QTL mapping of the domestication traits pre-harvest sprouting and dormancy in wheat (<Emphasis Type="BoldItalic">Triticum aestivum L.</Emphasis>)

A set of 75 recombinant inbred lines (RILs) of the ITMI mapping population was grown under field conditions in Gatersleben. The lines were evaluated for the domestication traits pre-harvest sprouting and dormancy (germinability). Main QTLs could be localized for pre-harvest sprouting on chromosome 4AL and dormancy on chromosome 3AL. In addition, 85 Triticum aestivum cv. “Chinese Spring”-Aegilops tauschii introgression lines grown under greenhouse conditions were researched. No QTL could be found for pre-harvest sprouting but a major QTL could be detected for dormancy on chromosome 6DL.     

Identification of molecular markers associated with sugar-related traits in a <Emphasis Type="Italic">Saccharum</Emphasis> interspecific cross

The cultivated sugarcane (Saccharum spp. hybrids, 2n = 100–130) is one crop for which interspecific hybridization involving wild germplasm has provided a major breakthrough in its improvement. Few clones were used in the initial hybridization event leading to a narrow genetic base for continued cultivar development. Molecular breeding would facilitate the identification and introgression of novel alleles/genes from the wild germplasm into cultivated sugarcane. We report the identification of molecular markers associated with sugar-related traits using an F₁ population derived from a cross between S. officinarum ‘Louisiana Striped’ × S. spontaneum ‘SES 147B’, the two major progenitor species of cultivated sugarcane. Genetic linkage maps of the S. officinarum and S. spontaneum parents were produced using the AFLP, SRAP and TRAP molecular marker techniques. The mapping population was evaluated for sugar-related traits namely, Brix (B) and pol (P) at the early (E) and late (L) plant growing season in the plant cane (04) and first ratoon (05) crops (04EB, 04LB, 04LP, 05EB and 05EP). For S. officinarum, combined across all the traits, a total of 30 putative QTLs was observed with LOD scores ranging from 2.51 to 7.48. The phenotypic variation (adj. R²) explained by all QTLs per trait ranged from 22.1% (04LP) to 48.4% (04EB). For S. spontaneum, a total of 11 putative QTLs was observed with LOD scores ranging from 2.62 to 4.70 and adj. R² ranging from 9.3% (04LP) to 43.0% (04LB). Nine digenic interactions (iQTL) were observed in S. officinarum whereas only three were observed in S. spontaneum. About half of the QTLs contributed by both progenitor species were associated with effects on the trait that was contrary to expectations based on the phenotype of the parent contributing the allele. Quantitative trait loci and their associated effects were consistent across crop-years and growing seasons with very few QTLs being unique to the early season. When the data were reanalyzed using the non-parametric discriminant analysis (DA) approach, significant marker-trait associations were detected for markers that were either identical to or in the vicinity of markers previously identified using the traditional QTL approach. Discriminant analysis also pointed to previously unidentified markers some of which remained unlinked on the map. These preliminary results suggest that DA could be used as a complementary approach to traditional QTL analysis in a crop like sugarcane for which saturated linkage maps are unavailable or difficult to obtain.     

QTL identification of yield-related traits and their association with flowering and maturity in soybean

Two soybean recombinant inbred line populations, Jinpumkong 2 × SS2-2 (J × S) and Iksannamulkong × SS2-2 (I x S) showed population-specific quantitative trait loci (QTLs) for days to flowering (DF) and days to maturity (DM) and these were closely correlated within population. In the present study, we identified QTLs for six yield-related traits with simple sequence repeat markers, and biological correlations between flowering traits and yield-related traits. The yield-related traits included plant height (PH), node numbers of main stem (NNMS), pod numbers per plant (PNPP), seed numbers per pod (SNPP), 100-seed weight (SW), and seed yield per plant (SYPP). Eighteen QTLs for six yield-related traits were detected on nine chromosomes (Chrs), containing four QTLs for PH, two for NNMS, two for PNPP, three for SNPP, five for SW, and two for SYPP. Two highly significant QTLs for PH and NNMS were identified on Chr 6 (LG C2) in both populations where the major flowering gene, E1, and two DF and DM QTLs were located. One other PNPP QTL was also located on this region, explaining 12.9% of phenotypic variation. Other QTLs for yield-related traits showed population-specificity. Two significant SYPP QTLs potentially related with QTLs for SNPP and PNPP were found on the same loci of Chrs 8 (Satt390) and 10 (Sat_108). Also, highly significant positive phenotypic correlations (P < 0.01) were found between DF with PH, NNMS, PNPP, and SYPP in both populations, while flowering was negatively correlated with SNPP and SW in the J × S (P < 0.05) and I × S (P < 0.01) populations. Similar results were also shown between DM and yield-related traits, except for one SW. These QTLs identified may be useful for marker-assisted selection by soybean breeders.     

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.     

Genetic mapping of quantitative trait loci for fiber quality and yield trait by RIL approach in Upland cotton

The improvement of cotton fiber quality has become more important because of changes in spinning technology. Stable quantitative trait loci (QTLs) for fiber quality will enable molecular marker-assisted selection to improve fiber quality of future cotton cultivars. A simple sequence repeat (SSR) genetic linkage map consisting of 156 loci covering 1,024.4 cM was constructed using a series of recombinant inbred lines (RIL) developed from an F₂ population of an Upland cotton (Gossypium hirsutum L.) cross 7235 × TM-1. Phenotypic data were collected at Nanjing and Guanyun County in 2002 and 2003 for 5 fiber quality and 6 yield traits. We found 25 major QTLs (LOD ≥ 3.0) and 28 putative QTLs (2.0 < LOD < 3.0) for fiber quality and yield components in two or four environments independently. Among the 25 QTLs with LOD ≥ 3, we found 4 QTLs with large effects on fiber quality and 7 QTLs with large effects on yield components. The most important chromosome D8 in the present study was densely populated with markers and QTLs, in which 36 SSR loci within a chromosomal region of 72.7 cM and 9 QTLs for 8 traits were detected.     

Analysis of contributors to grain yield in wheat at the individual quantitative trait locus level

In wheat, strong genetic correlations have been found between grain yield (GY) and tiller number per plant (TN), fertile spikelet number per spike (FSN), kernel number per spike (KN) and thousand‐kernel weight (TKW). To investigate their genetic relationships at the individual quantitative trait locus (QTL) level, we performed both normal and multivariate conditional QTL analysis based on two recombinant inbred lines (RILs) populations. A total of 79 and 48 normal QTLs were identified in the International Triticeae Mapping Initiative (ITMI)/SHW‐L1 × Chuanmai 32 (SC) populations, respectively, as well as 55 and 35 conditional QTLs. Thirty‐two QTL clusters in the ITMI population and 18 QTL clusters in the SC population explained 0.9%–46.2% of phenotypic variance for two to eight traits. A comparison between the normal and conditional QTL mapping analyses indicated that FSN made the smallest contribution to GY among the four GY components that were considered at the QTL level. The effects of TN, KN and TKW on GY were stronger at the QTL level.     

Molecular mapping and identification of QTL's associated to oat crown rust partial resistance

Summary Molecular mapping is a promising strategy for studying and understanding traits with complex genetic control, such as partial resistance to oat crown rust. The objectives of this research were to develop molecular maps from the progenies of the cross UFRGS7 (susceptible) × UFRGS910906 (partially resistant) and to identify QTLs (quantitative trait loci) associated to partial resistance to oat crown rust in two generations of that population.DNA of 86 genotypes of the F₂ and 90 genotypes of the F₆ UFRGS7 × UFRGS910906 population were used to generate AFLP markers. Molecular maps were constructed using Mapmaker Exp. 3.0 and QTLs for partial resistance to oat crown rust were identified with Mapmaker/QTL software. Five hundred and fifty seven markers in the F₂ and 243 markers in the F₆ generations were identified. The F₂ map integrated 250 markers in 37 linkage groups. The F₆ map integrated 86 markers in 17 linkage groups.Five QTLs were identified for partial resistance to oat crown rust in the F₂ generation and three QTLs in the F₆. The QTL identified on F₆ through the PaaaMctt340 AFLP marker showed consistency across two environments and two generations (F₄ and F₆), and appear to have potential for marker-assisted selection in oat.     

大豆耐旱选择群体QTL定位

以红丰11为轮回亲本、Clark为供体亲本构建回交群体进行耐旱性鉴定,对获得选择群体进行全基因组SSR标记扫描,计算供体基因型导入频率,利用卡方测验检测偏分离SSR位点,并结合GGT软件对各连锁群分析, 对5个耐旱相关性状进行QTL定位。以卡方测验检测到23个SSR偏分离位点(超导入),分布于10条连锁群。方差分析表明,8个叶片持水能力QTL分布于A₁、B₁、C₂、E、L和N连锁群;9个根长QTL分布于C₂、F、G和I连锁群;11个根干重QTL分布于A₂、B₁、B₂、E、F、K、L、M和O连锁群;12个产量QTL分布于B₁、D₁a、E、F、G、I、L、M和O连锁群;7个生物量QTL分布于E、F、G、K、L和N连锁群。在E连锁群的Sat_136位点,对于叶片持水能力、根干重、产量和生物量具有一致性;在F连锁群的GMRUBP位点,对于根干重和生物量具有一致性,Satt586位点,对于根长、根干重和产量具有一致性;在K连锁群的Satt167位点,对于根干重和生物量具有一致性,SOYPRP1位点,对于根长和生物量具有一致性;在L连锁群的Satt398位点,对于根长和产量具有一致性,Satt694位点对于叶片持水能力和生物量具有一致性;在M连锁群的GMSL514位点,对于根干重和产量具有一致性;以上位点均与卡方测验检测到的“超导入”位点具有一致性。经过供体等位基因卡方测验和耐旱QTL定位,共检测到33个QTL,其中有17个同时被检测到。这些位点可能是控制大豆耐旱性的重要位点。     

Identification of QTLs controlling rice drought tolerance at seedling stage in hydroponic culture

‘Drought avoidance’ and ‘drought tolerance’ are two mechanisms by which plants adapt under water stress. These mechanisms are difficult to evaluate separately in field experiments. Using hydroponic culture, we studied the genetic control of drought tolerance in rice (Oryza sativa L.) without the effect of drought avoidance. A backcross inbred population of ‘Akihikari’ (lowland cultivar) × ‘IRAT109’ (upland cultivar) with 106 lines was cultured with (stressed condition) and without (non-stressed condition) polyethylene glycol (PEG) at seedling stage. The relative growth rate (RGR), specific water use (SWU), and water use efficiency (WUE) showed significant genotype × environment interactions with or without PEG, indicating that each line responded differently to water stress. A quantitative trait locus (QTL) analysis revealed that these interactions were QTL specific. A total of three QTLs on chromosomes 2, 4, and 7 were detected for RGR. The QTL on chromosome 7 had a constant effect across environments, while the QTL on chromosome 4 had an effect only under non-stressed condition and that on chromosome 2 only under stressed condition. The stress-specific QTL on chromosome 2 was not co-located with any QTLs for root system depth previously reported from the same mapping population. However, this QTL was co-located with a stress-specific QTL for SWU, suggesting that the control of transpiration was relevant to dry matter production under drought. We concluded that PEG-treated hydroponic culture is very effective for use in genetic analyses of drought tolerance at seedling stage.     

QTL-based analysis of genotype-by-environment interaction for grain yield of rice in stress and non-stress environments

Use of DNA-based markers can accelerate cultivar development in variable cultivation environments since, in contrast to phenotype, DNA markers are environment-independent. In an effort to elucidate the genetic basis of genotype-by-environment interaction (G × E) for yield of rice (Oryza sativa L.), the associations between 139 AFLP markers and grain yield were determined for rice grown in fresh water (EC of 0.65 dS m⁻¹) and saline conditions (EC of 4–8 dS m⁻¹) with 0 kg ha⁻¹ or 100 kg ha⁻¹ nitrogen fertilizer in the years 2000 and 2001. A population of recombinant inbred lines of rice, developed from an IR29 × Pokkali cross, was used in the study. Both genotype × salinity and genotype × nitrogen level interactions were significant, with the genotype × salinity interaction being stronger. Through multiple regression analysis using a stepwise procedure for selecting markers, 36 markers were detected for grain yield in the four test conditions and of these 28 were detected in only one test condition implying strong environmental specificity for yield QTL expression. However, the fact that eight QTLs were detected in more than one test condition points to the existence of wide-adaptability genes in this cross. Markers with significant associations with yield explained between 37% and 48% of the yield variation in each test condition. Superior genotypes of rice were identified in all four test conditions based on their marker signatures. Furthermore, across N fertilizer regimes, yield predicted from summed additive effects of QTLs were significantly correlated with observed yield in the same year and across years. Thus marker-assisted selection can help breeders overcome the problem of low selection efficiency encountered during phenotypic selection for yield in stress environments.     

Identification of Quantitative Trait Loci Associated with Aluminum Tolerance in Rice (Oryza Sativa L.)

Summary Quantitative trait loci (QTL) analysis for Al tolerance was performed in rice using a mapping population of 98 BC₁F₁₀ lines (backcross inbred lines: BILs), derived from a cross of Al-tolerant cultivar of rice (Oryza sativa L. cv. Nipponbare) and Al-sensitive cultivar (cv. Kasalath). Three characters related to Al tolerance, including root elongation under non-stress conditions (CRE), root elongation under Al stress (SRE) and the relative root elongation (RRE) under Al stress versus non-stress conditions, were evaluated for the BILs and the parents at seedling stage. A total of seven QTLs for the three traits were identified. Among them, three putative QTLs for CRE (qCRE-6, qCRE-8 and qCRE-9) were mapped on chromosomes 6, 8 and 9, respectively. One QTL for SRE (qSRE-4) was identified on chromosome 4. Three QTLs (qRRE-5, qRRE-9 and qRRE-10) for RRE were detected on chromosomes 5, 9, 10 and accounted for 9.7–11.8% of total phenotypic variation. Interestingly, the QTL qRRE-5 appears to be syntenic with the genomic region carrying a major Al tolerance gene on chromosome 6 of maize. Another QTL, qRRE-9, appears to be similar among different rice populations, while qRRE-10 is unique in the BIL population. The common QTLs for CRE and RRE indicate that candidate genes conferring Al tolerance in the rice chromosome 9 may be associated with root growth rates. The existence of QTLs for Al tolerance was confirmed in substitution lines for corresponding chromosomal segments. These results also provide the possibilities of enhancing Al tolerance in rice through using marker-assisted selection (MAS) and pyramiding QTLs.     

A quantitative trait locus influencing tolerance to Phytophthora root rot in the soybean cultivar ‘Conrad’

‘Conrad’, a soybean cultivar tolerant to Phytophthora root rot (PRR), and ‘OX760-6-1’, a breeding line with low tolerance to PRR, were crossed. F₂ derived recombinant inbred lines were advanced to F₆ to generate a population through single-seed descent. This population was used to identify quantitative trait loci (QTLs) influencing PRR tolerance in ‘Conrad’. A total of 99 simple sequence repeat (SSR), or microsatellite, markers that were polymorphic and clearly segregated in the F₆ mapping population were used for QTL detection. Based on the data of PRR in the field at two planting locations, Woodslee and Weaver, for the years 2000 and 2001, one putative QTL, designated as Qsatt414-596, was detected using MapMaker/QTL. Qsatt414-596 was flanked by two SSR markers from the linkage group MLG J, Satt414 and Satt596. Satt414 and Satt596 were also detected to be significantly (P < 0.005) associated with PRR using the SAS GLM procedure and were estimated to explain 13.7% and 21.5% of the total phenotypic variance, respectively.     

Inheritance of foliar zingiberene contents and their relationship to trichome densities and whitefly resistance in tomatoes

The sesquiterpene zingiberene, present in leaf glandular trichomes, is reportedly responsible for the high level of arthropod resistance found in Lycopersicon hirsutum var. hirsutum. This paper reports on the inheritance of zingiberene contents and of the various types of glandular trichomes in the interspecific cross L. esculentum × Lycopersicon hirsutum var. hirsutum. Plants of L. esculentum ‘TOM-556’ (= P₁), L. hirsutum var. hirsutum ‘PI-127826’(= P₂), F₁ (P₁ × P₂) and F₂ (P₁ ×P₂) were evaluated for zingiberene contents and densitities of and glandular (types I, IV, VI and VII) trichomes. Broad sense heritabilities were high for all traits studied (0.678, 0.831, 0.996, 0.799 and 0.717 respectively for zingiberene and trichome types I, IV, VI, VII). There were significant positive genetic correlations between zingiberene contents and densities of trichomes types IV, VI and VII. Inheritance of zingiberenecontents can be explained mostly by the action of a single major locus, inwhich the allele from L. hirsutum that conditions high content is incompletely recessive over the allele from L. esculentum. Action of an incompleteley recessive allele in one major locus appears to be evident for densities of trichome types IV, VI and VII, but there is also evidence of the action of other epistatic loci for types IV and VI. F₂ genotypes selected for high zingiberene levels showed higher levels of resistance to the silverleaf whitefly Bemisia argentifolii than L. esculentum ‘TOM-556’, levels that were comparable those found in L. hirsutum var. hirsutum ‘PI-127826’ and other whitefly resistant accessions. This revised version was published online in August 2006 with corrections to the Cover Date.     

Quantitative trait loci controlling rice seed germination under salt stress

Salt tolerance of rice (Oryza sativa L.) at the seed germination stage is one of the major determinants for the stable stand establishment in salinity soil. One population of recombinant inbred lines (RILs, F_2:9), derived from a cross between a japonica rice landrace tolerant to salt stress and a sensitive indica rice variety, was used to determine the germination traits including imbibition rate and germination percentage under control (water) and salt stress (100 mM NaCl) for 10 days at 30 °C. The multiple interval mapping (MIM) were applied to conduct QTL for the traits. The results showed that seed germination was a quantitative trait controlled by several genes, and strongly affected by salt stress. A total of 16 QTLs were detected in this study, and each QTL could explain 4.6–43.7% of the total phenotypic variance. The expression of these QTLs might be developmentally regulated and growth stage-specific. In addition, only one digenic interaction was detected under salt stress, showing small effect on germination percentage with R² 2.7%. Among sixteen QTLs detected in this study, four were major QTLs with R² > 30%, and some novel alleles of salt tolerance genes in rice. The results demonstrated that the japonica rice Jiucaiqing is a good source of gene(s) for salt tolerance and the major or minor QTLs identified could be used to improve the salt tolerance by marker-assisted selection (MAS) in rice.     

QTL mapping of volatile compounds in ripe apples detected by proton transfer reaction-mass spectrometry

The availability of genetic linkage maps enables the detection and analysis of QTLs contributing to quality traits of the genotype. Proton Transfer Reaction Mass Spectrometry (PTR-MS), a relatively novel spectrometric technique, has been applied to measure the headspace composition of the Volatile Organic Compounds (VOCs) emitted by apple fruit genotypes of the progeny ‘Fiesta’ × ‘Discovery’. Fruit samples were characterised by their PTR-MS spectra normalised to total area. QTL analysis for all PTR-MS peaks was carried out and 10 genomic regions associated with the peaks at m/z = 28, 43, 57, 61, 103, 115 and 145 were identified (LOD > 2.5). We show that it is possible to find quantitative trait loci (QTLs) related to PTR-MS characterisation of the headspace composition of single whole apple fruits indicating the presence of a link between molecular characterisation and PTR-MS data. We provide tentative information on the metabolites related to the detected QTLs based on available chemical information. A relation between apple skin colour and peaks related to carbonyl compounds was established. The two authors contributed equally to this work.     

Molecular mapping of genomic regions harboring QTLs for stalk rot resistance in sorghum

Stalk rot, also called as charcoal rot in India, caused by Macrophomina phaseolina, is an economically important, soil borne disease in major sorghum growing areas across the world. A population of F₉ generation recombinant inbred lines (RILs), derived from IS22380 (susceptible) × E36-1 (resistant), along with parents were phenotyped in sick plots at two locations (Dharwad and Bijapur, Karnataka, India). A total of 85 polymorphic marker loci (62 nuclear and 4 genic SSRs, 19 RAPDs) was available for the construction of genetic map, spanning 650.3 cM in all the ten linkage groups. Analysis with QTL Cartographer (2.5b), adopting composite interval mapping method (LOD > 2.0) at both locations, revealed 5 QTLs at Dharwad and 4 QTLs at Bijapur locations for the component traits of charcoal rot disease resistance. QTLs for number of internodes crossed, length of infection and per cent lodging accounted for 31.83, 10.76 and 18.90 per cent at Dharwad location and 14.87, 10.47 and 26.44 per cent phenotypic variability at Bijapur location, respectively. The QTLs for number of internodes crossed by the rot, length of infection and percent lodging were common across two locations. These QTLs, consistent over environments for the component traits, are likely to assist in marker-assisted selection (MAS) for charcoal rot resistance in sorghum.     

Validating a segment on the short arm of chromosome 6 responsible for genetic variation in the hull silicon content and yield traits of rice

Rice is a typical silicon-accumulating plant and the beneficial effect of silicon on rice has long been recognized. In a previous study using 244 recombinant inbred lines (RILs) of an indica rice cross, Zhenshan 97B/Milyang 46 grown in 2003, four QTLs were detected for hull silicon content. QTL qHUS-6 had the largest effect among these, and the same interval also had significant effects on yield traits in the same population. The primary objective of this study was to validate the QTL effect in this region on HUS and yield traits. The same RIL population and another RIL population of lower heterogeneity were grown in 2004. QTL qHUS-6 was found to have significant additive effects on hull silicon content with a consistent direction in the two populations. From a residual heterozygous line selected from RILs of the same cross, 15 F_2:3 lines that differed only in a 2.15-Mb segment extending from RM587 to RM6119 on the short arm of chromosome 6 were derived. In these lines, qHUS-6 displayed a major effect, so did QTLs for yield traits previously detected in the same region. Two more QTLs for HUS detected in 2003, qHUS-1-1 and qHUS-1-2, also had consistent effects in the Zhenshan 97B/Milyang 46 RIL population in 2004. Thus this study verified three candidate regions for fine mapping HUS QTLs and determining the genetic relationship between silicon content and yield traits in rice.     

QTL mapping for economic traits based on a dense genetic map of cotton with PCR-based markers using the interspecific cross of Gossypium hirsutum × Gossypium barbadense

A high-density molecular marker linkage map of cotton based entirely on polymerase chain reaction-based markers is useful for a marker-assisted breeding program. Four kinds of markers—simple sequence repeats (SSRs), sequence-related amplified polymorphism (SRAP), random amplified polymorphic DNA (RAPD), and retrotransposon-microsatellite amplified polymorphism (REMAP)—were used to assay an F₂ population from a cross between “Handan208” (Gossypium hirsutum) and “Pima90” (Gossypium barbadense). Sixty-nine F₂ plants were used for map construction using 834 SSRs, 437 SRAPs, 107 RAPDs, and 16 REMAPs. Linkage analysis revealed that 1,029 loci could be mapped to 26 linkage groups that extended for 5,472.3 cM, with an average distance between 2 loci of 5.32 cM. The corresponding 69 F_2:3 families were grown, arranged in two replicates, and scored for eight phenotypes. Quantitative trait loci (QTL) analysis was performed by means of composite interval mapping using WinQtlCart ver 2.0. A total of 52 distinct QTLs were detected: 4 QTLs for lint index, 8 for seed index, 11 for lint yield, 4 for seed cotton yield, 9 for number of seed per boll, 3 for fiber strength, 5 for fiber length, and 8 for micronaire value. The present map and QTL analysis may provide a useful tool for breeders to transfer desirable traits from G. barbadense to the mainly cultivated species, G. hirsutum.     

Mapping QTLs of traits contributing to yield and analysis of genetic effects in tetraploid cotton

Fiber yield and yield components – including lint index (LI), seed index (SI), lint yield (LY), seed cotton yield (SCY) and number of seeds per boll (NSPB) – were investigated on the farm of Huazhong Agricultural University in a population of 69 F₂ individuals and corresponding F_2:3 families derived from a cross between high-fiber-yield Gossypium hirsutum CV Handan 208 and a low-fiber-yield Gossypium barbadense CV Pima 90. On the basis of the genetic map constructed previously from the same population by Lin et al. (Plant Breed., 2005), quantitative trait locus (QTL) analysis was performed with the software QTL Cartographer V2.0 using composite interval mapping method (LOD ≥ 3.0). A total of 21 QTLs were identified, which were located in 15 linkage groups. The number of QTLs per trait ranged from one to seven. Of these QTLs detected, one affecting LI explained 24.3% of phenotypic variation (PV), five influencing SI explained 16.15–39.21% of PV, seven controlling LY explained 13.01–28.35% of PV, and two controlling SCY explained 22.76 and 39.97% of PV, respectively. Simultaneously, the detected six QTLs for NSPB were located on five linkage groups, which individually explained 28.01–38.32% of the total phenotypic variation. The results would give breeders further insight into the genetic basis of fiber yield.     

QTL mapping of bio-energy related traits in Sorghum

Plant height (PHT), stem and leaf fresh weight (SLFW), juice weight (JW) and sugar content of stem (Brix) are important traits for biofuel production in sweet Sorghum. QTL analysis of PHT, SLFW, JW and Brix was conducted with composite interval mapping using F₂ and F_2:3 populations derived from the cross between grain Sorghum (Shihong137) × sweet Sorghum (L-Tian). Three QTLs controlling PHT were mapped on SBI-01, SBI-07 and SBI-09 under four different environments. These QTLs could explain 10.16 to 45.29% of the phenotypic variance. Two major effect QTLs on SBI-07 and SBI-09 were consistently detected under four environments. Eight QTLs controlling SLFW were mapped across three environments and accounted for 5.49–25.36% of the phenotypic variance. One major QTL on SBI-09 located between marker Sb5-206 and SbAGE03 was observed under three environments. Four QTLs controlling Brix were identified under two environments and accounted for 11.03–17.65% of the phenotypic variance. Six QTLs controlling JW were detected under two environments, and explained 6.63–23.56% of the phenotypic variance. QTLs for JW on SBI-07 and SBI-09 were consistent in two environments showing higher environmental stability. In addition, two chromosome regions on SBI-07 and SBI-09 were identified in our study having major effect on PHT, SFLW and JW. The results would be useful for the genetic improvement of sweet Sorghum to be used for biofuel production.