Genetic mapping of grapevine ( Vitis vinifera L.) applied to the detection of QTLs for seedlessness and berry weight

Parental and consensus genetic maps of Vitis vinifera L. (2n = 38) were constructed using a F₁ progeny of 139 individuals from a cross between two partially seedless genotypes. The consensus map contained 301 markers [250 amplification fragment length polymorphisms (AFLPs), 44 simple sequence repeats (SSRs), three isozymes, two random amplified polymorphic DNAs (RAPDs), one sequence-characterized amplified region (SCAR), and one phenotypic marker, berry color] mapped onto 20 linkage groups, and covered 1,002 cM. The maternal map consisted of 157 markers covering 767 cM (22 groups). The paternal map consisted of 144 markers covering 816 cM (23 groups). Differences in recombination rates between these maps and another unpublished map are discussed. The major gene for berry color was mapped on both the paternal and consensus maps. Quantitative trait loci (QTLs) for several quantitative subtraits of seedlessness in 3 successive years were searched for, based on parental maps: berry weight, seed number, seed total fresh and dry weights, seed percent dry matter, and seed mean fresh and dry weights. QTLs with large effects (R² up to 51%) were detected for all traits and years at the same location on one linkage group, with some evidence for the existence of a second linked major QTL for some of them. For these major QTLs, differences in relative parental effects were observed between traits. Three QTLs with small effects (R² from 6% to 11%) were also found on three other linkage groups, for berry weight and seed number in a single year, and for seed dry matter in 2 different years.     

QTL analysis for fruit yield components in table grapes (Vitis vinifera)

A segregation population of 184 genotypes derived from a pseudo-testcross of table grapes (Vitis vinifera), together with 203 AFLP and 110 SSR markers was used to detect quantitative trait loci (QTLs) for fruit yield components. Diffferent QTLs, a low percentage of phenotypic variance explained by the QTLs detected and QTL instability over years were detected for each fruit yield component. These results confirm the complex genetic architecture of the yield components in grapevine due to the perennial nature of this species, which has to adapt to yearly variations in climate. Phenotypic correlation analyses between fruit yield components were also performed. The negative correlation between berry weight and the number of berries per cluster seems to have an indirect negative effect on cluster weight, as revealed by the path coefficient analysis; however, this negative correlation was not supported at the molecular level because no coincident QTLs were observed between these traits. Nonetheless, the possibility to select seedless genotypes with large berries without affecting cluster weight needs to be substantiated in future experiments because factors such as sample size and heritability might influence QTL identification in table grapes.     

QTL analysis of potato tuberization

Quantitative trait loci (QTLs) affecting tuberization were detected in reciprocal backcrosses between Solanum tuberosum and S. berthaultii. Linkage analyses were performed between traits and RFLP alleles segregating from both the hybrid and the recurrent parent using a set of framework markers from the potato map. Eleven distinct loci on seven chromosomes were associated with variation in tuberization. Most of the loci had small effects, but a QTL explaining 27% of the variance was found on chromosome 5. More QTLs were detected while following alleles segregating from the recurrent S. tuberosum parent used to make the backcross than were detected by following alleles segregating from the hybrid parent. More than half of the alleles favoring tuberization were at least partly dominant. Tuberization was favored by an allele from S. berthaultii at 3 of the 5 QTLs detected by segregation from the hybrid parent. The additive effects of the QTLs for tuberization explained up to 53% of the phenotypic variance, and inclusion of epistatic effects increased this figure to 60%. The most common form of epistasis was that in which presence of an allele at each of 2 loci favoring tuberization was no more effective than the presence of a favorable allele at 1 of the 2 loci. The QTLs detected for tuberization traits are discussed in relationship to those previously detected for trichome-mediated insect resistance derived from the unadapted wild species.Paper number 54 of the Department of Fruit and Vegetable Science, Cornell University     

Identification of quantitative trait loci associated with salt tolerance at seedling stage from Oryza rufipogon

Soil salinity is one of the major abiotic stresses affecting plant growth and crop production.In the present study,salt tolerance at rice seedling stage was evaluated using 87 introgression lines (ILs),which were derived from a cross between an elite indica cultivar Teqing and an accession of common wild rice (Oryza rufipogon Griff.).Substantial variation was observed for four traits including salt tolerance score (STS),relative root dry weight (RRW),relative shoot dry weight (RSW) and relative total dry weight (RTW).STS was significantly positively correlated with all other three traits.A total of 15 putative quantitative trait loci (QTLs) associated with these four traits were detected using single-point analysis,which were located on chromosomes 1,2,3,6,7,9 and 10 with 8％-26％ explaining the phenotypic variance.The O.rufipogon-derived alleles at 13 QTLs (86.7％) could improve the salt tolerance in the Teqing background.Four QTL clusters affecting RRW,RSW and RTW were found on chromosomes 6,7,9 and 10,respectively.Among these four QTL clusters,a major cluster including three QTLs (qRRW10,qRSW10 and qRTW10) was found near the maker RM271 on the long arm of chromosome 10,and the O.rufipogon-derived alleles at these three loci increased RRW,RSW and RTW with additive effects of 22.7％,17.3％ and 18.5％,respectively,while the phenotypic variance explained by these three individual QTLs for the three traits varied from 19％ to 26％.In addition,several salt tolerant ILs were selected and could be used for identifying and utilizing favorable salt tolerant genes from common wild rice and used in the salt tolerant rice breeding program.     

QTL mapping of root traits in a doubled haploid population from a cross between upland and lowland japonica rice in three environments

To genetically dissect drought resistance associated with japonica upland rice, we evaluated a doubled haploid (DH) population from a cross between two japonica cultivars for seven root traits under three different growing conditions (upland, lowland and upland in PVC pipe). The traits included basal root thickness (BRT), total root number (RN), maximum root length (MRL), root fresh weight (RFW), root dry weight (RDW), ratio of root fresh weight to shoot fresh weight (RFW/SFW) and ratio of root dry weight to shoot dry weight (RDW/SDW). The BRT was significantly correlated with the index of drought resistance, which was defined as the ratio of yield under the stress of the upland condition to that under the normal lowland condition. A complete genetic linkage map with 165 molecular markers covering 1,535 cM was constructed. Seven additive quantitative trait loci (QTLs) and 15 pairs of epistatic loci for BRT and RN were identified under upland and lowland conditions, and 12 additive QTLs and 17 pairs of epistatic QTLs for BRT, RN, MRL, RFW, RFW/SFW and RDW/SDW were identified under the PVC pipe condition. Four additive QTLs and one pair of epistatic QTLs controlling IDR were also found. These QTLs individually explained up to 25.6% of the phenotypic variance. QTL × environment (Q × E) interactions were detected for all root traits, and the contributions of these interactions ranged from 1.1% to 19.9%. Five co-localized QTLs controlling RFW and RDW, RFW/SFW, RDW/SDW and IDR, BRT and RN, RN, MRL and IDR were found. Four types of QTLs governing BRT and RN were classified by their detection in the upland and lowland conditions. Some common QTLs for root traits across different backgrounds were also revealed. These co-localized QTLs and common QTLs will facilitate marker-assisted selection for root traits in rice breeding programs.     

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.     

Analysis of factors influencing milling yield and their association to other traits by QTL analysis in two hexaploid oat populations

Milling yield, or the grain weight from which 100 kg of rolled groats is obtained upon milling, is an important quality characteristic of cultivated oat (Avena sativa L.). Kernel morphology and the groat (caryopsis) percentage of the whole kernel including hull are factors that influence milling yield. We mapped QTLs for kernel area, kernel length, kernel width, and groat percentage in two populations of 137 recombinant inbred lines by RFLP and AFLP analysis to evaluate the prospects of marker-assisted selection (MAS). Phenotypic correlations between kernel morphology traits and groat percentage were not significant. For kernel morphology traits and groat percentage, one to five QTLs were detected, explaining 7.0–60.7% of the total phenotypic variance depending on the trait. One QTL for kernel length in each population and one QTL for kernel width in one population were found at the same location as a QTL for groat percentage, indicating that a change in kernel size or shape could have an influence on groat percentage. The positions and effects of QTLs for kernel morphology and groat percentage were compared to QTLs detected previously for chemical grain composition (oil andβ-glucanconcentration) and agronomic traits to evaluate the selection response on these traits through MAS. Several regions of the oat genome were identified that contained clusters of QTLs influencing two or more traits. While the allele from one parent at a QTL could simultaneously improve two or more traits in one population, it could have opposite effects on the same traits at another QTL or in the other population. Associations among traits were complex and will require careful consideration when employing QTL-marker associations in MAS to avoid negative selection response. Future research to discover candidate genes for those QTL clusters could provide information about trait associations and help in designing selection programs. Received: 17 February 2000 / Accepted: 27 October 2000     

Interval mapping of QTLs controlling yield-related traits and seed protein content in Pisum sativum

A linkage map of garden pea was constructed on the basis of 114 plants (F2 generation) derived from a cross combination Wt10245 x Wt11238. The map, consisting of 204 morphological, isozyme, AFLP, ISSR, STS, CAPS and RAPD markers, was used for interval mapping of quantitative trait loci (QTLs) controlling seed number, pod number, 1000-seed weight, 1000-yield, and seed protein content. Characterization of each QTL included identification of QTL position with reference to the flanking markers, estimation of the part of variance explained by this QTL, and determination of its gene action. The yield-related traits were measured in F2 plants and in F4 recombinant inbred lines (RILs). The interval mapping revealed two to six QTLs per trait, demonstrating linkage to seven pea chromosomes. A total of 37 detected QTLs accounted for 9.1-55.9% of the trait's phenotypic variation and showed different types of gene action. As many as eight and ten QTLs influencing the analysed traits were mapped in linkage groups III and V, respectively, indicating an important role of these regions of the pea genome in the control of yield and seed protein content.     

Molecular mapping of quantitative trait loci for drought tolerance in maize plants

Drought tolerance is one of the most important but complex traits of crops. We looked for quantitative trait loci (QTLs) that affect drought tolerance in maize. Two maize inbreds and their advanced lines were evaluated for drought-related traits. A genetic linkage map developed using RFLP markers was used to identify QTLs associated with drought-related traits. Twenty-two QTLs were detected, with a minimum of one and a maximum of nine for drought-related traits. A single-QTL was detected for sugar concentration accounting for about 52.2% of the phenotypic variation on chromosome 6. A single-QTL was also identified for each of the traits root density, root dry weight, total biomass, relative water content, and leaf abscisic acid content, on chromosomes 1 and 7, contributing to 24, 0.2, 0.4, 7, and 19% of the phenotypic variance, respectively. Three QTLs were identified for grain yield on chromosomes 1, 5, and 9, explaining 75% of the observed phenotypic variability, whereas four QTLs were detected for osmotic potential on chromosomes 1, 3, and 9, together accounting for 50% of the phenotypic variance. Nine QTLs were detected for leaf surface area on chromosomes 3 and 9, with various degrees of phenotypic variance, ranging from 25.8 to 42.2%. Four major clusters of QTLs were identified on chromosomes 1, 3, 7, and 9. A QTL for yield on chromosome 1 was found co-locating with the QTLs for root traits, total biomass, and osmotic potential in a region of about 15 cM. A cluster of QTLs for leaf surface area were coincident with a QTL for osmotic potential on chromosome 3. The QTLs for leaf area also clustered on chromosome 9, whereas QTLs for leaf abscisic acid content and relative water content coincided on chromosome 7, 10 cM apart. Co-location of QTLs for different traits indicates potential pleiotropism or tight linkage, which may be useful for indirect selection in maize improvement for drought tolerance.     

Identification of QTLs for seed germination capability after various storage periods using two RIL populations in rice

Seed germination capability of rice is one of the important traits in the production and storage of seeds. Quantitative trait loci (QTL) associated with seed germination capability in various storage periods was identified using two sets of recombinant inbred lines (RILs) which derived from crosses between Milyang 23 and Tong 88-7 (MT-RILs) and between Dasanbyeo and TR22183 (DT-RILs). A total of five and three main additive effects (QTLs) associated with seed germination capability were identified in MT-RILs and DT-RILs, respectively. Among them, six QTLs were identified repeatedly in various seed storage periods designated as qMT-SGC5.1, qMT-SGC7.2, and qMT-SGC9.1 on chromosomes 5, 7, and 9 in MT-RILs, and qDT-SGC2.1, qDT-SGC3.1, and qDT-SGC9.1 on chromosomes 2, 3, and 9 in DT-RILs, respectively. The QTL on chromosome 9 was identified in both RIL populations under all three storage periods, explaining up to 40% of the phenotypic variation. Eight and eighteen pairs additive × additive epistatic effect (epistatic QTL) were identified in MT-RILs and DT-RILs, respectively. In addition, several near isogenic lines (NILs) were developed to confirm six repeatable QTL effects using controlled deterioration test (CDT). The identified QTLs will be further studied to elucidate the mechanisms controlling seed germination capability, which have important implications for long-term seed storage.     

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.     

Quantitative trait loci for lodging resistance in a segregating wheat×spelt population

 Lodging can strongly affect both the grain yield and the quality of wheat. Lodging represents a quantitative trait and is difficult to assess on a phenotypic basis. Marker-assisted selection (MAS) could therefore become an important tool in breeding for lodging resistance. In this study, we mapped and characterised quantitative trait loci (QTLs) for lodging resistance, as well as morphological traits correlated with lodging, in a segregating population of 226 recombinant inbred lines derived from the cross of the lodging-resistant wheat variety Forno with the susceptible spelt variety Oberkulmer. Lodging, plant height, leaf width, leaf-growth habit, culm stiffness, culm swinging, culm thickness, days to ear emergence and days to flowering were assessed in field trials at two locations in 1996 and at one location in 1997. Additionally, at one location weight and length parameters were also assessed. Plant height and culm stiffness explained 77% of the phenotypic variance of lodging in a multiple regression model over all three environments. QTL analysis of lodging and morphological parameters was based on a genetic map containing 230 loci with 23 linkage groups (2469 cM). With the method of composite interval mapping nine QTLs for lodging resistance were detected, explaining 63% of the phenotypic variance in a simultaneous fit. Seven of these QTLs coincided with QTLs for morphological traits, reflecting the correlations between these traits and lodging. In our population the most efficient way to improve lodging resistance would be by a combination of indirect selection on plant height and culm stiffness together with MAS on the two QTLs for lodging resistance which did not coincide with QTLs for morphological traits. Received: 3 August 1998 / Accepted: 28 November 1998     

Identification of quantitative trait loci for cold-tolerance of photosynthesis in maize (Zea mays L.)

The effects of low growth temperature (15 degrees C) on the photosynthetic apparatus of maize were investigated in a set of 233 recombinant inbred lines by means of chlorophyll fluorescence, gas exchange measurements and analysis of photosynthetic pigments. A quantitative trait loci (QTL) analysis of five traits related to the functioning of the photosynthetic apparatus revealed a total of eight genomic regions that were significantly involved in the expression of the target traits. Four of these QTLs, located on chromosomes 1 (around 146 cM), 2 (around 138 cM), 3 (around 70 cM), and 9 (around 62 cM), were identified across several traits and the phenotypic correlation observed among those traits confirmed at the genetic level. The two QTLs on chromosomes 1 and 9 were also expressed in leaves developed at near-optimal temperature (25 degrees C) whilst the two QTLs on chromosomes 2 and 3 were specific to leaves developed at sub-optimal temperature. A QTL analysis conducted on traits related to the pigment composition of the leaves developed at 15 degrees C detected the QTL on chromosome 3 around 70 cM in 7 of the 11 traits analysed. This QTL accounted for up to 28% of the phenotypic variance of the quantum yield of electron transport at PSII in the fourth leaf after about 3 weeks at a sub-optimal temperature. The results presented here suggest that key gene(s) involved in the development of functional chloroplasts of maize at low temperature should be located on chromosome 3, close to the centromere.     

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

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

Identification of genome regions controlling cotyledon,pod wall/seed coat and pod wall resistance to pea weevil through QTL mapping

Pea weevil, Bruchus pisorum, is one of the limiting factors for field pea (Pisum sativum) cultivation in the world with pesticide application the only available method for its control. Resistance to pea weevil has been found in an accession of Pisum fulvum but transfer of this resistance to cultivated pea (P. sativum) is limited due to a lack of easy-to-use techniques for screening interspecific breeding populations. To address this problem, an interspecific population was created from a cross between cultivated field pea and P. fulvum (resistance source). Quantitative trait locus (QTL) mapping was performed to discover the regions associated with resistance to cotyledon, pod wall/seed coat and pod wall resistance. Three major QTLs, located on linkage groups LG2, LG4 and LG5 were found for cotyledon resistance explaining approximately 80 % of the phenotypic variation. Two major QTLs were found for pod wall/seed coat resistance on LG2 and LG5 explaining approximately 70 % of the phenotypic variation. Co-linearity of QTLs for cotyledon and pod wall/seed coat resistance suggested that the mechanism of resistance for these two traits might act through the same pathways. Only one QTL was found for pod wall resistance on LG7 explaining approximately 9 % of the phenotypic variation. This is the first report on the development of QTL markers to probe Pisum germplasm for pea weevil resistance genes. These flanking markers will be useful in accelerating the process of screening when breeding for pea weevil resistance.     

Mapping QTLs for root morphological traits inBrassica rapa L. based on AFLP and RAPD markers

Root growth and thickening plays a key role in the final productivity and even the quality of storage roots in root crops. This study was conducted to identify and map quantitative trait loci (QTLs) affecting root morphological traits in Brassica rapa by using molecular markers. An F2 population was developed from a cross between Chinese cabbage (Brassica rapa ssp. chinensis) and turnip (B. rapa ssp. rapifera), which differed greatly in root characters. A genetic map covering 1837.1 cM, with 192 marker loci and 11 linkage groups, was constructed by using this F2 population. The F3 families derived from F2 plants were grown in the field and evaluated for taproot traits (thickness, length, and weight). QTL analysis via simple interval mapping detected 18 QTLs for the 3 root traits, including 7 QTLs for taproot thickness, 5 QTLs for taproot length, and 6 QTLs for taproot weight. Individually, the QTLs accounted for 8.4-27.4% of the phenotypic variation. The 2 major QTLs, qTRT4b for taproot thickness and qTRW4 for taproot weight, explained 27.4% and 24.8% of the total phenotypic variance, respectively. The QTLs for root traits, firstly detected in Brassica crops, may provide a basis for marker-assisted selection to improve productivity in root-crop breeding.     

Construction of a Genetic Linkage Map and Identification of QTLs for Seed Weight and Seed Size Traits in Lentil (Lens culinaris Medik.)

Seed weight and seed size both are quantitative traits and have been considered as important components of grain yield, thus identification of quantitative trait loci (QTL) for seed traits in lentil (Lens culinaris) would be beneficial for the improvement of grain yield. Hence the main objective of this study was to identify QTLs for seed traits using an intraspecific mapping population derived from a cross between L. culinaris cv. Precoz (seed weight-5.1g, seed size-5.7mm) and L. culinaris cv. L830 (seed weight-2.2g, seed size-4mm) comprising 126 F₈-RILs. For this, two microsatellite genomic libraries enriched for (GA/CT) and (GAA/CTT) motif were constructed which resulted in the development of 501 new genomic SSR markers. Six hundred forty seven SSR markers (including 146 previously published) were screened for parental polymorphism and 219 (33.8%) were found to be polymorphic among the parents. Of these 216 were mapped on seven linkage groups at LOD4.0 spanning 1183.7cM with an average marker density of 5.48cM. Phenotypic data from the RILs was used to identify QTLs for the seed weight and seed size traits by single marker analysis (SMA) followed by composite interval mapping (CIM) which resulted in one QTL each for the 2 traits (qSW and qSS) that were co-localized on LG4 and explained 48.4% and 27.5% of phenotypic variance respectively. The current study would serve as a strong foundation for further validation and fine mapping for utilization in lentil breeding programs.     

利用双单倍体群体剖析水稻产量及其相关性状的遗传基础

主效QTL、上位性效应和它们与环境的互作(QE)都是数量性状的重要遗传因素。利用籼粳交珍汕97／武育粳2号F1植株上的花药进行组织培养得到的190个双单倍体群体和179个微卫星标记,通过两年两重复田间试验,采用混合线性模型方法分析了9个控制水稻产量及其相关性状的遗传效应,得到57个主效QTL,41对上位性互作,8对QTL与环境的互作和7对上位性效应与环境的互作。单个主效QTL解释这些性状1．3％～25．8％的表型方差。各性状QTL的累积表型贡献率达11．5％～66．8％。大多数性状之间具有显著的表型相关性,相关性较高的性状之间常具有较多共同或紧密连锁的QTL。结果表明,基因的多效性或紧密连锁可能是性状相关的重要遗传基础。     

Genetic mapping of seed shape in three populations of recombinant inbred lines of soybean (Glycine max L. Merr.)

Round soybean seeds are sought-after for food-type soybean. Also the genetic control of seed geometry is of scientific interest. The objectives of this study were to estimate heritability and map quantitative trait loci (QTLs) responsible for seed shape traits. Three densely mapped recombinant inbred populations each with 192 segregants were used, Minsoy × Archer, Minsoy × Noir1, and Noir1 × Archer. A two rep two location experiment was conducted in Los Andes, Chile, and East Lansing, MI, USA. Seed height (SH), width (SW), length (SL), and seed volume (SV) as width × height × length were measured to determine seed shape. Heritability was estimated by variance component analysis. A total of 19 significant QTLs (LOD ≥ 3.7) in ten linkage groups (LG) were detected for all the traits. Only one QTL was stable across populations and environments and six were stable in at least two populations in both environments. The amount of phenotypic variation explained by a single QTL varied from 7.5% for SH, to 18.5% for SW and at least 30% of the genetic variation for the traits is controlled by four QTL or less. All traits were highly correlated with each other in all populations with values ranging from 0.5 to 0.9, except for SL and SW that were not significantly correlated or had a low correlation in all populations. Narrow sense heritabilities for all traits ranged from 0.42 to 0.88. We note that LG u9, u11, and u14 are hot points of the genome for QTLs for various traits. The number and genomic distribution of the QTLs confirms the complex genetic control of seed shape. Transgressive segregation was observed for all traits suggesting that careful selection of parents with similar phenotypes but different genotypes using molecular markers can result in desirable transgressive segregants.     

Genetic analysis of seed size, yield and days to flowering in a chickpea recombinant inbred line population derived from a Kabuli × Desi cross

Quantitative traits, seed size, yield and days to flowering were studied in a chickpea intraspecific recombinant inbred line (RIL) population (F_6:7) derived from a Kabuli × Desi cross. The population was evaluated in two locations over 2 years. Days to flowering was also evaluated in the greenhouse under short-day conditions. Seed size was the most heritable trait (0.90), followed by days to flowering (0.36) and yield (0.14). Negative and significant correlation was found between yield and seed size in the second year where environmental homogeneity was tested by analysing the controls included in each assay. During the first year, the environment was not considered homogeneous for yield in either location. Quantitative trait loci (QTLs) for the three characters were detected in linkage group (LG) 4. In relation to seed size, two QTLs were located in LG4 (QTL_SW1) and LG8 (QTL_SW2). QTL_SW1 accounted 20.3% of the total phenotypic variation and QTL_SW2 explained 10.1%. A QTL for yield (QTL_YD) was located in LG4 explaining around 13% of variation. QTL_YD might be pleiotropic with QTL_SW1. For days to flowering, a QTL (QTL_DF1) was located in LG4 for all environments analysed explaining around 20% of variation. QTL_DF1 was closely linked to QTL_SW1 and QTL_YD in LG4.