Dynamic expression of nine QTLs for tiller number detected with single segment substitution lines in rice

Nine single segment substitution lines (SSSLs) in rice, which contain quantitative trait loci (QTLs) for tiller number on substituted segments detected in previous studies, were selected as materials to analyse dynamic expression of the QTLs in this study. These SSSLs and their recipient parent, Hua-jing-xian 74 (HJX74), were grown in four different environments and were measured for tiller number at nine different growth stages. An indirect methodology was applied in QTL mapping through analyzing multi-environment phenotypic data. Dynamics of three types of effects (including total effect, main effect, and QE interaction effect) of QTLs was released. It was shown that nine QTLs exhibited statistically significant effects only at certain stages. Effects of a QTL, although insignificant at certain stages, displayed dynamic change with the growth of rice plants. Two common features of nine QTLs were detected, one is no expression within 7 days after transplanting, and the other is opposite expression existed during the whole growth period. Nine QTLs largely focused on expression in certain stages, and accordingly were suggested to partition into three types, expression in prophase, both in prophase and in anaphase, and evenly during the whole stage. It may be reasonable explanation that dynamics of main effects of QTLs are likely due to gene expression selectly at certain times, while dynamics of QE interaction effects of QTLs might attribute to the subrogation of environmental factors. Examination of the association between QE interaction effect and specified environmental factors across stages may provide useful information on how an environmental factor regulates QTL expression. G. Liu and R. Zeng contributed equally to this work.     

QTL analysis for rice grain length and fine mapping of an identified QTL with stable and major effects

Grain length in rice plays an important role in determining rice appearance, milling, cooking and eating quality. In this study, the genetic basis of grain length was dissected into six main-effect quantitative trait loci (QTLs) and twelve pairs of epistatic QTLs. The stability of these QTLs was evaluated in four environments using an F₇ recombinant inbred line (RIL) population derived from the cross between a Japonica variety, Asominori, and an Indica variety, IR24. Moreover, chromosome segment substitution lines (CSSLs) harboring each of the six main-effect QTLs were used to evaluate gene action of QTLs across eight environments. A major QTL denoted as qGL-3a, was found to express stably not only in the isogenic background of Asominori but also in the recombinant background of Asominori and IR24 under multiple environments. The IR24 allele at qGL-3a has a positive effect on grain length. Based on the test of advanced backcross progenies, qGL-3a was dissected as a single Mendelian factor, i.e., long rice grain was controlled by a recessive gene gl-3. High-resolution genetic and physical maps were further constructed for fine mapping gl-3 by using 11 simple sequence repeat (SSR) markers designed using sequence information from seven BAC/PAC clones and a BC₄F₂ population consisting of 2,068 individuals. Consequently, the gl-3 gene was narrowed down to a candidate genomic region of 87.5 kb long defined by SSR markers RMw357 and RMw353 on chromosome 3, which provides a basis for map-based cloning of this gene and for marker-aided QTL pyramiding in rice quality breeding.     

Identification of main effects, epistatic effects and their environmental interactions of QTLs for yield traits in rice

Quantitative trait loci (QTLs) controlling yield and yield components were identified by using a doubled haploid (DH) population of 120 lines from a sub-specific cross between ‘Samgang’ (Indica) and ‘Nagdong’ (Japonica). Main effects, epistatic effects, their environment interactions of QTLs were analyzed via mixed linear model approach across different environments. A total of 17 putative QTLs were identified on 8 chromosomes and five QTLs were detected over two years. 7 QTLs of main effects and 23 epistatic interactions were observed for five traits. Epistatic interactions played an important role in controlling the expression of yield related traits. The epistatic effects explained higher percentages of phenotype variation for panicles per plant, seed set percentage and yield. Significant QTL×environment (QE) interactions effects were identified for all traits, including 5 main effect QTLs. However, the present study failed to identify the significant interactions between epistatic loci containing main effect QTLs and the environment. The information provided in the present study could be used in the marker-assisted selection to enhance selection efficiency and to improve yield in rice.     

Identification and pyramiding of QTLs for cold tolerance at the bud bursting and the seedling stages by use of single segment substitution lines in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Cold stress is one of the main constraints in rice production, and damage from cold can occur at different developmental stages in rice. Understanding the genetic basis of cold tolerance is the key for breeding cold-tolerant variety. In this study, we used single segment substitution lines (SSSLs) derived from a cross between cold-tolerant japonica variety “Nan-yang-zhan” and a popular indica variety “Hua-jing-xian 74” to detect and pyramid QTLs for cold tolerance at the bud bursting and the seedling stages. Evaluation of cold tolerance of these SSSLs and their recurrent parent helped identify two cold-tolerant QTLs (qCTBB-5 and qCTBB-6) at the bud bursting stage and two cold-tolerant QTLs (qCTS-6 and qCTS-12) at the seedling stage. The SSSLs carrying these QTLs showed stronger cold tolerance than their recurrent parent HJX74 did in three independent experiments. The qCTBB-6 and qCTS-6 were mapped to the same chromosomal region. QTL pyramiding was performed by intercrossing of SSSLs carrying the respective QTLs for cold tolerance at the bud bursting stage and the seedling stage and marker-assisted selection (MAS). The selected pyramiding line SC1-1 with different cold-tolerant QTLs showed cumulative effects on cold tolerance. Our results suggest that different genes (QTLs) control cold tolerance at bud bursting and seedling stages, and pyramiding of stable expression QTLs for cold tolerance at different developmental stages through MAS is a good strategy to prevent cold damage in rice.     

Mapping QTLs with digenic epistasis under multiple environments and predicting heterosis based on QTL effects

Mixed linear model approach was proposed for mapping QTLs with the digenic epistasis and QTL by environment (QE) interaction as well as additive and dominant effects. Monte Carlo simulations indicated that the proposed method could provide unbiased estimations for both positions and genetic main effects of QTLs, as well as unbiased predictions for QE interaction effects. A method was suggested for predicting heterosis based on individual QTL effects. The immortalized F₂ (IF₂) population constructed by random mating among RI or DH lines is appropriate for mapping QTLs with epistasis and their QE interaction. Based on the models and methodology proposed, we developed a QTL mapping software, QTLMapper 2.0 on the basis of QTLmapper 1.0, which is suitable for analyzing populations of DH, RIL, F₂ and IF₂. Data of thousand grain weight of IF₂ population with 240 lines derived from elite hybrid rice Shanyou 63 were analyzed as a worked example.     

Effects of genetic background and environment on QTLs and epistasis for rice (Oryza sativa L.) panicle number

A double-haploid (DH) population and a recombinant inbred (RI) line population, derived from a cross between a tropical japonica variety, Azucena, as male parent and two indica varieties, IR64 and IR1552, as female parents respectively, were used in both field and pot experiments for detecting QTLs and epistasis for rice panicle number in different genetic backgrounds and different environments. Panicle number (PN) was measured at maturity. A molecular map with 192 RFLP markers for the DH population and a molecular map with 104 AFLP markers and 103 RFLP markers for the RI population were constructed, in which 70 RFLP markers were the same. Six QTLs were identified in the DH population, including two detected from field experiments and four from pot experiments. The two QTLs, mapped on chromosomes 1 and 12, were identical in both field and pot experiments. In the RI population, nine QTLs were detected, five QTLs from field conditions and four from the pot experiments. Three of these QTLs were identical in both experimental conditions. Only one QTL, linked to CDO344 on chromosome 12, was detected across the populations and experiments. Different epistasitic interaction loci on PN were found under different populations and in different experimental conditions. One locus, flanked by RG323 and RZ801 on chromosome 1, had an additive effect in the DH population, but epistatic effects in the RI population. These results indicate that the effect of genetic background on QTLs is greater than that of environments, and epistasis is more sensitive to genetic background and environments than main-effect QTLs. QTL and epistatic loci could be interchangeable depending on the genetic backgrounds and probably on the environments where they are identified. Received: 26 May 2000 / Accepted: 19 October 2000     

Grain yield responses to moisture regimes in a rice population: association among traits and genetic markers

Drought is a major constraint to rice (Oryza sativa L.) production in rainfed and poorly irrigated environments. Identifying genomic regions influencing the response of yield and its components to water deficits will aid our understanding of the genetic mechanism of drought tolerance (DT) of rice and the development of DT varieties. Grain yield (GY) and its components of a recombinant inbred population developed from a lowland rice and an upland rice were investigated under different water levels in 2003 and 2004 in a rainout DT screening facility. Correlation and path analysis indicated that spikelet fertility (SF) was particularly important for grain yield with direct effect (P=0.60) under drought stress, while spikelet number per panicle (SN) contributed the most to grain yield (P=0.41) under well-watered condition. A total of 32 quantitative trait loci (QTLs) for grain yield and its components were identified. The phenotypic variation explained by individual QTLs varied from 1.29% to 14.76%. Several main effect QTLs affecting SF, 1,000-grain weight (TGW), panicle number (PN), and SN were mapped to the same regions on chromosome 4 and 8. These QTLs were detected consistently across 2 years and under both water levels in this study. Several digenic interactions among yield components were also detected. The identification of genomic regions associated with GY and its components under stress will be useful to improve drought tolerance of rice by marker-aided approaches.G. H. Zou and H. W. Mei contribute equally to this work.     

Identification of quantitative trait loci for yield and yield components in an advanced backcross population derived from the<Emphasis Type="Italic"> Oryza sativa</Emphasis> variety IR64 and the wild relative<Emphasis Type="Italic"> O. rufipogon</Emphasis>

A BC₂F₂ population developed from an interspecific cross between Oryza sativa (cv IR64) and O. rufipogon (IRGC 105491) was used in an advanced backcross QTL analysis to identify and introduce agronomically useful genes from this wild relative into the cultivated gene pool. The objectives of this study were: (1) to identify putative yield and yield component QTLs that can be useful to improve the elite cultivar IR64; (2) to compare the QTLs within this study with previously reported QTLs in rice as the basis for identifying QTLs that are stable across different environments and genetic backgrounds; and (3) to compare the identified QTLs with previously reported QTLs from maize to examine the degree of QTL conservation across the grass family. Two hundred eighty-five families were evaluated in two field environments in Indonesia, with two replications each, for 12 agronomic traits. A total of 165 markers consisting of 131 SSRs and 34 RFLPs were used to construct the genetic linkage map. By employing interval mapping and composite interval mapping, 42 QTLs were identified. Despite its inferior performance, 33% of the QTL alleles originating from O. rufipogon had a beneficial effect for yield and yield components in the IR64 background. Twenty-two QTLs (53.4%) were located in similar regions as previously reported rice QTLs, suggesting the existence of stable QTLs across genetic backgrounds and environments. Twenty QTLs (47.6%) were exclusively detected in this study, uncovering potentially novel alleles from the wild, some of which might improve the performance of the tropical indica variety IR64. Additionally, several QTLs for plant height, grain weight, and flowering time detected in this study corresponded to homeologous regions in maize containing previously detected maize QTLs for these traits.     

Detection of epistatic interactions of three QTLs for heading date in rice using single segment substitution lines

A library consisting of 1123 single segment substitution lines (SSSLs) in the same genetic back-ground of an elite rice variety Huajingxian74 (HJX74) was evaluated for heading date. From this library, two SSSLs, W23-03-8-9-1 and W15-03-1-31, with substitution segments on chromosome 3 and 2, respectively, were found to have significantly different heading date from the recipient HJX74. For genetic dissection and epistatic interaction of quantitative trait loci (QTLs) for heading date in two SSSLs, three secondary SSSLs with smaller substitution segments and genic pyramiding lines (GPLs) were developed from an F₂ segregating population of a cross between the two donor SSSLs, W23-03-8-9-1 and W15-03-1-31, using marker-assisted selection (MAS). The QTL analysis revealed that QTL for heading date detected in SSSL W23-03-08-9-1 was genetically dissected into two QTLs, qHD3-1 and qHD3-2, by overlapping mapping. At the same time, one QTL, qHD2-1 in the donor SSSL W15-03-1-31 was also identified. Analysis of GPLs for heading date showed epistatic interactions between qHD3-1 and qHD3-2, between qHD3-1 and qHD2-1, and between qHD3-2 and qHD2-1. These QTLs and epistatic interactions were confirmed in three cropping seasons under different natural daylength conditions, and their physiological functions for heading date were performed.     

The main effects, epistatic effects and environmental interactions of QTLs on the cooking and eating quality of rice in a doubled-haploid line population

Amylose content (AC), gel consistency (GC) and gelatinazation temperature (GT) are three important traits that influence the cooking and eating quality of rice. The objective of this study was to characterize the genetic components, including main-effect quantitative trait loci (QTLs), epistatic QTLs and QTL-by-environment interactions (QEs), that are involved in the control of these three traits. A population of doubled haploid (DH) lines derived from a cross between two indica varieties Zhenshan 97 and H94 was used, and data were collected from a field experiment conducted in two different environments. A genetic linkage map consisting of 218 simple sequence repeat (SSR) loci was constructed, and QTL analysis performed using qtlmapper 1.6 resolved the genetic components into main-effect QTLs, epistatic QTLs and QEs. The analysis detected a total of 12 main-effect QTLs for the three traits, with a QTL corresponding to the Wx locus showing a major effect on AC and GC, and a QTL corresponding to the Alk locus having a major effect on GT. Ten digenic interactions involving 19 loci were detected for the three traits, and six main-effect QTLs and two pairs of epistatic QTLs were involved in QEs. While the main-effect QTLs, especially the ones corresponding to known major loci, apparently played predominant roles in the genetic basis of the traits, under certain conditions epistatic effects and QEs also played important roles in controlling the traits. The implications of the findings for rice quality improvement are discussed.     

Mapping QTL for Biomass Yield and Its Components in Rice (Oryza sativa L.)

Addicive effects, additive by additive epistatic effects, and their environmental interactions of QTLs are important genetic components of quantitative traits. Genetic architecture underlying rice biomass yield and its two component traits (straw yield and grain yield) were analyzed for a population of 125 DH lines from an inter-subspecific cross of IR64/Azucena. The mixed-model based composite interval mapping approach (MCIM) was used to detect QTLs, There were 12 QTLs detected with additive main effects, 27 QTLs involved in digenic interaction with aa and/or aae effects, and 18 QTLs affected by environments with ae and/or aae effects. It was revealed that epistatic effects and QE interaction effects existed on biomass yield and its component traits in rice. In addition, the genetic basis of relationships among these traits were investigated. Four QTLs and one pair of epistatic QTLs were detected to be responsible for the positive correlation between biomass yield and straw yield. Three QTLs might be responsible for the negative correlation between straw yield and grain yield. This result could partially explain the genetic basis of correlation among the three traits, and provide useful information for genetic improvement of these traits by marker-assisted selection.     

利用染色体单片段代换系定位水稻株高QTL

株高是典型的数量性状,易受遗传背景的干扰和环境因素的影响,利用单片段代换系（single segment substitution lines,SSSLs）能减少遗传背景的干扰。以85个单片段代换系为材料,其受体亲本为籼稻广陆矮4号（Oryzasativassp.in-dica）,供体亲本为粳稻日本晴（Oryza sativa ssp.japonica）。通过单因素方差分析和Dunnett’s多重比较,分析单片段代换系与受体亲本之间株高的差异,对代换片段上的株高QTL进行鉴定。以P≤0.001为阈值共检测到24个株高QTLs,分别分布于除第10染色体外的其它11条染色体上,其中3个QTLs的加性效应表现为减效作用,另外21个株高QTLs的加性效应表现为增效作用。QTLs加性效应变化范围为-6.5-31.74,加性效应百分率的变化范围为-8.81%-41.96%。该研究对进一步发掘和利用新的矮秆或半矮秆基因资源具有重要意义。     

Genetic basis of pre-harvest sprouting tolerance using single-locus and two-locus QTL analyses in bread wheat

Quantitative trait loci (QTL) analysis for pre-harvest sprouting tolerance (PHST) in bread wheat was conducted following single-locus and two-locus analyses, using data on a set of 110 recombinant inbred lines (RILs) of the International Triticeae Mapping Initiative population grown in four different environments. Single-locus analysis following composite interval mapping (CIM) resolved a total of five QTLs with one to four QTLs in each of the four individual environments. Four of these five QTLs were also detected following two-locus analysis, which resolved a total of 14 QTLs including 8 main effect QTLs (M-QTLs), 8 epistatic QTLs (E-QTLs) and 5 QTLs involved in QTL × environment (QE) or QTL × QTL × environment (QQE) interactions, some of these QTLs being common. The analysis revealed that a major fraction (76.68%) of the total phenotypic variation explained for PHST is due to M-QTLs (47.95%) and E-QTLs (28.73%), and that only a very small fraction of variation (3.24%) is due to QE and QQE interactions. Thus, more than three-quarters of the genetic variation for PHST is fixable and would contribute directly to gains under selection. Two QTLs that were detected in more than one environment and at LOD scores above the threshold values were located on 3BL and 3DL presumably in the vicinity of the dormancy gene TaVp1. Another QTL was found to be located on 3B, perhaps in close proximity to the R gene for red grain colour. However, these associations of QTLs for PHST with genes for dormancy and grain colour are only suggestive. The results obtained in the present study suggest that PHST is a complex trait controlled by large number of QTLs, some of them interacting among themselves or with the environment. These QTLs can be brought together through marker-aided selection, leading to enhanced PHST.     

Compensatory effects of elevated CO<Subscript>2</Subscript> concentration on the inhibitory effects of high temperature and irradiance on photosynthetic gas exchange in carrots

We determined the interactive effects of irradiance, elevated CO₂ concentration (EC), and temperature in carrot (Daucus carota var. sativus). Plants of the cv. Red Core Chantenay (RCC) were grown in a controlled environmental plant growth room and exposed to 3 levels of photosynthetically active radiation (PAR) (400, 800, 1 200 μmol m⁻² s⁻¹), 3 leaf chamber temperatures (15, 20, 30 °C), and 2 external CO₂ concentrations (C _a), AC and EC (350 and 750 μmol mol⁻¹, respectively). Rates of net photosynthesis (P _N) and transpiration (E) and stomatal conductance (g _s ) were measured, along with water use efficiency (WUE) and ratio of internal and external CO₂ concentrations (C _i/C _a). P _N revealed an interactive effect between PAR and C _a. As PAR increased so did P _N under both C _a regimes. The g _s showed no interactive effects between the three parameters but had singular effects of temperature and PAR. E was strongly influenced by the combination of PAR and temperature. WUE was interactively affected by all three parameters. Maximum WUE occurred at 15 °C and 1 200 μmol m⁻² s^{− 1} PAR under EC. The C _i /C _a was influenced independently by temperature and C _a. Hence photosynthetic responses are interactively affected by changes in irradiance, external CO₂ concentration, and temperature. EC significantly compensates the inhibitory effects of high temperature and irradiance on P _N and WUE.     

Substitution mapping of QTLs controlling seed dormancy using single segment substitution lines derived from multiple cultivated rice donors in seven cropping seasons

Key message

A permanent advanced population containing 388 SSSLs was used for genetic analysis of seed dormancy; 25 QTLs including eight stable, six major and five new were identified.

Abstract

Seed dormancy (SD) is not only a complex biological phenomenon, but also a key practical problem in agricultural production closely related with pre-harvest sprouting (PHS). However, the genetic mechanisms of SD remain elusive. Here, we report the genetic dissection of SD in rice using 388 single segment substitution lines (SSSLs) derived from 16 donor parents. Continuous variation and positive correlations in seed germination percentages were observed in seven seasons. Genetic analysis revealed the narrow sense heritability in different seasons varied from 31.4 to 82.2% with an average value of 56.8%. In addition, 49 SSSLs exhibited significant difference to recipient parent HJX74 on SD in at least two seasons, and 12 of them were stably identified with putative QTLs in all of their corresponding cropping seasons. Based on substitution mapping, a total of 25 dormancy QTLs were detected on 11 chromosomes except the chromosome 5 with an interval length of 1.1 to 31.3 cM. The additive effects of these QTLs changed from ?0.31 to ?0.13, and the additive effect contributions ranged from 16.7 to 41.4%. Six QTLs, qSD3-2, qSD4-1, qSD7-1, qSD7-2, qSD7-3 and qSD11-2, showed large additive effect contributions (≥30%). Five QTLs, qSD3-3, qSD7-1, qSD7-4, qSD9-1 and qSD10-1, may represent novel ones. Furthermore, linkage and recombinant analysis delimited qSD7-1 to a locus 1.5 cM away from marker Oi2 and a 355-kb fragment flanked by RM1134 and Ui159, respectively. Taken together, this work conducts a comprehensive genetic dissection of SD and will provide more selections for breeding elite PHS-resistant rice varieties.

     

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.     

Erratum to: Development and application of gene-based markers for the major rice QTL Phosphorus uptake 1

Marker-assisted breeding is a very useful tool for breeders but still lags behind its potential because information on the effect of quantitative trait loci (QTLs) in different genetic backgrounds and ideal molecular markers are unavailable. Here, we report on some first steps toward the validation and application of the major rice QTL Phosphate uptake 1 (Pup1) that confers tolerance of phosphorus (P) deficiency in rice (Oryza sativa L.). Based on the Pup1 genomic sequence of the tolerant donor variety Kasalath that recently became available, markers were designed that target (1) putative genes that are partially conserved in the Nipponbare reference genome and (2) Kasalath-specific genes that are located in a large insertion-deletion (INDEL) region that is absent in Nipponbare. Testing these markers in 159 diverse rice accessions confirmed their diagnostic value across genotypes and showed that Pup1 is present in more than 50% of rice accessions adapted to stress-prone environments, whereas it was detected in only about 10% of the analyzed irrigated/lowland varieties. Furthermore, the Pup1 locus was detected in more than 80% of the analyzed drought-tolerant rice breeding lines, suggesting that breeders are unknowingly selecting for Pup1. A hydroponics experiment revealed genotypic differences in the response to P deficiency between upland and irrigated varieties but confirmed that root elongation is independent of Pup1. Contrasting Pup1 near-isogenic lines (NILs) were subsequently grown in two different P-deficient soils and environments. Under the applied aerobic growth conditions, NILs with the Pup1 locus maintained significantly higher grain weight plant⁻¹ under P deprivation in comparison with intolerant sister lines without Pup1. Overall, the data provide evidence that Pup1 has the potential to improve yield in P-deficient and/or drought-prone environments and in diverse genetic backgrounds.     

Mapping and validation of quantitative trait loci for spikelets per panicle and 1,000-grain weight in rice (Oryza sativa L.)

This study identified four and five quantitative trait loci (QTLs) for 1,000-grain weight (TGW) and spikelets per panicle (SPP), respectively, using rice recombinant inbred lines. QTLs for the two traits (SPP3a and TGW3a, TGW3b and SPP3b) were simultaneously identified in the two intervals between RM3400 and RM3646 and RM3436 and RM5995 on chromosome 3. To validate QTLs in the interval between RM3436 and RM5995, a BC₃F₂ population was obtained, in which TGW3b and SPP3b were simultaneously mapped to a 2.6-cM interval between RM15885 and W3D16. TGW3b explained 50.4% of the phenotypic variance with an additive effect of 1.81 g. SPP3b explained 29.1% of the phenotypic variance with an additive effect of 11.89 spikelets. The interval had no effect on grain yield because it increased SPP but decreased TGW and vice versa. Grain shape was strongly associated with TGW and was used for QTL analysis in the BC₃F₂ population. Grain length, grain width, and grain thickness were also largely controlled by TGW3b. At present, it is not clear whether one pleiotropic QTL or two linked QTLs were located in the interval. However, the conclusion could be made ultimately by isolation of TGW3b. The strategy for TGW3b isolation is discussed.     

Identification of candidate genes for key fibre‐related QTLs and derivation of favourable alleles in Gossypium hirsutum recombinant inbred lines with G. barbadense introgressions

Fine mapping QTLs and identifying candidate genes for cotton fibre‐quality and yield traits would be beneficial to cotton breeding. Here, we constructed a high‐density genetic map by specific‐locus amplified fragment sequencing (SLAF‐seq) to identify QTLs associated with fibre‐quality and yield traits using 239 recombinant inbred lines (RILs), which was developed from LMY22 (a high‐yield Gossypium hirsutumL. cultivar) × LY343 (a superior fibre‐quality germplasm with G. barbadenseL. introgressions). The genetic map spanned 3426.57 cM, including 3556 SLAF‐based SNPs and 199 SSR marker loci. A total of 104 QTLs, including 67 QTLs for fibre quality and 37 QTLs for yield traits, were identified with phenotypic data collected from 7 environments. Among these, 66 QTLs were co‐located in 19 QTL clusters on 12 chromosomes, and 24 QTLs were detected in three or more environments and determined to be stable. We also investigated the genomic components of LY343 and their contributions to fibre‐related traits by deep sequencing the whole genome of LY343, and we found that genomic components from G. hirsutum races (which entered LY343 via its G. barbadense parent) contributed more favourable alleles than those from G. barbadense. We further identified six putative candidate genes for stable QTLs, including Gh_A03G1147 (GhPEL6), Gh_D07G1598 (GhCSLC6) and Gh_D13G1921 (GhTBL5) for fibre‐length QTLs and Gh_D03G0919 (GhCOBL4), Gh_D09G1659 (GhMYB4) and Gh_D09G1690 (GhMYB85) for lint‐percentage QTLs. Our results provide comprehensive insight into the genetic basis of the formation of fibre‐related traits and would be helpful for cloning fibre‐development‐related genes as well as for marker‐assisted genetic improvement in cotton.     

QTL Analysis of Aluminum Resistance in Rice (Oryza sativa L.)

Aluminum (Al) toxicity is considered as one of the primary causes of low-rice productivity in acid soils. In the present study, quantitative trait loci (QTLs) controlling Al resistance based on relative root elongation (RRE) were dissected using a complete linkage map and a recombinant inbred lines (RILs) derived from a cross of Al-tolerant japonica cultivar Asominori (Oryza sativa L.) and Al-sensitive indica cultivar IR24 (O. sativa L.). A total of three QTLs (qRRE-1, qRRE-9, and qRRE-11) were detected on chromosomes 1, 9, and 11 with LOD score ranging from 2.64 to 3.60 and the phenotypic variance explained from 13.5 to 17.7%. The Asominori alleles were all associated with Al resistance at all the three QTLs. The existence of these QTLs was confirmed using Asominori chromosome segment substitution lines (CSSLs) in IR24 genetic background (IAS). By QTL comparative analysis, the two QTLs (qRRE-1and qRRE-9) on chromosomes 1 and 9 appeared to be consistent among different rice populations while qRRE-11 was newly detected and syntenic with a major Al resistance gene on chromosome 10 of maize. This region may provide an important case for isolating genes responsible for different mechanisms of Al resistance among different cereals. These results also provide the possibilities of enhancing Al resistance in rice breeding program by marker-assisted selection (MAS) and pyramiding QTLs.