QTL mapping of downy and powdery mildew resistances in PI 197088 cucumber with genotyping-by-sequencing in RIL population

Key message

Host resistances in PI 197088 cucumber to downy and powdery mildew pathogens are conferred by 11 (3 with major effect) and 4 (1 major effect) QTL, respectively, and three of which are co-localized.

Abstract

The downy mildew (DM) and powdery mildew (PM) are the two most important foliar diseases of cucurbit crops worldwide. The cucumber accession PI 197088 exhibits high-level resistances to both pathogens. Here, we reported QTL mapping results for DM and PM resistances with 148 recombinant inbred lines from a cross between PI 197088 and the susceptible line ‘Coolgreen’. Phenotypic data on responses to natural DM and PM infection were collected in multi-year and multi-location replicated field trials. A high-density genetic map with 2780 single nucleotide polymorphisms (SNPs) from genotyping-by-sequencing and 55 microsatellite markers was developed, which revealed genomic regions with segregation distortion and mis-assemblies in the ‘9930’ cucumber draft genome. QTL analysis identified 11 and 4 QTL for DM and PM resistances accounting for more than 73.5 and 63.0% total phenotypic variance, respectively. Among the 11 DM resistance QTL, dm5.1, dm5.2, and dm5.3 were major-effect contributing QTL, whereas dm1.1, dm2.1, and dm6.2 conferred susceptibility. Of the 4 QTL for PM resistance, pm5.1 was the major-effect QTL explaining 32.4% phenotypic variance and the minor-effect QTL pm6.1 contributed to disease susceptibility. Three PM QTL, pm2.1, pm5.1, and pm6.1, were co-localized with DM QTL dm2.1, dm5.2, and dm6.1, respectively, which was consistent with the observed linkage of PM and DM resistances in PI 197088. The genetic architecture of DM resistance in PI 197088 and another resistant line WI7120 (PI 330628) was compared, and the potential of using PI 197088 in cucumber breeding for downy and powdery mildew resistances is discussed.

     

QTL mapping of powdery mildew resistance in WI 2757 cucumber (Cucumis sativus L.)

Powdery mildew (PM) is a very important disease of cucumber (Cucumis sativus L.). Resistant cultivars have been deployed in production for a long time, but the genetic mechanisms of PM resistance in cucumber are not well understood. A 3-year QTL mapping study of PM resistance was conducted with 132 F_2:3 families derived from two cucumber inbred lines WI 2757 (resistant) and True Lemon (susceptible). A genetic map covering 610.4 cM in seven linkage groups was developed with 240 SSR marker loci. Multiple QTL mapping analysis of molecular marker data and disease index of the hypocotyl, cotyledon and true leaf for responses to PM inoculation identified six genomic regions in four chromosomes harboring QTL for PM resistance in WI 2757. Among the six QTL, pm1.1 and pm1.2 in chromosome 1 conferred leaf resistance. Minor QTL pm3.1 (chromosome 3) and pm4.1 (chromosome 4) contributed to disease susceptibility. The two major QTL, pm5.1 and pm5.2 were located in an interval of ~40 cM in chromosome 5 with each explaining 21.0–74.5 % phenotypic variations. Data presented herein support two recessively inherited, linked major QTL in chromosome 5 plus minor QTL in other chromosomes that control the PM resistance in WI 2757. The QTL pm5.2 for hypocotyl resistance plays the most important role in host resistance. Multiple observations in the same year revealed the importance of scoring time in the detection of PM resistance QTL. Results of this study provided new insights into phenotypic and genetic mechanisms of powdery mildew resistance in cucumber.     

Quantitative trait locus analysis of lateral branch– related traits in cucumber (Cucumis sativus L.) using recombinant inbred lines

A group of 224 recombinant inbred lines (RILs) was derived from a narrow cross between 2 cucumber (Cucumis sativus L.) lines, namely, S94 (Northern China type with weak lateral branch growth potential and early lateral branch sprouting time) and S06 (Northern European type with strong lateral branch growth potential and late lateral branch sprouting time). These lines were then used for investigating lateral branch-related traits. A total of 36 quantitative trait loci (QTLs) were detected for the following 4 lateral branch-related traits: lateral branch average length (LBAL), lateral branch total length (LBTL), lateral branch number (LBN), and first lateral branch node (FLBN). Further, each QTL explained 3.1% (lbtl2.1, spring) to 32.3% (lbn2.3, spring) of the observed phenotypic variance. Eleven QTLs (lbal1.1, lbtl1.1, lbn1.2, flbn1.2, etc.) for different traits were found to be clustered on the e23m18d-ME23EM6c section (7.4 cM) of linkage group (LG) 1; further, 15 QTLs (lbal2.1, lbtl2.1, lbn2.1, flbn2.1, etc.) were found to be clustered on the S94A1-ME4SA4a section (13.9 cM) of LG2. Twenty-one QTLs explained more than 10% of the phenotypic variance. Moreover, lbtl1.3 (autumn, 26.2%, logarithm of odds (LOD) = 17.4; spring, 26.9%, LOD = 17.9) had stable position and contribution in both seasons. Several se-quence-anchor markers (CMBR40, F, CS30, S94A1, CSWTA11B, etc.) were closely linked with some QTLs for LBAL, LBTL, LBN, and FLBN, which can be used for the marker-assisted selection to improve the plant architecture in cucumber breeding.     

Quantitative trait locus analysis of lateral branch-related traits in cucumber (<Emphasis Type="Italic">Cucumis sativus</Emphasis> L.) using recombinant inbred lines

A group of 224 recombinant inbred lines (RILs) was derived from a narrow cross between 2 cucumber (Cucumis sativus L.) lines, namely, S94 (Northern China type with weak lateral branch growth potential and early lateral branch sprouting time) and S06 (Northern European type with strong lateral branch growth potential and late lateral branch sprouting time). These lines were then used for investigating lateral branch-related traits. A total of 36 quantitative trait loci (QTLs) were detected for the following 4 lateral branch-related traits: lateral branch average length (LBAL), lateral branch total length (LBTL), lateral branch number (LBN), and first lateral branch node (FLBN). Further, each QTL explained 3.1% (lbtl2.1, spring) to 32.3% (lbn2.3, spring) of the observed phenotypic variance. Eleven QTLs (lbal1.1, lbtl1.1, lbn1.2, flbn1.2, etc.) for different traits were found to be clustered on the e23m18d-ME23EM6c section (7.4 cM) of linkage group (LG) 1; further, 15 QTLs (lbal2.1, lbtl2.1, lbn2.1, flbn2.1, etc.) were found to be clustered on the S94A1-ME4SA4a section (13.9 cM) of LG2. Twenty-one QTLs explained more than 10% of the phenotypic variance. Moreover, lbtl1.3 (autumn, 26.2%, logarithm of odds (LOD) = 17.4; spring, 26.9%, LOD = 17.9) had stable position and contribution in both seasons. Several sequence-anchor markers (CMBR40, F, CS30, S94A1, CSWTA11B, etc.) were closely linked with some QTLs for LBAL, LBTL, LBN, and FLBN, which can be used for the marker-assisted selection to improve the plant architecture in cucumber breeding.     

粳稻蒸煮食味品质相关性状的QTL分析

以沈农265和丽江新团黑谷杂交衍生的重组自交系群体(RILs)为实验材料,对12个粳稻(Oryza sativa subsp.japonica)蒸煮食味品质相关性状进行QTL分析。共检测到29个蒸煮食味品质相关的QTLs,分布于除第8染色体外的11条染色体上,LOD值介于2.50–16.47之间,加性效应值为–132.69–471.85,单个QTL贡献率为10.36%–73.24%。在第6染色体RM508–RM253区域检测到1个蒸煮营养食味品质多效性QTL簇,其中q AC6表型贡献率最大,解释73.24%的表型变异;在第10染色体PM166–RM258区域检测到2个与蒸煮食味品质相关的QTLs,分别是控制口感的q CTS10和综合评分的q CCS10。此外,检测到15个与RVA特征谱相关的QTLs,在第6染色体RM253–RM402区域检测到3个与RVA谱特征值相关的QTLs,表型贡献率均大于12%。这些定位结果将为粳稻蒸煮食味相关品质的分子遗传机理研究奠定基础。     

Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

Quantitative trait loci (QTLs) for plant height in wheat (Triticum aestivum L.) were studied using a set of 168 doubled haploid (DH) lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai'an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTLxenvironment interactions (QEs). Two major QTLs, QphAB and Qph4D, which accounted for 14.51 % and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rhtl and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL ef fects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection (MAS).     

Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

Quantitative trait loci (QTLs) for plant height in wheat (Triticum aestivum L.) were studied using a set of 168 doubled haploid (DH) lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai’an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTL×environment interactions (QEs). Two major QTLs, Qph4B and Qph4D, which accounted for 14.51% and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rht1 and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL effects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection (MAS).     

水稻生育后期叶绿素含量的QTLs及其与环境的互作分析

利用Dular和Lemont杂交后代单粒传衍生的123个F12家系所组成的重组自交系（RILs）群体,研究水稻剑叶叶绿素含量的数量性状基因座（QTL）.分别在2005年和2006年考察该RIL群体齐穗期剑叶叶绿素含量,并进行QTL定位和上位性分析及其与环境的互作效应分析.结果表明：在4对染色体上共检测到10个控制叶绿素含量的加性QTLs,共解释了73.51%的遗传变异,单个QTL的表型贡献率为2.08%～20.14%,其中6个和环境存在显著互作;同时也检测到13对影响叶绿素含量的加性×加性上位性互作,其中6对具有显著的上位性环境互作效应.     

Identification of QTLs with additive,epistatic and QTL × development interaction effects for seed dormancy in rice

Seed dormancy (SD) is an important agronomic trait affecting crop yield and quality. In this study, one rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of SD at the early (4 weeks after heading), middle (5 weeks after heading) and late (6 weeks after heading) development stages. The level of SD decreased with the process of seed development, and the SD was significantly affected by the heading date (HD) and temperature at the early and middle development stages. A total of eight additive quantitative trait loci (QTLs) for SD were identified at three development stages, and more QTLs were expressed at the early and late development stages. Among them, four, one and three additive QTLs were identified at the early, middle and late development stages, respectively. Epistatic QTLs and QTL-by-development interactions were detected by the joint analysis of multi-development phenotypic values, and one additive and two epistatic QTLs were identified. The phenotypic variation of SD explained by each additive, epistatic QTL and QTL × development interaction ranged from 8.0 to 13.5 %, 0.7 to 3.9 % and 1.3 to 2.8 %, respectively. One major QTL qSD7.1 for SD was tightly linked to the major QTL qHD7.4 for HD, which might be applied to reveal the relationship of SD and HD. By comparing chromosomal positions of these additive QTLs with those previously identified, five additive QTLs qSD1.1, qSD2.1, qSD2.2, qSD4.1 and qSD4.2 might represent novel genes. The best three cross combinations for the development of RIL populations were predicted to improve SD. The selected RILs and the identified QTLs might be applicable to improve the rice pre-harvest sprouting tolerance by the marker-assisted selection approach.     

QTL analysis for grain quality traits in 2 BC2F2 populations derived from crosses between Oryza sativa cv Swarna and 2 accessions of O. nivara

The appearance and cooking quality of rice determine its acceptability and price to a large extent. Quantitative trait loci (QTLs) for 12 grain quality traits were mapped in 2 mapping populations derived from Oryza sativa cv Swarna × O. nivara. The BC(2)F(2) population of the cross Swarna × O. nivara IRGC81848 (population 1) was evaluated during 2005 and that from Swarna × O. nivara IRGC81832 (population 2) was evaluated during 2006. Linkage maps were constructed using 100 simple sequence repeat (SSR) markers in population 1 and 75 SSR markers in population 2. In all, 21 QTLs were identified in population 1 (43% from O. nivara) and 37 in population 2 (38% QTLs from O. nivara). The location of O. nivara-derived QTLs mp1.2 for milling percent, kw6.1 for kernel width, and klac12.1 for kernel length after cooking coincided in the 2 populations and appear to be useful for Marker Assisted Selection (MAS). Four QTLs for milling percent, 1 QTL each for amylose content, water uptake, elongation ratio, 2 QTLs for kernel width, and 3 QTLs for gel consistency, each explained more than 20% phenotypic variance. Three QTL clusters for grain quality traits were close to the genes/QTLs for shattering and seed dormancy. QTLs for 4 quality traits were associated with 5 of the 7 major yield QTLs reported in the same 2 mapping populations. Useful introgression lines have been developed for several agronomic traits. It emerges that 40% O. nivara alleles were trait enhancing in both populations, and QTLs for grain quality overlapped with yield meta-QTLs and QTLs for dormancy and seed shattering.     

Quantitative trait loci for resistance to pre-harvest sprouting in US hard white winter wheat Rio Blanco

Pre-harvest sprouting (PHS) of wheat is a major problem that severely limits the end-use quality of flour in many wheat-growing areas worldwide. To identify quantitative trait loci (QTLs) for PHS resistance, a population of 171 recombinant inbred lines (RILs) was developed from the cross between PHS-resistant white wheat cultivar Rio Blanco and PHS-susceptible white wheat breeding line NW97S186. The population was evaluated for PHS in three greenhouse experiments and one field experiment. After 1,430 pairs of simple sequence repeat (SSR) primers were screened between the two parents and two bulks, 112 polymorphic markers between two bulks were used to screen the RILs. One major QTL, QPhs.pseru-3AS, was identified in the distal region of chromosome 3AS and explained up to 41.0% of the total phenotypic variation in three greenhouse experiments. One minor QTL, QPhs.pseru-2B.1, was detected in the 2005 and 2006 experiments and for the means over the greenhouse experiments, and explained 5.0-6.4% of phenotypic variation. Another minor QTL, QPhs.pseru-2B.2, was detected in only one greenhouse experiment and explained 4.5% of phenotypic variation for PHS resistance. In another RIL population developed from the cross of Rio Blanco/NW97S078, QPhs.pseru-3AS was significant for all three greenhouse experiments and the means over all greenhouse experiments and explained up to 58.0% of phenotypic variation. Because Rio Blanco is a popular parent used in many hard winter wheat breeding programs, SSR markers linked to the QTLs have potential for use in high-throughput marker-assisted selection of wheat cultivars with improved PHS resistance as well as fine mapping and map-based cloning of the major QTL QPhs.pseru-3AS.     

QTL mapping for downy mildew resistance in cucumber via bulked segregant analysis using next-generation sequencing and conventional methods

Key message

QTL mapping using NGS-assisted BSA was successfully applied to an F ₂ population for downy mildew resistance in cucumber. QTLs detected by NGS-assisted BSA were confirmed by conventional QTL analysis.

Abstract

Downy mildew (DM), caused by Pseudoperonospora cubensis, is one of the most destructive foliar diseases in cucumber. QTL mapping is a fundamental approach for understanding the genetic inheritance of DM resistance in cucumber. Recently, many studies have reported that a combination of bulked segregant analysis (BSA) and next-generation sequencing (NGS) can be a rapid and cost-effective way of mapping QTLs. In this study, we applied NGS-assisted BSA to QTL mapping of DM resistance in cucumber and confirmed the results by conventional QTL analysis. By sequencing two DNA pools each consisting of ten individuals showing high resistance and susceptibility to DM from a F₂ population, we identified single nucleotide polymorphisms (SNPs) between the two pools. We employed a statistical method for QTL mapping based on these SNPs. Five QTLs, dm2.2, dm4.1, dm5.1, dm5.2, and dm6.1, were detected and dm2.2 showed the largest effect on DM resistance. Conventional QTL analysis using the F₂ confirmed dm2.2 (R ² = 10.8–24 %) and dm5.2 (R ² = 14–27.2 %) as major QTLs and dm4.1 (R ² = 8 %) as two minor QTLs, but could not detect dm5.1 and dm6.1. A new QTL on chromosome 2, dm2.1 (R ² = 28.2 %) was detected by the conventional QTL method using an F₃ population. This study demonstrated the effectiveness of NGS-assisted BSA for mapping QTLs conferring DM resistance in cucumber and revealed the unique genetic inheritance of DM resistance in this population through two distinct major QTLs on chromosome 2 that mainly harbor DM resistance.

     

A genetic analysis of seed and berry weight in grapevine.

Fruit size and seedlessness are highly relevant traits in many fruit crop species, and both are primary targets of breeding programs for table grapes. In this work we performed a quantitative genetic analysis of size and seedlessness in an F1 segregating population derived from the cross between a classical seeded (Vitis vinifera L. 'Dominga') and a newly bred seedless ('Autumn Seedless') cultivar. Fruit size was scored as berry weight (BW), and for seedlessness we considered both seed fresh weight (SFW) and the number of seeds and seed traces (SN) per berry. Quantitative trait loci (QTL) analysis of BW detected 3 QTLs affecting this trait and accounting for up to 67% of the total phenotypic variance. QTL analysis for seedlessness detected 3 QTLs affecting SN (explaining up to 35% of total variance) and 6 affecting SFW (explaining up to 90% of total variance). Among them, a major effect QTL explained almost half of the phenotypic variation for SFW. Comparative analysis of QTLs for these traits reduced the number of grapevine genomic regions involved, one of them being a major effect QTL for seedlessness. Association analyses showed that microsatellite locus VMC7F2, closely linked to this QTL, is a useful marker for selection of seedlessnes.     

Identification of quantitative trait loci associated with small brown planthopper (Laodelphax striatellus Fallén) resistance in rice (Oryza sativa L.)

F(2) and BC(1) populations derived from the cross between 02428 / Rathu Heenati were used to investigate small brown planthopper (SBPH) resistance. Using the F(2) population, three QTLs for antixenosis against SBPH were located on chromosomes 2, 5 and 6, and accounted for 30.75% of the phenotypic variance; three QTLs for antibiosis against SBPH were detected on chromosomes 8, 9 and 12. qSBPH5-c explaining 7.21% of phenotypic variance for antibiosis was identified on chromosome 5 using the BC(1) population. A major QTL, qSBPH12-a1, explained about 40% of the phenotypic variance, and a minor QTL, qSBPH4-a, was detected by the SSST method in both the F(2) and BC(1) populations. The QTLs indentified in the present study will be useful for marker assisted selection of SBPH resistance in rice.     

QTL Mapping of Downy Mildew Resistance in an Introgression Line Derived from Interspecific Hybridization Between Cucumber and Cucumis hystrix

Downy mildew (DM), caused by Pseudoperonospora cubensis (Berk. & M.A. Curtis) Rostovzev, is a worldwide major disease of cucumbers (Cucumis sativus L.). By screening 10 introgression lines (ILs) derived from interspecific hybridization between cucumber and the wild Cucumis, C. hystrix, through a whole plant assay, one introgression line (IL52) was identified with high DM‐resistance. IL52 was further used as a resistant parent to make an F₂ population with ‘changchunmici’ (susceptible parent). The F₂ population (300 plants) was investigated for DM‐yellowing, DM‐necrosis and DM‐resistance in the adult stage. A genetic map spanning 642.5 cM with 104 markers was constructed and used for QTL analysis from the population. Three QTL regions were identified on chromosome 5 and chromosome 6. By interval mapping analysis, two QTLs for DM‐resistance were determined on chromosome 5 (DM_5.1 and DM_5.2), which explained 17.9% and 14.2% of the variation, respectively. QTLs for DM‐yellowing were in the same regions as DM‐resistance. For DM‐necrosis, by interval mapping analysis, one QTL was determined on chromosome 5 (Necr_5.1) that explained 18.3% of the variation and one on chromosome 6 (Necr_6.1) that explained 13.9% of the variation. Our results indicated that the identification of molecular markers linked to the QTLs could be further applied for marker‐assisted selection (MAS) of downy mildew resistance in cucumber.     

云南软米直链淀粉含量的遗传分析与基因定位

以低直链淀粉( 软米)品种毫木细与高直链淀粉品种桂朝2号杂交F2代分离群体为试验材料,研究水稻低直链淀粉含量的遗传规律和基因定位。结果表明,软米品种毫木细直链淀粉含量受一个隐性主效基因/QTL控制,该基因位点（QTL）与糯性基因(wx)为非等位。除主效QTL外,可能还有微效基因的作用。用SSR标记检测到该主效基因/QTL位于11号染色体上RM224附近,LOD值为15.4,可解释的表型变异率为32%。     

Dynamic quantitative trait locus analysis of seed dormancy at three development stages in rice

In this study, a rice population of recombinant inbred lines (RILs) was used to determine the genetic characteristics of seed dormancy (SD) at 4 (early), 5 (middle) and 6 (late) weeks after heading stages. Dynamic analysis showed that the indica IR28 variety tended to have deeper dormancy than the japonica Daguandao at the middle and late development stages. The level of SD decreased with the process of seed development. The significant interaction between heading date (HD) and SD occurred only in those seeds collected at the early development stage. A total of nine additive quantitative trait loci (QTLs) and eight epistatic QTLs for SD were identified at three seed development stages. Of them, one additive and four epistatic QTLs were identified for the early stage, six additive and one epistatic QTL for the middle stage and two additive and three epistatic QTLs for the late stage. The phenotypic variation explained by each additive and epistatic QTL ranged from 5.8 to 30.6 % and from 3.8 to 13.1 %, respectively. Compared with the additive QTLs, epistatic interactions were much more important for SD at the early and late development stages. Two major additive QTLs, qSD3.1 and qSD4.1, were identified; each QTL could explain more than 20 % of the total phenotypic variance and each dormancy-enhancing allele could decrease the germination percentage by about 10 %. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSD1.2, qSD2.1, qSD3.2, qSD4.1 and qSD9.1, might represent novel genes. One QTL identified here, qHD1, and nine QTLs identified in previous studies for HD were co-located with our QTLs for SD, which indicated that the significant correlation between SD and HD might be due to the linkage of QTLs for SD and HD. Four RILs with deep dormancy at development stages but non-dormancy after post-ripening under different germination conditions were selected. Using the selected RILs, three cross combinations of SD for the development of RIL populations were predicted. The selected RILs and the identified QTLs might be applicable for the improvement of pre-harvest sprouting tolerance by marker-assisted selection in rice.     

Mapping quantitative trait loci controlling seed dormancy and heading date in rice, Oryza sativa L., using backcross inbred lines

 To detect quantitative trait loci (QTLs) controlling seed dormancy, 98 BC₁F₅ lines (backcross inbred lines) derived from a backcross of Nipponbare (japonica)/Kasalath (indica)//Nipponbare were analyzed genetically. We used 245 RFLP markers to construct a framework linkage map. Five putative QTLs affecting seed dormancy were detected on chromosomes 3, 5, 7 (two regions) and 8, respectively. Phenotypic variations explained by each QTL ranged from 6.7% to 22.5% and the five putative QTLs explained about 48% of the total phenotypic variation in the BC₁F₅ lines. Except for those of the QTLs on chromosome 8, the Nipponbare alleles increased the germination rate. Five putative QTLs controlling heading date were detected on chromosomes 2, 3, 4, 6 and 7, respectively. The phenotypic variation explained by each QTL for heading date ranged from 5.7% to 23.4% and the five putative QTLs explained about 52% of the total phenotypic variation. The Nipponbare alleles increased the number of days to heading, except for those of two QTLs on chromosomes 2 and 3. The map location of a putative QTL for heading date coincided with that of a major QTL for seed dormancy on chromosome 3, although two major heading-date QTLs did not coincide with any seed dormancy QTLs detected in this study. Received: 10 October 1997 / Accepted: 12 January 1998     

小麦株高性状的QTL分析

自20世纪60年代农林10号矮秆基因被用于小麦育种以来,矮化育种成为世界范围内势不可挡的趋势,矮秆基因研究被越来越多的育种专家重视,先后鉴定出20余个矮秆基因,并应用其中6,7个基因,培育了大批丰产潜力大的半矮秆品种,应用矮秆冬小麦吕系DN3338（♀）和F390（♂）杂交得到的F2：3群体,研究小麦株高的遗传基础,以控制株高的数量性状基因座进行定位,利用240个F2：3家系,构建了含215个微卫星标记,覆盖3600cM,由21个连锁群组成的遗传 连锁图谱,并对该群体进行了4个环境（2年：2000年和2001年,2点：北京和石家庄）3重复的田间种植;采用区间作图法,对该群体的株高性状进行了QTL分析。结果表明：7个影响株高的QTL分别位于染色体1B,4B(2个）,6A（2个）,6D和7A上,每个QTL能解释5．2％－50．1％的表型变异,每个环境条件下检测出的所有QTL能解释64．8％－75％,的表型变异,除了7A上的QTL外,其他6个降低株高的QTL均来自ND3338,其效应介于0．94cm-9.33cm之间,且其中的4个在所有的环境下都能被检测出来,具有较高的稳定性,在4BS的Xgwm113标记附近有一主效QTL,其在不同的环境下能降低株高7．91cm-9.33cm,解释27．8％－36．2％,的表型变异,有着同农林10号中Rht-Blb相近的效应;同时在4BS上还发现一个和地点互作的QTL,该QTL在石家庄的两年试验中均被检测到,且有较大的效应值（80cm和7.6cm),因此,认为大部分的QTL能在所有的环境中检测到,这些QTO可以被用于品种改良和分子标记辅助选择育种。     

Quantitative trait loci identified for sugar related traits in a sugarcane (<Emphasis Type="Italic">Saccharum</Emphasis> spp.) cultivar × <Emphasis Type="Italic">Saccharum officinarum</Emphasis> population

The identification of markers linked to quantitative trait loci (QTLs) for increased sugar accumulation could improve the effectiveness of current breeding strategies in sugarcane. Progeny from a cross between a high sucrose producing cultivar, (denotes Australian plant breeding rights), and a Saccharum officinarum clone, IJ76-514 were grown in two field experiments in different years, and evaluated in the early and mid-season phases of crop maturity, to identify robust QTLs in affecting sucrose content in cane. Using an extensive genetic map constructed for with over 1,000 AFLP and SSR markers, a total of 37 QTLs were identified for brix and pol of which, 16 were detected in both experiments. Of these 37 QTL, 30 were clustered into 12 genomic regions in six of the eight homo(eo)logous groups. Each QTL explained from 3 to 9% of the phenotypic variation observed. Both positive and negative effects were identified and the location of the QTLs on linkage groups belonging to the same homo(eo)logy group suggested that a number of the QTLs were allelic forms of the same genes. Of the 37 QTLs identified, the majority were significant in both early and mature cane, but 8 were identified as early specific QTLs and 9 as mature cane QTLs. In total, 97 interactions were significant (P<10⁻⁵) and these were localised to 32 genomic regions of which 6 were detected with both years’ data. Models including all the QTLs explained from 37 to 66% of the total phenotypic variation, depending on the trait. The results will be subsequently applied in marker assisted breeding. denotes variety covered by Australian plant breeding rights.