Genome mapping and molecular markers identification for yield,yield component and fibre quality traits in tetraploid cotton

The identification of quantitative trait loci (QTL) across different environments is a prerequisite for marker‐assisted selection (MAS) in crop improvement programmes. CottonSNP63k Illumina infinium array was used for genotyping 178 inter‐specific recombinant inbred lines and the parents, and identified 1,667 homozygous polymorphic markers between the parents. Of these, 1,430 markers were used for the construction of linkage map after removing 237 redundant markers. The genetic map spans a total genetic length of 3,149.8 cM with an average marker interval size of 2.2 cM. The phenotypic data from five environments were analysed separately using inclusive composite interval mapping which identified a total of 56 QTL explaining phenotypic variances (PVE) in the range of 8.18%–28.91%. There were 11 and 24 major QTL found for fibre quality and yield components, respectively. A total of 64 QTL were identified through Multi‐Environment Trials analysis, of which 34 recorded QTL × Environment interactions.     

Mapping QTLs for salt tolerance with additive,epistatic and QTL × treatment interaction effects at seedling stage in wheat

Quantitative trait loci (QTLs) for salt tolerance with additive, epistatic and QTL × treatment interaction effects at seedling stage in wheat were identified. A set of 131 recombinant inbred lines derived from cross Chuan 35050 × Shannong 483 were evaluated under salt stress and normal conditions. Wide variation was found for all studied traits. A total of 18 additive and 16 epistatic QTLs were detected, among which five and 11 were with significant QTL × treatment effects. Ten QTL clusters were identified, and each may represent a single gene or closely linked genes. The locus controlling shoot K⁺/Na⁺ concentration ratio and shoot Na⁺ concentration on chromosome 5A may be identical to Nax2. The interval Xgwm6‐Xgwm538 on chromosome 4B for total dry weight was also identified in a previous study, both near the marker Xgwm6. The marker Xgwm6 may be useful for marker‐assisted selection. Six pairs of homoeologous QTLs were detected, showing synteny among the A, B and D genomes. These results facilitate understanding the mechanisms and the genetic basis of salt tolerance in wheat.     

Detection of epistatic and environmental interaction QTLs for leaf orientation‐related traits in maize

Leaf architecture traits in maize are quantitative and have been studied by quantitative trait loci (QTLs) mapping. However, additional QTLs for these traits require mapping and the interactions between mapped QTLs require studying because of the complicated genetic nature of these traits. To detect common QTLs and to find new ones, we investigated the maize traits of leaf angle, leaf flagging‐point length, leaf length and leaf orientation value using a set of recombinant inbred line populations and single nucleotide polymorphism markers. In total, 19 QTLs contributed 4.13–13.52% of the phenotypic effects to the corresponding traits that were mapped, and their candidate genes are provided. Common and major QTLs have also been detected. All of the QTLs showed significant additive effects and non‐significant additive × environment effects in combined environments. The majority showed additive × additive epistasis effects and non‐significant QTL × environment effects under single environments. Common and major QTLs provided information for fine mapping and gene cloning, and SNP markers can be used for marker‐assisted selection breeding.     

Genetic structure composed of additive QTL,epistatic QTL pairs and collective unmapped minor QTL conferring oil content and fatty acid components of soybeans

The relative importance of various types of quantitative trait locus (QTL) conferring oil content and its fatty acid components in soybean seeds was assessed through testing a recombinant inbred line (RIL) population (derived from KF1 × NN1138-2) in randomized blocks experiments in 2004–2006. The contents of oil and oleic, linoleic, linolenic, palmitic and stearic acids were determined with automatic Soxhlet extraction system and gas chromatography, respectively. Based on the established genetic linkage map with 834 markers, QTLNetwork2.0 was used to detect QTL under the genetic model composed of additive, additive × additive (epistasis), additive × year and epistasis × year effects. The contributions to the phenotypic variances of additive QTL and epistatic QTL pairs were 15.7% (3 QTL) and 10.8% (2 pairs) for oil content, 10.4% (3 QTL) and 10.3% (3 pairs) for oleic acid, 11.6% (3 QTL) and 8.5% (2 pairs) for linoleic acid, 28.5% (7 QTL) and 7.6% (3 pairs) for linolenic acid, 27.0% (6 QTL) and 16.6% (7 pairs) for palmitic acid and 29.7% (5 QTL) and 4.3% (1 pair) for stearic acid, respectively. Those of additive QTL by year interaction were small and no epistatic QTL pair by year interaction was found. Among the 27 additive QTL and 36 epistatic QTL (18 pairs), three are duplicated between the two QTL types. A large difference was found between the genotypic variance among RILs and the total variance of mapped QTL, which accounted for 52.9–74.8% of the genotypic variation, much larger than those of additive QTL and epistatic QTL pairs. This part of variance was recognized as that due to a collection of unmapped minor QTL, like polygenes in biometrical genetics, and was designated as collective unmapped minor QTL. The results challenge the breeders for how to pyramid different types of QTL. In addition, the present study supports the mapping strategy of a full model scanning followed by verification with other procedures corresponding to the first results.     

QTL epistatic analysis for yield components with single‐segment substitution lines in rice

Epistasis is an important genetic component in determining the phenotype of complex quantitative trait. In this article, 12 single‐locus heterozygotes and 66 double‐locus heterozygotes were developed and then were applied to assay QTL epistasis for four yield‐associated traits under two planting densities. Of 264 (66 × 4) tested interactions, 130 (49.2%) were significant at the p < .05 level. QTL with the same effect directions had higher probabilities of interactions. The negative epistasis at least included one positive effect QTL but the positive epistasis one negative QTL. The detected epistasis was sensitive to planting density. Epistasis also exhibited pleiotropic effects.     

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.     

Quantitative trait loci (QTL) mapping of silique length and petal colour in Brassica rapa

Recombinant inbred lines (RILs) derived from a cross between Brassica rapa L. cv. ‘Sampad’, and an inbred line 3‐0026.027 was used to map the loci controlling silique length and petal colour. The RILs were evaluated under four environments. Variation for silique length in the RILs ranged from normal, such as ‘Sampad’, to short silique, such as 3‐0026.027. Three QTL, SLA3, SLA5 and SLA7, were detected on the linkage groups A3, A5 and A7, respectively. These QTL explained 36.0 to 42.3% total phenotypic variance in the individual environments and collectively 32.5% phenotypic variance. No additive × additive epistatic interaction was detected between the three QTL. Moreover, no QTL × environment interaction was detected in any of the four environments. The number of loci for silique length detected based on QTL mapping agrees well with the results from segregation analysis of the RILs. In case of petal colour, a single locus governing this trait was detected on the linkage group A2.     

Identification of QTL for oil content,seed yield,and flowering time in oilseed rape (<Emphasis Type="Italic">Brassica napus</Emphasis>)

A genetic map was constructed with 353 sequence-related amplified polymorphism and 34 simple sequence repeat markers in oilseed rape (Brassica napus L.). The map consists of 19 linkage groups and covers 1,868 cM of the rapeseed genome. A recombinant doubled haploid (DH) population consisting of 150 lines segregating for oil content and other agronomic traits was produced using standard microspore culture techniques. The DH lines were phenotyped for days to flowering, oil content in the seed, and seed yield at three locations for 3 years, generating nine environments. Data from each of the environments were analyzed separately to detect quantitative trait loci (QTL) for these three phenotypic traits. For oil content, 27 QTL were identified on 14 linkage groups; individual QTL for oil content explained 4.20–30.20% of the total phenotypic variance. For seed yield, 18 QTL on 11 linkage groups were identified, and the phenotypic variance for seed yield, as explained by a single locus, ranged from 4.61 to 24.44%. Twenty-two QTL were also detected for days to flowering, and individual loci explained 4.41–48.28% of the total phenotypic variance.     

Molecular genetic analysis of flour color using a doubled haploid population in bread wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Flour color is an important trait in the assessment of flour quality for the production of many end products. In this study, quantitative trait loci (QTLs) with additive effects, epistatic effects, and QTL × environment (QE) interactions for flour color in bread wheat (Triticum aestivum L.) were studied, using a set of 168 doubled haploid (DH) lines derived from a Huapei 3 × Yumai 57 cross. A genetic map was constructed using 283 simple sequence repeats (SSR) and 22 expressed sequence tags (EST)-SSR markers. The DH and parents were evaluated for flour color in three environments. QTL analyses were performed using QTLNetwork 2.0 software based on a mixed linear model approach. A total of 18 additive QTLs and 24 pairs of epistatic QTLs were detected for flour color, which were distributed on 19 of the 21 chromosomes. One major QTL, qa1B, closely linked to barc372 0.1 cM, could account for 25.64% of the phenotypic variation of a* without any influence from the environments. So qa1B could be used in the molecular marker-assisted selection (MAS) in wheat breeding programs. The results showed that both additive and epistatic effects were important genetic basis for flour color, and were also sometimes subject to environmental modifications. The information obtained in this study should be useful for manipulating the QTLs for flour color by MAS in wheat breeding programs. Kun-Pu Zhang and Guang-Feng Chen contributed equally to this study.     

Genetic analysis of plant height using two immortalized populations of “CRI12 × J8891” in Gossypium hirsutum L.

Plant height is an important plant architecture trait that determines the canopy structure, photosynthetic capacity and lodging resistance of upland cotton populations. To understand the genetic basis of plant height for marker-assisted breeding, quantitative trait loci (QTL) analysis was conducted based on the genetic map of recombinant inbred lines (RILs) derived from the cross “CRI12 × J8891” (Gossypium hirsutum L.). Three methods, including composite interval mapping, multiple interval mapping and multi-marker joint analysis, were used to detect QTL across multiple environments in the RILs and in the immortalized F₂ population developed through intermating between RILs. A total of 19 QTL with genetic main effects and/or genetic × environment interaction effects were identified on 15 chromosomes or linkage groups, each explaining 5.8–14.3 % of the phenotypic variation. Five digenic epistatic QTL pairs, mainly involving additive × additive and/or dominance × dominance, were detected in different environments. Seven out of eight interacting loci were main-effect QTL, suggesting that these loci act as major genes as well as modifying genes in the expression of plant height. The results demonstrate that additive effects, dominance and epistasis are all important for the genetic constitution of plant height, with additive effects playing a more important role in reducing plant height. QTL showing stability across environments that were repeatedly detected by different methods can be used in marker-assisted breeding.     

甘蓝型油菜产量及其构成因素的QTL定位与分析

产量性状是复杂的数量性状, 对种子的单株产量及其构成因素(全株总有效角果数、每角粒数、千粒重)进行QTL定位和上位性分析,确定其在染色体上的位置及其遗传效应,可以探讨油菜杂种优势产生原因,提高育种中对产量性状优良基因型选择的效率,达到提高油菜产量的目的。在双低油菜细胞质雄性不育保持系1141B和双高恢复系垦C₁构建的F₂作图群体中,运用SRAP、AFLP和SSR三种标记技术构建了一个甘蓝型油菜(Brassica napus L.)的分子标记遗传连锁图谱。共包含244个标记,分布于20个主要连锁群、1个三联体上,图谱总长度为2 769.5 cM。采用Windows QTL Cartographer Version 2.0统计软件及复合区间作图法,对油菜单株产量及其3大构成因素进行QTL定位,共检测到QTLs 16个分布在9个连锁群上,其中第6和13连锁群最多,均有3个。单个QTL解释性状表型变异的0.38%～73.34%。对于同一性状,等位基因的增效作用既来自母本,亦源自父本;采用双向方差分析法对位点间互作及其上位性进行分析,检测到26对影响产量构成性状的上位性互作效应QTL,说明油菜基因组中存在大量控制产量的互作位点,油菜产量性状的上位性存在着多效性,上位性互作包括QTL与非QTL位点,其中以非QTL位点较多。一般互作位点的独立效应值较小,而互作的效应值显著增大,且一般超过两位点独立效应值之和。反映了控制产量性状基因的复杂性。上位性是甘蓝型油菜产量性状杂种优势的重要遗传基础。     

Dissection of genetic architecture for oil content in soybean seed using two backcross populations

Soybean seed oil was valued in foods, animal feed and some industrial applications. Molecular marker‐assisted selection (MAS) for high‐oil‐content cultivars was an important method for soybean breeders. The objective of this study was to identify quantitative trait loci (QTL) and epistatic QTL underlying the seed oil content of soybeans across two backcross (BC) populations (with one common male parent ‘Dongnong47’) and two different environments. Two molecular genetic maps were constructed. They encompassed 1046.8 cM [with an average distance of 6.75 cM in the ‘Dongnong47’  ×  ‘Jiyu89’ (DJ) population] and 846.10 cM [with an average distance of 5.76 cM in the ‘Dongnong47’  ×  ‘Zaoshu18’ (DZ) population]. Nine and seven QTL were identified to be associated with oil content in the DJ and DZ populations, respectively. The phenotypic variation explained by most of the QTL was usually less than 10%. Among the identified QTL, those stable ones across multiple environments and populations often had stronger additive effects. In addition, three stable QTL in the DZ populations were identified in the similar genomic region of the three QTL in the DJ population [qDJE and qDZE‐1 were located near Satt151 of Chromosome 15 (Chr15), qDJA1 and qDZA1 were located near Satt200 of Chr15 (LG A1), and qDJD2‐1 and qDZD2‐1 were located near Sat365 of Chr17]. In conclusion, MAS will be able more effectively to combine beneficial alleles of the different donors to design new genotypes with higher soybean seed oil content using the BC populations.     

Genetic association of within‐boll yield components and boll morphological traits with fibre properties in upland cotton (Gossypium hirsutum L.)

Knowledge of genetic relationships between within‐boll yield components and fibre quality is essential for simultaneous improvement of lint yield and fibre quality in upland cotton (Gossypium hirsutum L.). Nine parents and their F₁ progeny with reciprocals from a 3 × 6 factorial mating design were grown in 2008 and 2009. Seven within‐boll yield components and two boll morphological traits and the three fibre quality parameters were analysed based on a conditional additive/dominance (AD) genetic model. Results showed that boll length contributed to the largest proportion of phenotypic, additive and dominance variances for UHM length; seed index contributed to the largest proportion of phenotypic and additive variances for fibre strength; boll width made the largest contribution to phenotypic and additive variances and the second largest contribution to dominance‐by‐environment interaction variance for micronaire, indicating that they played an important role than the other traits for fibre length, strength and fineness/maturity, respectively. It is worthy of note that those correlations between boll shape and fibre quality apply only to the nine parents and the resultant hybrids in this study and do not imply a cause and effect relationship.     

陆地棉重组自交系产量及产量构成因子性状的上位性QTL分析

棉花的产量及产量构成因子性状是以复杂的方式遗传,遗传力较低并易受环境条件影响。经典数量遗传学指出,上位性是复杂性状的遗传基础。本研究以湘杂棉2号F8和F9世代重组自交系为材料,调查了3个环境下的产量及产量构成因子性状,并构建了遗传连锁图。旨在定位产量及产量构成因子性状的上位性QTL并分析QTL与环境的互作效应。所有产量及产量构成因子性状均检测到上位性QTL,共检测到16对加性互作QTL（AA）,涉及的位点中仅4个有单位点效应,这反映了上位性的复杂性及其对产量和产量构成因子性状的重要贡献。共检测到17对QTL加性和环境互作（AE）,以及14对上位性QTL与环境的互作,表明环境因素对产量和产量构成因子性状起重要影响作用。研究结果还表明上位性效应作为湘杂棉2号的遗传基础起着重要作用。对各性状在不同环境的优良基因型进行了预测。综合优良家系（GSL）和特定环境下的优良家系（SL）的性状表现高于两亲本,表明湘杂棉2号重组自交系各性状都有提高的潜力。由于QTL加性和环境互作以及上位性QTL与环境互作的影响,预测的优良家系基因型会随着环境的改变而不同,表明应针对特定环境开展棉花育种。     

多环境下水稻DH群体剑叶长度的QTL分析

种植由籼稻品种和粳稻品种杂交衍生的DH群体,连续4年测定剑叶长度,运用基于混合模型的复合区间作图法,定位其QTL及上位性互作,估算遗传主效应和环境互作效应。结果表明,全部18个QTL都参与了上位性的形成,其中3个没有自身的遗传效应,但参与了3对上位性互作,这是传统方法不能发现的。另外,一个QTL可与多个QTL发生互作,这可能预示着存在更高阶互作。QTL与上位性互作可以具有不受环境影响而稳定表达的效应,以及与环境的互作效应。有些QTL与环境的互作效应可以在多环境下被检测到,但却不具有主效应,这种QTL可能易受环境因子的影响。QTL与环境的互作效应为随机效应,一个QTL或一对上位性与环境的互作效应总和理论上应等于零,否则会影响对遗传效应的估算,因此多环境下估算的遗传效应更可靠。     

A QTL controlling low temperature induced spikelet sterility at booting stage in rice

Low temperature is a major abiotic stress for rice cultivation, causing serious yield loss in many countries. To identify QTL controlling low temperature induced spikelet sterility in rice, the progeny of F2, BC1F1 and BC2F1 populations derived from a Reiziq × Lijiangheigu cross were exposed to 21/15°C for 15 days at the booting stage, and spikelet sterility was assessed. For genotyping, 92 polymorphic markers from 373 SSR and 325 STS primer pairs were used. A major QTL was initially indentified on the short arm of chromosome 10 by selective genotyping using highly tolerant and susceptible progeny from F2 and BC1F1 populations. The QTL (qLTSPKST10.1) was validated and mapped by genotyping the entire F2 (282 progeny) and BC1F1 (84 progeny) populations. The results from the F2 population showed that qLTSPKST10.1 could explain 20.5% of the variation in spikelet sterility caused by low temperature treatment with additive (a = 14.4) and dominant effect (d = −7.5). From the analysis of 98 selected BC2F1 progeny, the QTL located in the 3.5 cM interval between S10010.9 and S10014.4 was further confirmed. Based on the studies of 3 generations in 2 years, it was clear that the QTL on chromosome 10 is a major determinant of the control of low temperature induced spikelet sterility at booting stage.     

Identification of QTL underlying the filling rate of protein at different developmental stages of soybean seed

Seed protein content at the harvest stage is the sum of protein accumulation during seed filling. The aim of our investigation was to identify loci underlying the filling rate of seed protein at different developmental stages. To this end, we used 143 recombinant inbred lines (RILs) derived from the cross of soybean cultivars ‘Charleston’ and ‘Dongnong 594’ and composite interval mapping with a mixed genetic model. The genotype × environment interactions of the quantitative trait loci (QTL) were also evaluated. Thirty-nine unconditional QTL underlying the filling rate of seed protein at five developmental stages were mapped onto 14 linkage groups. The proportion of phenotypic variation explained by these QTL ranged from 4.88 to 26.05%. Thirty-eight conditional QTL underlying the filling rate of seed protein were mapped onto 16 linkage groups. The proportion of phenotypic variation explained by these QTL ranged from 1.87 to 31.34%. The numbers and types of QTL and their genetic effects on the filling rate of seed protein were different at each developmental stage. A G × E interaction effect was observed for some QTL.     

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.     

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.     

Genetic analysis of genotype × iron nutrition interaction on coleoptile elongation rate in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

High iron levels in rice soils represent a major problem for seedling establishment and crop growth, and rapid coleoptile elongation is the mechanism for the rice to cope with the induced stress. Quantitative trait loci (QTLs) analysis for coleoptile elongation rate (CER) in rice (Oryza sativa L.) was performed to study the inheritance of CER and its response to Fe nutrition. A recombinant inbred line (RIL) population of 244 lines derived from the cross zhenshan97B/miyang46 was germinated in 2004 under four Fe concentrations (0, 1.79, 7.16, and 14.32 mM). Seven QTLs with additive effects of stimulating CER were detected under the four Fe concentrations and they were localized on chromosome 1, 4, 5 and 7 with LOD ranging from 2.88 to 15.94 and their contribution to total phenotypic variance ranging from 4.17% to 15.87%, respectively. In addition, 21 QTLs with additive × additive epistasis were detected on all chromosomes but 4 and 9. The detected QTLs with additive effect mainly came from the male parent ZS97B. The detected number of QTLs with additive and epistatic effects for CER varied with Fe concentration. An additive QTL with G × Fe effect was detected between RZ460 and RZ730 markers of chromosome 1 using multi-environmental model of QTL Mapper 1.6 and considering Fe concentration as an environmental factor. The pattern of CER in the different Fe concentrations was well characterized by the genetic model of quantitative traits. It was found that some RILs had higher CER than both parents in each Fe concentration.