Sensitivities of rice grain yield and other panicle characters to late-stage drought stress revealed by phenotypic correlation and QTL analysis

A lowland rice variety (Zhenshan97B) was crossed with an upland variety (IRAT109) to construct a set of recombinant inbred lines (RILs). The population was evaluated under both well-watered (control) and drought-stress (drought) conditions for 2 years. Panicle water potential (PWP), panicle length (PL), grain number per plant (GNP), primary branch number (PBN), second branch number (SBN), spikelet density (SPD), and dry grain weight per plant (DGW) were measured. Phenotypic correlation and path analysis were used to interpret the ranking of importance of other panicle traits to grain yield. Comparison of such rankings under control and drought conditions showed varied responses of panicle traits to drought stress. It was indicated that GNP was an important contributor to DGW under control, and even more important under drought. Thirty-two quantitative trait loci (QTLs) for panicle traits and dry grain weight were identified, with contribution rates ranging from 3.33% to 22.66%. Eleven epistatic QTLs were detected. Cases of collocated QTLs under control and drought were found for PL, SPD, GNP, PBN, and SBN, but not for DGW and PWP. The effectiveness of selection under normal and stressed conditions is discussed.     

Construction of introgression lines carrying wild rice (Oryza rufipogon Griff.) segments in cultivated rice (Oryza sativa L.) background and characterization of introgressed segments associated with yield-related traits

Introgression lines (ILs) are useful tools for precise mapping of quantitative trait loci (QTLs) and the evaluation of gene action or interaction in theoretical studies. A set of 159 ILs carrying variant introgressed segments from Chinese common wild rice (Oryza rufipogon Griff.), collected from Dongxiang county, Jiangxi Province, in the background of Indica cultivar (Oryza sativa L.), Guichao 2, was developed using 126 polymorphic simple sequence repeats (SSR) loci. The 159 ILs represented 67.5% of the genome of O. rufipogon. All the ILs have the proportions of the recurrent parent ranging from 92.4 to 99.9%, with an average of 97.4%. The average proportion of the donor genome for the BC₄F₄ population was about 2.2%. The mean numbers of homozygous and heterozygous donor segments were 2 (ranging 0–8) and 1 (ranging 0–7), respectively, and the majority of these segments had sizes less than 10 cM. QTL analysis was conducted based on evaluation of yield-related traits of the 159 ILs at two sites, in Beijing and Hainan. For 6 out of 17 QTLs identified at two sites corresponding to three traits (panicles per plant, grains per panicle and filled grains per plant, respectively), the QTLs derived from O. rufipogon were usually associated with an improvement of the target trait, although the overall phenotypic characters of O. rufipogon were inferior to that of the recurrent parent. Of the 17 QTLs, 5 specific QTLs strongly associated with more than one trait were observed. Further analysis of the high-yielding and low-yielding ILs revealed that the high-yielding ILs contained relatively less introgressed segments than the low-yielding ILs, and that the yield increase or decrease was mainly due to the number of grain. On the other hand, low-yielding ILs contained more negative QTLs or disharmonious interactions between QTLs which masked trait-enchancing QTLs. These ILs will be useful in identifying the traits of yield, tolerance to low temperature and drought stress, and detecting favorable genes of common wild rice.     

RFLP mapping of QTLs for yield and related characters in rice (Oryza sativa L.)

Quantitative triat loci (QTLs) for yield and related traits in rice were mapped based on RFLP maps from two indica/indica F₂ populations, Tesanai 2/CB and Waiyin 2/CB. In Tesanai 2/CB, 14 intervals carrying QTLs for eight traits were detected, including 3 for grain weight per plant (GWT), 2 for number of panicles per plant (NP), 2 for number of grains per panicle (NG), 1 for total number of spikelets per panicle (TNS), 1 for spikelet fertility (SF), 3 for 1000-grain weight (TGWT), 1 for spikelet density (SD), and 1 for number of first branches per main panicle. The 3 QTLs for GWT were located on chromosomes 1, 2, and 4, with 1 in each chromosome. The additive effect of the single locus ranged from 2.0 g to 9.1 g. A major gene (np4) for NP was detected on chromosome 4 within the interval of RG143–RG214, about 4cM for RG143, and this locus explained 26.1% of the observed phenotypic variance for NP. The paternal allele of this locus was responsible for reduced panicles per plant (3 panicles per plant). In another population, Waiyin 2/CB, 12 intervals containing QTLs for six of the above-mentioned traits were detected, including 3 for GWT, 2 for each of NP, TNS, TGWT and SD, 1 for SF. Three QTLs for GWT were located on chromosome 1, 4, and 5, respectively. The additive effect of the single locus for GWT ranged from 6.7 g to 8.8 g, while the dominance effect was 1.7–11.5 g. QTL mapping in two populations with a common male parent is compared and discussed.     

Gene actions of QTLs affecting several agronomic traits resolved in a recombinant inbred rice population and two testcross populations

To understand the types of gene action controlling seven quantitative traits in rice, QTL mapping was performed to dissect the main effect (M-QTLs) and digenic epistatic (E-QTLs) QTLs responsible for the trait performance of 254 recombinant inbred lines (RILs) of "Lemont/Teqing", and two testcross (TC) F(1) populations derived from these RILs. The correlation analyses reveal a general pattern, i.e. trait heritability in the RILs was negatively correlated to trait heterosis in the TC hybrids. A large number of M-QTLs and E-QTLs affecting seven traits, including heading date (HD), plant height (PH), flag leaf length (FLL), flag leaf width (FLW), panicle length (PL), spikelet number per panicle (SN) and spikelet fertility (SF), were identified and could be classified into two predominant groups, additive QTLs detected primarily in the RILs, and overdominant QTLs identified exclusively in the TC populations. There is little overlap between QTLs identified in the RILs and in the TC populations. This result implied that additive gene action is largely independent from non-additive gene action in the genetic control of quantitative traits of rice. The detected E-QTLs collectively explained a much greater portion of the total phenotypic variation than the M-QTLs, supporting prior findings that epistasis has played an important role in the genetic control of quantitative traits in rice. The implications of these results to the development of inbred and hybrid cultivars were discussed.     

QTL analysis of potato tuberization

Quantitative trait loci (QTLs) affecting tuberization were detected in reciprocal backcrosses between Solanum tuberosum and S. berthaultii. Linkage analyses were performed between traits and RFLP alleles segregating from both the hybrid and the recurrent parent using a set of framework markers from the potato map. Eleven distinct loci on seven chromosomes were associated with variation in tuberization. Most of the loci had small effects, but a QTL explaining 27% of the variance was found on chromosome 5. More QTLs were detected while following alleles segregating from the recurrent S. tuberosum parent used to make the backcross than were detected by following alleles segregating from the hybrid parent. More than half of the alleles favoring tuberization were at least partly dominant. Tuberization was favored by an allele from S. berthaultii at 3 of the 5 QTLs detected by segregation from the hybrid parent. The additive effects of the QTLs for tuberization explained up to 53% of the phenotypic variance, and inclusion of epistatic effects increased this figure to 60%. The most common form of epistasis was that in which presence of an allele at each of 2 loci favoring tuberization was no more effective than the presence of a favorable allele at 1 of the 2 loci. The QTLs detected for tuberization traits are discussed in relationship to those previously detected for trichome-mediated insect resistance derived from the unadapted wild species.Paper number 54 of the Department of Fruit and Vegetable Science, Cornell University     

Novel pleiotropic loci controlling panicle architecture across environments in japonica rice （Oryza sativa L.）

To identify quantitative trait loci (QTLs) controlling panicle architecture in japonica rice, a genetic map was constructed based on simple sequence repeat (SSR) markers and 254 recombinant inbred lines (RILs) derived from a cross between cultivars Xiushui 79 and C Bao. Seven panicle traits were investigated under three environments. Single marker analysis indicated that a total of 27 SSR markers were highly associated with panicle traits in all the three environments. Percentage of phenotypic variation explained by single locus varied from 2% to 35%. Based on the mixed linear model, a total of 40 additive QTLs for seven panicle traits were detected by composite interval mapping, explaining 1.2%-35% of phenotypic variation. Among the 9 QTLs with more than 10% of explained phenotypic variation, two QTLs were for the number of primary branches per panicle (NPB), two for panicle length (PL), two for spikelet density (SD), one for the number of secondary branches per panicle (NSB), one for secondary branch distribution density (SBD), and one for the number of spikelets per panicle (NS), respectively. qPLSD-9-1 and qPLSD-9-2 were novel pleiotropic loci, showing effects on PL and SD simultaneously. qPLSD-9-1 explained 34.7% of the phenotypic variation for PL and 25.4% of the phenotypic variation for SD, respec- tively. qPLSD-9-2 explained 34.9% and 24.4% of the phenotypic variation for PL and SD, respectively. The C Bao alleles at the both QTLs showed positive effects on PL, and the Xiushui 79 alleles at the both QTLs showed positive effects on SD. Genetic variation of panicle traits are mainly attributed to additive effects. QTL × environment interactions were not significant for additive QTLs and additive × additive QTL pairs.     

Comparison of molecular linkage maps and QTLs for morphological traits in two reciprocal backcross populations of rice

Comparison of maps and QTLs between populations may provide us with a better understanding of molecular maps and the inheritance of traits. We developed and used two reciprocal BC1F1 populations, IP/DS//IP and IP/DS//DS, for QTL analysis. DS (Dasanbyeo) is a Korean tongil-type cultivar (derived from an indica x japonica cross and similar to indica in its genetic make-up) and IP (Ilpumbyeo) is a Korean japonica cultivar. We constructed two molecular linkage maps corresponding to each backcross population using 196 markers for each map. The length of each chromosome was longer in the IP/DS//IP population than in the IP/DS//DS population, indicating that more recombinants were produced in the IP/DS//IP population. Distorted segregation was observed for 44 and 19 marker loci for the IP/DS//IP and IP/DS//DS populations, respectively; these were mostly skewed in favor of the indica alleles. A total of 36 main effect QTLs (M-QTLs) and 15 digenic epistatic interactions (E-QTLs) were detected for the seven traits investigated. The phenotypic variation explained (PVE) by M-QTLs ranged from 3.4% to 88.2%. Total PVE of the M-QTLs for each trait was significantly higher than that of the E-QTLs. The total number of M-QTLs identified in the IP/DS//IP population was higher than in the IP/DS//DS population. However, the total PVE by the M-QTLs and E-QTLs together for each trait was similar in the two populations, suggesting that the two BC1F1 populations are equally useful for QTL analysis. Maps and QTLs in the two populations were compared. Eleven new QTLs were identified for SN, SF, GL, and GW in this study, and they will be valuable in marker-assisted selection, particularly for improving grain traits in tongil-type varieties.     

Identification of salt-tolerant QTLs with strong genetic background effect using two sets of reciprocal introgression lines in rice

Effect of genetic background on detection of quantitative trait locus (QTL) governing salinity tolerance (ST) was studied using two sets of reciprocal introgression lines (ILs) derived from a cross between a moderately salinity tolerant japonica variety, Xiushui09 from China, and a drought tolerant but salinity susceptible indica breeding line, IR2061-520-6-9 from the Philippines. Salt toxicity symptoms (SST) on leaves, days to seedling survival (DSS), and sodium and potassium uptake by shoots were measured under salinity stress of 140?mmol/L of NaCl. A total of 47 QTLs, including 26 main-effect QTLs (M-QTLs) and 21 epistatic QTLs (E-QTLs), were identified from the two sets of reciprocal ILs. Among the 26?M-QTLs, only four (15.4%) were shared in the reciprocal backgrounds while no shared E-QTLs were detected, indicating that ST QTLs, especially E-QTLs, were very specific to the genetic background. Further, 78.6% of the M-QTLs for SST and DSS identified in the reciprocal ILs were also detected in the recombinant inbred lines (RILs) from the same cross, which clearly brings out the background effect on ST QTL detection and its utilization in ST breeding. The detection of ILs with various levels of pyramiding of nonallelic M-QTL alleles for ST from Xiushui09 into IR2061-520-6-9 allowed us to further improve the ST in rice.     

Gene actions of QTLs affecting several agronomic traits resolved in a recombinant inbred rice population and two backcross populations

To understand the types of gene action controlling seven quantitative traits in rice, we carried out quantitative trait locus (QTL) mapping in order to distinguish between the main-effect QTLs (M-QTLs) and digenic epistatic QTLs (E-QTLs) responsible for the trait performance of 254 recombinant inbred lines (RILs) from rice varieties Lemont/Teqing and two backcross hybrid (BCF₁) populations derived from these RILs. We identified 44 M-QTL and 95 E-QTL pairs in the RI and BCF₁ populations as having significant effects on the mean values and mid-parental heterosis of heading date, plant height, flag leaf length, flag leaf width, panicle length, spikelet number and spikelet fertility. The E-QTLs detected collectively explained a larger portion of the total phenotypic variation than the M-QTLs in both the RI and BCF₁ populations. In both BCF₁ populations, over-dominant (or under-dominant) loci were more important than additive and complete or partially dominant loci for M-QTLs and E-QTL pairs, thereby supporting prior findings that overdominance resulting from epistatic loci are the primary genetic basis of inbreeding depression and heterosis in rice.     

水稻穗部性状的QTL与环境互作分析

分别在两年收集珍汕97／明恢63的重组自交系群体的表现数据,运用混合线性模型的QTL定位方法,联合分析穗部5个性状的QTLs7及QTL与环境互作关系。每穗颖花数、每穗实粒数、结实率、穗长和穗着密度分别检测到10、3、6、8和7个QTLs分别解释各性状变异的29．13％、19．2％、29．46％、26．39％和35．76％。对于同一性状,高值亲本和低值亲本中均存在增效和减效QTL。相关性状QTL的位置表现相同或相似,高值亲本和低值亲本中均存在增效和减效QTL。相关性状QTL的位置表现相同或相似,成族分布。1个穗长QTL,2个每穗颖花数QTL3,3个结实率QTLs表现与环境显著互作,QTL与环境互作效应的贡献率比相应的QTL贡献率略大。遗传力稍高的每穗实粒数和穗着粒密度的DQTL与环境不互作。     

云南元江普通野生稻穗颈维管束和穗部性状的QTL分析

以云南元江普通野生稻为供体亲本,籼稻品种特青为轮回亲本构建高代回交群体,用SSR标记构建连锁图谱,在第1、2、3、4、7和10染色体上定位到7个控制穗颈大维管束数的QTL,在第1、2、3、4和8染色体上定位到5个控制穗颈小维管束数的QTL,在第11和12以外的10条染色体上,共定位到15个控制穗一、二次枝梗数和穗颖花数QTL。来自野生稻的等位基因大多表现负效,能显著减少群体的穗颈维管束数、枝梗数和颖花数,说明从野生稻演化成栽培稻的过程中,可能淘汰了一些对产量不利的QTL,保留了有利的QTL。相当一部分控制穗颈维管束数、枝梗数及颖花数的QTL在染色体上成簇分布或紧密连锁,且加性效应的方向一致,从理论上解释了这些性状表型显著相关的遗传基础,同时也说明在人工选择或自然选择下,这些性状可能存在平行进化或协同进化的关系。     

水稻产量库相关穗部性状的遗传分析

收人李源于珍汕97/明恢63的重组系群体中与产量库容有关的10个穗部性状的表现型数据。总体上,每穗颖花数与每穗二次枝梗数、每个二次枝梗上的颖花数、颖花密度有更大的相关性。对所研究的10个性状,两年间共检测到53个QTLs。约43.4%的QTLs能在两年同时检测到。5个染色体区域（第1染色体上G359-RG532和C567-C86-RG236,第2染色体上R712-RM29,第6染色体上P-RG424,第10染色体上C148-RM258）分别对多个穗部性状表现出效应。结果显示相关性状的QTLs大致定位在相似的染色体区域,这表明基因的多效性或紧密连锁是穗部性状间相关的遗传基因。在检测到的大量2位点互作对中,约18.2%在两年都能被检测到。不同性状的共同互作对的比例为8.7%～32.6%。在两年都能检测到2位点组合中,约26.7%的组合同时影响着多个性状,表现出多效效应。结果表明每个性状都由数个QTL、基因型与环境互作、大量的上位性互作所控制。     

Quantitative trait loci for grain quality, productivity, morphological and agronomical traits in sorghum (Sorghum bicolor L. Moench)

 Quantitative trait loci (QTLs) for grain quality, yield components and other traits were investigated in two Sorghum caudatum×guinea recombinant inbred line (RIL) populations. A total of 16 traits were evaluated (plant height, panicle length, panicle compactness, number of kernels/panicle, thousand-kernel weight, kernel weight/panicle, threshing percentage, dehulling yield, kernel flouriness, kernel friability, kernel hardness, amylose content, protein content, lipid content, germination rate and molds during germination and after harvest) and related to two 113- and 100-point base genetic maps using simple (SIM) and composite (CIM) interval mapping. The number, effects and relative position of QTLs detected in both populations were generally in agreement with the distributions, heritabilities and correlations among traits. Several chromosomal segments markedly affected multiple traits and were suspected of harbouring major genes. The positions of these QTLs are discussed in relation to previously reported studies on sorghum and other grasses. Many QTLs, depending on their relative effects and position, could be used as targets for marker-assisted selection and provide an opportunity for accelerating breeding programmes. Received: 14 February 1998 / Accepted: 4 March 1998     

QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

Genetic dissection and pyramiding of quantitative traits for panicle architecture by using chromosomal segment substitution lines in rice

To understand the genetic basis of yield-related traits of rice, we developed 39 chromosome segment substitution lines (CSSLs) from a cross between an average-yielding japonica cultivar, Sasanishiki, as the recurrent parent and a high-yielding indica cultivar, Habataki, as the donor. Five morphological components of panicle architecture in the CSSLs were evaluated in 2 years, and 38 quantitative trait loci (QTLs) distributed on 11 chromosomes were detected. The additive effect of each QTL was relatively small, suggesting that none of the QTLs could explain much of the phenotypic difference in sink size between Sasanishiki and Habataki. We developed nearly isogenic lines for two major QTLs, qSBN1 (for secondary branch number on chromosome 1) and qPBN6 (for primary branch number on chromosome 6), and a line containing both. Phenotypic analysis of these lines revealed that qSBN1 and qPBN6 contributed independently to sink size and that the combined line produced more spikelets. This suggests that the cumulative effects of QTLs distributed throughout the genome form the major genetic basis of panicle architecture in rice. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. T. Ando and T. Yamamoto contributed equally to this work.     

Additive and over-dominant effects resulting from epistatic loci are the primary genetic basis of heterosis in rice

A set of 148 F9 recombinant inbred lines (RILs) was developed from the cross of an indica cultivar 93-11 and japonica cultivar DTT13,showing strong F1 heterosis.Subsequently,two backcross F1 (BCF1) populations were constructed by backcrossing these 148 RILs to two parents,93-11 and DT713.These three related populations (281BCF1 lines,148 RILs) were phenotyped for six yield-related traits in two locations.Significant inbreeding depression was detected in the population of RILS and a high level of heterosis was observed in the two BCF1 populations.A total of 42 main-effect quantitative trait loci (M-QTLs) and 109 epistatic effect QTL pairs (E-QTLs) were detected in the three related populations using the mixed model approach.By comparing the genetic effects of these QTLs detected in the RILs,BCF1 performance and mid-parental heterosis (HMp),we found that,in both BCF1 populations,the QTLs detected could be classified into two predominant types:additive and over-domlnant loci,which indicated that the additive and over-dominant effect were more important than complete or partially dominance for M-QTLs and E-QTLs.Further,we found that the E-QTLs detected collectively explained a larger portion of the total phenotypic variation than the M-QTLs in both RILs and BCF1 populations.All of these results suggest that additive and over-dominance resulting from epistatic loci might be the primary genetic basis of heterosis in rice.     

Identification of several small main-effect QTLs and a large number of epistatic QTLs for drought tolerance related traits in groundnut (Arachis hypogaea L.)

Cultivated groundnut or peanut (Arachis hypogaea L.), an allotetraploid (2n = 4x = 40), is a self pollinated and widely grown crop in the semi-arid regions of the world. Improvement of drought tolerance is an important area of research for groundnut breeding programmes. Therefore, for the identification of candidate QTLs for drought tolerance, a comprehensive and refined genetic map containing 191 SSR loci based on a single mapping population (TAG 24 x ICGV 86031), segregating for drought and surrogate traits was developed. Genotyping data and phenotyping data collected for more than ten drought related traits in 2-3 seasons were analyzed in detail for identification of main effect QTLs (M-QTLs) and epistatic QTLs (E-QTLs) using QTL Cartographer, QTLNetwork and Genotype Matrix Mapping (GMM) programmes. A total of 105 M-QTLs with 3.48-33.36% phenotypic variation explained (PVE) were identified using QTL Cartographer, while only 65 M-QTLs with 1.3-15.01% PVE were identified using QTLNetwork. A total of 53 M-QTLs were such which were identified using both programmes. On the other hand, GMM identified 186 (8.54-44.72% PVE) and 63 (7.11-21.13% PVE), three and two loci interactions, whereas only 8 E-QTL interactions with 1.7-8.34% PVE were identified through QTLNetwork. Interestingly a number of co-localized QTLs controlling 2-9 traits were also identified. The identification of few major, many minor M-QTLs and QTL × QTL interactions during the present study confirmed the complex and quantitative nature of drought tolerance in groundnut. This study suggests deployment of modern approaches like marker-assisted recurrent selection or genomic selection instead of marker-assisted backcrossing approach for breeding for drought tolerance in groundnut.     

Over-expression of the rice LRK1 gene improves quantitative yield components

In rice ( Oryza sativa L.), the number of panicles, spikelets per panicle and grain weight are important components of grain yield. These characteristics are controlled by quantitative trait loci (QTLs) and are derived from variation inherent in crops. As a result of the complex genetic basis of these traits, only a few genes involved in their control have been cloned and characterized. We have previously map-cloned a gene cluster including eight leucine-rich repeat receptor-like kinase ( LRK ) genes in Dongxiang wild rice ( Oryza rufipogon Griff.), which increased the grain yield by 16%. In the present study, we characterized the LRK1 gene, which was contained in the donor parent (Dongxiang wild rice) genome and absent from the recurrent parent genome (Guichao2, Oryza sativa L. ssp. indica ). Our data showed that rice LRK1 is a plasma membrane protein expressed constitutively in leaves, young panicles, roots and culms. The over-expression of rice LRK1 results in increased panicles, spikelets per panicle, weight per grain and enhanced cellular proliferation, leading to a 27.09% increase in total grain yield per plant. The increased number of panicles and spikelets per panicle are associated with increased branch number. Our data suggest that rice LRK1 regulates rice branch number by enhancing cellular proliferation. The functional characterization of rice LRK1 facilitates an understanding of the mechanisms involved in cereal crop yield, and may have utility in improving grain yield in cereal crops.     

水稻穗颈维管束及穗部性状的QTL分析

以籼稻(Oryza sativa L. ssp. indica ZYQ8)和粳稻(O. sativa ssp. japonica JX17)的杂交F1代花培加倍的DH群体为材料考察了该群体的穗颈节大小维管束数、一次枝梗数、每穗颖花数、穗颈节顶部直径和穗长,并用该群体构建的分子图谱进行数量性状座位(QTL)分析.检测到控制大维管束的3个QTL (qLVB-1、qLVB-6和qLVB-7)分别位于第1、第6和第7染色体;控制小维管束的2个QTL (qSVB-4和qSVB-6)分别位于第4和第6染色体;控制一次枝梗的4个QTL (qPRB-4a、qPRB-4b、qPRB-6和qPRB-7)分别位于第4 (2个)、第6和第7染色体;每穗颖花数的3个QTL (qSPN-4a、qSPN-4b和 qSPN-6)分别位于第4 (2个)和第6染色体上;穗颈节顶部直径的5个QTL (qPTD-2、qPTD-5、qPTD-6、qPTD-8和qPTD-12)分别位于第2、第5、第6、第8和第12染色体;穗长的3个QTL (qPL-4、qPL-6和qPL-8)分别位于第4、第6、第8染色体上.其中qLVB-6、qSVB-6、qSPN-6、qPTD-6和qPL-6均位于第6染色体的G122-G1314b之间;qPL-8和qPTD-8位于第8染色体的GA408-BP127a之间;qPRB-4a和qSPN-4a位于第4染色体的G177-CT206之间;qPL-4和qSPN-4b位于第4染色体CT404-CT500之间;qSVB-4所在的区间与qPL-4、qSPN-4b和qPRB-4b所在的区间相邻.     

水稻穗颈维管束及穗部性状的QTL分析

以籼稻 (OryzasativaL .ssp .indicaZYQ8)和粳稻 (O .sativassp .japonicaJX17)的杂交F1代花培加倍的DH群体为材料考察了该群体的穗颈节大小维管束数、一次枝梗数、每穗颖花数、穗颈节顶部直径和穗长 ,并用该群体构建的分子图谱进行数量性状座位 (QTL)分析。检测到控制大维管束的 3个QTL (qLVB_1、qLVB_6和qLVB_7)分别位于第 1、第 6和第 7染色体 ;控制小维管束的 2个QTL (qSVB_4和qSVB_6 )分别位于第 4和第 6染色体 ;控制一次枝梗的 4个QTL (qPRB_4a、qPRB_4b、qPRB_6和qPRB_7)分别位于第 4(2个 )、第 6和第 7染色体 ;每穗颖花数的 3个QTL (qSPN_4a、qSPN_4b和qSPN_6 )分别位于第 4(2个 )和第 6染色体上 ;穗颈节顶部直径的 5个QTL (qPTD_2、qPTD_5、qPTD_6、qPTD_8和qPTD_12 )分别位于第 2、第 5、第 6、第 8和第 12染色体 ;穗长的 3个QTL (qPL_4、qPL_6和qPL_8)分别位于第 4、第 6、第 8染色体上。其中qLVB_6、qSVB_6、qSPN_6、qPTD_6和qPL_6均位于第 6染色体的G12 2_G1314b之间 ;qPL_8和qPTD_8位于第 8染色体的GA40 8_BP12 7a之间 ;qPRB_4a和qSPN_4a位于第 4染色体的G177_CT2 0 6之间 ;qPL_4和qSPN_4b位于第 4染色体CT40 4_CT5 0 0之间 ;qSVB_4所在的区间与qPL_4、qSPN_4b和qPRB_4b所在的区间相邻。