Identification of QTL for sugar-related traits in a sweet × grain sorghum (<Emphasis Type="Italic">Sorghum bicolor</Emphasis> L. Moench) recombinant inbred population

QTL for stem sugar-related and other agronomic traits were identified in a converted sweet (R9188) × grain (R9403463-2-1) sorghum population. QTL analyses were conducted using phenotypic data for 11 traits measured in two field experiments and a genetic map comprising 228 SSR and AFLP markers grouped into 16 linkage groups, of which 11 could be assigned to the 10 sorghum chromosomes (SBI-01 to SBI-10). QTL were identified for all traits and were generally co-located to five locations (SBI-01, SBI-03, SBI-05, SBI-06 and SBI-10). QTL alleles from R9188 were detected for increased sucrose content and sugar content on SBI-01, SBI-05 and SBI-06. R9188 also contributed QTL alleles for increased Brix on SBI-05 and SBI-06, and increased sugar content on SBI-03. QTL alleles from R9403463-2-1 were found for increased sucrose content and sucrose yield on SBI-10, and increased glucose content on SBI-07. QTL alleles for increased height, later flowering and greater total dry matter yield were located on SBI-01 of R9403463-2-1, and SBI-06 of R9188. QTL alleles for increased grain yield from both R9403463-2-1 and R9188 were found on SBI-03. As an increase in stem sugars is an important objective in sweet sorghum breeding, the QTL identified in this study could be further investigated for use in marker-assisted selection of sweet sorghum.     

Study of strategies for selecting quantitative trait locus mapping procedures by computer simulation

Along with the development and integration of molecular genetics and quantitative genetics, many quantitative trait locus (QTL) mapping studies have been conducted using different mapping populations in various crop species. Existing QTLs can be used for marker-assisted breeding and map-based cloning, whereas the false-positive QTLs are no use. The purpose of this study is to evaluate the suitability of different mapping procedures for data from different genetic models. In this study, four types of recombinant inbred lines (RILs) with different genetic models, viz. additive QTLs (Model I), additive and epistatic QTLs (Model II), additive QTLs and QTL × environment interaction (Model III), additive, epistatic QTLs and QTL × environment interaction (Model IV), were simulated by computer. Six types of QTL mapping procedures, viz. CIM, MIMF, MIMR, ICIM, MQM and NWIM, on four kinds of QTL mapping software, viz. WinQTL Cartographer Version 2.5, IciMapping Version 2.0, MapQTL Version 5.0 and QTLnetwork Version 2.0, were used for screening QTLs of the simulated RILs. The results showed that different mapping procedures have different suitability for different genetic models. CIM and MQM can only screen Model I data. MIMR, MIMF and ICIM can only screen Model I and Model II data. NWIM can screen all four models’ data. It can be concluded that different genetic models’ data have different most suitable mapping procedures. In practical experiments where the genetic model of the data is unknown, a multiple model mapping strategy should be used, that is a full model scanning with complex model procedure followed by verification with other procedures corresponding to the scanning results.     

Mapping of QTLs governing agronomic and yield traits in chickpea

Chickpea is one of the most important leguminous cool season food crops, cultivated prevalently in South Asia and Middle East. The main objective of this study was to identify quantitative trait loci (QTLs) associated with seven agronomic and yield traits in two recombinant inbred line populations of chickpea derived from the crosses JG62 × Vijay (JV population) and Vijay × ICC4958 (VI population) from at least three environments. Single locus QTL analysis involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated traits to detect pleiotropic QTLs. Two-locus analysis was conducted to identify the main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions. Through CIM analysis, a total of 106 significant QTLs (41 in JV and 65 in VI populations) were identified for the seven traits, of which one QTL each for plant height and days to maturity was common in both the populations. Six pleiotropic QTLs that were consistent over the environments were also identified. LG2 in JV and LG1a in VI contained at least one QTL for each trait. Hence, concentrating on these LGs in molecular breeding programs is most likely to bring simultaneous improvement in these traits.     

QTL for fibre-related traits in grain × sweet sorghum as a tool for the enhancement of sorghum as a biomass crop

Compared to maize and temperate grasses, sorghum has received less attention in terms of improving cell wall components. The objectives of this study were to identify quantitative trait loci (QTL) with main effects, epistatic and pleiotropic effects along with QTL × environment (QE) interactions controlling fibre-related traits in sorghum. Neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), cellulose, hemicellulose, fresh leaf mass, stripped stalk mass, dry stalk mass, fresh biomass and dry biomass were analysed from a population of 188 grain × sweet sorghum recombinant inbred lines. A genetic map consisting of 157 DNA markers was constructed, and QTL were detected using composite interval mapping (CIM). CIM detected more than 5 additive QTL per trait explaining 7.1–24.7% of the phenotypic variation. Abundant co-localization of these QTL was observed across all chromosomes, and the highest cluster was identified on chromosome 6. Searching for candidate genes using the confidence interval of our QTL clusters reveals that these clusters might comprise a set of genes that are tightly linked. Some QTL showed multiple effects; however, the allele for each trait was favouring the parent with the increasing effect. QE interactions were observed for QTL showing multiple effects. Additive × additive interaction was observed for 7 out of 10 traits, indicating the importance of epistatic analysis. However, the phenotypic variation explained by digenic interactions was lower compared to the individual QTL. Our results indicate that various genetic components contribute to fibre-related traits and should be considered during the enhancement of sorghum for lignocellulosic biomass.     

Dynamic gene action at QTLs for resistance to Setosphaeria turcica in maize

 Cultivars with quantitative resistance are widely used to control Setosphaeria turcica (Luttrell) Leonard & Suggs, the causal organism of northern corn leaf blight (NCLB). Here the effectiveness of quantitative trait loci (QTLs) for NCLB resistance was investigated over the course of host plant development in inoculated field trials. A population of 194–256 F_2:3 lines derived from a cross between a susceptible Italian (Lo951) and a highly resistant African inbred line (CML202) was tested in three environments in Kenya. The traits assessed were the incubation period (IP), the percentage disease severity (DS 1 to 5, taken biweekly), and the area under the disease progress curve (AUDPC). Considering all resistance traits and environments, a total of 19 putative QTLs were detected by composite interval mapping using a linkage map with 110 RFLP markers. In the combined analysis across environments, nine QTLs were significant (LOD >3.0) for DS 3, recorded around flowering time, explaining 71% of the genotypic variance. Four of these nine QTLs displayed significant (P<0.05) QTL×environment (QTL×E) interaction. Most QTLs were already significant in the juvenile stage (IP) and became less effective after flowering. Across environments, three QTLs conditioned adult-plant resistance, in the sense that they were only significant after flowering. Six QTL alleles on chromosomes 2, 4, 5, 8, and 9 of CML202 should be useful for marker-assisted backcrossing. Received: 24 August 1998 / Accepted: 29 September 1998     

Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum

The identification of quantitative trait loci (QTLs) affecting agronomically important traits enable to understand their underlying genetic mechanisms and genetic basis of their complex interactions. The aim of the present study was to detect QTLs for 12 agronomic traits related to staygreen, plant early development, grain yield and its components, and some growth characters by analyzing replicated phenotypic datasets from three crop seasons, using the population of 168 F₇ RILs of the cross 296B × IS18551. In addition, we report mapping of a subset of genic-microsatellite markers. A linkage map was constructed with 152 marker loci comprising 149 microsatellites (100 genomic- and 49 genic-microsatellites) and three morphological markers. QTL analysis was performed by using MQM approach. Forty-nine QTLs were detected, across environments or in individual environments, with 1–9 QTLs for each trait. Individual QTL accounted for 5.2–50.4% of phenotypic variance. Several genomic regions affected multiple traits, suggesting the phenomenon of pleiotropy or tight linkage. Stable QTLs were identified for studied traits across different environments, and genetic backgrounds by comparing the QTLs in the study with previously reported QTLs in sorghum. Of the 49 mapped genic-markers, 18 were detected associating either closely or exactly as the QTL positions of agronomic traits. EST marker Dsenhsbm19, coding for a key regulator (EIL-1) of ethylene biosynthesis, was identified co-located with the QTLs for plant early development and staygreen trait, a probable candidate gene for these traits. Similarly, such exact co-locations between EST markers and QTLs were observed in four other instances. Collectively, the QTLs/markers identified in the study are likely candidates for improving the sorghum performance through MAS and map-based gene isolations.     

Comparative analysis of QTLs affecting plant height and flowering among closely-related diploid and polyploid genomes.

Quantitative trait loci (QTLs) affecting plant height and flowering were studied in the two Saccharum species from which modern sugarcane cultivars are derived. Two segregating populations derived from interspecific crosses between Saccharum officinarum and Saccharum spontaneum were genotyped with 735 DNA markers. Among the 65 significant associations found between these two traits and DNA markers, 35 of the loci were linked to sugarcane genetic maps and 30 were unlinked DNA markers. Twenty-one of the 35 mapped QTLs were clustered in eight genomic regions of six sugarcane homologous groups. Some of these could be divergent alleles at homologous loci, making the actual number of genes implicated in these traits much less than 35. Four QTL clusters controlling plant height in sugarcane corresponded closely to four of the six plant-height QTLs previously mapped in sorghum. One QTL controlling flowering in sugarcane corresponded to one of three flowering QTLs mapped in sorghum. The correspondence in locations of QTLs affecting plant height and flowering in sugarcane and sorghum reinforce the notion that the simple sorghum genome is a valuable "template" for molecular dissection of the much more complex sugarcane genome.     

Mapping QTLs and meta-QTLs for two inflorescence architecture traits in multiple maize populations under different watering environments

Drought significantly affects the architectural development of maize inflorescence, which leads to massive losses in grain yield. However, the genetic mechanism for traits involved in inflorescence architecture in different watering environments, remains poorly understood in maize. In this study, 19 QTLs for tassel primary branch number (TBN) and ear number per plant (EN) were detected in 2 F_2:3 populations under both well-watered and water-stressed environments by single environment mapping with composite interval mapping (CIM); 11/19 QTLs were detected under water-stressed environments. Moreover, 21 QTLs were identified in the 2 F_2:3 populations by joint analysis of all environments with a mixed linear model based on composite interval mapping (MCIM), 11 QTLs were involved in QTL × environment interactions, seven epistatic interactions were identified with additive by additive/dominance effects. Remarkably, 12 stable QTLs (sQTLs) were simultaneously detected by single environment mapping with CIM and joint analysis through MCIM, which were concentrated in ten bins across the chromosomes: 1.05_1.07, 1.08_1.10, 2.01_2.04, 3.01, 4.06, 4.09, 5.06_5.07, 6.05, 7.00, and 7.04 regions. Twenty meta-QTLs (mQTLs) were detected across 19 populations under 51 watering environments using a meta-analysis, and 34 candidate genes were predicted in corresponding mQTLs regions to be involved in the regulation of inflorescence development and drought resistance. Therefore, these results provide valuable information for finding quantitative trait genes and to reveal the genetic mechanisms responsible for TBN and EN under different watering environments. Furthermore, alleles for TBN and EN provide useful targets for marker-assisted selection to generate high-yielding maize varieties.     

Identification of QTL for agronomic traits of importance for olive breeding

Development of methodologies for early selection is one of the most important goals of olive breeding programs at present. In this context, the identification of molecular markers associated with beneficial alleles could allow the development of marker-assisted selection (MAS) strategies in olive breeding programs. Fruit-related and plant vigor traits, which are of key importance for olive selection and breeding, were analyzed during two seasons in a progeny derived from the cross ‘Picual’ × ‘Arbequina.’ Quantitative trait loci (QTL) analyses were performed using MapQTL 4.0. A total of 22 putative QTLs were identified in the map of ‘Arbequina.’ QTLs clustered in linkage groups (LG) 1, 10 and 17. QTLs for oil-related traits located in LG 1 and 10 explained around 20–30 % of the phenotypic variability depending on the season and the trait. QTL for moisture-related traits were detected in LG 1, 10 and 17, and QTLs for the ratio pulp to stone were identified in LG 10 and 17 explaining around 15–20 %. Interaction between QTLs for the same trait was investigated. The significance of these results was discussed considering the co-localization of QTLs and Pearson correlations among traits. Five additional QTLs were detected in the map of ‘Picual.’ Four of them clustered in LG 17 indicating the presence of a QTL for fruit weight explaining around 12.7–15.2 % of the variability. Additionally, a QTL for trunk diameter was detected in LG 14 explaining 16 % of the variation. These results represent an important step toward the application of MAS in olive breeding programs.     

Impact of epistasis and QTL×environment interaction on the developmental behavior of plant height in rice (Oryza sativa L.)

QTLs with epistatic effects and environmental interaction effects for the developmental behavior of plant height in rice were studied by conventional and conditional methods for quantitative trait loci (QTLs) by mapping with a doubled-haploid population of 123 lines from IR64/Azucena in three environments. The results showed that epistatic effects were important and most epistasis could be detected only by conditional QTL mapping, while most non–epistatic QTLs could be detected by both conventional and conditional methods. Many modificative QTLs showed only epistatic effects without their own additive effects at some stages. QTL×environment (QE) interaction effects were detected more often than QTL main effects for plant-height behavior, which might indicate that gene expression could be greatly affected by the environment. No QTLs had effects during the whole of ontogeny. Conditional QTL mapping might be a valid way to reveal dynamic gene expression for the development of quantitative traits, especially for epistatic effects. Received: 19 May 2000 / Accepted: 27 October 2000     

Quantitative trait loci for agronomic traits in an elite barley population for Mediterranean conditions

Advances in plant breeding through marker-assisted selection (MAS) are only possible when genes or quantitative trait loci (QTLs) can contribute to the improvement of elite germplasm. A population of recombinant inbred lines (RILs) was developed for one of the best crosses of the Spanish National Barley Breeding Program, between two six-row winter barley cultivars Orria and Plaisant. The objective of this study was to identify favourable QTLs for agronomic traits in this population, which may help to optimise breeding strategies for these and other elite materials for the Mediterranean region. A genetic linkage map was developed for 217 RILs, using 382 single nucleotide polymorphism markers, selected from the barley oligonucleotide pool assay BOPA1 and two genes. A subset of 112 RILs was evaluated for several agronomic traits over a period of 2 years at three locations, Lleida and Zaragoza (Spain) and Fiorenzuola d’Arda (Italy), for a total of five field trials. An important segregation distortion occurred during population development in the region surrounding the VrnH1 locus. A QTL for grain yield and length of growth cycle was also found at this locus, apparently linked to a differential response of the VrnH1 alleles to temperature. A total of 33 QTLs was detected, most of them for important breeding targets such as plant height and thousand-grain weight. QTL × environment interactions were prevalent for most of the QTLs detected, although most interactions were of a quantitative nature. Therefore, QTLs suitable for MAS for most traits were identified.     

Cytonuclear epistatic quantitative trait locus mapping for plant height and ear height in maize

Plant height (PH) and ear height (EH) are important traits in maize (Zea mays L.) breeding. Previous research has indicated that these traits are influenced by quantitative trait loci (QTL). However, previous studies attempting to identify the genetic bases of PH and EH have ignored the possibility that cytoplasmic effects and cytonuclear interactions may influence these traits. The objectives of this study were to identify the cytonuclear epistatic QTL and to evaluate the contributions of cytoplasm and QTL × cytoplasm interactions to phenotypic variation of PH and EH. A reciprocal mating design was conducted to generate F2 mapping populations comprising 120 F2 plants from the direct cross (JB × Y53) and 120 F2 plants from the reciprocal cross (Y53 × JB). F2:3 mapping populations were further generated with 91 direct F2:3 families and 120 reciprocal F2:3 families (ten plants per family). The PH and EH of the above F2 and F2:3 mapping populations were evaluated in the same field at the same experimental station in 2007 and 2008. A genetic linkage map with 154 microsatellite markers was constructed, which covered 1,735.0 cM of the maize genome with an average marker spacing of 11.3 cM. A joint-analysis method incorporating the cytonuclear interaction mapping approach was proposed and performed to detect cytonuclear interacting QTL affecting PH and EH. We identified six cytonuclear epistatic QTL affecting PH and five affecting EH. The average phenotypic variance explained by the genetic components of the QTL × cytoplasm interaction for each QTL was 18 % for PH and 9 % for EH. In addition, we observed cytoplasmic effects contributing substantially to phenotypic variance, reaching 9 and 40 % of the phenotypic contributions to PH and EH, respectively.     

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.     

Quantitative trait loci for growing degree days to flowering and photoperiod response in Sunflower (Helianthus annuus L.)

The number of days from seedling emergence to flowering (DTF) is a major consideration in sunflower breeding programs. This is a complex trait determined by the genotype, environmental conditions and interactions. Photoperiod and temperature have major effects on DTF and could be important sources of genotype× environment interaction. The objectives of this study were to locate quantitative trait loci (QTLs) associated with growing degree days (GDD) to flowering and photoperiod (PP) response in an elite sunflower population. Two hundred and thirty five F₂-generation plants and their F_2:3 and F_2:4 progenies of a single-cross population of two divergent inbred lines were evaluated in six environments (locations, years and sowing dates) with photoperiods known to elicit a PP response between the inbred lines. Detection of QTLs was facilitated with a genetic linkage map of 205 RFLP loci and composite interval mapping. The 205 restriction fragment length polymorphism (RFLP) loci covered 1380 cM and were arranged in 17 linkage groups, which is the haploid number of chromosomes in this species. The average interval size was 5.9 cM. Six QTLs in linkage groups A, B, F, I, J and L were associated with GDD to flowering and accounted for 76% of the genotypic variation in the mean environment. QTLs in linkage groups A and B accounted for 72% of the genetic variation. QTL×environment (QTL×E) interactions were highly significant for linkage groups A, B, F and J (P<0.01). QTLs in linkage groups A and B were highly dependent on PP. Also, QTL mapping of the ratio of the GDD required by a progeny to flower at a PP of 12.1 and 15.0 h, defined as the photoperiod response (PPR), suggested that alleles at QTLs in linkage groups A and B were responsive to PP. QTLs in linkage groups F and J showed QTL×E interaction but the LOD values were not associated with PP. QTL×E interactions for additive effects were highly significant (P<0.01) for linkage groups A, B and F. QTL×E interactions for QTLs with dominant effects were significant (P<0.01) for linkage groups A, B and J. The dominant effect of QTLs in linkage group B increased in environments with a longer PP. The knowledge of how these QTLs influence the GDD for flowering and how they interact with the environment will facilitate marker- assisted selection and backcross conversion of photoperiod-sensitive germplasm. Received: 7 February 2000 / Accepted: 13 June 2000     

Supermodels: sorghum and maize provide mutual insight into the genetics of flowering time

Nested association mapping (NAM) offers power to dissect complex, quantitative traits. This study made use of a recently developed sorghum backcross (BC)-NAM population to dissect the genetic architecture of flowering time in sorghum; to compare the QTL identified with other genomic regions identified in previous sorghum and maize flowering time studies and to highlight the implications of our findings for plant breeding. A subset of the sorghum BC-NAM population consisting of over 1,300 individuals from 24 families was evaluated for flowering time across multiple environments. Two QTL analysis methodologies were used to identify 40 QTLs with predominately small, additive effects on flowering time; 24 of these co-located with previously identified QTL for flowering time in sorghum and 16 were novel in sorghum. Significant synteny was also detected with the QTL for flowering time detected in a comparable NAM resource recently developed for maize (Zea mays) by Buckler et al. (Science 325:714–718, 2009). The use of the sorghum BC-NAM population allowed us to catalogue allelic variants at a maximal number of QTL and understand their contribution to the flowering time phenotype and distribution across diverse germplasm. The successful demonstration of the power of the sorghum BC-NAM population is exemplified not only by correspondence of QTL previously identified in sorghum, but also by correspondence of QTL in different taxa, specifically maize in this case. The unification across taxa of the candidate genes influencing complex traits, such as flowering time can further facilitate the detailed dissection of the genetic control and causal genes.     

Molecular mapping of genomic regions harbouring QTLs for root and yield traits in sorghum (Sorghum bicolor L. Moench)

Root system is a vital part of plants for absorbing soil moisture and nutrients and it influences the drought tolerance. Identification of the genomic regions harbouring quantitative trait loci (QTLs) for root and yield traits, and the linked markers can facilitate sorghum improvement through marker-assisted selection (MAS) besides the deeper understanding of the plant response to drought stress. A population of 184 recombinant inbred lines (RILs), derived from E36-1 × SPV570, along with parents were phenotyped for component traits of yield in field and root traits in an above ground rhizotron. High estimates of heritability and genetic advance for all the root traits and for most of the yield traits, presents high scope for improvement of these traits by simple selection. A linkage map constructed with 104 marker loci comprising 50 EST-SSRs, 34 non-genic nuclear SSRs and 20 SNPs, and QTL analysis was performed using composite interval mapping (CIM) approach. A total of eight and 20 QTLs were mapped for root and yield related traits respectively. The QTLs for root volume, root fresh weight and root dry weight were found co-localized on SBI-04, supported by a positive correlation among these traits. Hence, these traits can be improved using the same linked markers. The lack of overlap between the QTLs of component traits of root and yield suggested that these two sets of parameters are independent in their influence and the possibility of combining these two traits might enhance productivity of sorghum under receding moisture condition.     

Mapping of shoot fly tolerance loci in sorghum using SSR markers

Sorghum (Sorghum bicolor (L.) Moench) is one of the most important crops in the semiarid regions of the world. One of the important biotic constraints to sorghum production in India is the shoot fly which attacks sorghum at the seedling stage. Identification of the genomic regions containing quantitative trait loci (QTLs) for resistance to shoot fly and the linked markers can facilitate sorghum improvement programmes through marker-assisted selection. A simple sequence repeat (SSR) marker- based skeleton linkage map of two linkage groups of sorghum was constructed in a population of 135 recombinant inbred lines (RIL) derived from a cross between IS18551 (resistant to shoot fly) and 296B (susceptible to shoot fly). A total of 14 SSR markers, seven each on linkage groups A and C were mapped. Using data of different shoot fly resistance component traits, one QTL which is common for glossiness, oviposition and dead hearts was detected following composite interval mapping (CIM) on linkage group A. The phenotypic variation explained by this QTL ranged from 3.8%–6.3%. Besides the QTL detected by CIM, two more QTLs were detected following multi-trait composite interval mapping (MCIM), one each on linkage groups A and C for the combinations of traits which were correlated with each other. Results of the present study are novel as we could find out the QTLs governing more than one trait (pleiotropic QTLs). The identification of pleiotropic QTLs will help in improvement of more than one trait at a time with the help of the same linked markers. For all the QTLs, the resistant parent IS18551 contributed resistant alleles.     

The Sweet Passion Fruit (<Emphasis Type="Italic">Passiflora alata</Emphasis>) Crop: Genetic and Phenotypic Parameter Estimates and QTL Mapping for Fruit Traits

Despite their economic importance, some tropical crop species are largely neglected when it comes to conducting genetic studies characterizing target traits for breeding. Herein, genetic and phenotypic parameters as well quantitative trait loci (QTL) are described for the first time in a full-sib progeny of sweet passion fruit (Passiflora alata). A hundred F₁ individuals were evaluated in two locations for seven fruit traits: diameter of fruit (DF, in mm), length of fruit (LF, in mm), weight of fruit (WF, in g), thickness of fruit skin (TS, in mm), weight of fruit skin (WS, in g), weight of fruit pulp (WP, in g) and soluble solids (SS, in °Brix). Mixed models fitted complex, unstructured genetic variance-covariance matrices for all traits in phenotypic analysis. Because of important genetic correlations among skin and pulp traits, multiplicative index selection to select the most promising individuals was successfully applied. A previously reported integrated map supported composite interval mapping (CIM) analyses. In total, we found 22 QTLs mapped in seven out of nine linkage groups. Heritabilities (from 59.8 % to 82.7 %) and proportion of phenotypic variance explained by the QTLs (from 42.0 % to 64.3 %) were comparable for each trait. Principal component analysis on TS, WS and WP showed that the first two principal components (PCs) accounted for 93.6 % of the total variability. CIM analyses on these two PCs revealed five putative QTLs controlling variation for these three traits simultaneously. Thus, genetic improvement for sweet passion fruit should be based on correlations between traits and QTL-related information can be a useful tool.     

Multi-environment QTL analyses for drought-related traits in a recombinant inbred population of chickpea (Cicer arientinum L.)

A recombinant inbred line (RIL) population, comprising 181 lines derived from ILC588 × ILC3279, was evaluated in 10 environments across three locations with different moisture gradients. A drought resistance score (DRS) and three phenology traits—plant height (PLHT), days to flowering (DFLR), and days to maturity (MAT)—were recorded along with seven yield-related traits—grain yield (GY), biological yield (BY), harvest index (HI), the number of pods/3 plants (Pod), percentage of empty pods (%Epod), 100 seed weight (100 sw), and seed number/3 plants (SN). Two RILs (152, 162) showed the best GYs and DRSs under stressed and non-stressed environments. The quantitative trait loci (QTLs) analyses detected 93 significant QTLs (LOD ≥ 2.0) across the genome × environment interactions. The highest phenotypic variation (>24 %) was explained by the QTL_DFLR in Terbol-11. Four common possible pleiotropic QTLs on LG3 and LG4 were identified as associated with DFLR, DRS, GY, MAT, HI, SN, and Pod. No significant epistatic interactions were found between these QTLs and the other markers. However, the QTL for DRS was detected as a conserved QTL in three late planting environments. The markers H6C-07 (on LG3) and H5G01 (on LG4) were associated with QTLs for many traits in all environments studied except two. The allele ‘A’ of marker H6C07 (from the tolerant parent ILC588) explained 80 % of the yield increase under late planting and 29.8 % of that under dry environments. Concentrating on LG3 and LG4 in molecular breeding programs for drought could speed up improvement for these traits.     

Quantitative trait locus (QTL) analysis of fruit-quality traits for mandarin breeding in Japan

The improvement of fruit quality is an important objective in citrus breeding. Using an F₁ segregating population from a cross between citrus cultivars ‘Harehime’ (‘E647’—‘Kiyomi’ [Citrus unshiu Marcow. ‘Miyagawa Wase’ × Citrus sinensis (L.) Osbeck ‘Trovita’] × ‘Osceola’—a cultivar of clementine [Citrus clementina hort. ex Tanaka] × ‘Orland’ [Citrus paradisi Macfad. ‘Duncan’ × Citrus tangerina hort. ex Tanaka] × ‘Miyagawa Wase’) and ‘Yoshida’ ponkan (Citrus reticulata Blanco ‘Yoshida’), a SNP-based genetic linkage map was constructed and quantitative trait locus (QTL) mapping of four fruit-quality traits (fruit weight, sugar content, peel puffing, and water rot) was performed. The constructed genetic linkage map of ‘Harehime’ consisted of 442 single nucleotide polymorphisms (SNPs) on 9 linkage groups (LGs) and covered 635.8 cM of the genome, while that of ‘Yoshida’ ponkan consisted of 332 SNPs on 9 LGs and covered 892.9 cM of its genome. We identified four QTLs associated with fruit weight, one QTL associated with sugar content, three QTLs associated with peel puffing, and one QTL associated with water rot. For these QTL regions, we estimated the haplotypes of the crossed parents and verified the founding cultivars that these QTLs were originated from and their inheritance in descendant cultivars using pedigree information. QTLs identified in this study provide useful information for marker-assisted breeding of citrus in Japan.