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 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.

     

High-Density Genetic Linkage Map Construction and QTL Mapping of Grain Shape and Size in the Wheat Population Yanda1817 × Beinong6

High-density genetic linkage maps are necessary for precisely mapping quantitative trait loci (QTLs) controlling grain shape and size in wheat. By applying the Infinium iSelect 9K SNP assay, we have constructed a high-density genetic linkage map with 269 F ₈ recombinant inbred lines (RILs) developed between a Chinese cornerstone wheat breeding parental line Yanda1817 and a high-yielding line Beinong6. The map contains 2431 SNPs and 128 SSR & EST-SSR markers in a total coverage of 3213.2 cM with an average interval of 1.26 cM per marker. Eighty-eight QTLs for thousand-grain weight (TGW), grain length (GL), grain width (GW) and grain thickness (GT) were detected in nine ecological environments (Beijing, Shijiazhuang and Kaifeng) during five years between 2010–2014 by inclusive composite interval mapping (ICIM) (LOD≥2.5). Among which, 17 QTLs for TGW were mapped on chromosomes 1A, 1B, 2A, 2B, 3A, 3B, 3D, 4A, 4D, 5A, 5B and 6B with phenotypic variations ranging from 2.62% to 12.08%. Four stable QTLs for TGW could be detected in five and seven environments, respectively. Thirty-two QTLs for GL were mapped on chromosomes 1B, 1D, 2A, 2B, 2D, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6B, 7A and 7B, with phenotypic variations ranging from 2.62% to 44.39%. QGl.cau-2A.2 can be detected in all the environments with the largest phenotypic variations, indicating that it is a major and stable QTL. For GW, 12 QTLs were identified with phenotypic variations range from 3.69% to 12.30%. We found 27 QTLs for GT with phenotypic variations ranged from 2.55% to 36.42%. In particular, QTL QGt.cau-5A.1 with phenotypic variations of 6.82–23.59% was detected in all the nine environments. Moreover, pleiotropic effects were detected for several QTL loci responsible for grain shape and size that could serve as target regions for fine mapping and marker assisted selection in wheat breeding programs.     

Characterization of QTL for oil content in maize kernel

Kernel oil content in maize is a complex quantitative trait. Phenotypic variation in kernel oil content can be dissected into its component traits such as oil metabolism and physical characteristics of the kernel, including embryo size and embryo-to-endosperm weight ratio (EEWR). To characterize quantitative trait loci (QTL) for kernel oil content, a recombinant inbred population derived from a cross between normal line B73 and high-oil line By804 was genotyped using 228 molecular markers and phenotyped for kernel oil content and its component traits [embryo oil content, embryo oil concentration, EEWR, embryo volume, embryo width, embryo length, and embryo width-to-length ratio (EWLR)]. A total of 58 QTL were identified for kernel oil content and its component traits in 26 genomic regions across all chromosomes. Eight main-effect QTL were identified for kernel oil content, embryo oil content, embryo oil concentration, EEWR, embryo weight, and EWLR, each accounting for over 10?% of the phenotypic variation in six genomic regions. Over 90?% of QTL identified for kernel oil content co-localized with QTL for component traits, validating their molecular contribution to kernel oil content. On chromosome 1, the QTL that had the largest effect on kernel oil content (qKO1-1) was associated with embryo width; on chromosome 9, the QTL for kernel oil content (qKO9) was related to EEWR (qEEWR9). Embryo oil concentration and embryo width were identified as the most important component traits controlling the second largest QTL for kernel oil content on chromosome 6 (qKO6) and a minor QTL for kernel oil content on chromosome 5 (qKO5-2), respectively. The dissection of kernel oil QTL will facilitate future cloning and/or functional validation of kernel oil content, and help to elucidate the genetic basis of kernel oil content in maize.     

Identification of QTLs for Yield and Associated Traits in F₂ Population of Rice

Identification of quantitative trait loci (QTLs) controlling yield and yield-related traits in rice was performed in the F₂ mapping population derived from parental rice genotypes DHMAS and K343. A total of 30 QTLs governing nine different traits were identified using the composite interval mapping (CIM) method. Four QTLs were mapped for number of tillers per plant on chromosomes 1 (2 QTLs), 2 and 3; three QTLs for panicle number per plant on chromosomes 1 (2 QTLs) and 3; four QTLs for plant height on chromosomes 2, 4, 5 and 6; one QTL for spikelet density on chromosome 5; four QTLs for spikelet fertility percentage (SFP) on chromosomes 2, 3 and 5 (2 QTLs); two QTLs for grain length on chromosomes 1 and 8; three QTLs for grain width on chromosomes1, 3 and 8; three QTLs for 1000-grain weight (TGW) on chromosomes 1, 4 and 8 and six QTLs for yield per plant (YPP) on chromosomes 2 (3 QTLs), 4, 6 and 8. Most of the QTLs were detected on chromosome 2, so further studies on chromosome 2 could help unlock some new chapters of QTL for this cross of rice variety. Identified QTLs elucidating high phenotypic variance can be used for marker-assisted selection (MAS) breeding. Further, the exploitation of information regarding molecular markers tightly linked to QTLs governing these traits will facilitate future crop improvement strategies in rice.     

Genetic analysis of kernel hardness in bread wheat using PCR-based markers

In wheat, kernel hardness is a complex genetic trait involving various directly and indirectly contributing components such as kernel hardness per se, protein content, hectolitre weight and 1,000-kernel weight. In an attempt to identify DNA markers associated with this trait, 100 recombinant inbred lines (RILs) derived from a cross between a hard grain land-race, NP4, and a soft grain variety, HB 208, were screened with 100 ISSR and 360 RAPD primers. Eighteen markers were assigned to seven linkage groups covering 223.6 cM whereas 11 markers remained unlinked. A multiple-marker model explained the percentage of phenotypic variation for kernel hardness as 20.6%, whereas that for protein content, hectolitre weight and 1,000-kernel weight was 18.8%, 13.5% and 12.1%, respectively. Our results indicate that phenotypic expression of kernel hardness is controlled by many QTLs and is interdependent on various related traits. Received: 25 July 2000 / Accepted: 24 November 2000     

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.

     

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.     

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.     

Conditional and unconditional QTL mapping of drought-tolerance-related traits of wheat seedling using two related RIL populations

For discovering the quantitative trait loci (QTLs) contributing to early seedling growth and drought tolerance during germination, conditional and unconditional analyses of 12 traits of wheat seedlings: coleoptile length, seedling height, longest root length, root number, seedling fresh weight, stem and leaves fresh weight, root fresh weight, seedling dry weight, stem and leaves dry weight, root dry weight, root to shoot fresh weight ratio, root-to-shoot dry weight ratio, were conducted under two water conditions using two F_8:9 recombinant inbred line (RIL) populations. The results of unconditional analysis are as follows: 88 QTLs accounting for 3.33–77.01% of the phenotypic variations were detected on chromosomes 1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B and 7D. Among these QTLs, 19 were main-effect QTLs with a contribution rate greater than 10%. The results of the conditional QTL analysis of 12 traits under osmotic stress on normal water conditions were as follows: altogether 22 QTLs concerned with drought tolerance were detected on chromosomes 1B, 2A, 2B, 3B, 4A, 5D, 6A, 6D, 7B, and 7D. Of these QTLs, six were main-effect QTLs. These 22 QTLs were all special loci directly concerned with drought tolerance and most of them could not be detected by unconditional analysis. The finding of these QTLs has an important significance for fine-mapping technique, map-based cloning, and molecular marker-assisted selection of early seedling traits, such as growth and drought tolerance.     

Mapping quantitative trait loci for quality factors in an inter-class cross of US and Chinese wheat

Wheat quality factors are critical in determining the suitability of wheat (Triticum aestivum L.) for end-use product and economic value, and they are prime targets for marker-assisted selection. Objectives of this study were to identify quantitative trait loci (QTLs) that ultimately influence wheat market class and milling quality. A population of 132 F₁₂ recombinant inbred lines (RILs) was derived by single-seed descent from a cross between the Chinese hard wheat line Ning7840 and the soft wheat cultivar Clark and grown at three Oklahoma locations from 2001 to 2003. Milling factors such as test weight (volumetric grain weight, TW), kernel weight (KW), and kernel diameter (KD) and market class factors such as wheat grain protein content (GPC) and kernel hardness index (HI) were characterized on the basis of a genetic map constructed from 367 SSR and 241 AFLP markers covering all 21 chromosomes. Composite interval mapping identified eight QTLs for TW, seven for KW, six for KD, two each for GPC and HI measured by near-infrared reflectance (NIR) spectroscopy, and four for HI measured by single kernel characterization system. Positive phenotypic correlations were found among milling factors. Consistent co-localized QTLs were identified for TW, KW, and KD on the short arms of chromosomes 5A and 6A. A common QTL was identified for TW and KD on the long arm of chromosome 5A. A consistent major QTL for HI peaked at the Pinb-D1 locus on the short arm of chromosome 5D and explained up to 85% of the phenotypic variation for hardness. We identified QTLs for GPC on 4B and the short arm of 3A chromosomes. The consistency of quality factor QTLs across environments reveals their potential for marker-assisted selection.     

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.     

Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments

Key Message

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement.

Abstract

Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F_2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02–1.03, 1.04–1.06, 2.05–2.07, 4.07–4.08 and 9.03–9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.     

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.     

Quantitative trait locus analysis of drought tolerance and yield in Maize in China

Drought accounts for significant yield losses in crops. Maize (Zea mays L.) is particularly sensitive to water stress at reproductive stages, and breeding to improve drought tolerance has been a challenge. By use of a linkage map with 121 single sequence repeat (SSR) markers, quantitative trait loci (QTLs) for grain yield and yield components were characterized in the population of the cross X178×B73 under water-stressed and well-watered conditions. Under the well-watered regime, 2, 4, 4, 1, 2, 2, and 3 QTLs were identified for grain yield, 100-kernel weight, kernel number per ear, cob weight per ear, kernel weight per ear, ear weight, and ear number per plant, respectively, whereas under the water-stressed conditions, 1, 5, 2, 6, 1, 3, and 2 QTLs, respectively, were found. The significant phenotypic correlations among yield and yield components to some extent were observed under both water conditions, and some overlaps between the corresponding QTLs were also found. QTLs for grain yield and kernel weight per ear under well-watered conditions and ear weight under both well-watered and water-stressed conditions over-lapped, and all were located on chromosome 1.03 near marker bnlg176. Two other noticeable QTL regions were on chromosome 9.05 and 9.07 near markers umc1657 and bnlg1525; the first corresponded to grain yield, kernel weight per ear, and ear weight under well-watered conditions and kernel number per ear under both water conditions, and the second to grain yield and cob weight per ear under water-stressed conditions and ear number per plant under both water conditions. A comparative analysis of the QTLs herein identified with those described in previous studies for yield and yield components in different maize populations revealed a number of QTLs in common. These QTLs have potential use in molecular marker-assisted selection.     

Identification of quantitative trait loci affecting shank length, body weight and carcass weight from the Japanese cockfighting chicken breed, Oh-Shamo (Japanese Large Game)

We performed a quantitative trait locus (QTL) analysis to map QTLs controlling shank length, body weight, and carcass weight in a resource family of 245 F(2) birds developed from a cross of the large-sized, native, Japanese cockfighting breed, Oh-Shamo (Japanese Large Game), and the White Leghorn breed of chickens. Interval mapping revealed three significant QTLs for shank length on chromosomes 1, 4 and 24 at the experiment-wise 5% level, and a suggestive shank length QTL on chromosome 27 at the experiment-wise 10% level. For body weight two QTLs, one significant and the other suggestive, were identified on chromosomes 4 and 24, respectively. As expected, QTLs for carcass weight, which was highly correlated with body weight (r = 0.95), were detected at the same chromosomal locations as the detected body weight QTLs. Interestingly, the chromosomal locations containing these body weight and carcass weight QTLs coincided with those of two of the four shank length QTLs detected. No QTL with an epistatic interaction effect was discovered for any trait. The total contribution of all detected QTLs to genetic variance was 98.4%, 27.0% and 25.9% for shank length, body weight and carcass weight, respectively, indicating that most shank length QTLs have been identified but many body weight and carcass weight QTLs have been overlooked by the present analysis because of a low coverage rate of the 88 microsatellite markers used here (approximately 46% of the whole genome).     

Genetic dissection of yield-related traits in a recombinant inbred line population created using a key breeding parent in China’s wheat breeding

Understanding the genetics underlying yield formation of wheat is important for increasing wheat yield potential in breeding programs. Nanda2419 was a widely used cultivar for wheat production and breeding in China. In this study, we evaluated yield components and a few yield-related traits of a recombinant inbred line (RIL) population created by crossing Nanda2419 with the indigenous cultivar Wangshuibai in three to four trials at different geographical locations. Negative and positive correlations were found among some of these evaluated traits. Five traits had over 50 % trial-wide broad sense heritability. Using a framework marker map of the genome constructed with this population, quantitative trait loci (QTL) were identified for all traits, and epistatic loci were identified for seven of them. Our results confirmed some of the previously reported QTLs in wheat and identified several new ones, including QSn.nau-6D for effective tillers, QGn.nau-4B.2 for kernel number, QGw.nau-4D for kernel weight, QPh.nau-4B.2 and QPh.nau-4A for plant height, and QFlw.nau-5A.1 for flag leaf width. In the investigated population, Nanda2419 contributed all QTLs associated with higher kernel weight, higher leaf chlorophyll content, and a major QTL associated with wider flag leaf. Seven chromosome regions were related to more than one trait. Four QTL clusters contributed positively to breeding goal-based trait improvement through the Nanda2419 alleles and were detected in trials set in different ecological regions. The findings of this study are relevant to the molecular improvement of wheat yield and to the goal of screening cultivars for better breeding parents.     

Genomic dissection of drought resistance in durum wheat × wild emmer wheat recombinant inbreed line population

Drought is the major factor limiting wheat productivity worldwide. The gene pool of wild emmer wheat, Triticum turgidum ssp. dicoccoides , harbours a rich allelic repertoire for morpho-physiological traits conferring drought resistance. The genetic and physiological bases of drought responses were studied here in a tetraploid wheat population of 152 recombinant inbreed lines (RILs), derived from a cross between durum wheat (cv. Langdon) and wild emmer (acc# G18-16), under contrasting water availabilities. Wide genetic variation was found among RILs for all studied traits. A total of 110 quantitative trait loci (QTLs) were mapped for 11 traits, with LOD score range of 3.0–35.4. Several QTLs showed environmental specificity, accounting for productivity and related traits under water-limited (20 QTLs) or well-watered conditions (15 QTLs), and in terms of drought susceptibility index (22 QTLs). Major genomic regions controlling productivity and related traits were identified on chromosomes 2B, 4A, 5A and 7B. QTLs for productivity were associated with QTLs for drought-adaptive traits, suggesting the involvement of several strategies in wheat adaptation to drought stress. Fifteen pairs of QTLs for the same trait were mapped to seemingly homoeologous positions, reflecting synteny between the A and B genomes. The identified QTLs may facilitate the use of wild alleles for improvement of drought resistance in elite wheat cultivars.     

QTL mapping for grain filling rate and yield-related traits in RILs of the Chinese winter wheat population Heshangmai × Yu8679

A set of 142 winter wheat recombinant inbred lines (RILs) deriving from the cross Heshangmai x Yu8679 were tried in four ecological environments during the seasons 2006 and 2007. Nine agronomic traits comprising mean grain filling rate (GFR(mean)), maximum grain filling rate (GFR(max)), grain filling duration (GFD), grain number per ear (GNE), grain weight per ear (GWE), flowering time (FT), maturation time (MT), plant height (PHT) and thousand grain weight (TGW) were evaluated in Beijing (2006 and 2007), Chengdu (2007) and Hefei (2007). A genetic map comprising 173 SSR markers and two EST markers was generated. Based on the genetic map and phenotypic data, quantitative trait loci (QTL) were mapped for these agronomic traits. A total of 99 putative QTLs were identified for the nine traits over four environments except GFD, PHT and MT, measured in two environments (BJ07 and CD07), respectively. Of the QTL detected, 17 for GFR(mean), 16 for GFR(max), 21 for TGW and 10 for GWE involving the chromosomes 1A, 1B, 2A, 2D, 3A, 3B, 3D, 4A, 4D, 5A, 5B, 6D and 7D were identified. Moreover, 13 genomic regions showing pleiotropic effects were detected in chromosomes 1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 4B, 4D, 5B, 6D and 7D; these QTL revealing pleiotropic effects may be informative for a better understanding of the genetic basis of grain filling rate and other yield-related traits, and represent potential targets for multi-trait marker aided selection in wheat.     

Detection of loci controlling seed dormancy on group 4 chromosomes of wheat and comparative mapping with rice and barley genomes

Three quantitative trait loci (QTLs) controlling seed dormancy were detected on group 4 chromosomes of wheat (Triticum aestivum L.) using 119 doubled haploid lines (DHLs) derived from a cross between AC Domain and Haruyutaka. A major QTL, designated QPhs.ocs-4A.1, was identified within the marker interval between Xcdo795 and Xpsr115 in the proximal region of the long arm of chromosome 4A. Two minor QTLs, QPhs.ocs-4B.2 on 4B and QPhs.ocs-4D.2 on 4D, were flanked by common markers, Xbcd1431.1 and Xbcd1431.2 in the terminal region of the long arms, suggesting a homoeologous relationship. These three QTLs explained more than 80% of the total phenotypic variance in seed dormancy of DHLs grown in the field and under glasshouse conditions. The AC Domain alleles at the three QTLs contributed to increasing seed dormancy. Comparative maps across wheat, barley and rice demonstrated the possibility of a homoeologous relationship between QPhs.ocs-4A.1 and the barley gene SD4, while no significant effects of the chromosome regions of wheat and barley orthologous to rice chromosome 3 region carrying a major seed dormancy QTL were detected. Received: 5 June 2000 / Accepted: 31 August 2000