Identification of population-specific QTLs for flowering in soybean

Flowering is an important stage in plant development and crucial for adaptation of plant species to different environments. Two soybean mapping populations were used to identify quantitative trait loci (QTLs) for days to flowering (DF) and days to maturity (DM) by genotyping simple sequence repeat (SSR) markers. Single-factor analysis of variance detected association of phenotypic data with SSR markers in each population. DF QTLs were identified on four chromosomes (chrs.); two QTLs located on chrs. 2 and 13 with Satt041 and Satt206 in the Jinpumkong 2 × SS2-2 population and other two DF QTLs were detected on chrs. 6 and 19 with Satt100 and Satt373 in the Iksannamulkong × SS2-2 population. The major QTLs associated with Satt100 explained 30.3% of maximum phenotypic variation. Especially, all DF QTLs included QTLs for DM, except Satt206 on chr. 13. Moreover, two additional DM QTLs were mapped on chrs. 10 and 11 with Satt243 and Satt359, respectively. DF QTL on chr. 2 with Satt041 was the newly identified QTL only in the Jinpumkong 2 × SS2-2 population and explained 10.3% of the phenotypic variation. The single locus of Satt100 on chr. 6 and Satt373 on chr. 19 were located on soybean genomic regions of the known flowering gene loci E1 and E3, respectively. These population-specific QTLs (Satt100 and Satt373) are the major QTLs for flowering time, putatively, they may be related to maturity QTLs with large effect. Additionally, these QTLs are valuable for marker-assisted approaches and could be widely adopted by soybean breeders.     

Identification of QTLs for growth period traits in soybean using association analysis and linkage mapping

The growth period traits of soybean (Glycine max L. Merr.) are quantitatively inherited and crucial for its adaptation to different environments. Association analysis and linkage mapping were used to identify the quantitative trait loci (QTLs) for days to flowering (DF), days from flowering to maturity (DFM) and days to maturity (DM). Considering the effect of sowing date, the phenotypes were evaluated in three or four sowing‐date‐experiments in each year. A total of 96 associations, involving 19 SSRs corresponding to DF, DFM and/or DM, were identified by association mapping. Six, eight and two QTLs were observed relating to DF, DFM and DM by linkage mapping, respectively, and some QTLs were shared by DF, DFM and DM. Four SSRs (Satt150, Satt489, Satt172 and Sat_312) were found to be related to the growth period traits using the two mapping methods. In summary, association analysis and linkage mapping can complement and verify results from both methods to identify QTLs in soybean, and these findings may be useful in facilitating the selection of growth period–related traits via marker‐assisted selection.     

Linkage fine-mapping and QTLs affecting morpho-agronomic traits of a Mesoamerican × Andean RIL common bean population

This paper proposes the construction of a genetic linkage map with 376 recombinant inbred lines (RILs) derived from a cross between Mesoamerican?×?Andean common bean (Phaseolus vulgaris L) parents based on single nucleotide polymorphism (SNP) markers; and to detect quantitative trait loci (QTLs) associated with seven morpho-agronomic traits: number of days to flowering (DF), number of days to maturity (DM) or crop cycle; plant architecture (ARC); seed yield (YLD); degree of seed flatness (SF); seed shape (SS); and 100-seed weight (SW). A total of 3060 polymorphic SNP markers were used and 2041 segregated at a 1:1 ratio in the RIL population, as expected. These markers were subjected to linkage analysis in each chromosome. The genetic linkage analysis resulted in linkage maps with a total of 1962 markers spanning 1079.21 cM. A total of 29 QTLs associated with seven morpho-agronomic traits were detected on the 11 chromosomes, which explained between 3.83 and 32.92% of the phenotypic variation in DF. A total of 18 candidate genes associated with the detected QTLs were identified and related with biological processes, molecular functions and cellular components.     

Quantitative trait loci mapping for yield-related traits under low and high planting densities in maize (Zea mays)

Average maize yield per hectare has increased significantly because of the improvement in high-density tolerance, but little attention has been paid to the genetic mechanism of grain yield response to high planting density. Here, we used a population of 301 recombinant inbred lines (RILs) derived from the cross YE478 × 08–641 to detect quantitative trait loci (QTLs) for 16 yield-related traits under two planting densities (57,000 and 114,000 plants per ha) across four environments. These yield-related traits responded differently to high-density stress. A total of 110 QTLs were observed for these traits: 33 QTLs only under low planting density, 50 QTLs under high planting density and 27 QTLs across both densities. Only two major QTLs, qCD6 and qWKEL2-2, were identified across low- and high-density treatments. Seven environmentally stable QTLs were also observed containing qED6, qWKEL3, qRN3-3, qRN7-2, qRN9-2 and qRN10 across both densities, as well as qRN9-1 under low density. In addition, 16 and eight pairs of loci with epistasis interaction (EPI) were detected under low and high planting densities, respectively. Additionally, nine and 17 loci showed QTL × environment interaction (QEI) under low- and high-density conditions, respectively. These interactions are of lesser importance than the main QTL effects. We also observed 26 pleiotropic QTL clusters, and the hotspot region 3.08 concentrated nine QTLs, suggesting its great importance for maize yield. These findings suggested that multiple minor QTLs, loci with EPI and QEI, pleiotropy and the complex network of “crosstalk” among them for yield-related traits were greatly influenced by plant density, which increases our understanding of the genetic mechanism of yield-related traits for high-density tolerance.     

Genetic control of yield and yield components in winter oilseed rape (Brassica napus L.) grown under nitrogen limitation

Despite its high nitrogen absorption capacity, oilseed rape (OSR) has a low apparent nitrogen use efficiency (NUE), which makes its production highly dependent on nitrogen fertilization. Improving NUE in OSR is therefore a main target in breeding. The objectives of the present work were to determine the genomic regions (QTLs) associated with yield and to assess their stability under contrasted nitrogen nutrition regimes. One mapping population, AM, was tested in a French location for three growing seasons (2011, 2012 and 2013), under two nitrogen conditions (optimal and low). Eight yield-related traits were scored and nitrogen-responsive traits were calculated. A total of 104 QTLs were detected of which 28 controlled flowering time and 76 were related to yield and yield components. Very few genotype × nitrogen interactions were detected and the QTLs were highly stable between the nitrogen conditions. In contrast, only a few QTLs were stable across the years of the trial, suggesting a strong QTL × year interaction. Finally, eleven critical genomic regions that were stable across nitrogen conditions and/or trial years were identified. One particular region located on the A5 linkage group appears to be a promising candidate for marker assisted selection programs. The different strategies for OSR breeding using the QTLs found in the present study are discussed.     

Genetic Analysis of Developmental Traits in Chickpea

Chickpea (Cicer arietinum L.) is an important legume crop in India. The present study was conducted to investigate the inheritance of several developmental traits in three crosses of chickpea, viz., WFWG III’בT20’, ‘T88’בBold Seeded’, and ‘NP34’בP1528-1-1’, each having seven generations. The seven generations were P₁, P₂, F₁, B₁, B₂, F₂, and F₃. The experimental lay-out was randomized complete block design with three replications. Data were acquired on days to flowering (DF), days to maturity (DM), plant height in cm (PH), number of primary branches (PB), and number of secondary branches (SB). Generation mean analysis was used to estimate the genetic components; narrow sense heritability was estimated using variance components; and correlation analysis to estimate correlation coefficients among different traits. Genetic differences were found in all three crosses for all traits studied. Additive, dominance, and epistatic effects were found for many traits'. Duplicate epistasis was observed for all traits except number of PB. Higher order interactions and/or linkage were detected for DM and SB. For many traits the relative magnitudes of the genetic effects differed among crosses, thus the extrapolation to other crosses may be difficult. The inheritance becomes more complex as the fate of the character is decided at a later stage in the life cycle. Positive heterosis was observed for some traits, but the exploitation of this component may not Feasible since stable male sterile lines are not available. Early maturity and high yield ‘may be selected independently because of the absence of any significant correlation between these two traits.     

烤烟6个农艺性状的QTL定位

由于烟草的分子标记开发和遗传图谱构建十分困难,迄今烟草中有关数量性状基因座(QTL)的定位研究仍非常有限。本研究利用一个由207个株系组成的烤烟DH群体及基于该群体所构建的含有24个连锁群、611个SSR标记,总长为1 882.1 cM的遗传图谱,采用复合区间作图方法,对株高(PH)、茎围(SG)、节距(IL)、叶片数(LN)、最大腰叶长(LWL)和最大腰叶宽(WWL) 6个与叶片产量有关的农艺性状进行QTL定位分析。共检测到69个QTL,大部分QTL的效应值较小,仅有4个具有较大的效应值,可解释大约15%~20%的表型变异。6个性状之间大多彼此相关。与此相符,在基因组中发现存在许多小区域,每个区域包含两个或两个以上紧密连锁的不同性状的QTL。     

利用单片段代换系研究水稻产量相关性状QTL加性及上位性效应

产量及其相关性状如单株有效穗数、千粒重、穗实粒数、穗总粒数和结实率等是水稻重要的农艺性状,了解产量及其相关性状QTL的加性及上位性效应对以分子标记聚合育种改良水稻产量具有重要意义。本文以16个单片段代换系及15个双片段代换系分析了水稻产量相关性状QTL的加性及上位性效应。共检出影响产量及其相关性状的13个QTL,包括产量性状1个、单株有效穗数1个、千粒重4个、穗实粒数4个、穗总粒数2个和结实率1个,分布于第2、第3、第4、第7和第10染色体上。此外,检出12对双基因互作。结果显示,2个正向(或负向)产量性状QTL聚合,往往会产生负向(或正向)的上位性效应,能否产生更大(或更小)的目标性状,取决于双片段遗传效应(加性效应与上位效应代数和)绝对值与单片段最大加性效应绝对值的差。本研究结果对实施高产分子标记聚合育种方法有重要参考价值。     

QTL Mapping of Important Agronomic Traits on Chromosomes Showing Marker Segregation Distortion in Interspecific Backcross Cotton Populations

[Objective] The purpose of this study was to map QTLs for yield, maturity, and fiber quality traits of cotton interspecific hybrid populations and to mine favorable alleles from the cotton genome for QTL fine mapping, gene cloning, and molecular marker-assisted selection. [Method] Four BC₁F₁ populations, E(E3), E(3E), (E3)E, and (3E)E, were developed by crossing sea island cotton 3-79 ("3") and upland cotton 'Emian 22' ("E"), and with "Emian 22" as the recurrent parent. BC1F2 populations derived from BC1F1 populations were phenotyped to allow mapping of QTLs for yield-, maturity-, and fiber quality-related traits on chromosomes showing segregation distortion. [Result] A normal distribution test indicated that six fiber quality-related traits, except for the micronaire value in E (E3) and the short fiber ratio in all populations, followed a normal distribution pattern; in contrast, earliness-related traits were not normally distributed in all populations, which suggests that genotype-environment interactions have strong effects on maturity-related traits. Using composite interval mapping, 47 QTLs were detected on three chromosomes (2, 16, and 18): 15 in (E3)E, 13 in (3E)E, 12 in E(E3), and 7 in E(3E), with the phenotypic variation explained (PVE) ranging from 8.8%–30.9%. A total of 27 fiber-related QTLs were discovered, with the PVE ranging from 9.0%–30.9%. Five yield-related QTLs (PVE = 8.8%–17.4%) and 13 maturity-related QTLs (PVE = 9.4%–19.4%) were also identified. [Conclusion] Comparison analysis showed that most QTLs in our study were consistent with the MetaQTL, including QTLs for boll weight, fiber length, fiber length uniformity, lint percentage, and micronaire value on chromosome 2; QTLs for fiber length, fiber strength, and fiber length uniformity on chromosome 16; and QTLs for boll weight, date of boll opening, fiber length, fiber strength, fiber length uniformity, and micronaire value on chromosome 18. Several detected QTLs in our study were not found in the MetaQTL, such as QTLs for date of boll opening and lint percentage on chromosome 16 and a QTL for the date from seedling to flowering stages on chromosome 18. These newly identified QTLs may provide novel insights for cotton QTL analysis.     

Allelic relationships of genes controlling number of flowers per axis in chickpea

Genetic variation for number of flowers per axis in chickpea (Cicer arietinum L.) includes single-flower, double-flower, triple-flower and multi-flower traits. A double-flowered (DF) line ICC 4929, a triple-flowered (TF) line IPC 99-18 and a multi-flowered (MF) line JGM 7 were intercrossed in all possible combinations and flowering behavior of parents, F₁s and F₂s was studied to establish allelic relationships, penetrance and expressivity of genes controlling number of flowers per axis in chickpea. The F₁ from ICC 4929 (DF) × IPC 99-18 (TF) cross were double-flowered, whereas F₁ from ICC 4929 (DF) × JGM 7 (MF) and IPC 99-18 (TF) × JGM 7 (MF) crosses were single-flowered. The F₂ from ICC 4929 (DF) × IPC 99-18 (TF) cross gave a good fit to a 3:1 ratio for double-flowered and triple-flowered plants. The F₂ from ICC 4929 (DF) × JGM 7 (MF) cross segregated in a ratio of 9:3:3:1 for single-flowered, double-flowered, multi-flowered and double-multi-flowered plants. The F₂ from IPC 99-18 (TF) × JGM 7 (MF) cross segregated in a ratio of 9:3:4 for single-flowered, triple-flowered and multi-flowered plants. The results clearly established that two loci control number of flowers per axis in chickpea. The double-flower and triple-flower traits are controlled by a single-locus (Sfl) and the allele for double-flowered trait (sfl ^d ) is dominant over the allele for triple-flower trait (sfl ^t ). The three alleles at the Sfl locus has the dominance relationship Sfl > sfl ^d > sfl ^t . The multi-flower trait is controlled by a different gene (cym). Single-flowered plants have dominant alleles at both the loci (Sfl_ Cym_). The double-flower, the triple-flower and the multi-flower traits showed complete penetrance, but variable expressivity. The expressivity was 96.3% for double-flower and 76.4% for double-pod in ICC 4929, 81.2% for triple-flower and 0.0% for triple-pod in IPC 99-18, and 51.3% for multi-flower and 24.7% for multi-pod in JGM 7. Average number of flowers per axis and average number of pods per axis were higher in JGM 7 than double-flowered line ICC 4929 and triple-flowered line IPC 99-18. The results of this study will help in development of breeding strategies for exploitation of these flowering and podding traits in chickpea improvement.     

Identification of QTLs for BYDV tolerance in bread wheat

We searched for QTLs involved in tolerance to barley yellow dwarf (BYD), a serious viral disease of small grain cereals in two wheat populations, Opata × Synthetic (ITMI)and Frontana × INIA66 (F × I), for which marker data had previously been generated. The populations were evaluated in replicated field trials under artificial inoculation with a BYDV-PAV-Mex isolate and under disease-free conditions. Disease symptoms (yellowing, dwarfism and biomass reduction) were visually recorded and agronomic traits (number of tillers,height, biomass, yield and thousand-kernel weight) were measured on five plants per plot. Phenotypic data on all evaluated traits showed normal distribution with high correlation between visual estimates and measured values. Heritabilities were mostly moderate to high in the 114 lines of the ITMI population, and from low to moderate in the 117 lines of the F × I population. QTL analyses were based on genetic maps containing 443 loci for the ITMI population and 317 loci for the F × I population. Using composite interval mapping, 22 QTLs in the ITMI population and seven in the F × I population were detected, explaining9.8–43.3% of total phenotypic variation (σ² _P)per agronomic trait in the first population, and 4.1–13.7% in the second. Individual QTLs explained less than 15.8%of σ² _P. In the F × I population a minor QTL explaining 7% of σ² _P for yellowing was detected on the short arm of 7D, linked to leaf tip necrosis, a morphological marker for linked genes Bdv1, Yr18 andLr34. A QTL consistently detected for several traits on 2D in the ITMI population and on the short arm of group 6 chromosome(6S) in F × I explained 10–15% of σ² _P. The large number of QTLs having mostly small effects and the continuous distribution of all evaluated traits confirmed the polygenic nature and complexity of BYD tolerance in wheat. This revised version was published online in August 2006 with corrections to the Cover Date.     

QTL mapping of sheath blight resistance in a deep-water rice cultivar

Sheath blight, caused by the pathogen Rhizoctonia solani Kühn, is one of the most serious diseases of rice and leads to severe yield loss worldwide. A recombinant inbred line (RIL) population consisting of 121 lines was constructed from a cross between HH1B and RSB03, the latter of which is a deep-water rice variety. Five traits were used to evaluate sheath blight resistance, namely disease rating (DR), lesion length (LL), lesion height (LH), relative lesion length [RLL, the ratio of LL to plant height (PH)], and relative LH (RLH, the ratio of LH to PH). Using the RIL population and 123 molecular markers, we identified 28 quantitative trait loci (QTLs) for the five traits in two environments. These QTLs are located on nine chromosomes and most of them are environment specific. A major QTL for DR (qSBR1) on chromosome 1 was identified with contributions of 12.7% at Shanghai and 42.6% at Hainan, and it collocated with a QTL for PH. The allele at this locus from RSB03 enhances sheath blight resistance and increases PH. Another QTL for DR on chromosome 7 was adjacent to QTLs for heading date (HD) and four other disease traits. RSB03 also carries the resistant allele at this locus and shortens HD. The susceptible parent, HH1B, provides the resistance allele at the locus qSBR8, where QTLs for four other disease traits were identified. QTL mapping results showed that most QTLs for LL, LH, RLL, and RLH are collocated with QTLs for DR. Three QTLs for DR are independent from HD, PH, and four other disease traits, while four QTLs are closely related to HD and PH. Four QTLs for LL, LH, RLL, and RLH are independent from DR, HD, and PH, while there is only one region harboring QTLs for these four traits and HD. Correlation analysis and QTL mapping results indicated that LL, LH, RLL, and RLH might be important indices, like DR, for evaluating the level of resistance to rice sheath blight.     

Advanced-backcross QTL analysis in spring barley: IV. Localization of QTL × nitrogen interaction effects for yield-related traits

The advanced backcross quantitative trait locus (AB-QTL) analysis has proven its usefulness to identify and localize favourable alleles from exotic germplasm and to transfer those alleles into elite varieties. In a balanced design with up to six environments and two nitrogen fertilization (N treatment) levels, a 4-factorial mixed model analysis of variance (ANOVA) was used to identify QTL main effects, QTL × environment interaction effects and QTL × N treatment interaction effects in the spring barley BC₂DH population S42. The yield-related traits studied were number of ears per m², days until heading, plant height, thousand grain weight (TGW) and grain yield. In total, 82 QTLs were detected for all traits. This finding was compared to a previous QTL study of the same population S42, where the current field data was reduced to one half through restriction of the analysis to the standard N treatment level (von Korff et al., Theor Appl Genet 112: 1221–1231, 2006). These authors located 54 QTLs for the same traits by applying a 3-factorial mixed model similar to the current model but excluding the factor N treatment. We found that QTL × environment interaction, alone or in combination, accounted for 24 of the newly uncovered QTLs, whereas QTL × N treatment interaction was of lesser importance with six new cases in total. A valuable QTL interacting with N treatment has been identified on chromosome 7H where lines carrying the wild barley allele were superior in number of ears per m² in either N treatment. We conclude that in population S42 the extension of the phenotype data set and the inclusion of N treatment into the mixed model increased the power of QTL detection by providing an additional replication rather than by revealing specific N treatment QTLs.     

影响水稻穗部性状及籽粒碾磨品质的QTL及其环境互作分析

利用优质恢复系测258为轮回亲本与粳型糯稻新品系IR75862杂交创制的BC₁F₇回交导入系群体,在广西南宁和海南三亚定位了产量相关性状(二次枝梗数、穗总粒数、穗实粒数、粒重和穗重)、粒型(粒长、宽、厚)和碾磨品质(糙米率、精米率和整精米率)的主效QTL并剖析其环境互作效应。双亲在穗实粒数、千粒重、粒长和粒宽及整精米率等性状上存在显著差异。各产量相关性状间呈极显著正相关,而与千粒重和粒长呈极显著负相关。多数产量及粒型相关性状与3种碾磨品质相关不显著。在南宁和三亚环境下检测到影响产量相关性状、粒型及碾磨品质的主效QTL共计57个,包括二次枝梗数的6个,穗实粒数4个,穗总粒数、粒重和穗重各5个,粒长9个,粒宽7个,粒厚1个,糙米率4个,精米率5个和整精米率6个,分布在除第11染色体外的所有染色体上。多数影响枝梗数、穗粒数和粒重的QTL成簇分布,而且与影响BR、MR和HR的QTL分布在不同染色体区域。在第2、第3、第4、第5和第6染色体上鉴定出影响穗粒数、粒重、粒型及碾磨品质的重要QTL,这些QTL在以往不同遗传背景和环境下被多次检测到。在第8染色体RM152~RM310区间鉴定到1个影响粒长和粒宽的新的QTL,能同步增加粒宽和粒长。鉴定出的这些稳定表达的QTL具有标记辅助选择育种的应用价值。整精米率是受环境影响最大的性状,其QTL的环境互作效应明显。对QTL的环境互作效应特点及其在品种标记辅助改良中的作用进行了深入探讨。     

越冬栽培稻产量性状相关QTL定位

越冬栽培稻是一类能越过自然冷冬季节并在第2年春季萌芽、正常开花结实、收获稻谷的水稻品种。本文通过对越冬栽培稻产量性状QTL分析,明确产量相关性状的遗传规律,旨在进一步解析越冬栽培稻产量性状的遗传机制,为育种创新利用提供理论依据。以3份越冬栽培稻构建的3个半同胞F2群体为材料。各考察15个产量相关性状,利用Excel 2003、GraphPad Prism 5.0和QTL IciMapping 4.10软件分析数据、绘制遗传图谱、定位QTL和联合分析。结果表明,产量性状表型值在3群体中呈连续正态分布,表现为数量性状遗传。共检测到37个QTL和26对上位性QTL,贡献率分别介于2.32%～36.31%和1.04%～2.05%;检测到9个同时影响2个及以上产量性状(一因多效)QTL标记区间;以联合分析检测到13个产量性状相关QTL,其中4个QTL区间与单群体检测QTL区间重叠;越冬栽培稻产量相关性状QTL以加–显性效应遗传为主、上位性遗传效应为辅。本研究将为越冬栽培稻产量相关基因挖掘及育种创新利用奠定基础。     

甘蓝型油菜产量及相关性状的QTL分析

高产是甘蓝型油菜育种的重要目标之一，产量是多基因控制的数量性状。本文通过QTL作图分析了产量及其相关性状的数量性状位点，以甘蓝型油菜中油821和保604 F₁代小孢子培养获得的DH系为作图群体，构建了由20个连锁群组成的，包括251个分子标记( 2个RFLP标记，72个RAPD标记，91个SSR标记，86个SRAP标记)的遗传连锁图(10个标记没有分配到连锁群中)。图谱的平均图距为6.96 cM，共覆盖油菜基因组1 746.5 cM。在此图谱基础上采取复合区间作图法，检测到与油菜产量及其相关性状有关的QTL共17个。其中控制株高的3个分别位于第4、第9和第10连锁群上，对性状的解释率为9.42%～17.58%；与分枝部位有关的4个分别位于第4、第6和第7连锁群上，其中Bp1 和Bp2 均位于第4连锁群，对性状的解释率为8.13%～15.20%；与主花序有效长有关的3个分别位于第4、第10和第16连锁群上，对性状的解释率为7.49%～23.36%；与一次有效分枝有关的2个分别位于第1、第4连锁群上，对性状的解释率为15.29%～19.58%；与角果总数和千粒重有关的分别位于第4连锁群和第9连锁群上，贡献率分别为17.42%和7.64%；与单株产量有关的3个分别位于第3、第4和第15连锁群，共解释26.60%的表型变异。部分性状的QTL在连锁群上成簇分布,对性状贡献率很大，表现主效QTLs的特点，相应的性状之间也呈显著相关，这表明一因多效或者相关的QTLs之间紧密连锁是性状相关的遗传基础。本研究中与主效QTLs连锁的标记可用于油菜产量性状的分子标记辅助选择。     

转基因抗虫棉产量相关性状QTL的分子标记及定位

 采用亚洲棉渐渗的纤维强度突出的陆地棉优质新品系0-153与陆地棉转基因抗虫新品系sGK9708为亲本,构建了F₂及F_2∶3分离群体。利用3869对SSR引物筛选亲本,得到125对多态性引物。进一步对183个F₂群体单株分析得到150个多态性标记位点,其中100个标记位点连锁,构建20个连锁群,共覆盖660 cM,占棉花总基因组的14.67%,每个连锁群平均包含5个标记位点,标记间平均相距6.6 cM,其中13个连锁群确定了对应的染色体。利用F₂和F_2:3数据,通过复合区间作图,共检测到28个产量及相关因素的QTLs。这些控制产量性状的QTLs只存在于5个连锁群上,成簇分布。与皮棉产量性状有关的2个QTLs,均与其它多个产量相关性状的QTLs在同一个连锁区段内,增效基因遗传效应方向一致,有必要研究其在标记辅助选择中的效果。本研究没有检测到在多世代表现稳定的QTL。因此,需要培育重组自交系,进一步明确产量性状有关QTL的遗传效应。     

利用品质性状的回交选择导入系挖掘水稻抗纹枯病QTL

将优质、抗纹枯病的高秆供体Tarom Molaii和Binam导入半矮秆IR64和特青背景,培育品质性状回交选择构建的4个导入系群体IR64/Tarom Molaii、特青/Tarom Molaii、IR64/Binam和特青/Binam,定位了影响水稻抗纹枯病病级(disease scale, DS)、相对病斑高度(relative lesion height, RH)和株高(plant height, PH)的QTL。结果表明,4个导入系群体的DS与RH高度相关,两者与PH呈显著负相关。导入系后代各性状均呈现超亲分离,出现抗性明显优于双亲的抗病个体,其中40%左右属半矮秆抗病类型。采用单向方差分析,在这4个群体中分别定位到10、8、8和6个影响3个性状的QTL,多数基因座上降低DS和RH即增强抗病性同时增加株高的等位基因均来自两个供体。未在同一供体两个不同背景下检测到影响3个性状的相同QTL,表明抗纹枯病QTL表达有明显的遗传背景效应。PH与DS及PH与RH被定位在同一个显著标记位点的QTL数分别占两个性状QTL总数的38%和52%,表明水稻纹枯病抗性与株高关系密切,两者存在许多连锁位点。与以往相同群体品质性状QTL的定位结果相比,发现品质性状QTL与抗纹枯病QTL大多分布在染色体的不同区域,彼此独立遗传。对利用目标性状选择导入系定位非目标性状QTL的效果、影响因素及育种应用进行了探讨,强调了目标性状选择导入系对非目标性状QTL发掘及育种应用的重要性。     

Analysis of quantitative trait loci associated with leaflet types in two recombinant inbred lines of soybean

Leaf area, length and width affect the photosynthetic capability of a plant and so increasing the photosynthetic rate per unit leaf area may improve seed yield in soybean. In this study, simple sequence repeat (SSR) markers were used to identify the genomic regions significantly associated with the quantitative trait locus (QTL) that controls length, width and the length/width ratio of the terminal and lateral leaflet in two segregating F_2:10 recombinant inbred line (RIL) populations, ‘Keounolkong’ × ‘Shinpaldalkong’ (K/S) and ‘Keounolkong’ × ‘Iksan10’ (K/I). In the K/S population, one QTL was identified for terminal leaflet length (TLL), two for lateral leaflet length (LLL), four for terminal leaflet width (TLW), four for lateral leaflet width (LLW), two for terminal leaflet length/width ratio (TLR) and four for lateral leaflet length/width ratio (LLR), with total phenotypic variations of 7.43, 10.9, 26.57, 23.46, 20.25 and 23.31%, respectively. In the K/I population, two QTLs were identified for TLL, two for LLL, three for TLW, and two for LLW, four for TLR and two for LLR with total phenotypic variations of 29.89, 22.77, 18.5, 12.15, 22.96 and 17.85%, respectively. Only a few QTLs coincided among the leaflet traits and no relationships were observed between the two populations. Many QTLs were associated with leaflet traits but each single QTL made only a minimal contribution. Thus, pyramiding the favourable alleles for leaflet traits in soybean breeding programmes may accelerate vegetative growth and perhaps lead to higher yields by maximizing total photosynthetic performance.     

Population-specific QTLs and their different epistatic interactions for pod dehiscence in soybean [<Emphasis Type="Italic">Glycine max</Emphasis> (L.) Merr.]

Pod dehiscence (PD) prior to harvest results in serious yield loss in soybean. Two linkage maps with simple sequence repeat (SSR) markers were independently constructed using recombinant inbred lines (RILs) developed from Keunolkong (pod-dehiscent) × Sinpaldalkong (pod-indehiscent) and Keunolkong × Iksan 10 (pod-indehiscent). These soybean RIL populations were used to identify quantitative trait loci (QTLs) conditioning resistance to PD. While a single major QTL on linkage group (LG) J explained 46% of phenotypic variation in PD in the Keunolkong × Sinpaldalkong population with four minor QTLs, three minor QTLs were identified in the Keunolkong × Iksan 10 population. Although these two populations share the pod dehiscent parent, no common QTL has been identified. In addition, epistatic interactions among three marker loci partially explained phenotypic variation in PD in both populations. The result of this study indicates that different breeding strategies will be required for PD depending on genetic background.