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

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

Genetic basis of pre-harvest sprouting tolerance using single-locus and two-locus QTL analyses in bread wheat

Quantitative trait loci (QTL) analysis for pre-harvest sprouting tolerance (PHST) in bread wheat was conducted following single-locus and two-locus analyses, using data on a set of 110 recombinant inbred lines (RILs) of the International Triticeae Mapping Initiative population grown in four different environments. Single-locus analysis following composite interval mapping (CIM) resolved a total of five QTLs with one to four QTLs in each of the four individual environments. Four of these five QTLs were also detected following two-locus analysis, which resolved a total of 14 QTLs including 8 main effect QTLs (M-QTLs), 8 epistatic QTLs (E-QTLs) and 5 QTLs involved in QTL × environment (QE) or QTL × QTL × environment (QQE) interactions, some of these QTLs being common. The analysis revealed that a major fraction (76.68%) of the total phenotypic variation explained for PHST is due to M-QTLs (47.95%) and E-QTLs (28.73%), and that only a very small fraction of variation (3.24%) is due to QE and QQE interactions. Thus, more than three-quarters of the genetic variation for PHST is fixable and would contribute directly to gains under selection. Two QTLs that were detected in more than one environment and at LOD scores above the threshold values were located on 3BL and 3DL presumably in the vicinity of the dormancy gene TaVp1. Another QTL was found to be located on 3B, perhaps in close proximity to the R gene for red grain colour. However, these associations of QTLs for PHST with genes for dormancy and grain colour are only suggestive. The results obtained in the present study suggest that PHST is a complex trait controlled by large number of QTLs, some of them interacting among themselves or with the environment. These QTLs can be brought together through marker-aided selection, leading to enhanced PHST.     

Quantitative Trait Loci Mapping of Leaf Morphological Traits and Chlorophyll Content in Cultivated Tetraploid Cotton

Genetic mapping provides a powerful tool for quantitative trait loci （QTL） analysis at the molecular level. A simple sequence repeat （SSR） genetic map containing 590 markers and a BCI population from two cultivated tetraploid cotton （Gossypium hirsutum L.） cultivars, namely TM-1 and Hai 7124 （G. barbadense L.）, were used to map and analyze QTL using the composite interval mapping （CIM） method. Thirty one QTLs, 10 for lobe length, 13 for lobe width, six for lobe angle, and two for leaf chlorophyll content, were detected on 15 chromosomes or linkage groups at logarithm of odds （LOD）≥2.0, of which 15 were found for leaf morphology at LOD≥3.0. The genetic effects of the QTL were estimated. These results are fundamental for marker-assisted selection （MAS） of these traits in tetraploid cotton breeding.     

QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

Mapping of post-flowering drought resistance traits in grain sorghum: association between QTLs influencing premature senescence and maturity

The identification of genetic factors underlying the complex responses of plants to drought stress provides a solid basis for improving drought resistance. The stay-green character in sorghum (Sorghum bicolor L. Moench) is a post-flowering drought resistance trait, which makes plants resistant to premature senescence under drought stress during the grainfilling stage. The objective of this study was to identify quantitative trait loci (QTLs) that control premature senescence and maturity traits, and to investigate their association under post-flowering drought stress in grain sorghum. A genetic linkage map was developed using a set of recombinant inbred lines (RILs) obtained from the cross B35 × Tx430, which were scored for 142 restriction fragment length polymorphism (RFLP) markers. The RILs and their parental lines were evaluated for post-flowering drought resistance and maturity in four environments. Simple interval mapping identified seven stay-green QTLs and two maturity QTLs. Three major stay-green QTLs (SGA, SGD and SGG) contributed to 42% of the phenotypic variability (LOD 9.0) and four minor QTLs (SGB, SGI.1, SGI.2, and SGJ) significantly contributed to an additional 25% of the phenotypic variability in stay-green ratings. One maturity QTL (DFB) alone contributed to 40% of the phenotypic variability (LOD 10.0), while the second QTL (DFG) significantly contributed to an additional 17% of the phenotypic variability (LOD 4.9). Composite interval mapping confirmed the above results with an additional analysis of the QTL × Environment interaction. With heritability estimates of 0.72 for stay-green and 0.90 for maturity, the identified QTLs explained about 90% and 63% of genetic variability for stay-green and maturity traits, respectively. Although stay-green ratings were significantly correlated (r=0.22, P ≤ 0.05) with maturity, six of the seven stay-green QTLs were independent of the QTLs influencing maturity. Similarly, one maturity QTL (DFB) was independent of the stay-green QTLs. One stay-green QTL (SGG), however, mapped in the vicinity of a maturity QTL (DFG), and all markers in the vicinity of the independent maturity QTL (DFB) were significantly (P ≤ 0.1) correlated with stay-green ratings, confounding the phenotyping of stay-green. The molecular genetic analysis of the QTLs influencing stay-green and maturity, together with the association between these two inversely related traits, provides a basis for further study of the underlying physiological mechanisms and demonstrates the possibility of improving drought resistance in plants by pyramiding the favorable QTLs. Received: 10 October 1998 / Accepted: 12 July 1999     

利用两种方法构建的近等基因系对水稻两个多效区段遗传效应进行评价

水稻每穗颖花数是水稻产量的重要构成因子之一。适当的抽穗期和株高对水稻高产是非常必要的。依据珍汕97和HR5衍生的重组自交系初步定位的结果, 利用高世代回交的方法构建了第7染色体同时控制抽穗期、株高和每穗颖花数的靶区段近等基因系(BC4F2); 利用基于重组自交系群体的杂合区段自交的方法构建了第8染色体同时控制抽穗期、株高和每穗颖花数的靶区段近等基因系, 并利用两个近等基因系对这两个多效区段的遗传效应进行了准确的评价。两个近等基因系的QTL分析结果表明, 3个性状都是由一个QTL或紧密连锁的QTL控制, 而且加性效应和显性效应的方向均相同; 同时3个性状在各自的近等基因系中呈现典型的双峰分布或不连续分布,这些结果暗示3个性状可能是一因多效的结果。文章还对抽穗期和株高与水稻产量的关系、3个性状显著正相关在育种中的应用及两种构建近等基因系方法的优缺点也进行了讨论。     

Mapping of QTLs governing agronomic and yield traits in chickpea

Chickpea is one of the most important leguminous cool season food crops, cultivated prevalently in South Asia and Middle East. The main objective of this study was to identify quantitative trait loci (QTLs) associated with seven agronomic and yield traits in two recombinant inbred line populations of chickpea derived from the crosses JG62 × Vijay (JV population) and Vijay × ICC4958 (VI population) from at least three environments. Single locus QTL analysis involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated traits to detect pleiotropic QTLs. Two-locus analysis was conducted to identify the main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions. Through CIM analysis, a total of 106 significant QTLs (41 in JV and 65 in VI populations) were identified for the seven traits, of which one QTL each for plant height and days to maturity was common in both the populations. Six pleiotropic QTLs that were consistent over the environments were also identified. LG2 in JV and LG1a in VI contained at least one QTL for each trait. Hence, concentrating on these LGs in molecular breeding programs is most likely to bring simultaneous improvement in these traits.     

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.     

Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive × additive interaction and QTL × environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0–26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL × environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.     

Genetic dissection of grain weight in bread wheat through quantitative trait locus interval and association mapping

Genetic dissection of grain weight in bread wheat was undertaken through both genome-wide quantitative trait locus (QTL) interval mapping and association mapping. QTL interval mapping involved preparation of a framework linkage map consisting of 294 loci {194 simple sequence repeats (SSRs), 86 amplified fragment length polymorphisms (AFLPs) and 14 selective amplifications of microsatellite polymorphic loci (SAMPL)} using a bi-parental recombinant inbred line (RIL) mapping population derived from Rye Selection111 × Chinese Spring. Using the genotypic data and phenotypic data on grain weight (GW) of RILs collected over six environments, genome-wide single locus QTL analysis was conducted to identify main effect QTL. This led to identification of as many as ten QTL including four major QTL (three QTL were stable), each contributing >20% phenotypic variation (PV) for GW. The above study was supplemented with association mapping, which allowed identification of 11 new markers in the genomic regions that were not reported earlier to harbour any QTL for GW. It also allowed identification of closely linked markers for six known QTL, and validation of eight QTL reported earlier. The QTL identified through QTL interval mapping and association mapping may prove useful in marker-assisted selection (MAS) for the development of cultivars with high GW in bread wheat.     

Quantitative trait loci mapping for yield and its components by using two immortalized populations of a heterotic hybrid in Gossypium hirsutum L.

Quantitative trait loci (QTL) mapping provides a powerful tool for unraveling the genetic basis of yield and yield components as well as heterosis in upland cotton. In this research, a molecular linkage map of Xiangzamian 2 (Gossypium hirsutum L.)-derived recombinant inbred lines (RILs) was reconstructed based on increased expressed sequence tag–simple sequence repeat markers. Both the RILs and immortalized F2s (IF2) developed through intermating between RILs were grown under multiple environments. Yield and yield components including seed-cotton yield, lint yield, bolls/plant, boll weight, lint percentage, seed index, lint index and fruit branch number were measured and their QTL were repeatedly identified across environments by the composite interval mapping (CIM) method. From a total of 111 non-redundant QTL, 23 were detected in both two populations. In the meantime, multi-marker joint analyses showed that 16 of these QTL had significant environmental interaction. QTL for correlated traits tended to be collocated and most of the QTL for seed-cotton yield and lint yield were associated with QTL for at least one yield component, consistent with the results observed in correlation analyses. For many QTL with significant additive effects, positive alleles from CRI12, the inferior parent with lower yield performance, were associated with trait improvement. Trait performance of IF2s and the large number of QTL with positive dominant effects implied that dominance plays an important role in the genetic basis of heterosis in Xiangzamian 2 and that non-additive inheritance is also an important genetic mode for lint percentage in the population. These QTL can provide the bases for marker-assisted breeding programs of upland cotton.     

Identification of QTLs for early blight ( Alternaria solani) resistance in tomato using backcross populations of a Lycopersicon esculentum x L. hirsutum cross

Most commercial cultivars of tomato, Lycopersicon esculentum Mill., are susceptible to early blight (EB), a devastating fungal ( Alternaria solani Sorauer) disease of tomato in the northern and eastern parts of the U.S. and elsewhere in the world. The disease causes plant defoliation, which reduces yield and fruit quality, and contributes to significant crop loss. Sources of resistance have been identified within related wild species of tomato. The purpose of this study was to identify and validate quantitative trait loci (QTLs) for EB resistance in backcross populations of a cross between a susceptible tomato breeding line (NC84173; maternal and recurrent parent) and a resistant Lycopersicon hirsutum Humb. and Bonpl. accession (PI126445). Sixteen hundred BC(1) plants were grown to maturity in a field in 1998. Plants that were self-incompatible, indeterminant in growth habit, and/or extremely late in maturity, were discarded in order to eliminate confounding effects of these factors on disease evaluation, QTL mapping, and future breeding research. The remaining 145 plants (referred to as the BC(1) population) were genotyped for 141 restriction fragment length polymorphism (RFLP) markers and 23 resistance gene analogs (RGAs), and a genetic linkage map was constructed. BC(1) plants were evaluated for disease symptoms throughout the season, and the area under the disease progress curve (AUDPC) and the final percent defoliation (disease severity) were determined for each plant. BC(1) plants were self-pollinated and produced BC(1)S(1) seed. The BC(1)S(1) population, consisting of 145 BC(1)S(1) families, was grown and evaluated for disease symptoms in replicated field trials in two subsequent years (1999 and 2000) and AUDPC and/or final percent defoliation were determined for each family in each year. Two QTL mapping approaches, simple interval mapping (SIM) and composite interval mapping (CIM), were used to identify QTLs for EB resistance in the BC(1) and BC(1)S(1) populations. QTL results were highly consistent across generations, years and mapping approaches. Approximately ten significant QTLs (LOD >/= 2.4, P 57% of the total phenotypic variation. All QTLs had the positive alleles from the disease-resistant parent. The good agreement between results of the BC(1) and 2 years of the BC(1)S(1) generations indicated the stability of the identified QTLs and their potential usefulness for improving tomato EB resistance using marker-assisted selection (MAS). Further inspections using SIM and CIM indicated that six of the ten QTLs had independent additive effects and together could account for up to 56.4% of the total phenotypic variation. These complementary QTLs, which were identified in two generations and 3 years, should be the most useful QTLs for MAS and improvement of tomato EB resistance using PI126445 as a gene resource. Furthermore, the chromosomal locations of 10 of the 23 RGAs coincided with the locations of three QTLs, suggesting possible involvement of these RGAs with EB resistance and a potential for identifying and cloning genes which confer EB resistance in tomato.     

Molecular mapping for resistance to pea rust caused by <Emphasis Type="Italic">Uromyces fabae</Emphasis> (Pers.) de-Bary

Pea rust caused by Uromyces fabae (Pers.) de-Bary is a major problem in warm humid regions causing huge economic losses. A mapping population of 136 F_6:7 recombinant inbred lines (RILs) derived from the cross between pea genotypes, HUVP 1 (susceptible) and FC 1 (resistant) was evaluated in polyhouse as well as under field conditions during two consecutive years. Infection frequency (IF) and area under disease progress curve (AUDPC) were used for evaluation of rust reaction of the RILs. A linkage map was constructed with 57 polymorphic loci selected from 148 simple sequence repeats (SSRs), 3 sequence tagged sites (STS), and 2 random amplified polymorphic (RAPD) markers covering 634 cM of genetic distance on the seven linkage groups of pea with an average interval length of 11.3 cM. Composite interval mapping (CIM) revealed one major (Qruf) and one minor (Qruf1) QTL for rust resistance on LGVII. The LOD (5.2–15.8) peak for Qruf was flanked by SSR markers, AA505 and AA446 (10.8 cM), explaining 22.2–42.4% and 23.5–58.8% of the total phenotypic variation for IF and AUDPC, respectively. The minor QTL was environment-specific, and it was detected only in the polyhouse (LOD values 4.2 and 4.8). It was flanked by SSR markers, AD146 and AA416 (7.3 cM), and explained 11.2–12.4% of the total phenotypic variation. The major QTL Qruf was consistently identified across all the four environments. Therefore, the SSR markers flanking Qruf would be useful for marker-assisted selection for pea rust (U. fabae) resistance.     

High Resolution Consensus Mapping of Quantitative Trait Loci for Fiber Strength,Length and Micronaire on Chromosome 25 of the Upland Cotton (Gossypium hirsutum L.)

Cotton (Gossypium hirsutum L.) is an important agricultural crop that provides renewable natural fiber resources for the global textile industry. Technological developments in the textile industry and improvements in human living standards have increased the requirement for supplies and better quality cotton. Upland cotton 0–153 is an elite cultivar harboring strong fiber strength genes. To conduct quantitative trait locus (QTL) mapping for fiber quality in 0–153, we developed a population of 196 recombinant inbred lines (RILs) from a cross between 0–153 and sGK9708. The fiber quality traits in 11 environments were measured and a genetic linkage map of chromosome 25 comprising 210 loci was constructed using this RIL population, mainly using simple sequence repeat markers and single nucleotide polymorphism markers. QTLs were identified across diverse environments using the composite interval mapping method. A total of 37 QTLs for fiber quality traits were identified on chromosome 25, of which 17 were stably expressed in at least in two environments. A stable fiber strength QTL, qFS-chr25-4, which was detected in seven environments and was located in the marker interval between CRI-SNP120491 and BNL2572, could explain 6.53%–11.83% of the observed phenotypic variations. Meta-analysis also confirmed the above QTLs with previous reports. Application of these QTLs could contribute to improving fiber quality and provide information for marker-assisted selection.     

Quantitative trait loci associated with androgenic responsiveness in triticale (×Triticosecale Wittm.) anther culture

Quantitative trait loci (QTLs) associated with androgenic responsiveness in triticale were analyzed using a population of 90 DH lines derived from the F1 cross between inbred line ‘Saka 3006’ and cv. ‘Modus’, which was used in a number of earlier studies on molecular mapping in this crop. Using Windows QTL Cartographer and MapQTL 5.0, composite interval mapping (CIM) and association studies (Kruskal–Wallis test; K–W) for five androgenesis parameters (androgenic embryo induction, total regeneration and green plant regeneration ability, and two characteristics describing final androgenesis efficiency) were conducted. For the studied components of androgenic response, CIM detected in total 28 QTLs which were localized on 5 chromosomes from A and R genomes. Effects of all QTLs that were identified at 2.0 or above of the LOD score explained 5.1–21.7?% of the phenotypic variation. Androgenesis induction was associated with seven QTLs (LOD between 2.0 and 5.8) detected on chromosomes 5A, 4R, 5R and 7R, all of them confirmed by K–W test as regions containing the markers significantly linked to the studied trait. What is more, K–W test revealed additional markers on chromosomes: 5A, 2BL, 7B and 5R. Both total and green regeneration ability were controlled by genes localized on chromosome 4A. Some of the QTLs that affected final androgenesis efficiency were identical with those associated with androgenic embryo induction efficiency, suggesting that the observed correlation may be either due to tight linkage or to pleiotropy. Key message Five regions of the triticale genome were indicated as revealing significant marker/trait association. Markers located in these regions are potentially useful for triticale breeding through marker-assisted selection.     

Genetic analysis of vertical root pulling resistance (VRPR) in maize using two genetic populations

Root traits are important in improving nutrient and water use efficiency. Vertical root pulling resistance (VRPR) has been shown to be closely related to root system characteristics in maize (Zea mays L.). In the present study, two genetic populations derived from the same parents, one containing 218 recombinant inbred lines (RILs) and the other containing 187 advanced backcross BC₄F₃ lines, were genotyped using 184 SSR markers and evaluated for VRPR, grain yield (GY), stover yield (SY), and nitrogen uptake (N_up) under field conditions over 2 years. Our aims were (1) to locate QTLs associated with VRPR, SY, GY, and N_up, (2) to determine whether QTL detection is consistent between the BC₄F₃ and RIL populations, and (3) to identify backcross lines harboring favorable VRPR QTLs for use in future breeding programs. Using composite interval mapping (CIM), 12 and 17 QTLs were detected in BC₄F₃ and RIL populations, respectively. An important QTL region in bin 6.02 within the interval umc1006-umc1257 was found to control VRPR, SY, and N_up in both populations. These favorable alleles were contributed by the large-rooted parent Ye478. A significant positive correlation was detected between VRPR, SY, and N_up, but not between VRPR and GY. Backcross lines harboring VRPR QTLs could be useful germplasm for developing near isogenic lines (NILs) and for map-based cloning of genes controlling root growth.     

Mapping of shoot fly tolerance loci in sorghum using SSR markers

Sorghum (Sorghum bicolor (L.) Moench) is one of the most important crops in the semiarid regions of the world. One of the important biotic constraints to sorghum production in India is the shoot fly which attacks sorghum at the seedling stage. Identification of the genomic regions containing quantitative trait loci (QTLs) for resistance to shoot fly and the linked markers can facilitate sorghum improvement programmes through marker-assisted selection. A simple sequence repeat (SSR) marker- based skeleton linkage map of two linkage groups of sorghum was constructed in a population of 135 recombinant inbred lines (RIL) derived from a cross between IS18551 (resistant to shoot fly) and 296B (susceptible to shoot fly). A total of 14 SSR markers, seven each on linkage groups A and C were mapped. Using data of different shoot fly resistance component traits, one QTL which is common for glossiness, oviposition and dead hearts was detected following composite interval mapping (CIM) on linkage group A. The phenotypic variation explained by this QTL ranged from 3.8%–6.3%. Besides the QTL detected by CIM, two more QTLs were detected following multi-trait composite interval mapping (MCIM), one each on linkage groups A and C for the combinations of traits which were correlated with each other. Results of the present study are novel as we could find out the QTLs governing more than one trait (pleiotropic QTLs). The identification of pleiotropic QTLs will help in improvement of more than one trait at a time with the help of the same linked markers. For all the QTLs, the resistant parent IS18551 contributed resistant alleles.     

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.     

QTL mapping of resistance to Fusarium ear rot using a RIL population in maize

Fusarium ear rot is a prevalent disease in maize, reducing grain yields and quality. Resistance breeding is an efficient way to minimize losses caused by the disease. In this study, 187 lines from a RIL population along with the resistant (87-1) and susceptible (Zong 3) parents were planted in Zhengzhou and Beijing with three replications in years 2004 and 2006. Each line was artificially inoculated using the nail-punch method. Significant genotypic variation in response to Fusarium ear rot was detected in both years. Based on a genetic map containing 246 polymorphic SSR markers with average genetic distances of 9.1 cM, the ear-rot resistance QTL were firstly analyzed by composite interval mapping (CIM). Three QTL were detected in both Zhengzhou and Beijing in 2004; and three and four QTL, respectively, were identified in 2006. The resistant parent contributed all resistance QTL. By using composite interval mapping and a mixed model (MCIM), significant epistatic effects on Fusarium ear rot as well as interactions between mapped loci and environments were observed across environments. Two QTL on chromosome 3 (3.04 bin) were consistently identified across all environments by the two methods. The major resistant QTL with the largest effect was flanked by markers umc1025 and umc1742 on chromosome 3 (3.04 bin), explaining 13–22% of the phenotypic variation. The SSR markers closely flanking the major resistance QTL will facilitate marker-assisted selection (MAS) of resistance to Fusarium ear rot in maize breeding programs.     

Molecular mapping in tropical maize (Zea mays L.) using microsatellite markers. 2. Quantitative trait loci (QTL) for grain yield, plant height, ear height and grain moisture

A previous genetic map containing 117 microsatellite loci and 400 F(2) plants was used for quantitative trait loci (QTL) mapping in tropical maize. QTL were characterized in a population of 400 F(2:3) lines, derived from selfing the F(2) plants, and were evaluated with two replications in five environments. QTL determinations were made from the mean of these five environments. Grain yield (GY), plant height (PH), ear height (EH) and grain moisture (GM) were measured. Variance components for genotypes (G), environments (E) and GxE interaction were highly significant for all traits. Heritability was 0.69 for GY, 0.66 for PH, 0.67 for EH and 0.23 for GM. Using composite interval mapping (CIM), a total of 13 distinct QTLs were identified: four for GY, four for PH and five for EH. No QTL was detected for GM. The QTL explained 32.73 % of the phenotypic variance of GY, 24.76 % of PH and 20.91 % of EH. The 13 QTLs displayed mostly partial dominance or overdominance gene action and mapped to chromosomes 1, 2, 7, 8 and 9. Most QTL alleles conferring high values for the traits came from line L-14-4B. Mapping analysis identified genomic regions associated with two or more traits in a manner that was consistent with correlation among traits, supporting either pleiotropy or tight linkage among QTL. The low number of QTLs found, can be due to the great variation that exists among tropical environments.