Mapping and characterisation of QTL × E interactions for traits determining grain and stover yield in pearl millet

A mapping population of 104 F(3) lines of pearl millet, derived from a cross between two inbred lines H 77/833-2 x PRLT 2/89-33, was evaluated, as testcrosses on a common tester, for traits determining grain and stover yield in seven different field trials, distributed over 3 years and two seasons. The total genetic variation was partitioned into effects due to season (S), genotype (G), genotype x season interaction (G x S), and genotype x environment-within-season interaction [G x E(S)]. QTLs were determined for traits for their G, G x S, and G x E(S) effects, to assess the magnitude and the nature (cross over/non-crossover) of environmental interaction effects on individual QTLs. QTLs for some traits were associated with G effects only, while others were associated with the effects of both G and G x S and/or G, G x S and G x E(S) effects. The major G x S QTLs detected were for flowering time (on LG 4 and LG 6), and mapped to the same intervals as G x S QTLs for several other traits (including stover yield, harvest index, biomass yield and panicle number m(-2)). All three QTLs detected for grain yield were unaffected by G x S interaction however. All three QTLs for stover yield (mapping on LG 2, LG 4 and LG 6) and one of the three QTLs for grain yield (mapping on LG 4) were also free of QTL x E(S) interactions. The grain yield QTLs that were affected by QTL x E(S) interactions (mapping on LG 2 and LG 6), appeared to be linked to parallel QTL x E(S) interactions of the QTLs for panicle number m(-2) on (LG 2) and of QTLs for both panicle number m(-2) and harvest index (LG 6). In general, QTL x E(S) interactions were more frequently observed for component traits of grain and stover yield, than for grain or stover yield per se.     

QTLs for grain carbon isotope discrimination in field-grown barley

In several crops including cereals, carbon isotope discrimination (Delta) has been associated with drought tolerance in terms of water-use efficiency and yield stability in drought-prone environments. By using a complete genetic map generated from 167 recombinant inbred lines from a cross between Tadmor and Er/Apm, QTLs associated with grain Delta have been detected in barley grown in three Mediterranean field environments, two differing only in water availability. Ten QTLs were identified: one was specific to one environment, two presented interaction with the environment, six presented main effects across three or two environments and one presented both effects. Heading date did not contribute to the environment (E) and G x E effects acting on Delta. Seasonal rainfall and the ratio of rainfall to evapo-transpiration made large contributions to the environmental effect, but their influence on G x E was weaker. Eight QTLs for Delta co-located with QTLs for physiological traits related to plant water status and/or osmotic adjustment, and/or for agronomic traits previously measured on the same population. Some perspectives in terms of characterising drought tolerance are evoked.     

Mapping quantitative trait loci for yield,yield components and morphological traits in an advanced backcross population between <Emphasis Type="Italic">Oryza rufipogon</Emphasis> and the <Emphasis Type="Italic">Oryza sativa</Emphasis> cultivar Jefferson

An advanced backcross population between an accession of Oryza rufipogon (IRGC 105491) and the U.S. cultivar Jefferson (Oryza sativa ssp. japonica) was developed to identify quantitative trait loci (QTLs) for yield, yield components and morphological traits. The genetic linkage map generated for this population consisted of 153 SSR and RFLP markers with an average interval size of 10.3 cM. Thirteen traits were examined, nine of which were measured in multiple environments. Seventy-six QTLs above an experiment-wise significance threshold of P<0.01 (corresponding to an interval mapping LOD>3.6 or a composite interval mapping LOD>3.9) were identified. For the traits measured in multiple environments, 47% of the QTLs were detected in at least two environments. The O. rufipogon allele was favorable for 53% of the yield and yield component QTLs, including loci for yield, grains per panicle, panicle length, and grain weight. Morphological traits related to the domestication process and/or weedy characteristics, including plant height, shattering, tiller type and awns, were found clustered on chromosomes 1 and 4. Comparisons to previous studies involving wild x cultivated crosses revealed O. rufipogon alleles with stable effects in multiple genetic backgrounds and environments, several of which have not been detected in studies between Oryza sativa cultivars, indicating potentially novel alleles from O. rufipogon. Some O. rufipogon-derived QTLs, however, were in similar regions as previously reported QTLs from Oryza sativa cultivars, providing evidence for conservation of these QTLs across the Oryza genus. In addition, several QTLs for grain weight, plant height, and flowering time were localized to putative homeologous regions in maize where QTLs for these traits have been previously reported, supporting the hypothesis of functional conservation of QTLs across the grasses.     

Phenotype/genotype associations for yield and salt tolerance in a barley mapping population segregating for two dwarfing genes

Barley traits related to salt tolerance are mapped in a population segregating for a dwarfing gene associated with salt tolerance. Twelve quantitative trait loci (QTLs) were detected for seven seedling traits in doubled haploids from the spring barley cross Derkado x B83-12/21/5 when given saline treatment in hydroponics. The location of QTLs for seedling growth stage (leaf appearance rate), stem weight prior to elongation, and tiller number are reported for the first time. In addition, four QTLs were found for the mature plant traits grain nitrogen and plot yield. In total, seven QTLs are co-located with the dwarfing genes sdw1, on chromosome 3H, and ari-e.GP, on chromosome 5H, including seedling leaf response (SGa) to gibberellic acid (GA(3)). QTLs controlling the growth of leaves (GS2) on chromosomes 2H and 3H and emergence of tillers (TN2) and grain yield were independent of the dwarfing genes. Field trials were grown in eastern Scotland and England to estimate yield and grain composition. A genetic map was used to compare the positions of QTLs for seedling traits with the location of QTLs for the mature plant traits. The results are discussed in relation to the study of barley physiology and the location of genes for dwarf habit and responses to GA.     

Bayesian mapping of multiple traits in maize: the importance of pleiotropic effects in studying the inheritance of quantitative traits

Pleiotropy has played an important role in understanding quantitative traits. However, the extensiveness of this effect in the genome and its consequences for plant improvement have not been fully elucidated. The aim of this study was to identify pleiotropic quantitative trait loci (QTLs) in maize using Bayesian multiple interval mapping. Additionally, we sought to obtain a better understanding of the inheritance, extent and distribution of pleiotropic effects of several components in maize production. The design III procedure was used from a population derived from the cross of the inbred lines L-14-04B and L-08-05F. Two hundred and fifty plants were genotyped with 177 microsatellite markers and backcrossed to both parents giving rise to 500 backcrossed progenies, which were evaluated in six environments for grain yield and its components. The results of this study suggest that mapping isolated traits limits our understanding of the genetic architecture of quantitative traits. This architecture can be better understood by using pleiotropic networks that facilitate the visualization of the complexity of quantitative inheritance, and this characterization will help to develop new selection strategies. It was also possible to confront the idea that it is feasible to identify QTLs for complex traits such as grain yield, as pleiotropy acts prominently on its subtraits and as this "trait" can be broken down and predicted almost completely by the QTLs of its components. Additionally, pleiotropic QTLs do not necessarily signify pleiotropy of allelic interactions, and this indicates that the pervasive pleiotropy does not limit the genetic adaptability of plants.     

Deciphering genetic basis of developmental and agronomic traits by integrating high-throughput optical phenotyping and genome-wide association studies in wheat

Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield-related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non-invasive and high-throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield-related traits was further explored. Whole genome re-sequencing of the panel provided 12.64 million markers for a high-resolution genome-wide association analysis using 190 image-based traits and 17 agronomic traits. A total of 8327 marker-trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield-related traits are largely predictable using models developed based on i-traits and provide possibility for high-throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield-related traits by combining high-throughput phenotyping and genotyping, which further unravelled the complex and stage-specific contributions of genetic loci to optimize growth and yield in wheat.     

Genetic mapping of QTLs affecting productivity and plant architecture in a full-sib cross from non-inbred parents in Cassava (<Emphasis Type="Italic">Manihot esculenta</Emphasis> Crantz)

An attempt was made to identify quantitative trait loci (QTLs) for several productivity and plant architecture traits in a full-sib progeny of 144 individuals from two non-inbred parents in cassava. A molecular linkage map of this cross constructed previously with over 250 markers was the source of molecular markers. The progeny were grown under field conditions at two locations (Palmira and Quilichao) in Colombia and evaluated in 2 years (1998 and 1999) for architecture and productivity traits. Architecture traits evaluated were plant height (PH), branching height (BH), branching levels (BL), branching index (BI), stem portion with leaves (SPL) and leaf area index (LAI). Productivity traits were those related to total dry matter production and distribution, namely fresh root yield (FRY), fresh shoot yield (FSY), harvest index (HI) and the number of storage roots (NR). Phenotypic evaluation of the traits in this population revealed continuous variation for all traits. Broad-sense heritability estimates, ranged from 36% (for NR) to 94% (for BH). Several significant phenotypic correlations were observed between architecture and productivity traits. Primary QTLs, using the single-QTL model, and secondary QTLs, by a primary QTL interaction model, were detected by interval mapping. A total of 30 primary QTLs and 84 secondary QTLs were detected. We identified 35% of detected QTLs in two or more trials, the other QTLs were environment-specific. These results underscore the significant genotype × environment interactions found for most of the traits. Several genomic segments affecting multiple traits were identified and were in agreement with correlation among traits. All QTLs identified for FRY were found associated with either component traits of productivity or architecture traits. This study suggests that QTLs for plant architecture can be used to improve productivity. However an exhaustive search and analysis of QTLs controlling architecture is required before marker-assisted selection (MAS) for increasing productivity can be initiated.Communicated by H. C. Becker     

水稻籼粳交DH群体收获指数及源库性状的QTL分析

以 1个水稻籼粳交 (圭 6 30 0 2 4 2 8)来源的DH群体为材料 ,利用 1张含有 2 32个标记的RFLP连锁图谱和基于混合线性模型的定位软件QTLMapper1 0对水稻收获指数及生物量、籽粒产量、库容量和株高 5个性状进行QTL分析 ,共检测到 2 1个主效应QTLs和 9对上位性互作位点。其中 ,控制籽粒产量的 3个QTLs合计贡献率为 4 2 % ,LOD值为 7 10 ;这 3个QTLs或者与收获指数的QTL同位 ,或者与生物量的QTL同位 ,且加性效应的方向一致 ,从而揭示了“籽粒产量 =生物量×收获指数”的遗传基础所在。控制收获指数的 4个QTLs合计贡献率为 4 6 % ,LOD值为 10 3;控制生物量的 4个QTLs合计贡献率为 6 4 % ,LOD值为 14 0 9;收获指数的 4个QTLs与生物量的 4个QTLs均不同位。因此 ,通过基因重组 ,可能实现控制收获指数和生物量的增效基因的聚合 ,由此获得收获指数和生物量“双高”的基因型。检测到 5个株高QTLs,其合计贡献率为 6 4 % ,LOD值为 11 6 2 ;其中 ,有 3个效应较小的QTLs与生物量、库容量和 或籽粒产量QTLs同位 ,且同位QTLs的加性效应方向一致 ;未发现株高QTLs与收获指数QTLs的同位性。由此表明 ,株高与“源 流 库”概念中的“源”和“库”在遗传上有一定程度的关联 ,而与“流”无关联。此外还发现 ,在上述同位性QTL     

Genetic analysis of leaf morphology underlying the plant density response by QTL mapping in maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.)

Maize yield increase has been strongly linked to plant population densities over time with changes in plant architecture, but the genetic basis for the plant architecture response to plant density is unknown, as is its stability across environments. To elucidate the genetic basis of the plant architecture response to density in maize, we mapped quantitative trait loci (QTLs) for leaf morphology-related traits in four sets of recombinant inbred line (RIL) populations under two plant density conditions. Forty-five QTLs for six traits were detected in both high and low plant density conditions. Thirty-seven QTLs were only detected when grown under high plant density, and 34 QTLs were only detected when grown under low plant density. Twenty-two meta-QTLs (mQTLs) were identified by meta-analysis, and mQTL1-1, mQTL3-2 and mQTL8 were identified when grown under high and low plant densities, with R ² of some initial QTLs > 10 %, suggesting the mQTLs might be hot spots of the important QTLs for the related traits under planting density stress conditions. The results presented here provide useful information for further research and the marker-assisted selection of varieties targeting increased plant density and will help to reveal the molecular mechanisms related to leaf morphology in response to density.     

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

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

Identification of QTLs affecting traits of agronomic importance in a recombinant inbred population derived from a subspecific rice cross

To detect QTLs controlling traits of agronomic importance in rice, two elite homozygous lines 9024 and LH422, which represent the indica and japonica subspecies of rice (Oryza sativa), were crossed. Subsequently a modified single-seed-descent procedure was employed to produce 194 recombinant inbred lines (F₈). The 194 lines were genotyped at 141 RFLP marker loci and evaluated in a field trial for 13 quantitative traits including grain yield. Transgressive segregants were observed for all traits examined. The number of significant QTLs (LOD 2.0) detected affecting each trait ranged from one to six. The percentage of phenotypic variance explained by each QTL ranged from 5.1% to 73.7%. For those traits for which two or more QTLs were detected, increases in the traits were conditioned by indica alleles at some QTLs Japonica alleles at others. No significant evidence was found for epistasis between markers associated with QTLs and all the other markers. Pleitropic effects of single QTLs on different traits are suggested by the observation of clustering of QTLs. No QTL for traits was found to map to the vicinity of major gene loci governing the same traits qualitatively. Evidence for putative orthologous QTLs across rice, maize, oat, and barley is discussed.     

玉米株高和穗位高的遗传基础与分子机制*

株高和穗位高是玉米重要育种性状,直接影响植株的养分利用效率及抗倒伏性,进而影响玉米产量。玉米株高和穗位高属于典型数量性状,目前通过数量性状位点(quantitative trait loci mapping,QTL)定位和全基因组关联分析(genome-wide association study, GWAS)等方法已挖掘到较多相关遗传位点,通过QTL精细定位及利用突变体克隆了一些调控株高和穗位高关键基因。但是由于各研究组所利用的群体类型和大小、标记类型和密度以及统计方法不同,所鉴定QTL差异较大,单个研究难以揭示玉米株高和穗位高遗传结构。早期QTL定位的结果多以遗传距离来展示,不同时期GWAS研究所使用参考基因组版本不同,这进一步增加了借鉴和利用前人研究结果的难度。首次将目前已鉴定株高和穗位高遗传定位信息统一锚定至玉米自交系B73参考基因组V4版本,构建了株高和穗位高性状定位的一致性图谱,并鉴定出可被多个独立研究定位的热点区间。进一步对已克隆玉米株高和穗位高调控基因进行总结与分类,揭示株高和穗位高性状调控机制,对深度解析株高和穗位高遗传结构、指导基因克隆和利用分子标记辅助选择优化玉米株高和穗位高性状均具有重要意义。     

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.     

Identification of QTLs for grain yield and grain-related traits of maize (Zeamays L.) using an AFLP map, different testers, and cofactor analysis

We exploited the AFLP®¹(AFLP® is a registered trademark of Keygene, N.V.) technique to map and characterise quantitative trait loci (QTLs) for grain yield and two grain-related traits of a maize segregating population. Two maize elite inbred lines were crossed to produce 229 F₂ individuals which were genotyped with 66 RFLP and 246 AFLP marker loci. By selfing the F₂ plants 229 F₃ lines were produced and subsequently crossed to two inbred testers (T1 and T2). Each series of testcrosses was evaluated in field trials for grain yield, dry matter concentration, and test weight. The efficiency of generating AFLP markers was substantially higher relative to RFLP markers in the same population, and the speed at which they were generated showed a great potential for application in marker-assisted selection. AFLP markers covered linkage group regions left uncovered by RFLPs; in particular at telomeric regions, previously almost devoided of markers. This increase of genome coverage afforded by the inclusion of the AFLPs revealed new QTL locations for all the traits investigated and allowed us to map telomeric QTLs with higher precision. The present study has also provided an opportunity to compare simple (SIM) and composite interval mapping (CIM) for QTL analysis. Our results indicated that the method of CIM employed in this study has greater power in the detection of QTLs, and provided more precise and accurate estimates of QTL positions and effects than SIM. For all traits and both testers we detected a total of 36 QTLs, of which only two were in common between testers. This suggested that the choice of a tester for identifying QTL alleles for use in improving an inbred is critical and that the expression of QTL alleles identified may be tester-specific.     

QTL and QTL x environment effects on agronomic and nitrogen acquisition traits in rice

Agricultural environments deteriorate due to excess nitrogen application.Breeding for low nitrogen responsive genotypes can reduce soil nitrogen input.Rice genotypes respond variably to soil available nitrogen.The present study attempted quantification of genotype x nitrogen level interaction and mapping of quantitative trait loci (QTLs) associated with nitrogen use efficiency (NUE) and other associated agronomic traits.Twelve parameters were observed across a set of 82 double haploid (DH) lines derived from IR64/Azucena.Three nitrogen regimes namely,native (0 kg/ha; no nitrogen applied),optimum (100 kg/ha) and high (200 kg/ha) replicated thrice were the environments.The parents and DH lines were significantly varying for all traits under different nitrogen regimes.All traits except plant height recorded significant genotype x environment interaction.Individual plant yield was positively correlated with nitrogen use efficiency and nitrogen uptake.Sixteen QTLs were detected by composite interval mapping.Eleven QTLs showed significant QTL x environment interactions.On chromosome 3,seven QTLs were detected associated with nitrogen use,plant yield and associated traits.A QTL region between markers RZ678,RZ574 and RZ284 was associated with nitrogen use and yield.This chromosomal region was enriched with expressed gene sequences of known key nitrogen assimilation genes.     

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

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

Identification of QTLs for 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.     

Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia

Rice double-haploid (DH) lines of an indica and japonica cross were grown at nine different locations across four countries in Asia. Genotype-by-environment (G x E) interaction analysis for 11 growth- and grain yield-related traits in nine locations was estimated by AMMI analysis. Maximum G x E interaction was exhibited for fertility percentage number of spikelets and grain yield. Plant height was least affected by environment, and the AMMI model explained a total of 76.2% of the interaction effect. Mean environment was computed by averaging the nine environments and subsequently analyzed with other environments to map quantitative trait loci (QTL). QTL controlling the 11 traits were detected by interval analysis using mapmaker/qtl. A threshold LOD of >/=3.20 was used to identify significant QTL. A total of 126 QTL were identified for the 11 traits across nine locations. Thirty-four QTL common in more than one environment were identified on ten chromosomes. A maximum of 44 QTL were detected for panicle length, and the maximum number of common QTL were detected for days to heading detected. A single locus for plant height (RZ730-RG810) had QTL common in all ten environments, confirming AMMI results that QTL for plant height were affected the least by environment, indicating the stability of the trait. Two QTL were detected for grain yield and 19 for thousand-grain weight in all DH lines. The number of QTL per trait per location ranged from zero to four. Clustering of the QTL for different traits at the same marker intervals was observed for plant height, panicle number, panicle length and spikelet number suggesting that pleiotropism and or tight linkage of different traits could be the possible reason for the congruence of several QTL. The many QTL detected by the same marker interval across environments indicate that QTL for most traits are stable and not essentially affected by environmental factors.     

玉米雌雄开花间隔天数、结穗率与产量的数量性状位点(QTL)分析

采用SSR标记连锁图谱和复合区间作图法在山西灌溉和干旱胁迫条件下,对玉米(Zea mays L.)自交系黄早四×掖107组合的F3群体雌雄开花间隔天数(ASI)、结穗率和籽粒产量进行了数量性状位点(QTL)定位及基因效应分析.结果表明,在两种水分处理下,ASI、结穗率与籽粒产量的相关性均达到显著水平(P＜0.05).在灌溉和干旱胁迫下,分别检测到3个和2个控制ASI的QTL,位于第1、2、3和第2、5染色体上.在灌溉条件下,在第3和第6染色体上各检测到1个控制结穗率的QTL,基因作用方式呈加性或部分显性,可解释19.9%的表型变异;在干旱条件下,在第3、 7、10染色体上共检测到4个控制结穗率的QTL,基因作用方式为显性或部分显性,可解释60.4%的表型变异.在灌溉和干旱胁迫下,控制产量的QTL分别定位在第3、6、7和第1、2、4、8染色体上,基因作用方式均以加性或部分显性为主,可解释的表型变异为7.3%～22.0%.在干旱条件下,借助连锁分子标记和基因效应分析,可构建包含ASI、结穗率和产量QTL的选择指数,用于分子标记辅助育种.     

Molecular-marker-facilitated studies of morphological traits in maize. II: Determination of QTLs for grain yield and yield components

Genetic factors controlling quantitative inheritance of grain yield and its components have not previously been investigated by using replicated lines of an elite maize (Zea mays L.) population. The present study was conducted to identify quantitative trait loci (QTLs) associated with grain yield and grain-yield components by using restriction fragment length polymorphism (RFLP) markers. A population of 150 random F₂₃ lines was derived from the single cross of inbreds Mo17 and H99, which are considered to belong to the Lancaster heterotic group. Trait values were measured in a replicated trial near Ames, Iowa, in 1989. QTLs were located on a linkage map constructed with one morphological and 103 RFLP loci. QTLs were found for grain yield and all yield components. Partial dominance to overdominance was the primary mode of gene action. Only one QTL, accounting for 35% of the phenotypic variation, was identified for grain yield. Two to six QTLs were identified for the other traits. Several regions with pleiotropic or linked effects on several of the yield components were detected.