Assessment of differences in morphological and physiological leaf lodging characteristics between two cultivars of Hippeastrum rutilum

Kernel weight and morphology are important traits affecting cereal yields and quality. Dissecting the genetic basis of thousand kernel weight (TKW) and its related traits is an effective method to improve wheat yield. In this study, we performed quantitative trait loci (QTL) analysis using recombinant inbred lines derived from the cross ‘PuBing3228 × Gao8901’ (PG-RIL) to dissect the genetic basis of kernel traits. A total of 17 stable QTLs related to kernel traits were identified, notably, two stable QTLs QTkw.cas-1A.2 and QTkw.cas-4A explained the largest portion of the phenotypic variance for TKW and kernel length (KL), and the other two stable QTLs QTkw.cas-6A.1 and QTkw.cas-7D.2 contributed more effects on kernel width (KW). Conditional QTL analysis revealed that the stable QTLs for TKW were mainly affected by KW. The QTLs QTkw.cas-7D.2 and QKw.cas-7D.1 associated with TKW and KW were delimited to the physical interval of approximately 3.82 Mb harboring 47 candidate genes. Among them, the candidate gene TaFT-D1 had a 1 bp insertions/deletion (InDel) within the third exon, which might be the reason for diversity in TKW and KW between the two parents. A Kompetitive Allele-Specific PCR (KASP) marker of TaFT-D1 allele was developed and verified by PG-RIL and a natural population consisted of 141 cultivar/lines. It was found that the favorable TaFT-D1 (G)-allele has been positively selected during Chinese wheat breeding. Thus, these results can be used for further positional cloning and marker-assisted selection in wheat breeding programs. Seventeen stable QTLs related to kernel traits were identified. The stable QTLs for thousand kernel weight were mainly affected by kernel width. TaFT-D1 could be the candidate gene for QTLs QTkw.cas-7D.2 and QKw.cas-7D.1.     

Molecular genetic analysis of grain protein content and flour whiteness degree using RILs in common wheat

Grain protein content (GPC) and flour whiteness degree (FWD) are important qualitative traits in common wheat. Quantitative trait locus (QTL) mapping for GPC and FWD was conducted using a set of 131 recombinant-inbred lines derived from the cross ‘Chuan 35050 × Shannong 483’ in six environmental conditions. A total of 22 putative QTLs (nine GPC and 13 FWD) were identified on 12 chromosomes with individual QTL explaining 4.5–34.0% phenotypic variation. Nine QTLs (40.9%) were detected in two or more environments. The colocated QTLs were on chromosomes 1B and 4B. Among the QTLs identified for GPC, QGpc.sdau-4A from the parent Shannong 483 represented some important favourable QTL alleles. QGpc.sdau-2A.1 and QFwd.sdau-2A.1 had a significant association with both GPC and FWD. The markers detected on top of QTL regions could be potential targets for marker-assisted selection.     

QTL mapping of 1000-kernel weight, kernel length, and kernel width in bread wheat (Triticum aestivum L.)

Kernel size and morphology influence the market value and milling yield of bread wheat (Triticum aestivum L.). The objective of this study was to identify quantitative trait loci (QTLs) controlling kernel traits in hexaploid wheat. We recorded 1000-kernel weight, kernel length, and kernel width for 185 recombinant inbred lines from the cross Rye Selection 111 × Chinese Spring grown in 2 agro-climatic regions in India for many years. Composite interval mapping (CIM) was employed for QTL detection using a linkage map with 169 simple sequence repeat (SSR) markers. For 1000-kernel weight, 10 QTLs were identified on wheat chromosomes 1A, 1D, 2B, 2D, 4B, 5B, and 6B, whereas 6 QTLs for kernel length were detected on 1A, 2B, 2D, 5A, 5B and 5D. Chromosomes 1D, 2B, 2D, 4B, 5B and 5D had 9 QTLs for kernel width. Chromosomal regions with QTLs detected consistently for multiple year-location combinations were identified for each trait. Pleiotropic QTLs were found on chromosomes 2B, 2D, 4B, and 5B. The identified genomic regions controlling wheat kernel size and shape can be targeted during further studies for their genetic dissection.     

QTL mapping for some grain traits in bread wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Grain traits are important agronomic attributes with the market value as well as milling yield of bread wheat. In the present study, quantitative trait loci (QTL) regulating grain traits in wheat were identified. Data for grain area size (GAS), grain width (GWid), factor form density (FFD), grain length-width ratio (GLWR), thousand grain weight (TGW), grain perimeter length (GPL) and grain length (GL) were recorded on a recombinant inbred line derived from the cross of NW1014?×?HUW468 at Meerut and Varanasi locations. A linkage map of 55 simple sequence repeat markers for 8 wheat chromosomes was used for QTL analysis by Composite interval mapping. Eighteen QTLs distributed on 8 chromosomes were identified for seven grain traits. Of these, five QTLs for GLWR were found on chromosomes 1A, 6A, 2B, and 7B, three QTLs for GPL were located on chromosomes 4A, 5A and 7B and three QTLs for GAS were mapped on 5D and 7D. Two QTLs were identified on chromosomes 4A and 5A for GL and two QTLs for GWid were identified on chromosomes 7D and 6A. Similarly, two QTLs for FFD were found on chromosomes 1A and 5D. A solitary QTL for TGW was identified on chromosome 2B. For several traits, QTLs were also co-localized on chromosomes 2B, 4A, 5A, 6A, 5D, 7B and 7D. The QTLs detected in the present study may be validated for specific crosses and then used for marker-assisted selection to improve grain quality in bread wheat.     

QTL analysis for grain protein content using SSR markers and validation studies using NILs in bread wheat

QTL interval mapping for grain protein content (GPC) in bread wheat was conducted for the first time, using a framework map based on a mapping population, which was available in the form of 100 recombinant inbred lines (RILs). The data on GPC for QTL mapping was recorded by growing the RILs in five different environments representing three wheat growing locations from Northern India; one of these locations was repeated for 3 years. Distribution of GPC values followed normal distributions in all the environments, which could be explained by significant g x e interactions observed through analyses of variances, which also gave significant effects due to genotypes and environments. Thirteen (13) QTLs were identified in individual environments following three methods (single-marker analysis or SMA, simple interval mapping or SIM and composite interval mapping or CIM) and using LOD scores that ranged from 2.5 to 6.5. Threshold LOD scores (ranging from 3.05 to 3.57), worked out and used in each case, however, detected only seven of the above 13 QTLs. Only four (QGpc.ccsu-2B.1; QGpc.ccsu-2D.1; QGpc.ccsu-3D.1 and QGpc.ccsu-7A.1) of these QTLs were identified either in more than one location or following one more method other than CIM; another QTL (QGpc.ccsu-3D.2), which was identified using means for all the environments, was also considered to be important. These five QTLs have been recommended for marker-assisted selection (MAS). The QTLs identified as above were also validated using ten NILs derived from three crosses. Five of the ten NILs possessed 38 introgressed segments from 16 chromosomes and carried 42 of the 173 markers that were mapped. All the seven QTLs were associated with one or more of the markers carried by the above introgressed segments, thus validating the corresponding markers. More markers associated with many more QTLs to be identified should become available in the future by effective MAS for GPC improvement.     

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     

Genetic Dissection of Wheat Kernel Hardness Using Conditional QTL Mapping of Kernel Size and Protein-Related Traits

Kernel hardness (KH) is one of the primary quality parameters for common wheat (Triticum aestivum L.) and has a major impact on milling, flour quality, and end-product properties. In addition to Puroindoline (Pin) mutations and differences in Pin expression, other factors, such as kernel size and protein-related traits, play noticeable roles in determining hardness, but at the quantitative trait locus (QTL) level, the influence of these factors remains unclear. In this study, genetic relationships between KH and kernel size traits and between KH and protein-related traits were demonstrated by unconditional and conditional mapping using a wheat 90K genotyping assay with a segregating population of 173 recombinant inbred lines in four environments. Eight additive QTL for KH were detected using unconditional QTL mapping analysis; these QTL were primarily distributed on chromosomes 4B, 5A, 5B, and 6D, with phenotypic variation that ranged from 0.2 to 17.7%. In addition, one pair of epistatic QTL (QKH3B.4-65/QKH4B.6-2) was identified by unconditional mapping, and this pair accounted for 1.6% of the phenotypic variation. Through conditional mapping, after excluding the influences of kernel size and protein-related traits, 14 QTL were discovered and accounted for 0.6–18.5% of the phenotypic variation. Of them, the stable QTL QKH4B.4-17 made the largest contribution, which was partially contributed by the kernel length (KL), kernel thickness (KT), and dry gluten content (DGC). Furthermore, QKH4B.4-17 was crucially contributed by the kernel width (KW), kernel diameter (KD), kernel protein content (KPC), and wet gluten content (WGC) and was independent of the sedimentation volume (SV) and gluten index (GI). Another major QTL, QKH5B.10-63, was independent of the KW and KT; partly due to the variations in KL, KD, DGC, and WGC; and conclusively contributed by the KPC, SV, and GI. Seven additional QTL were only detected in the conditional analysis and were crucially contributed by kernel size or protein-related traits. These results demonstrated that kernel size and protein-related traits play significant roles in determining KH. The present study increases the understanding of the relationships between KH and kernel size and between KH and protein-related traits at the QTL level.     

Genetic analysis of resistance to yellow rust in hexaploid wheat using a mixture model for multiple crosses

DNA-based molecular markers have been used in numerous studies for tagging specific genes in wheat for subsequent use in marker-assisted selection. Usually in plant breeding, procedures for mapping genes are based on analysis of a single segregating population. However, breeding programmes routinely evaluate large numbers of progeny derived from multiple-related crosses with some parental lines shared. In most such related crosses, the number of progeny is quite small. Thus, statistical techniques for detecting quantitative trait loci (QTLs) using data from conventional multi-cross breeding programmes are interesting. The objective of this study is to present a mixture model for QTL mapping in crosses of multiple inbred varieties with non-normal phenotype distributions and to use this model to map QTLs for yellow rust resistance in elite wheat breeding material. Three doubled haploid populations consisting of 41, 42 and 55 lines, respectively, originating from four parental varieties were studied. Multi-cross QTL analysis with three specific pathogen isolates of Puccinia striiformis f. sp. tritici and a mixture of the isolates revealed QTLs for resistance at four different genomic locations. These QTLs were found on chromosome 2AL, 2AS, 2BL and 6BL and explained between 21 and 41% of the phenotypic variation. Two of these QTLs, one on the long arm of chromosome 2A and one on the short arm of chromosome 2A were identical to the known yellow rust resistance genes Yr32 and Yr17, respectively, whereas the QTLs located on the long arms of chromosomes 2B and 6B may reflect types of resistance to yellow rust, which have not previously been mapped.     

Genome mapping of kernel characteristics in hard red spring wheat breeding lines

Kernel characteristics, particularly kernel weight, kernel size, and grain protein content, are important components of grain yield and quality in wheat. Development of high performing wheat cultivars, with high grain yield and quality, is a major focus in wheat breeding programs worldwide. Here, we report chromosome regions harboring genes that influence kernel weight, kernel diameter, kernel size distribution, grain protein content, and grain yield in hard red spring wheat breeding lines adapted to the Upper Midwest region of the United States. A genetic linkage map composed of 531 SSR and DArT marker loci spanned a distance of 2,505 cM, covering all 21 chromosomes of wheat. Stable QTL clusters influencing kernel weight, kernel diameter, and kernel size distribution were identified on chromosomes 2A, 5B, and 7A. Phenotypic variation explained by individual QTL at these clusters varied from 5 to 20% depending on the trait. A QTL region on chromosome 2B confers an undesirable pleiotropic effect or a repulsion linkage between grain yield (LOD = 6.7; R ² = 18%) and grain protein content (LOD = 6.2; R ² = 13.3%). However, several grain protein and grain yield QTL independent of each other were also identified. Because some of the QTL identified in this study were consistent across environments, DNA markers will provide an opportunity for increasing the frequency of desirable alleles through marker-assisted selection.     

Genetic analysis of grain and malt quality in an elite barley population

Quantitative trait loci (QTLs) associated with grain weight, grain width, kernel hardness and malting quality were mapped in a doubled haploid population derived from two elite Australian malting barley varieties, Navigator and Admiral. A total of 30 QTLs for grain weight, grain width and kernel hardness were identified in three environments, and 63 QTLs were identified for ten malting quality traits in two environments. Three malting quality traits, namely β-amylase, diastatic power and apparent attenuation limit, were mainly controlled by a QTL linked to the Bmy1 gene at the distal end of chromosome 4H encoding a β-amylase enzyme. Six other malting quality traits, namely α-amylase, soluble protein, Kolbach index, free amino-acid nitrogen, wort β-glucan and viscosity, had coincident QTL clustered on chromosomes 1HS, 4HS, 7HS and 7HL, which demonstrated the interdependence of these traits. There was a strong association between these malt quality QTL clusters on chromosomes 1HS and 7HL and the major QTL for kernel hardness, suggesting that the use of this trait to enable early selection for malting quality in breeding programs would be feasible. In contrast, the majority of QTLs for hot-water extract were not coincident with those identified for other malt quality traits, which suggested differences in the mechanism controlling this trait. Novel QTLs have been identified for kernel hardness on chromosomes 2HL and 7HL, hot-water extract on 7HL and wort β-glucan on 6HL, and the resulting markers may be useful for marker-assisted selection in breeding programs.     

Mixed-model QTL mapping for kernel hardness and dough strength in bread wheat

Plant breeding data comprise unbalanced phenotypic data for inbreds with complex pedigrees. As traditional methods to map quantitative trait loci (QTL) cannot exploit plant breeding data, an alternative approach is QTL mapping via a mixed-model procedure. Our objective was to validate mixed-model QTL mapping for self-pollinated crops by detecting QTL for kernel hardness and dough strength from data in a bread wheat (Triticum aestivum L.) breeding program. We studied 80 parental and 373 experimental inbreds genotyped for 65 simple sequence repeat (SSR) markers and three candidate loci. The methodology involved three steps: variance component estimation, single-marker analyses, and a final multiple-marker analysis with marker effects treated as fixed effects. Two QTLs for kernel hardness were detected on chromosomes 1A (close to candidate locus GluA3) and 5D (close to candidate locus Ha). Four QTLs were detected for dough strength on chromosomes 1A, 1B, 1D, and 5B. Candidate gene GluA1, which was associated with dough strength, was the only candidate locus found significant. Results were consistent with previously reported markers and QTLs associated with kernel hardness and dough strength. Unlike previous studies that have assumed QTL effects as random, the assumption of fixed marker effects identified the favorable marker alleles to select for. We conclude that the detection of previously mapped QTL validates the usefulness of mixed-model QTL mapping in the context of a plant-breeding program.     

Mapping QTLs controlling kernel dimensions in a wheat inter-varietal RIL mapping population

Key message

Seven kernel dimension QTLs were identified in wheat, and kernel thickness was found to be the most important dimension for grain weight improvement.

Abstract

Kernel morphology and weight of wheat (Triticum aestivum L.) affect both yield and quality; however, the genetic basis of these traits and their interactions has not been fully understood. In this study, to investigate the genetic factors affecting kernel morphology and the association of kernel morphology traits with kernel weight, kernel length (KL), width (KW) and thickness (KT) were evaluated, together with hundred-grain weight (HGW), in a recombinant inbred line population derived from Nanda2419?×?Wangshuibai, with data from five trials (two different locations over 3 years). The results showed that HGW was more closely correlated with KT and KW than with KL. A whole genome scan revealed four QTLs for KL, one for KW and two for KT, distributed on five different chromosomes. Of them, QKl.nau-2D for KL, and QKt.nau-4B and QKt.nau-5A for KT were newly identified major QTLs for the respective traits, explaining up to 32.6 and 41.5% of the phenotypic variations, respectively. Increase of KW and KT and reduction of KL/KT and KW/KT ratios always resulted in significant higher grain weight. Lines combining the Nanda 2419 alleles of the 4B and 5A intervals had wider, thicker, rounder kernels and a 14% higher grain weight in the genotype-based analysis. A strong, negative linear relationship of the KW/KT ratio with grain weight was observed. It thus appears that kernel thickness is the most important kernel dimension factor in wheat improvement for higher yield. Mapping and marker identification of the kernel dimension-related QTLs definitely help realize the breeding goals.

     

Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars

Association mapping is a method for detection of gene effects based on linkage disequilibrium (LD) that complements QTL analysis in the development of tools for molecular plant breeding. In this study, association mapping was performed on a selected sample of 95 cultivars of soft winter wheat. Population structure was estimated on the basis of 36 unlinked simple-sequence repeat (SSR) markers. The extent of LD was estimated on chromosomes 2D and part of 5A, relative to the LD observed among unlinked markers. Consistent LD on chromosome 2D was <1 cM, whereas in the centromeric region of 5A, LD extended for approximately 5 cM. Association of 62 SSR loci on chromosomes 2D, 5A, and 5B with kernel morphology and milling quality was analyzed through a mixed-effects model, where subpopulation was considered as a random factor and the marker tested was considered as a fixed factor. Permutations were used to adjust the threshold of significance for multiple testing within chromosomes. In agreement with previous QTL analysis, significant markers for kernel size were detected on the three chromosomes tested, and alleles potentially useful for selection were identified. Our results demonstrated that association mapping could complement and enhance previous QTL information for marker-assisted selection.     

Genetic analysis of grain protein-content,grain yield and thousand-kernel weight in bread wheat

Grain yield and grain protein content are two very important traits in bread wheat. They are controlled by genetic factors, but environmental conditions considerably affect their expression. The aim of this study was to determine the genetic basis of these two traits by analysis of a segregating population of 194 F(7) recombinant inbred lines derived from a cross between two wheat varieties, grown at six locations in France in 1999. A genetic map of 254 loci was constructed, covering about 75% of the bread wheat genome. QTLs were detected for grain protein-content (GPC), yield and thousand-kernel weight (TKW). 'Stable' QTLs (i.e. detected in at least four of the six locations) were identified for grain protein-content on chromosomes 2A, 3A, 4D and 7D, each explaining about 10% of the phenotypic variation of GPC. For yield, only one important QTL was found on chromosome 7D, explaining up to 15.7% of the phenotypic variation. For TKW, three QTLs were detected on chromosomes 2B, 5B and 7A for all environments. No negative relationships between QTLs for yield and GPC were observed. Factorial Regression on GxE interaction allowed determination of some genetic regions involved in the differential reaction of genotypes to specific climatic factors, such as mean temperature and the number of days with a maximum temperature above 25 degrees C during grain filling.     

Molecular mapping of resistance to <Emphasis Type="Italic">Pyrenophora tritici-repentis</Emphasis> race 5 and sensitivity to Ptr ToxB in wheat

Tan spot, caused by Pyrenophora tritici-repentis (Ptr), is an economically important foliar disease in the major wheat growing areas of the world. Multiple races of the pathogen have been characterized based on their ability to cause necrosis and/or chlorosis in differential wheat lines. Isolates of race 5 cause chlorosis only, and they produce a host-selective toxin designated Ptr ToxB that induces chlorosis when infiltrated into sensitive genotypes. The international Triticeae mapping initiative (ITMI) mapping population was used to identify genomic regions harboring QTLs for resistance to fungal inoculations of Ptr race 5 and to determine the chromosomal location of the gene conditioning sensitivity to Ptr ToxB. The toxin-insensitivity gene, which we are designating tsc2, mapped to the distal tip of the short arm of chromosome 2B. This gene was responsible for the effects of a major QTL associated with resistance to the race 5 fungus and accounted for 69% of the phenotypic variation. Additional minor QTLs were identified on the short arm of 2A, the long arm of 4A, and on the long arm of chromosome 2B. Together, the major QTL on 2BS identified by tsc2 and the QTL on 4AL explained 73% of the total phenotypic variation for resistance to Ptr race 5. The results of this research indicate that Ptr ToxB is a major virulence factor, and the markers closely linked to tsc2 and the 4A QTL should be useful for introgression of resistance into adapted germplasm.     

Validation of quantitative trait loci for Fusarium head blight and kernel discoloration in barley

Validation of quantitative trait loci (QTLs) is a prerequisite to marker assisted selection (MAS), however, only a fraction of QTLs identified for important plant traits have been independently tested for validation. Resistance to the diseases kernel discoloration (KD) and Fusarium head blight (FHB) in barley is complex and technically difficult to assess, and therefore QTLs for these traits are suitable targets for MAS. We selected two lines, from a QTL mapping population created using the resistant variety Chevron, and crossed them to susceptible parents to generate two validation populations. Genetic maps of both populations were developed for five chromosomes covering 15 selected regions containing QTLs for FHB severity, KD score and concentration of deoxynivalenol (DON), a mycotoxin produced by the FHB pathogen. QTL analyses using these validation populations confirmed that five of the possible 15 QTL regions were associated with at least one of the three traits. While some QTL were detected inconsistently across environments, QTL that could be subjected to validation in both populations were confirmed in both populations in seven out of eight instances. A QTL for KD score on chromosome 6(6H) was confirmed in both validation populations in eight of nine environments and was also associated with FHB in three of six environments. A QTL for FHB on chromosome 2(2H) was confirmed and was also associated with KD and heading date. Marker assisted selection at these two QTLs should enhance disease resistance, however, the QTL on chromosome 2(2H) will also delay heading date.     

Genetic analysis of resistance to septoria tritici blotch in the French winter wheat cultivars Balance and Apache

The ascomycete Mycosphaerella graminicola is the causal agent of septoria tritici blotch (STB), one of the most destructive foliar diseases of bread and durum wheat globally, particularly in temperate humid areas. A screening of the French bread wheat cultivars Apache and Balance with 30 M. graminicola isolates revealed a pattern of resistant responses that suggested the presence of new genes for STB resistance. Quantitative trait loci (QTL) analysis of a doubled haploid (DH) population with five M. graminicola isolates in the seedling stage identified four QTLs on chromosomes 3AS, 1BS, 6DS and 7DS, and occasionally on 7DL. The QTL on chromosome 6DS flanked by SSR markers Xgpw5176 and Xgpw3087 is a novel QTL that now can be designated as Stb18. The QTLs on chromosomes 3AS and 1BS most likely represent Stb6 and Stb11, respectively, and the QTLs on chromosome 7DS are most probably identical with Stb4 and Stb5. However, the QTL identified on chromosome 7DL is expected to be a new Stb gene that still needs further characterization. Multiple isolates were used and show that not all isolates identify all QTLs, which clearly demonstrates the specificity in the M. graminicola–wheat pathosystem. QTL analyses were performed with various disease parameters. The development of asexual fructifications (pycnidia) in the characteristic necrotic blotches of STB, designated as parameter P, identified the maximum number of QTLs. All other parameters identified fewer but not different QTLs. The segregation of multiple QTLs in the Apache/Balance DH population enabled the identification of DH lines with single QTLs and multiple QTL combinations. Analyses of the marker data of these DH lines clearly demonstrated the positive effect of pyramiding QTLs to broaden resistance spectra as well as epistatic and additive interactions between these QTLs. Phenotyping of the Apache/Balance DH population in the field confirmed the presence of the QTLs that were identified in the seedling stage, but Stb18 was inconsistently expressed and might be particularly effective in young plants. In contrast, an additional QTL for STB resistance was identified on chromosome 2DS that is exclusively and consistently expressed in mature plants over locations and time, but it was also strongly related with earliness, tallness as well as resistance to Fusarium head blight. Although to date no Stb gene has been reported on chromosome 2D, the data provide evidence that this QTL is only indirectly related to STB resistance. This study shows that detailed genetic analysis of contemporary commercial bread wheat cultivars can unveil novel Stb genes that can be readily applied in marker-assisted breeding programs.     

Quantitative trait loci for yield and related traits in the wheat population Ning7840 × Clark

Grain yield and associated agronomic traits are important factors in wheat (Triticum aestivum L.) improvement. Knowledge regarding the number, genomic location, and effect of quantitative trait loci (QTL) would facilitate marker-assisted selection and the development of cultivars with desirable characteristics. Our objectives were to identify QTLs directly and indirectly affecting grain yield expression. A population of 132 F₁₂ recombinant inbred lines (RILs) was derived by single-seed descent from a cross between the Chinese facultative wheat Ning7840 and the US soft red winter wheat Clark. Phenotypic data were collected for 15 yield and other agronomic traits in the RILs and parental lines from three locations in Oklahoma from 2001 to 2003. Twenty-nine linkage groups, consisting of 363 AFLP and 47 SSR markers, were identified. Using composite interval mapping (CIM) analysis, 10, 16, 30, and 14 QTLs were detected for yield, yield components, plant adaptation (shattering and lodging resistance, heading date, and plant height), and spike morphology traits, respectively. The QTL effects ranged from 7 to 23%. Marker alleles from Clark were associated with a positive effect for the majority of QTLs for yield and yield components, but gene dispersion was the rule rather than the exception for this RIL population. Often, QTLs were detected in proximal positions for different traits. Consistent, co-localized QTLs were identified in linkage groups 1AL, 1B, 4B, 5A, 6A, and 7A, and less consistent but unique QTLs were identified on 2BL, 2BS, 2DL, and 6B. Results of this study provide a benchmark for future efforts on QTL identification for yield traits.     

QTL mapping of flag leaf-related traits in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Key message

QTL controlling flag leaf length, flag leaf width, flag leaf area and flag leaf angle were mapped in wheat.

Abstract

This study aimed to advance our understanding of the genetic mechanisms underlying morphological traits of the flag leaves of wheat (Triticum aestivum L.). A recombinant inbred line (RIL) population derived from ND3331 and the Tibetan semi-wild wheat Zang1817 was used to identify quantitative trait loci (QTLs) controlling flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), and flag leaf angle (FLANG). Using an available simple sequence repeat genetic linkage map, 23 putative QTLs for FLL, FLW, FLA, and FLANG were detected on chromosomes 1B, 2B, 3A, 3D, 4B, 5A, 6B, 7B, and 7D. Individual QTL explained 4.3–68.52% of the phenotypic variance in different environments. Four QTLs for FLL, two for FLW, four for FLA, and five for FLANG were detected in at least two environments. Positive alleles of 17 QTLs for flag leaf-related traits originated from ND3331 and 6 originated from Zang1817. QTLs with pleiotropic effects or multiple linked QTL were also identified on chromosomes 1B, 4B, and 5A; these are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.

     

Mapping candidate QTLs related to plant persistency in red clover

Red clover (Trifolium pratense L.) is a diploid (2n = 14), self-incompatible legume that is widely cultivated as a forage legume in cold geographical regions. Because it is a short-lived perennial species, improvement of plant persistency is the most important objective for red clover breeding. To develop a marker-assisted selection (MAS) approach for red clover, we identified candidate QTLs related to plant persistency. Two full-sib mapping populations, 272 × WF1680 and HR × R130, were used for QTL identification. Resistance to Sclerotinia trifoliorum and Fusarium species, as well as to winter hardiness, was investigated in the laboratory and in field experiments in Moscow region (Russia), and Sapporo (Japan). With the genotype data derived from microsatellite and other DNA markers, candidate QTLs were identified by simple interval mapping (SIM), Kruskal–Wallis analysis (KW analysis) and genotype matrix mapping (GMM). A total of 10 and 23 candidate QTL regions for plant persistency were identified in the 272 × WF1680 and the HR × R130 mapping populations, respectively. The QTLs identified by multiple mapping approaches were mapped on linkage group (LG) 3 and LG6. The significant QTL interactions identified by GMM explained the higher phenotypic variation than single effect QTLs. Identification of haplotypes having positive effect QTLs in each parent were first demonstrated in this study for pseudo-testcross mapping populations in plant species using experimental data.