gested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.

Dissection of Genetic Effects of Quantitative Trait Loci(QTL) in Transgenic Cotton

When alien DNA inserts into cotton genome in multi-copy manner,several QTL in cotton genome are disrupted,which are called dQTL in this study.Transgenic mutant line is near-isogenic to its recipient which is divergent for the dQTL from remaining QTL.So,a set of data from a

Identification of Quantitative Trait Loci for Anthesis-Silking Interval and Yield Components Under Drought Stress in Maize

A genetic linkage map with 89 SSR marker loci was constructed based on a maize (Zea mays L.) population consisting of 184 F-2 individuals from the cross, Huangzao 4 X Ye 107. The 184 F-3 families were evaluated in the field under well-watered and drought-stressed regimes in Shanxi Province of China. The objectives of the study were to identify genetic segments responsible for the expression of anthesis-silking interval (ASI), ear setting and grain yield, and to examine if the quantitative trait loci (QTLs) for ASI or yield components can be used in marker-assisted selection (MAS) to improve grain yield under drought conditions. Results showed that under well-watered and drought-stressed regimes, three and two QTLs involved in the expression of ASI were detected on chromosomes 1, 2 and 3, and 2 and 5, respectively. Under well-watered regime, two QTLs for ear setting were detected on chromosomes 3 and 6, explaining about 19.9% of the phenotypic variance, and displayed additive and partial dominant effects, respectively. Under drought-stressed condition, four QTLs for ear setting were detected on chromosomes 3, 7 and 10, which were responsible for interpreting 60.4% of the phenotypic variance, and showed dominant or partial dominant effects. Under well-watered condition, four QTLs controlling grain yield were identified on chromosomes 3, 6 and 7, while five QTLs were identified under drought stress on chromosomes 1, 2, 4 and 8. The gene action was of additive or partial dominant effects, and each QTL could explain 7.3% to 22.0% of the phenotypic variance, respectively. Under drought conditions, ASI and ear setting percentage were highly correlated with grain yield, which can be used as secondary traits for grain yield selection. Based on linked markers detected and gene action analyzed, an MAS strategy for yield improvement under drought condition could be established, which consists of QTLs contributing to decreased ASI and to increased ear setting and grain yield, respectively.

Detection of Quantitative Trait Loci Associated with Several Internal Organ Traits and Teat Number Trait in a Pig Population

Quantitative trait loci （QTL） were detected for 8 internal organ traits, 3 carcass length traits, and teat number trait in 214 pigs in a resource population that included 180 F2 individuals. A total of 39 microsatellite markers were examined on SSC4, SSC6, SSC7, SSC8, and SSC13. The genetic traits included heart weight （HW）, lung weight （LW）, liver and gallbladder weight （LGW）, spleen weight （SPW）, stomach weight （STW）, small intestine weight （S1W）, large intestine weight （LIW）, kidney weight （KW）, carcass length to the first cervical vertebra （CL1）, carcass length to the first thoracic vertebra （CL2）, rib numbers （RNS）, and teat numbers （TNS）. Results indicated that, 3 highly significant QTL （P≤0.01 at chromosome-wise level） for HW （at 30 cM on SSC6）, RNS （at 115 cM on SSC7）, TNS （at 110 cM on SSC7）, and 6 significant QTL （P≤0.05 at chromosome-wise level） for LW （at 119 cM on SSC13）, LGW （at 94 cM on SSC6）, SPW （at 106 cM on SSC8）, SIW （0 cM on SSC4）, LIW （170 cM on SSC 4）, and TNS （at 95 cM on SSC6） were detected. The phenotypic variances for which these QTL were accounted ranged from 0.04 % to 14.06 %. Most of these QTL had not been previously reported.

Analysis of Quantitative Trait Loci for Starch Properties of Rice Based on an RIL Population

Rice (Oryza sativa L.) eating and cooking quality is mainly influenced by its starch properties. Mapping quantitative trait loci (QTL) for starch properties not only helps us understand their genetic basis leading to acceleration of quality improvement, but also helps us find possible genes participating in the synthesis of starch. A recombinant inbred line (RIL) population consisting of 107 lines, derived from an indica (Zaiyeqing 8, ZYQ 8) and a japonica (Jingxi 17, JX 17) rice, was used to investigate the genetic factors affecting starch quality parameters, such as apparent amylose content (AAC), gel consistency (GC), starch pasting viscosity parameters, gel textural properties, gelatinization temperature (GT) and starch retrogradation properties. A total of 44 QTLs covered chromosomes 2-6, 8, 9 and 11 were detected for the 22 traits, with at least one QTL and as many as four QTLs for each individual trait. The results indicated that two major genes were responsible for most starch property traits. The Wx gene that encodes granule bound starch synthase on chromosome 6 was significant for AAC, GC, starch pasting viscosity parameters, gel textural properties and starch retrogradation properties. The alk gene linked with Wx on chromosome 6 was significant for starch gelatinization temperature characteristics. All other QTLs were minor genes. One QTL on chromosome 9 flanked by RZ404 and G295 was significant for gel hardness (HD), gumminess (GUM), chewiness (CHEW), peak temperature of retrogradated starch (RTp), and percentage retrogradation (R%) and all these traits were not tested before.

Identification of quantitative trait loci for the dead leaf rate and the seedling dead rate under alkaline stress in rice

The quantitative trait loci （QTLs） for the dead leaf rate （DLR） and the dead seedling rate （DSR） at the different rice growing periods after transplanting under alkaline stress were identified using an F2：3 population, which included 200 individuals and lines derived from a cross between two japonica rice cultivars Gaochan 106 and Changbai 9 with microsatellite markers. The DLR detected at 20 days to 62 days after transplanting under alkaline stress showed continuous normal or near normal distributions in F3 lines, which was the quantitative trait controlled by multiple genes. The DSR showed a continuous distribution with 3 or 4 peaks and was the quantitative trait controlled by main and multiple genes when rice was grown for 62 days after transplanting under alkaline stress. Thirteen QTLs associated with DLR were detected at 20 days to 62 days after transplanting under alkaline stress. Among these, qDLR9-2 located in RM5786-RM160 on chromosome 9 was detected at 34 days, 41 days, 48 days, 55 days, and 62 days, respectively; qDLR4 located in RM3524-RM3866 on chromosome 4 was detected at 34 days, 41 days, and 48 days, respectively; qDLR7-1 located in RM3859-RM320 on chromosome 7 was detected at 20 days and 27 days; and qDLR6-2 in RM1340-RM5957 on chromosome 6 was detected at 55 days and 62 days, respectively. The alleles of both qDLR9-2 and qDLR4 were derived from alkaline sensitive parent ＂Gaochanl06＂. The alleles of both qDLR7-1 and qDLR6-2 were from alkaline tolerant parent Changbai 9. These gene actions showed dominance and over dominance primarily. Six QTLs associated with DSR were detected at 62 days after transplanting under alkaline stress. Among these, qDSR6-2 and qDSR8 were located in RM1340-RM5957 on chromosome 6 and in RM3752-RM404 on chromosome 8, respectively, which were associated with DSR and accounted for 20.32% and 18.86% of the observed phenotypic variation, respectively; qDSR11-2 and qDSR11-3 were located in RM536-RM479 and RM2596-RM286 on chromosome 11, respectively, which were associated with DSR explaining 25.85% and 15.41% of the observed phenotypic variation, respectively. The marker flanking distances of these QTLs were quite far except that of qDSR6-2, which should be researched further.

Quantitative Trait Loci for Grain Chalkiness and Endosperm Transparency Detected in Three Recombinant Inbred Line Populations of Indica Rice

Quantitative trait loci（QTL） for percentage of chalky grain,degree of chalkiness,and endosperm transparency were detected using 3 recombinant inbred line populations derived from crosses between parental lines of commercial three-line hybrids of indica rice.Two of the populations showed great variations on heading date,and the other had a short range of heading date variation.A total of 40 QTLs were detected and fell into 15 regions of 10 chromosomes,of which 5 regions were detected for 1 or more same traits over different populations,2 were detected for different traits in different populations,3 were detected for 2 or all the 3 traits in a single population,and 5 were detected for a single trait in a single population.Most of these QTLs have been reported previously,but a region located on the long arm of chromosome 10 showing significant effects in all the 3 populations has not been reported before.It was shown that a number of gene cloned,including the Wx and Alk for the physiochemical property of rice grain,and GW2,GS3 and GW5 for grain weight and grain size,could have played important roles for the genetic control of grain chalkiness in rice,but there are many more QTLs exerting stable effects for rice chalkiness over different genetic backgrounds.It is worth paying more attentions to these regions which harbor QTL such as the qPCG5.2/qDC5.2/qET5.2 and qPCG10/qDC10/qET10 detected in our study.Our results also showed that the use of segregating populations having high-uniform heading date could greatly increase the efficiency of the identification of QTL responsible for traits that are subjected to great environmental influence.

Quantitative Trait Loci Associated with Pollen Fertility under High Temperature Stress at Flowering Stage in Rice (Oryza sativa)

High temperature stress (HTS), an increasingly important problem in rice production, significantly reduces rice yield by reducing pollen fertility and seed setting rate. Breeding rice varieties with tolerance to HTS at the flowering stage is therefore essential for maintaining rice production as the climate continues to become warm. In this study, two quantitative trait loci (QTLs) underlying tolerance to HTS were identified using recombinant inbred lines derived from a cross between an HTS-tolerant rice cultivar 996 and a sensitive cultivar 4628. Pollen fertility was used as a heat-tolerance indicator for the lines subjected to HTS at the flowering stage in field experiments. Two QTLs that affected pollen fertility, qPF4 and qPF6, were detected between RM5687 and RM471 on chromosome 4, and between RM190 and RM225 on chromosome 6, by using the composite interval mapping (CIM) analysis. The two QTLs explained 15.1% and 9.31% of the total phenotypic variation in pollen fertility, and increased the pollen fertility of the plants subjected to HTS by 7.15% and 5.25%, respectively. The positive additive effects of the two QTLs were derived from the 996 alleles. The two major QTLs identified would be useful for further fine mapping and cloning of these genes and for molecular marker-assistant breeding of heat-tolerant rice varieties.

Identification of Quantitative Trait Loci for Grain Traits in Japonica Rice

Quantitative trait loci（QTLs） of grain traits were detected to provide theoretical basis for fine mapping and molecular marker-assisted breeding of grain traits in japonica rice.Using an F2 population including 200 individuals derived from a cross combination between two japonica rice DL115 with large grain and XL005 with small grain,the grain length,grain width,grain thickness,ratio of grain length to width and 1 000-grain weight were evaluated in Beijing;and the quantitative trait loci for above five grain traits were identified by composite interval mapping using SSR markers.The results showed that the five grain traits exhibited a normal continuous distribution in F2 population,indicating they were quantitative traits controlled by multiple genes.A total of 16 QTLs conferring the five grain traits were detected on chromosomes 2,3,5 and 12,respectively.Eight QTLs,namely qGL3a,qGW2,qGW5,qGT2,qRLW2,qRLW3,qGWT2 and qGWT3,were major QTLs and explained 15.42,40.89,13.54,33.43,13.82,13.61,12.51 and 10.1% of the observed phenotypic variance,respectively.Among them,qGW2,qGT2,qRLW2 and qGWT2 were mapped in same interval RM12776-RM324 on chromosome 2.The marker interval RM12776-RM324 on chromosome 2 was common marker intervals of four major QTLs,and the two SSR markers RM12776 and RM324 would be used in molecular markerassisted breeding in japonica rice.The modes of gene action were mainly additive and partial dominance.Four QTLs＇ alleles were derived from small grain parent XL005,and other 12 QTLs＇ alleles were derived from large grain parent DL115.The alleles from larger parent were showed significant effects to grain length,grain width,grain thickness and 1 000-grain weight.

Quantitative Trait Loci for Heading Date and Their Relationship with Genetic Control of Yield Traits in Rice (Oryza sativa)

Grain yield and heading date are key factors determining the commercial potential of a rice variety. Mapping of quantitative trait loci （QTLs） in rice has been advanced from primary mapping to gene cloning, and heading date and yield traits have always attracted the greatest attention. In this review, genomic distribution of QTLs for heading date detected in populations derived from intra-specific crosses of Asian cultivated rice （Oryza sativa） was summarized, and their relationship with the genetic control of yield traits was analyzed. The information could be useful in the identification of QTLs for heading date and yield traits that are promising for the improvement of rice varieties.

Confirmation of Novel Quantitative Trait Loci for Seed Dormancy at Different Ripening Stages in Rice

Seed dormancy contributes resistance to pre-harvest sprouting. Effects on respective quantitative trait loci （QTLs） for dormancy should be assessed by using fresh seeds before germinability altered through storage. We investigated QTLs related to seed dormancy using backcross inbred lines derived from a cross between Nipponbare and Kasalath. Four putative QTLs for seed dormancy were detected immediately after harvest using composite interval mapping. These putative QTLs were mapped near C1488 on chromosome 3 （qSD-3.1）, R2171 on chromosome 6 （qSD-6.1）, R1245 on chromosome 7 （qSD-7.1） and C488 on chromosome 10 （qSD-IO.1）. Kasalath alleles promoted dormancy for qSD-3.1, qSD-6.1 and qSD-7.1, and the respective proportions of phenotypic variation explained by each QTL were 12.9%, 9.3% and 8.1%. We evaluated the seed dormancy harvested at different ripening stages during seed development using chromosome segment substitution lines （CSSLs） to confirm gene effects. The germination rates of CSSL27 and CSSL28 substituted with the region including qSD-6.1 were significantly lower than those of Nipponbare and other CSSLs at the late ripening stage. Therefore, qSD-6.1 is considered the most effective novel QTL for pre-harvest sprouting resistance among the QTLs detected in this study.

Quantitative Trait Loci for Mercury Tolerance in Rice Seedlings

Mercury （Hg） is one of the most toxic heavy metals to living organisms and its conspicuous effect is the inhibition of root growth. However, little is known about the molecular genetic basis for root growth under excess Hg2＋ stress. To map quantitative trait loci （QTLs） in rice for Hg2＋ tolerance, a population of 120 recombinant inbred lines derived from a cross between two japonica cultivars Yuefu and IRAT109 was grown in 0.5 mmol/L CaCI2 solution. Relative root length （RRL）, percentage of the seminal root length in ＋HgCI2 to -HgCI2, was used for assessing Hg2＋ tolerance. In a dose-response experiment, Yuefu had a higher RRL than IRAT109 and showed the most significant difference at the Hg2＋ concentration of 1.5 tJmol/L. Three putative QTLs for RRL were detected on chromosomes 1, 2 and 5, and totally explained about 35.7% of the phenotypic variance in Hg2＋ tolerance. The identified QTLs for RRL might be useful for improving Hg2＋ tolerance of rice by molecular marker-assisted selection.

Mapping of quantitative trait loci using the skew-normal distribution

In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use the previous model after data transformation. However, an appropriate transformation may not exist or may be difficult to find. Also this approach can raise interpretation issues. An interesting alternative is to consider a skew-normal mixture model in standard IM, and the resulting method is here denoted as skew-normal IM. This flexible model that includes the usual symmetric normal distribution as a special case is important, allowing continuous variation from normality to non-normality. In this paper we briefly introduce the main peculiarities of the skew-normal distribution. The maximum likelihood estimates of parameters of the skew-normal distribution are obtained by the expectation-maximization (EM) algorithm. The proposed model is illustrated with real data from an intercross experiment that shows a significant departure from the normality assumption. The performance of the skew-normal IM is assessed via stochastic simulation. The results indicate that the skew-normal IM has higher power for QTL detection and better precision of QTL location as compared to standard IM and nonparametric IM.

Identification of Stable Quantitative Trait Loci for Sheath Blight Resistance Using Recombinant Inbred Line

To identify stable quantitative trait loci(QTLs)responsible for sheath blight resistance,a recombinant inbred line mapping population consisting of 219 lines was developed by crossing Lemont and Yangdao 4.Average disease rating,average lesion length,maximum disease rating and maximum lesion length were assayed in six different environments.A total of 128 minor effect QTLs were detected by multiple interval mapping.These QTLs explained less than 11.2%of the phenotypic variations individually,and 106 QTLs were clustered in 20 QTL-rich regions/putative loci.Significant QTL×environment interactions were detected at three putative loci(qSBR11.1,qSBR11.2 and qSBR11.3),indicating that these three loci were not stable.The other 17 stable loci(qSBR1.1,qSBR1.2,qSBR2.1,qSBR2.3,qSBR3.1,qSBR3.2,qSBR3.5,qSBR3.6,qSBR5.1,qSBR7.1,qSBR8.1,qSBR9.1,qSBR9.2,qSBR9.3,qSBR12.1,qSBR12.2 and qSBR12.4)provided a foundation for marker-assisted selection in breeding.Analysis of allelic effect on the 20 putative loci identified 7 highly stable loci,including qSBR3.2,qSBR7.1,qSBR8.1,qSBR9.2,qSBR9.3,qSBR12.1 and qSBR12.2.

Mapping quantitative trait loci associated with starch paste viscosity attributes by using double haploid populations of rice (Oryza sativa L.)

The paste viscosity attributes of starch,measured by rapid visco analyzer(RVA),are important factors for the evaluation of the cooking and eating qualities of rice in breeding programs.To determine the genetic roots of the paste viscosity attributes of rice grains,quantitative trait loci(QTLs)associated with the paste viscosity attributes were mapped,using a double haploid(DH)population derived from Zhongjiazao 17(YK17),a super rice variety,crossed with D50,a tropic japonica variety.Fifty-four QTLs,for seven parameters of the RVA profiles,were identified in three planting seasons.The 54 QTLs were located on all of the 12 chromosomes,with a single QTL explaining 5.99 to 47.11%of phenotypic variation.From the QTLs identified,four were repeatedly detected under three environmental conditions and the other four QTLs were repeated under two environments.Most of the QTLs detected for peak viscosity(PKV),trough viscosity(TV),cool paste viscosity(CPV),breakdown viscosity(BDV),setback viscosity(SBV),and peak time(PeT)were located in the interval of RM 6775-RM 3805 under all three environmental conditions,with the exception of pasting temperature(PaT).For digenic interactions,eight QTLs with six traits were identified for additivexenvironment interactions in all three planting environments.The epistatic interactions were estimated only for PKV,SBV and PaT.The present study will facilitate further understanding of the genetic architecture of eating and cooking quality(ECQ)in the rice quality improvement program.