Two consensus quantitative trait loci clusters controlling anthesis–silking interval, ear setting and grain yield might be related with drought tolerance in maize

Unravelling the molecular basis of drought tolerance will provide novel opportunities for improving crop yield under water-limited conditions. The present study was conducted to identify quantitative trait loci (QTLs) controlling anthesis–silking interval (ASI), ear setting percentage (ESP) and grain yield (GY). The mapping population included 234 F₂ plants derived from the cross X178 (drought tolerant) × B73 (drought susceptible). The corresponding F_2:3 progenies, along with their parents, were evaluated for the above-mentioned traits under both well-watered and water-stressed field conditions in three different trials carried out in central and southern China. Interval mapping and composite interval mapping identified 45 and 65 QTLs for the investigated traits, respectively. Two QTL clusters influencing ASI and ESP on chromosomes 1 (bin 1.03) and 9 (bins 9.03–9.05) were identified in more than two environments, showing sizeable additive effects and contribution to phenotypic variance; these two QTL clusters influenced GY only in one environment. No significant interaction was detected between the two genomic regions. A comparative analysis of these two QTL clusters with the QTLs controlling maize drought tolerance previously described in three mapping populations confirmed and extended their relevance for marker-assisted breeding to improve maize production under water-limited conditions.     

Use of genotype-environment interactions to elucidate the pattern of maize root plasticity to nitrogen deficiency

Maize(Zea mays L.) root morphology exhibits a high degree of phenotypic plasticity to nitrogen(N) de ficiency,but the underlying genetic architecture remains to be investigated Using an advanced BC_4F_3 population,we investigated the root growth plasticity under two contrasted N levels and identi fied the quantitative trait loci(QTLs) with QTL-environment(Q×E)interaction effects. Principal components analysis(PCA) on changes of root traits to N de ficiency(D LN-HN) showed that root length and biomass contributed for 45.8% in the same magnitude and direction on the first PC,while root traits scattered highly on PC_2 and PC_3. Hierarchical cluster analysis on traits for D LN-HN further assigned the BC_4F_3 lines into six groups,in which the special phenotypic responses to N de ficiency was presented These results revealed the complicated root plasticity of maize in response to N de ficiency that can be caused by genotype environment(G×E) interactions. Furthermore,QTL mapping using a multi-environment analysis identi fied 35 QTLs for root traits. Nine of these QTLs exhibited signi ficant Q×E interaction effects. Taken together,our findings contribute to understanding the phenotypic and genotypic pattern of root plasticity to N de ficiency,which will be useful for developing maize tolerance cultivars to N de ficiency.     

Genetic analysis of drought tolerance in maize by molecular markers I. Yield components

Grain yield is a complex trait, strongly influenced by the environment: severe losses can be caused by drought, a stress common in most maize-growing areas, including temperate climatic zones. Accordingly, drought tolerance is one of the main components of yield stability, and its improvement is a major challenge to breeders. The aim of the present work was the identification, in maize genotypes adapted to temperate areas, of genomic segments responsible for the expression of drought tolerance of yield components: ear length, ear weight, kernel weight, kernel number and 50-kernel weight. A linkage analysis between the expression of these traits and molecular markers was performed on a recombinant inbred population of 142 families, obtained by repeated selfing of the F₁ between lines B73 and H99. The population, genotyped at 173 loci (RFLPs, microsatellites and AFLPs), was evaluated in well-watered and water-stressed conditions. A drought tolerance index was calculated as the ratio between the mean value of the trait in the two environments. For the traits measured, a highly positive correlation was found over the two water regimes, and more than 50% of the quantitative trait loci (QTLs) detected were the same in both; moreover, the direction of the allelic contribution was always consistent, the allele increasing the trait value being mostly from line B73. Several QTLs were common to two or more traits. For the tolerance index, however, most of the QTLs were specific for a single component and different from those controlling the basic traits; in addition, a large proportion of the alleles increasing tolerance were provided by line H99. The data suggest that drought tolerance for yield components is largely associated with genetic and physiological factors independent from those determining the traits per se. The implications of these results for developing an efficient strategy of marker-assisted selection for drought tolerance are discussed. Received: 19 October 1998 / Accepted: 28 December 1998     

Quantitative Trait Loci Mapping of Maize Yield and Its Components Under Different Water Treatments at Flowering Time

Drought or water stress is a serious agronomic problem resulting in maize (Zea mays L.) yield loss throughout the world. Breeding hybrids with drought tolerance is one important approach for solving this problem. However, lower efficiency and a longer period of breeding hybrids are disadvantages of traditional breeding programs. It is generally recognized that applying molecular marker techniques to traditional breeding programs could improve the efficiency of the breeding of drought‐tolerant maize. To provide useful information for use in studies of maize drought tolerance, the mapping and tagging of quantitative trait loci (QTL) for yield and its components were performed in the present study on the basis of the principle of a mixed linear model. Two hundred and twenty‐one recombinant inbred lines (RIL) of Yuyu 22 were grown under both well‐watered and water‐stressed conditions. In the former treatment group, plants were well irrigated, whereas those in the latter treatment group were stressed at flowering time. Ten plants of each genotype were grown in a row that was 3.00 m × 0.67 m (length × width). The results show that a few of the QTL were the same (one additive QTL for ear length, two additive QTL and one pair of epistatic QTL for kernel number per row, one additive QTL for kernel weight per plant), whereas most of other QTL were different between the two different water treatment groups. It may be that genetic expression differs under the two different water conditions. Furthermore, differences in the additive and epistatic QTL among the traits under water‐stressed conditions indicate that genetic expression also differs from trait to trait. Major and minor QTL were detected for the traits, except for kernel number per row, underwater‐stressed conditions. Thus, the genetic mechanism of drought tolerance in maize is complex because the additive and epistatic QTL exist at the same time and the major and minor QTL all contribute to phenotype under water‐stressed conditions. In particular, epidemic QTL under water‐stressed conditions suggest that it is important to investigate the drought tolerance of maize from a genetic viewpoint. (Managing editor: Wei Wang)     

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 QTL for maize grain yield and kernel-related traits

Grain yield (GY) is one of the most important and complex quantitative traits in maize (Zea mays L.) breeding practice. Quantitative trait loci (QTLs) for GY and three kernel-related traits were detected in a set of recombinant inbred lines (RILs). One hundred and seven simple sequence repeats (SSRs) and 168 insertion/deletion polymorphism markers (Indels) were used to genotype RILs. Eight QTLs were found to be associated with four yield-related traits: GY, 100-kernel weight (HKW), 10-kernel length (KL), and 10-kernel length width (KW). Each QTL explained between 5.96 (qKL2-1) and 13.05 (qKL1-1) per cent of the phenotypic variance. Notably, one common QTL, located at the marker interval between bnlg1893 and chr2-236477 (chromosomal bin 2.09) simultaneously controlled GY and HKW; another common QTL, at bin 2.03 was simultaneously responsible for HKW and KW. Of the QTLs identified, only one pair of significant epistatic interaction involved in chromosomal region at bin 2.03 was detected for HKW; no significant QTL × environment interactions were observed. These results provide the common QTLs and for marker-assisted breeding.     

G × E Analysis of Growth Traits of <i>Betula platyphylla</i> Clones at Three Separated Sites in Northeastern China

To select elite materials, the growth traits of 32 Betula platyphylla clones at three separated northern sites in Northeast China were investigated and analyzed. The results showed that there were significant differences among all variation sources in the different investigated traits (P < 0.01). Except for the carbon contents, all the coefficients of phenotypic variation of the other investigated traits were higher than 10%. The repeatability of different traits ranged from 0.760 to 0.998. Correlation analysis showed that tree height were significantly correlated with diameter at breast height, but neither was significantly correlated with leaf traits nor element contents. Additive main effects and multiplicative interaction analysis showed that genotype, environment and genotype × environment interactions were significantly different in diameter at breast height, which indicated that environment had a significant effect on genotype. Comprehensive assessment results showed that three clones with high and stable diameters at breast height were selected, and the genetic gains of diameter at breast height on sites Maoershan, Qingan, and Yongji were 21.24%, 20.58%, and 38.65%, respectively. The results could provide a theoretical basis for elite clone selection in B. platyphylla and other broad leaved species.     

Differential Responses of Soil Organic Carbon Fractions and Carbon Turnover Related Enzyme Activities to Wheat Straw Incorporation in Subtropical China

Soil organic carbon (SOC) fractions and C turnover related enzyme activities are essential for nutrient cycling. This is because they are regarded as important indicators of soil fertility and quality. We measured the effects of wheat straw incorporation on SOC fractions and C turnover related enzyme activities in a paddy field in subtropical China. Soil samples were collected from 0–10 cm and 10–20 cm depths after rice harvesting. The total SOC concentrations were higher in the high rate of wheat straw incorporation treatment (NPKS2) than in the not fertilized control (CK) (P < 0.05). The concentrations of labile C fractions [i.e., water soluble organic C (WSOC), hot-water soluble organic C (HWSOC), microbial biomass C (MBC), and easily oxidizable C (EOC)], were higher in the moderate NPKS1 and NPKS2 treatments than in CK and the fertilized treatment without straw (NPK) (P < 0.05). The geometric means of labile C (GMC) and C pool management index (CPMI) values were highest in NPKS2 (P < 0.05). The SOC concentrations correlated positively with the labile C fractions (P < 0.05). Soil cellulase activity and the geometric mean of enzyme activities (GMea) were higher in NPKS2 than in CK in all soil layers (P < 0.05), and the invertase activity was higher in NPKS2 than in CK in the 0–10 cm layer (P < 0.05). Stepwise multiple linear regression indicated that the formation of the SOC, WSOC, HWSOC, MBC, and EOC was mostly enhanced by the cellulase and invertase activities (P < 0.05). Therefore, the high rate of wheat straw incorporation may be recommended to increase soil C pool levels and soil fertility in subtropical paddy soils.     

Mapping QTLs for root traits under different nitrate levels at the seedling stage in maize (Zea mays L.)

Nitrogen (N) loss is a worldwide problem in crop production. Apart from reasonable N fertilizer application, breeding N efficient cultivars provides an alternative way. Root architecture is an important factor determining N acquisition. However, little is known about the molecular genetic basis for root growth in relation to N supply. In the present study, an F₈ maize (Zea may L.) recombinant inbred (RI) population consisting of 94 lines was used to identify the QTLs for root traits under different nitrate levels. The lateral root length (LRL), axial root length (ARL), maximal axial root length (MARL), axial root number (ARN) and average axial root length (AARL) were evaluated under low N (LN) and high N (HN) conditions in a hydroponics system. A total of 17 QTLs were detected among which 14 loci are located on the same chromosome region as published QTLs for root traits. A major QTL on chromosome 1 (between bnlg1025 and umc2029) for the AARL under LN could explain 43.7% of the phenotypic variation. This QTL co-localizes with previously reported QTLs that associate with root traits, grain yield, and N uptake. Our results indicate that longer axial roots are important for efficient N acquisition and the major QTL for AARL may be used as a marker in breeding N efficient maize genotypes.     

Genetic and QTL analysis of maize tassel and ear inflorescence architecture

Maize (Zea mays L.) ear inflorescence architecture is directly relevant to grain yield components, and tassel architecture is relevant to hybrid seed production. The objectives of this study were to (1) determine heritabilities and correlations of a comprehensive set of tassel and ear inflorescence architecture traits in a set of (Illinois Low Protein×B73) B73 S₁ families, (2) identify chromosomal positions of QTL affecting tassel and ear architecture, and (3) identify possible candidate genes associated with these QTL. For tassel traits, the number of detected QTL ranged from one to five, and explained between 6.5 and 35.9% of phenotypic variation. For ear traits, the number of detected QTL ranged from one to nine and phenotypic variation explained by those QTL varied between 7.9 and 53.0%. We detected QTL for tassel architecture traits that required calculation of ratios from measured traits. Some of these calculated traits QTL were detected in regions that did not show QTL for the measured traits, suggesting that calculation of ratios may reveal developmentally relevant patterns of tassel architecture. We detected a QTL on chromosome 7 for tassel branch number near the gene ramosa1 (ra1), which is known to control tassel branch number, making ra1 a candidate gene for tassel branch number. We detected QTL for several traits on chromosomes 6, 8, and 9, where no inflorescence architecture genes have been mapped, thus providing initial information towards new gene discovery for control of inflorescence architecture.     

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.     

Proteomic profiling of two maize inbreds during early gibberella ear rot infection

Fusarium graminearum is the causal agent of gibberella ear rot in maize ears, resulting in yield losses due to mouldy and mycotoxin‐contaminated grain. This study represents a global proteomic approach to document the early infection by F. graminearum of two maize inbreds, B73 and CO441, which differ in disease susceptibility. Mock‐ and F. graminearum‐treated developing kernels were sampled 48 h post‐inoculation over three field seasons. Infected B73 kernels consistently contained higher concentrations of the mycotoxin deoxynivalenol than the kernels of the more tolerant inbred CO441. A total of 2067 maize proteins were identified in the iTRAQ analysis of extracted kernel proteins at a 99% confidence level. A subset of 878 proteins was identified in at least two biological replicates and exhibited statistically significantly altered expression between treatments and/or the two inbred lines of which 96 proteins exhibited changes in abundance >1.5‐fold in at least one of the treatments. Many proteins associated with the defense response were more abundant after infection, including PR‐10 (PR, pathogenesis‐related), chitinases, xylanase inhibitors, proteinase inhibitors, and a class III peroxidase. Kernels of the tolerant inbred CO441 contained higher levels of these defense‐related proteins than B73 kernels even after mock treatment, suggesting that these proteins may provide a basal defense against Fusarium infection in CO441.     

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.     

Identification of quantitative trait loci for nitrogen use efficiency in maize

Intensively managed crop systems are normally dependent on nitrogen input to maximize yield potential. Improvements in nitrogen- use efficiency (NUE) in crop plants may support the development of cropping systems that are more economically efficient and environment friendly. The objective of this study was to map and characterize quantitative trait loci (QTL) for NUE in a maize population. In preliminary experiments, inbred lines contrasting for NUE were identified and were used to generate populations of F2:3 families for genetic study. A total of 214 F2:3 families were evaluated in replicated trials under high nitrogen (280 kg/ha) and low nitrogen (30 kg/ha) conditions in 1996 and 1997. Analysis of ear-leaf area, plant height, grain yield, ears per plant, kernels number per ear, and kernel weight indicated significant genetic variation among F2:3 families. The heritability of these traits was found to be high (h2=0.57–0.81). The mapping population were genotyped using a set of 99 restriction fragment length polymorphism (RFLP) markers. A linkage map of these markers was developed and used to identify QTL. Between two and six loci were found to be associated with each trait. The correspondence of several genomic regions with traits measured under nitrogen limited conditions suggests the presence of QTL associated with NUE. QTLs will help breeders to improve their maize ideotype of a low-nitrogen efficiency by identifying those constitutive and adaptive traits involved in the expression of traits significantly correlated with yield, such as ear leaf area and number of ears per plant. This revised version was published online in June 2006 with corrections to the Cover Date.     

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.     

Mapping of quantitative trait loci for flesh colour and growth traits in Atlantic salmon (Salmo salar)

Background

Flesh colour and growth related traits in salmonids are both commercially important and of great interest from a physiological and evolutionary perspective. The aim of this study was to identify quantitative trait loci (QTL) affecting flesh colour and growth related traits in an F2 population derived from an isolated, landlocked wild population in Norway (Byglands Bleke) and a commercial production population.

Methods

One hundred and twenty-eight informative microsatellite loci distributed across all 29 linkage groups in Atlantic salmon were genotyped in individuals from four F2 families that were selected from the ends of the flesh colour distribution. Genotyping of 23 additional loci and two additional families was performed on a number of linkage groups harbouring putative QTL. QTL analysis was performed using a line-cross model assuming fixation of alternate QTL alleles and a half-sib model with no assumptions about the number and frequency of QTL alleles in the founder populations.

Results

A moderate to strong phenotypic correlation was found between colour, length and weight traits. In total, 13 genome-wide significant QTL were detected for all traits using the line-cross model, including three genome-wide significant QTL for flesh colour (Chr 6, Chr 26 and Chr 4). In addition, 32 suggestive QTL were detected (chromosome-wide P < 0.05). Using the half-sib model, six genome-wide significant QTL were detected for all traits, including two for flesh colour (Chr 26 and Chr 4) and 41 suggestive QTL were detected (chromosome-wide P < 0.05). Based on the half-sib analysis, these two genome-wide significant QTL for flesh colour explained 24% of the phenotypic variance for this trait.

Conclusions

A large number of significant and suggestive QTL for flesh colour and growth traits were found in an F2 population of Atlantic salmon. Chr 26 and Chr 4 presented the strongest evidence for significant QTL affecting flesh colour, while Chr 10, Chr 5, and Chr 4 presented the strongest evidence for significant QTL affecting growth traits (length and weight). These QTL could be strong candidates for use in marker-assisted selection and provide a starting point for further characterisation of the genetic components underlying flesh colour and growth.     

Mapping of QTL for downy mildew resistance in maize

Quantitative trait loci (QTLs) of maize involved in mediating resistance to Peronosclerospora sorghi, the causative agent of sorghum downy mildew (SDM), were detected in a population of recombinant inbred lines (RILs) derived from the Zea mays L. cross between resistant (G62) and susceptible (G58) inbred lines. Field tests of 94 RILs were conducted over two growing seasons using artificial inoculation. Heritability of the disease reaction was high (around 70%). The mapping population of the RILs was also scored for restriction fragment length polymorphic (RFLP) markers. One hundred and six polymorphic RFLP markers were assigned to ten chromosomes covering 1648 cM. Three QTLs were detected that significantly affected resistance to SDM combined across seasons. Two of these mapped quite close together on chromosome 1, while the third one was on chromosome 9. The percentage of phenotypic variance explained by each QTL ranged from 12.4% to 23.8%. Collectively, the three QTLs identified in this study explained 53.6% of the phenotypic variation in susceptibility to the infection. The three resistant QTLs appeared to have additive effects. Increased susceptibility was contributed by the alleles of the susceptible parent. The detection of more than one QTL supports the hypothesis that several qualitative and quantitative genes control resistance to P. sorghi.     

Detection of quantitative trait loci for body weight at 10 weeks from Philippine wild mice

A genome-wide scan for quantitative trait loci (QTLs) controlling body weight at 10 weeks after birth was carried out in a population of 387 intersubspecific backcross mice derived from a cross between C57BL/6J inbred mice (Mus musculus domesticus) and wild mice (M. m. castaneus) captured in the Philippines, in order to discover novel QTLs from the wild mice that have about 60% lower body weight than C57BL/6J. By interval mapping, we detected four QTLs: a highly significant QTL on Chromosome (Chr) 2, which was common in both sexes; two significant QTLs on Chr 13, one male-specific and the other female-specific; and a suggestive male-specific QTL on X Chr. By composite interval mapping, we confirmed the presence of the three QTLs on Chrs 2 and 13, but not of the male-specific X-linked QTL. The composite interval mapping analysis newly identified three QTLs: a significant male-specific QTL on Chr 11 and two highly significant female-specific QTLs on Chrs 9 and X. Individual QTLs explained 3.8–11.6% of the phenotypic variance, and all the QTL alleles derived from the wild mice decreased body weight. A two-way analysis of variance revealed a significant epistatic interaction between the Chr 2 QTL and the background marker locus D12Mit4 on Chr 12 only in males. The interaction effect unexpectedly increased body weight. The chromosomal region containing the Chr 2 QTL did not coincide with those of growth or fatness QTLs mapped in previous studies. These results suggest that a population of wild mice may play an important role as new sources of valuable QTLs. Received: 14 January 2000 / Accepted: 14 April 2000     

Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping

Kernel size‐related traits are the most direct traits correlating with grain yield. The genetic basis of three kernel traits of maize, kernel length (KL), kernel width (KW) and kernel thickness (KT), was investigated in an association panel and a biparental population. A total of 21 single nucleotide polymorphisms (SNPs) were detected to be most significantly (P < 2.25 × 10^?6) associated with these three traits in the association panel under four environments. Furthermore, 50 quantitative trait loci (QTL) controlling these traits were detected in seven environments in the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, of which eight were repetitively identified in at least three environments. Combining the two mapping populations revealed that 56 SNPs (P < 1 × 10^?3) fell within 18 of the QTL confidence intervals. According to the top significant SNPs, stable‐effect SNPs and the co‐localized SNPs by association analysis and linkage mapping, a total of 73 candidate genes were identified, regulating seed development. Additionally, seven miRNAs were found to situate within the linkage disequilibrium (LD) regions of the co‐localized SNPs, of which zma‐miR164e was demonstrated to cleave the mRNAs of Arabidopsis CUC1, CUC2 and NAC6 in vitro. Overexpression of zma‐miR164e resulted in the down‐regulation of these genes above and the failure of seed formation in Arabidopsis pods, with the increased branch number. These findings provide insights into the mechanism of seed development and the improvement of molecular marker‐assisted selection (MAS) for high‐yield breeding in maize.     

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