Comparative mapping of QTLs for agronomic traits of rice across environments by using a doubled-haploid population

We report here the RFLP mapping of quantitative trait loci (QTLs) which affect some important agronomic traits in cultivated rice. An anther culture-derived doubled-haploid (DH) population was established from a cross between indica and japonica rice varieties. A molecular linkage map comprising 137 markers was constructed based on this population which covered the rice genome at intervals of 14.8 cM on average. The linkage map was used to locate QTLs for such important agronomic traits as heading date, plant height, number of spikelets per panicle, number of grains per panicle, 1 000-grain weight and the percentage of seed set, by interval mapping. Evidence of genotype-by-environment interaction was found by comparing QTL maps of the same population grown in three diverse environments. A total of 22 QTLs for six agronomic traits was detected which were significant in at least one environment, but only seven were significant in all three environments; seven were significant in two environments and eight could only be detected in a single environment. However, QTLs-by-environment interaction was trait dependent. QTLs for spikelets and grains per panicle were common across environments while traits like heading date and plant height were more sensitive to environment. Received: 22 February 1996 / Accepted: 10 May 1996     

Mapping quantitative trait loci controlling agronomic traits in the spring wheat cross RL4452x'AC Domain'.

Relatively little is known about the genetic control of agronomic traits in common wheat (Triticum aestivum L.) compared with traits that follow Mendelian segregation patterns. A doubled-haploid population was generated from the cross RL4452x'AC Domain' to study the inheritance of the agronomic traits: plant height, time to maturity, lodging, grain yield, test weight, and 1000-grain weight. This cross includes the genetics of 2 western Canadian wheat marketing classes. Composite interval mapping was conducted with a microsatellite linkage map, incorporating 369 loci, and phenotypic data from multiple Manitoba environments. The plant height quantitative trait loci (QTLs), QHt.crc-4B and QHt.crc-4D, mapped to the expected locations of Rht-B1 and Rht-D1. These QTLs were responsible for most of the variation in plant height and were associated with other agronomic traits. An additional 25 agronomic QTLs were detected in the RL4452x'AC Domain' population beyond those associated with QHt.crc-4B and QHt.crc-4D. 'AC Domain' contributed 4 alleles for early maturity, including a major time to maturity QTL on 7D. RL4452 contributed 2 major alleles for increased grain yield at QYld.crc-2B and QYld.crc-4A, which are potential targets for marker-assisted selection. A key test weight QTL was detected on 3B and prominent 1000-grain weight QTLs were identified on 3D and 4A.     

水稻籼粳交DH群体收获指数及源库性状的QTL分析

以 1个水稻籼粳交 (圭 6 30 0 2 4 2 8)来源的DH群体为材料 ,利用 1张含有 2 32个标记的RFLP连锁图谱和基于混合线性模型的定位软件QTLMapper1 0对水稻收获指数及生物量、籽粒产量、库容量和株高 5个性状进行QTL分析 ,共检测到 2 1个主效应QTLs和 9对上位性互作位点。其中 ,控制籽粒产量的 3个QTLs合计贡献率为 4 2 % ,LOD值为 7 10 ;这 3个QTLs或者与收获指数的QTL同位 ,或者与生物量的QTL同位 ,且加性效应的方向一致 ,从而揭示了“籽粒产量 =生物量×收获指数”的遗传基础所在。控制收获指数的 4个QTLs合计贡献率为 4 6 % ,LOD值为 10 3;控制生物量的 4个QTLs合计贡献率为 6 4 % ,LOD值为 14 0 9;收获指数的 4个QTLs与生物量的 4个QTLs均不同位。因此 ,通过基因重组 ,可能实现控制收获指数和生物量的增效基因的聚合 ,由此获得收获指数和生物量“双高”的基因型。检测到 5个株高QTLs,其合计贡献率为 6 4 % ,LOD值为 11 6 2 ;其中 ,有 3个效应较小的QTLs与生物量、库容量和 或籽粒产量QTLs同位 ,且同位QTLs的加性效应方向一致 ;未发现株高QTLs与收获指数QTLs的同位性。由此表明 ,株高与“源 流 库”概念中的“源”和“库”在遗传上有一定程度的关联 ,而与“流”无关联。此外还发现 ,在上述同位性QTL     

Comparative mapping of QTLs for agronomic traits of rice across environments using a doubled haploid population

We report here the RFLP mapping of quantitative triat loci (QTLs) that affect some important agronomic traits in cultivated rice. An anther culturederived doubled haploid (DH) population was established from a cross between an indica and a japonica rice variety. On the basis of this population a molecular linkage map comprising 137 markers was constructed that covered the rice genome at intervals of 14.8cM on average. Interval mapping of the linkage map was used to locate QTLs for such important agronomic traits as heading date, plant height, number of spikelets per panicle, number of grains Per panicle, 1000-grain weight and percentage of seed set. Evidence of genotype-byenvironment interaction was found by comparing QTL maps of the same population grown in three diverse environments. A total of 22 QTLs for six agronomic traits were detected that were significant in at least one environment, but only 7 were significant in all three environments, 7 were significant in two environments and 8 could only be detected in a single environment. However, QTL-by-environment interaction was traitdependent. QTLs for spikelets and grains per panicle were common across environments, while traits like heading date and plant height were more sensitive to environment.     

Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive × additive interaction and QTL × environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0–26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL × environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.     

Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum

The identification of quantitative trait loci (QTLs) affecting agronomically important traits enable to understand their underlying genetic mechanisms and genetic basis of their complex interactions. The aim of the present study was to detect QTLs for 12 agronomic traits related to staygreen, plant early development, grain yield and its components, and some growth characters by analyzing replicated phenotypic datasets from three crop seasons, using the population of 168 F₇ RILs of the cross 296B × IS18551. In addition, we report mapping of a subset of genic-microsatellite markers. A linkage map was constructed with 152 marker loci comprising 149 microsatellites (100 genomic- and 49 genic-microsatellites) and three morphological markers. QTL analysis was performed by using MQM approach. Forty-nine QTLs were detected, across environments or in individual environments, with 1–9 QTLs for each trait. Individual QTL accounted for 5.2–50.4% of phenotypic variance. Several genomic regions affected multiple traits, suggesting the phenomenon of pleiotropy or tight linkage. Stable QTLs were identified for studied traits across different environments, and genetic backgrounds by comparing the QTLs in the study with previously reported QTLs in sorghum. Of the 49 mapped genic-markers, 18 were detected associating either closely or exactly as the QTL positions of agronomic traits. EST marker Dsenhsbm19, coding for a key regulator (EIL-1) of ethylene biosynthesis, was identified co-located with the QTLs for plant early development and staygreen trait, a probable candidate gene for these traits. Similarly, such exact co-locations between EST markers and QTLs were observed in four other instances. Collectively, the QTLs/markers identified in the study are likely candidates for improving the sorghum performance through MAS and map-based gene isolations.     

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。     

用RFLP标记剖析水稻穗颈维管束及穗部性状的遗传基础

采用籼、粳亚种间杂种F1(圭630×02428)花药培养获得的DH群体,对水稻穗颈大、小维管束数和倒数第2节间大、小维管束数等4个维管束性状,以及一、二次枝梗数,每穗颖花数3个穗部性状进行QTL分析,共检测到16个QTLs,其中有7个QTLs的加性效应较大,单个QTL的贡献率在20%以上。发现有4个QTLs成簇分布于第1染色体从RZ776到C11的大约35cM的区段上,来源于亲本"圭630"的这一染色体区段对穗颈大维管束、第2节间大维管束、第2节间小维管束和二次枝梗数4个性状的表达均具有增效作用。还讨论了利用分子标记辅助选择聚合增效QTLs、实现穗颈维管束性状遗传改良的策略。     

两个水稻骨干恢复系重要农艺性状的遗传基础研究

水稻骨干恢复系是指在杂交稻育种中广泛应用的一类恢复系。探明骨干恢复系的遗传基础,发掘其重要农艺性状基因/QTL,对分子标记辅助选择水稻恢复系育种具有重要应用价值。本研究以生产上广泛应用的三系骨干恢复系成恢727和两系骨干恢复系9311为亲本,培育了具有250个系的重组自交系群体。分别在2015年三亚和2016年合肥两个环境下进行了9个重要农艺性状表型和SSR分子标记基因型鉴定,用SAS9.2分析表型数据,用QTL Ici Mapping v4.1进行QTL定位分析。在三亚和合肥两个环境下共检测到39个QTL,三亚检测到16个,分布于第1、2、4、7、8、10、11和12染色体上;合肥检测24个,分布于第1、2、3、7、8、9、10和12染色体上。其中qPH1-1在三亚和合肥两个环境下都能检测到,加性效应分别为-1.75和-2.46。在检测到的39个QTL中,有24个QTL的增效等位基因来自恢复系成恢727,15个QTL的增效等位基因来自9311。共计有26个QTL曾被前人定位,13个属于尚未见文献报道的新QTL。另外,在RM279~RM521、RM336~RM3534、RM25~RM547、RM553~RM160、RM222~RM271区段内检测到5个多效性QTL位点。其中RM25~RM547位点与已经克隆的基因Ghd8位置相近。RM553~RM160位点是一个新的多效性位点,分别控制每穗实粒数、单株产量和结实率,而且效应和表型变异贡献率都较大。其余3个位点在前人的研究中分别有所报道,但其多效性则是在本研究中首次发现。在本研究新发掘到的QTL中,控制穗数的QTL qPN12-1,控制穗长的QTL qPL1-2和qPL10-1,控制总粒数的QTL qSNP2-1和qSNP10-1,控制结实率的QTL qSF3-1,控制千粒重QTL qTGW7-1和控制产量的QTL qGY1-1效应均比较大,解释的表型遗传变异比例也较高。本研究的结果将会为相关性状QTL的精细定位、克隆和育种应用奠定基础。     

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.     

Quantitative trait locus analysis for rice panicle and grain characteristics

 The development of molecular genetic maps has accelerated the identification and mapping of genomic regions controlling quantitative characters, referred to as quantitative trait loci or QTLs. A molecular map derived from an F₂ population of a tropical japonica×indica cross (Labelle/Black Gora) consisted of 116 restriction fragment length polymorphism (RFLP) markers. Composite interval mapping was used to identify the QTLs controlling six panicle and grain characteristics. Two QTLs were identified for panicle size at LOD>3.0, with one on chromosome 3 accounting for 16% of the phenotypic variation. Four loci controlling spikelet fertility accounted for 23% of the phenotypic variation. Seven, four, three and two QTLs were detected for grain length, breadth, shape and weight, respectively, with the most prominent QTLs being on chromosomes 3, 4, and 7. Grain shape, measured as the ratio of length to breadth, was mostly controlled by loci on chromosomes 3 and 7 that coincided with the most important QTLs identified for length and breadth, respectively. A model including three loci accounted for 45% of the phenotypic variation for this trait. The identification of economically important QTLs will be useful in breeding for improved grain characteristics. Received: 18 July 1997 / Accepted: 9 December 1997     

Quantitative trait loci for agronomic traits in an elite barley population for Mediterranean conditions

Advances in plant breeding through marker-assisted selection (MAS) are only possible when genes or quantitative trait loci (QTLs) can contribute to the improvement of elite germplasm. A population of recombinant inbred lines (RILs) was developed for one of the best crosses of the Spanish National Barley Breeding Program, between two six-row winter barley cultivars Orria and Plaisant. The objective of this study was to identify favourable QTLs for agronomic traits in this population, which may help to optimise breeding strategies for these and other elite materials for the Mediterranean region. A genetic linkage map was developed for 217 RILs, using 382 single nucleotide polymorphism markers, selected from the barley oligonucleotide pool assay BOPA1 and two genes. A subset of 112 RILs was evaluated for several agronomic traits over a period of 2 years at three locations, Lleida and Zaragoza (Spain) and Fiorenzuola d’Arda (Italy), for a total of five field trials. An important segregation distortion occurred during population development in the region surrounding the VrnH1 locus. A QTL for grain yield and length of growth cycle was also found at this locus, apparently linked to a differential response of the VrnH1 alleles to temperature. A total of 33 QTLs was detected, most of them for important breeding targets such as plant height and thousand-grain weight. QTL × environment interactions were prevalent for most of the QTLs detected, although most interactions were of a quantitative nature. Therefore, QTLs suitable for MAS for most traits were identified.     

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.     

Quantitative Trait Loci for Panicle Layer Uniformity Identified in Doubled Haploid Lines of Rice in Two Environments

Uniformity of stem height in rice directly affects crop yield potential and appearance, and has become a vital index for rice improvement. In the present study, a doubled haploid (DH) population, derived from a cross between japonica rice Chunjiang 06 and indica rice TN1 was used to analyze the quantitative trait locus (QTL) for three related traits of panicle-layer-uniformity; that is, the tallest panicle height, the lowest panicle height and panicle layer disuniformity in two locations: Hangzhou (HZ) and Hainan (HN). A total of 16 QTLs for three traits distributed on eight chromosomes were detected in two different environments. Two QTLs, qTPH -4 and qTPH -8 were co-located with the QTLs for qLPH -4 and qLPH -8, which were only significant in the HZ environment, whereas the qTPH -6 and qLPH -6 located at the same interval were only significant in the HN environment. Two QTLs, qPLD -10-1 and qPLD -10-2, were closely linked to qTPH-10 , and they might have been at the same locus. One QTL, qPLD -3, was detected in both environments, explaining more than 23% of the phenotypic variations. The CJ06 allele of qPLD -3 could increase the panicle layer disuniformity by 9.23 and 4.74 cm in the HZ and HN environments. Except for qPLD -3, almost all other QTLs for the same trait were detected only in one environment, indicating that these three traits were dramatically affected by environmental factors. The results may be useful for elucidation of the molecular mechanism of panicle-layer-uniformity and marker assisted breeding for super-rice.     

Mapping QTLs for improving grain yield using the USDA rice mini-core collection

Yield is the most important and complex trait for genetic improvement in crops, and marker-assisted selection enhances the improvement efficiency. The USDA rice mini-core collection derived from over 18,000 accessions of global origins is an ideal panel for association mapping. We phenotyped 203 O. sativa accessions for 14 agronomic traits and identified 5 that were highly and significantly correlated with grain yield per plant: plant height, plant weight, tillers, panicle length, and kernels/branch. Genotyping with 155 genome-wide molecular markers demonstrated 5 main cluster groups. Linkage disequilibrium (LD) decayed at least 20 cM and marker pairs with significant LD ranged from 4.64 to 6.06% in four main groups. Model comparisons revealed that different dimensions of principal component analysis affected yield and its correlated traits for mapping accuracy, and kinship did not improve the mapping in this collection. Thirty marker–trait associations were highly significant, 4 for yield, 3 for plant height, 6 for plant weight, 9 for tillers, 5 for panicle length and 3 for kernels/branch. Twenty-one markers contributed to the 30 associations, because 8 markers were co-associated with 2 or more traits. Allelic analysis of OSR13, RM471 and RM7003 for their co-associations with yield traits demonstrated that allele 126 bp of RM471 and 108 bp of RM7003 should receive greater attention, because they had the greatest positive effect on yield traits. Tagging the QTLs responsible for multiple yield traits may simultaneously help dissect the complex yield traits and elevate the efficiency to improve grain yield using marker-assisted selection in rice.     

Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia

Rice double-haploid (DH) lines of an indica and japonica cross were grown at nine different locations across four countries in Asia. Genotype-by-environment (G x E) interaction analysis for 11 growth- and grain yield-related traits in nine locations was estimated by AMMI analysis. Maximum G x E interaction was exhibited for fertility percentage number of spikelets and grain yield. Plant height was least affected by environment, and the AMMI model explained a total of 76.2% of the interaction effect. Mean environment was computed by averaging the nine environments and subsequently analyzed with other environments to map quantitative trait loci (QTL). QTL controlling the 11 traits were detected by interval analysis using mapmaker/qtl. A threshold LOD of >/=3.20 was used to identify significant QTL. A total of 126 QTL were identified for the 11 traits across nine locations. Thirty-four QTL common in more than one environment were identified on ten chromosomes. A maximum of 44 QTL were detected for panicle length, and the maximum number of common QTL were detected for days to heading detected. A single locus for plant height (RZ730-RG810) had QTL common in all ten environments, confirming AMMI results that QTL for plant height were affected the least by environment, indicating the stability of the trait. Two QTL were detected for grain yield and 19 for thousand-grain weight in all DH lines. The number of QTL per trait per location ranged from zero to four. Clustering of the QTL for different traits at the same marker intervals was observed for plant height, panicle number, panicle length and spikelet number suggesting that pleiotropism and or tight linkage of different traits could be the possible reason for the congruence of several QTL. The many QTL detected by the same marker interval across environments indicate that QTL for most traits are stable and not essentially affected by environmental factors.     

Genetic architecture of quantitative trait loci associated with morphological and agronomic trait differences in a wild by cultivated barley cross.

Hordeum vulgare subsp. spontaneum is the progenitor of cultivated barley (Hordeum vulgare L.). Domestication combined with plant breeding has led to the morphological and agronomic characteristics of modern barley cultivars. The objective of this study was to map the genetic factors that morphologically and agronomically differentiate wild barley from modern barley cultivars. To address this objective, we identified quantitative trait loci (QTLs) associated with plant height, flag leaf width, spike length, spike width, glume length in relation to seed length, awn length, fragility of ear rachis, endosperm width and groove depth, heading date, flag leaf length, number of tillers per plant, and kernel color in a Harrington/OUH602 advanced backcross (BC2F8) population. This population was genotyped with 113 simple sequence repeat markers. Thirty QTLs were identified, of which 16 were newly identified in this study. One to 4 QTLs were identified for each of the traits except glume length, for which no QTL was detected. The portion of phenotypic variation accounted for by individual QTLs ranged from about 9% to 54%. For traits with more than one QTL, the phenotypic variation explained ranged from 25% to 71%. Taken together, our results reveal the genetic architecture of morphological and agronomic traits that differentiate wild from cultivated barley.     

Identification of QTLs for Yield and Associated Traits in F₂ Population of Rice

Identification of quantitative trait loci (QTLs) controlling yield and yield-related traits in rice was performed in the F₂ mapping population derived from parental rice genotypes DHMAS and K343. A total of 30 QTLs governing nine different traits were identified using the composite interval mapping (CIM) method. Four QTLs were mapped for number of tillers per plant on chromosomes 1 (2 QTLs), 2 and 3; three QTLs for panicle number per plant on chromosomes 1 (2 QTLs) and 3; four QTLs for plant height on chromosomes 2, 4, 5 and 6; one QTL for spikelet density on chromosome 5; four QTLs for spikelet fertility percentage (SFP) on chromosomes 2, 3 and 5 (2 QTLs); two QTLs for grain length on chromosomes 1 and 8; three QTLs for grain width on chromosomes1, 3 and 8; three QTLs for 1000-grain weight (TGW) on chromosomes 1, 4 and 8 and six QTLs for yield per plant (YPP) on chromosomes 2 (3 QTLs), 4, 6 and 8. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety. Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection (MAS) breeding. Further, the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.     

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.     

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