Targeted discovery of quantitative trait loci for resistance to northern leaf blight and other diseases of maize

To capture diverse alleles at a set of loci associated with disease resistance in maize, heterogeneous inbred family (HIF) analysis was applied for targeted QTL mapping and near-isogenic line (NIL) development. Tropical maize lines CML52 and DK888 were chosen as donors of alleles based on their known resistance to multiple diseases. Chromosomal regions (“bins”; n = 39) associated with multiple disease resistance (MDR) were targeted based on a consensus map of disease QTLs in maize. We generated HIFs segregating for the targeted loci but isogenic at ~97% of the genome. To test the hypothesis that CML52 and DK888 alleles at MDR hotspots condition broad-spectrum resistance, HIFs and derived NILs were tested for resistance to northern leaf blight (NLB), southern leaf blight (SLB), gray leaf spot (GLS), anthracnose leaf blight (ALB), anthracnose stalk rot (ASR), common rust, common smut, and Stewart’s wilt. Four NLB QTLs, two ASR QTLs, and one Stewart’s wilt QTL were identified. In parallel, a population of 196 recombinant inbred lines (RILs) derived from B73 × CML52 was evaluated for resistance to NLB, GLS, SLB, and ASR. The QTLs mapped (four for NLB, five for SLB, two for GLS, and two for ASR) mostly corresponded to those found using the NILs. Combining HIF- and RIL-based analyses, we discovered two disease QTLs at which CML52 alleles were favorable for more than one disease. A QTL in bin 1.06–1.07 conferred resistance to NLB and Stewart’s wilt, and a QTL in 6.05 conferred resistance to NLB and ASR.     

Genomic Dissection of Leaf Angle in Maize (Zea mays L.) Using a Four-Way Cross Mapping Population

Increasing grain yield by the selection for optimal plant architecture has been the key focus in modern maize breeding. As a result, leaf angle, an important determinant of plant architecture, has been significantly improved to adapt to the ever-increasing plant density in maize production over the past several decades. To extend our understanding on the genetic mechanisms of leaf angle in maize, we developed the first four-way cross mapping population, consisting of 277 lines derived from four maize inbred lines with varied leaf angles. The four-way cross mapping population together with the four parental lines were evaluated for leaf angle in two environments. In this study, we reported linkage maps built in the population and quantitative trait loci (QTL) on leaf angle detected by inclusive composite interval mapping (ICIM). ICIM applies a two-step strategy to effectively separate the cofactor selection from the interval mapping, which controls the background additive and dominant effects at the same time. A total of 14 leaf angle QTL were identified, four of which were further validated in near-isogenic lines (NILs). Seven of the 14 leaf angle QTL were found to overlap with the published leaf angle QTL or genes, and the remaining QTL were unique to the four-way population. This study represents the first example of QTL mapping using a four-way cross population in maize, and demonstrates that the use of specially designed four-way cross is effective in uncovering the basis of complex and polygenetic trait like leaf angle in maize.     

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.     

Identification and validation of QTLs conferring resistance to sorghum downy mildew (Peronosclerospora sorghi) and Rajasthan downy mildew (P. heteropogoni) in maize

We have mapped the quantitative trait loci (QTLs) conferring resistance to sorghum downy mildew (Peronosclerospora sorghi; SDM) and Rajasthan downy mildew (P. heteropogoni; RDM), two species of DM prevalent throughout India. QTL mapping was carried out on a backcross population of 151 individuals derived from a cross between CM139 (susceptible parent) and NAI116 (highly resistant to both SDM and RDM). Heritability estimates were 0.74 for SDM and 0.67 for RDM. Composite interval mapping combined with a linkage map constructed with 80 simple sequence repeat (SSR) markers resulted in the identification of three QTLs (one each on chromosomes 2, 3 and 6) for SDM resistance and two QTLs (one each on chromosomes 3 and 6) for RDM resistance, all of which were contributed by NAI116. The significance of the major QTL on chromosome 6 (bin 6.05) that confers resistance to diverse DMs in tropical Asia, including SDM and RDM in India, was also verified. The results confirmed that some common QTLs contribute to both SDM and RDM resistance, while additional loci might specifically govern resistance to SDM. The QTL information generated in this study provide information that will aid in undertaking an integrated breeding strategy for the transfer of resistance to SDM and RDM in maize lines using marker-assisted selection.     

小麦全基因组抗赤霉病QTL关联位点特异性SSR标记的筛选、等位变异及效应解析

赤霉病是小麦主要的流行病害之一。借助标记辅助选择将不同数量性状基因座（quantitative trait loci，QTL）聚合是防治赤霉病有效且环保的方法，可以从源头上控制赤霉病并降低籽粒中毒素含量。抗赤霉病QTL在小麦全基因组均有分布，但除了Fhb1、Fhb2等少数位点有比较可靠的鉴别标记，绝大部分位点缺乏有效的位点特异性鉴别标记。简单重复序列（simple sequence repeat，SSR）标记多态性丰富，可以区分自然群体中不同等位变异，方便用于标记辅助育种。基于此，搜集了不同文献中报道的与赤霉病关联的SSR标记386个，并用这些标记构建全基因组赤霉病抗性QTL一致性图谱，接着对这些关联标记进行拷贝数分析，进而选择位点内的单拷贝SSR标记，将这些单拷贝标记在156个品种组成的自然群体中进行扩增，并与三季大田和三季温室环境下赤霉病抗性进行关联，筛选与赤霉病抗性关联的单拷贝SSR标记，明确这些标记在自然群体中的有效等位变异和效应。结果表明，共8个单拷贝SSR标记至少在两季试验中与表型显著关联(P<0.05)，涉及2B、2D、3B、5A、5B、6A、6D、7A染色体，有5个单拷贝标记位点存在有效等位变异。中国地方品种和日本品种携带更多的有利变异，且有利等位变异数目越多的品种赤霉病抗性越好。研究分析的QTL位点及其关联的单拷贝SSR标记可用于赤霉病抗病育种，有利于提高品种赤霉病抗性水平和育种效率。     

Unraveling Genomic Complexity at a Quantitative Disease Resistance Locus in Maize

Multiple disease resistance has important implications for plant fitness, given the selection pressure that many pathogens exert directly on natural plant populations and indirectly via crop improvement programs. Evidence of a locus conditioning resistance to multiple pathogens was found in bin 1.06 of the maize genome with the allele from inbred line “Tx303” conditioning quantitative resistance to northern leaf blight (NLB) and qualitative resistance to Stewart’s wilt. To dissect the genetic basis of resistance in this region and to refine candidate gene hypotheses, we mapped resistance to the two diseases. Both resistance phenotypes were localized to overlapping regions, with the Stewart’s wilt interval refined to a 95.9-kb segment containing three genes and the NLB interval to a 3.60-Mb segment containing 117 genes. Regions of the introgression showed little to no recombination, suggesting structural differences between the inbred lines Tx303 and “B73,” the parents of the fine-mapping population. We examined copy number variation across the region using next-generation sequencing data, and found large variation in read depth in Tx303 across the region relative to the reference genome of B73. In the fine-mapping region, association mapping for NLB implicated candidate genes, including a putative zinc finger and pan1. We tested mutant alleles and found that pan1 is a susceptibility gene for NLB and Stewart’s wilt. Our data strongly suggest that structural variation plays an important role in resistance conditioned by this region, and pan1, a gene conditioning susceptibility for NLB, may underlie the QTL.     

Production of haploids and doubled haploids in oil palm

Background

Studies on host-pathogen interactions in a range of pathosystems have revealed an array of mechanisms by which plants reduce the efficiency of pathogenesis. While R-gene mediated resistance confers highly effective defense responses against pathogen invasion, quantitative resistance is associated with intermediate levels of resistance that reduces disease progress. To test the hypothesis that specific loci affect distinct stages of fungal pathogenesis, a set of maize introgression lines was used for mapping and characterization of quantitative trait loci (QTL) conditioning resistance to Setosphaeria turcica, the causal agent of northern leaf blight (NLB). To better understand the nature of quantitative resistance, the identified QTL were further tested for three secondary hypotheses: (1) that disease QTL differ by host developmental stage; (2) that their performance changes across environments; and (3) that they condition broad-spectrum resistance.

Results

Among a set of 82 introgression lines, seven lines were confirmed as more resistant or susceptible than B73. Two NLB QTL were validated in BC₄F₂ segregating populations and advanced introgression lines. These loci, designated qNLB1.02 and qNLB1.06, were investigated in detail by comparing the introgression lines with B73 for a series of macroscopic and microscopic disease components targeting different stages of NLB development. Repeated greenhouse and field trials revealed that qNLB1.06 _Tx303 (the Tx303 allele at bin 1.06) reduces the efficiency of fungal penetration, while qNLB1.02 _B73 (the B73 allele at bin 1.02) enhances the accumulation of callose and phenolics surrounding infection sites, reduces hyphal growth into the vascular bundle and impairs the subsequent necrotrophic colonization in the leaves. The QTL were equally effective in both juvenile and adult plants; qNLB1.06 _Tx303 showed greater effectiveness in the field than in the greenhouse. In addition to NLB resistance, qNLB1.02 _B73 was associated with resistance to Stewart's wilt and common rust, while qNLB1.06 _Tx303 conferred resistance to Stewart's wilt. The non-specific resistance may be attributed to pleiotropy or linkage.

Conclusions

Our research has led to successful identification of two reliably-expressed QTL that can potentially be utilized to protect maize from S. turcica in different environments. This approach to identifying and dissecting quantitative resistance in plants will facilitate the application of quantitative resistance in crop protection.     

Discovery and validation of genomic regions associated with resistance to maize lethal necrosis in four biparental populations

In sub-Saharan Africa, maize is the key determinant of food security for smallholder farmers. The sudden outbreak of maize lethal necrosis (MLN) disease is seriously threatening the maize production in the region. Understanding the genetic basis of MLN resistance is crucial. In this study, we used four biparental populations applied linkage mapping and joint linkage mapping approaches to identify and validate the MLN resistance-associated genomic regions. All populations were genotyped with low to high density markers and phenotyped in multiple environments against MLN under artificial inoculation. Phenotypic variation for MLN resistance was significant and heritability was moderate to high in all four populations for both early and late stages of disease infection. Linkage mapping revealed three major quantitative trait loci (QTL) on chromosomes 3, 6, and 9 that were consistently detected in at least two of the four populations. Phenotypic variance explained by a single QTL in each population ranged from 3.9% in population 1 to 43.8% in population 2. Joint linkage association mapping across three populations with three biometric models together revealed 16 and 10 main effect QTL for MLN-early and MLN-late, respectively. The QTL identified on chromosomes 3, 5, 6, and 9 were consistent with the QTL identified by linkage mapping. Ridge regression best linear unbiased prediction with five-fold cross-validation revealed high accuracy for prediction across populations for both MLN-early and MLN-late. Overall, the study discovered and validated the presence of major effect QTL on chromosomes 3, 6, and 9 which can be potential candidates for marker-assisted breeding to improve the MLN resistance.     

QTL mapping for test weight by using F<Subscript><Emphasis Type="Bold">2:3</Emphasis></Subscript> population in maize

Test weight is an important trait in maize breeding. Understanding the genetic mechanism of test weight is important for effective selection of maize test weight improvement. In this study, quantitative trait loci (QTL) for maize test weight were identified. In the years 2007 and 2008, a F_2:3 population along with the parents Chang7-2 and Zheng58 were planted in Zhengzhou, People’s Republic of China. Significant genotypic variation for maize test weight was observed in both years. Based on the genetic map containing 180 polymorphic SSR markers with an average linkage distance of 11.0 cM, QTL for maize test weight were analysed by mixed-model composite interval mapping. Five QTL, including four QTL with only additive effects, were identified on chromosomes 1, 2, 3, 4 and 5, and together explained 25.2% of the phenotypic variation. Seven pairs of epistatic interactions were also detected, involving 11 loci distributed on chromosomes 1, 2, 3, 4, 5 and 7, respectively, which totally contributed 18.2% of the phenotypic variation. However, no significant QTL × environment (Q×E) interaction and epistasis × environment interaction effects were detected. The results showed that besides the additive QTL, epistatic interactions also formed an important genetic basis for test weight in maize.     

Identification of QTL for maize resistance to common smut by using recombinant inbred lines developed from the Chinese hybrid Yuyu22

Common smut in maize, caused by Ustilago maydis, reduces grain yield greatly. Agronomic and chemical approaches to control such diseases are often impractical or ineffective. Resistance breeding could be an efficient approach to minimize the losses caused by common smut. In this study, quantitative trait loci (QTL) for resistance to common smut in maize were identified. In 2005, a recombinant inbred line (RIL) population along with the resistant (Zong 3) and susceptible (87-1) parents were planted in Beijing and Zhengzhou. Significant genotypic variation in resistance to common smut was observed at both locations after artificial inoculation by injecting inoculum into the whorl of plants with a modified hog vaccinator. Basing on a genetic map containing 246 polymorphic SSR markers with an average linkage distance of 9.11 cM, resistance QTL were analysed by composite interval mapping. Six additive-effect QTL associated with resistance to common smut were identified on chromosomes 3 (three QTL), 5 (one QTL) and 8 (two QTL), and explained 3.2% to 12.4% of the phenotypic variation. Among the 6 QTL, 4 showed significant QTL x environment (Q x E) interaction effects, which accounted for 1.2% to 2.5% of the phenotypic variation. Nine pairs of epistatic interactions were also detected, involving 18 loci distributed on all chromosomes except 2, 6 and 10, which contributed 0.8% to 3.0% of the observed phenotypic variation. However, no significant epistasis x environment interactions were detected. In total, additive QTL effects and Q x E interactions explained 38.8% and 8.0% of the phenotypic variation, respectively. Epistatic effects contributed 15% of the phenotypic variation. The results showed that besides the additive QTL, both epistasis and Q x E interactions formed an important genetic basis for the resistance to Ustilago maydis in maize.     

High-resolution mapping of quantitative trait loci affecting increased life span in Drosophila melanogaster

Limited life span and senescence are near-universal characteristics of eukaryotic organisms, controlled by many interacting quantitative trait loci (QTL) with individually small effects, whose expression is sensitive to the environment. Analyses of mutations in model organisms have shown that genes affecting stress resistance and metabolism affect life span across diverse taxa. However, there is considerable segregating variation for life span in nature, and relatively little is known about the genetic basis of this variation. Replicated lines of Drosophila that have evolved increased longevity as a correlated response to selection for postponed senescence are valuable resources for identifying QTL affecting naturally occurring variation in life span. Here, we used deficiency complementation mapping to identify at least 11 QTL on chromosome 3 that affect variation in life span between five old (O) lines selected for postponed senescence and their five base (B) population control lines. Most QTL were sex specific, and all but one affected multiple O lines. The latter observation is consistent with alleles at intermediate frequency in the base population contributing to the response to selection for postponed senescence. The QTL were mapped with high resolution and contained from 12 to 170 positional candidate genes.     

Identification of novel QTL contributing resistance to aflatoxin accumulation in maize

The toxic metabolic product aflatoxin produced by the opportunistic fungus Aspergillus flavus (Link:Fr) in maize (Zea mays L.) can cause disease and economic harm when levels exceed very minute quantities. The selection of resistant germplasm has great potential to reduce the problem, but the highly quantitative nature of the trait makes this a difficult endeavor. The identification of aflatoxin accumulation resistance quantitative trait loci (QTL) from resistant donor lines and the discovery of linked markers could speed this task. To identify marker–trait associations for marker-assisted breeding, a genetic mapping population of F_2:3 families was developed from Mp715, a maize inbred line resistant to aflatoxin accumulation, and T173, a susceptible, southern-adapted maize inbred line. QTL, some with large phenotypic effects, were identified in multiple years on chromosomes 1, 3, 5, and 10, and smaller QTL identified in only 1 year were found on chromosomes 4 and 9. The phenotypic effect of each QTL ranged from 2.7 to 18.5%, and models created with multiple QTL could explain up to 45.7% of the phenotypic variation across years, indicating that the variation associated with the trait can be manipulated using molecular markers.     

Mapping of the quantitative trait locus (QTL) conferring partial resistance to rice leaf blast disease

Malaysian rice, Pongsu Seribu 2, has wide-spectrum resistance against blast disease. Chromosomal locations conferring quantitative resistance were detected by linkage mapping with SSRs and quantitative trait locus (QTL) analysis. For the mapping population, 188 F₃ families were derived from a cross between the susceptible cultivar, Mahsuri, and a resistant variety, Pongsu Seribu 2. Partial resistance to leaf blast in the mapping population was assessed. A linkage map covering ten chromosomes and consisting of 63 SSR markers was constructed. 13 QTLs, including 6 putative and 7 putative QTLs, were detected on chromosomes 1, 2, 3, 5, 6, 10, 11 and 12. The resulting phenotypic variation due to a single QTL ranged from 2 to 13 %. These QTLs accounted for approx. 80 % of the total phenotypic variation within the F₃ population. Therefore, partial resistance to blast in Pongsu Seribu 2 is due to combined effects of multiple loci with major and minor effects.     

Dissection and fine mapping of a major QTL for preharvest sprouting resistance in white wheat Rio Blanco

Preharvest sprouting (PHS) is a major constraint to white wheat production. Previously, we mapped quantitative trait loci (QTL) for PHS resistance in white wheat by using a recombinant inbred line (RIL) population derived from the cross Rio Blanco/NW97S186. One QTL, QPhs.pseru-3A, showed a major effect on PHS resistance, and three simple sequence repeat (SSR) markers were mapped in the QTL region. To determine the flanking markers for the QTL and narrow down the QTL to a smaller chromosome region, we developed a new fine mapping population of 1,874 secondary segregating F₂ plants by selfing an F6 RIL (RIL25) that was heterozygous in the three SSR marker loci. Segregation of PHS resistance in the population fitted monogenic inheritance. An additive effect of the QTL played a major role on PHS resistance, but a dominant effect was also observed. Fifty-six recombinants among the three SSR markers were identified in the population and selfed to produce homozygous recombinants or QTL near-isogenic lines (NIL). PHS evaluation of the recombinants delineated the QTL in the region close to Xbarc57 flanked by Xbarc321 and Xbarc12. To saturate the QTL region, 11 amplified fragment length polymorphism (AFLP) markers were mapped in the QTL region with 7 AFLP co-segregated with Xbarc57 by using the NIL population. Dissection of the QTL as a Mendelian factor and saturation of the QTL region with additional markers created a solid foundation for positional cloning of the major QTL.     

Mapping quantitative trait loci associated with barley net blotch resistance

Net blotch of barley, caused by Pyrenophora teres Drechs., is an important foliar disease worldwide. Deployment of resistant cultivars is the most economic and eco-friendly control method. This report describes mapping of quantitative trait loci (QTL) associated with net blotch resistance in a doubled-haploid (DH) barley population using diversity arrays technology (DArT) markers. One hundred and fifty DH lines from the cross CDC Dolly (susceptible)/TR251 (resistant) were screened as seedlings in controlled environments with net-form net blotch (NFNB) isolates WRS858 and WRS1607 and spot-form net blotch (SFNB) isolate WRS857. The population was also screened at the adult-plant stage for NFNB resistance in the field in 2005 and 2006. A high-density genetic linkage map of 90 DH lines was constructed using 457 DArT and 11 SSR markers. A major NFNB seedling resistance QTL, designated QRpt6, was mapped to chromosome 6H for isolates WRS858 and WRS1607. QRpt6 was associated with adult-plant resistance in the 2005 and 2006 field trials. Additional QTL for NFNB seedling resistance to the more virulent isolate WRS858 were identified on chromosomes 2H, 4H, and 5H. A seedling resistance QTL (QRpts4) for the SFNB isolate WRS857 was detected on chromosome 4H as was a significant QTL (QRpt7) on chromosome 7H. Three QTL (QRpt6, QRpts4, QRpt7) were associated with resistance to both net blotch forms and lines with one or more of these demonstrated improved resistance. Simple sequence repeat (SSR) markers tightly linked to QRpt6 and QRpts4 were identified and validated in an unrelated barley population. The major 6H QTL, QRpt6, may provide adequate NFNB field resistance in western Canada and could be routinely selected for using molecular markers in a practical breeding program.     

Mapping quantitative trait loci associated with southern leaf blight resistance in maize (Zea mays L.)

Southern leaf blight (SLB) caused by the fungus Cochliobolus heterostrophus (Drechs.) Drechs. is a major foliar disease of maize worldwide. Our objectives were to identify quantitative trait loci (QTL) for resistance to SLB and flowering traits in recombinant inbred line (RIL) population derived from the cross of inbred lines LM5 (resistant) and CM140 (susceptible). A set of 207 RILs were phenotyped for resistance to SLB at three time intervals for two consecutive years. Four putative QTL for SLB resistance were detected on chromosomes 3, 8 and 9 that accounted for 54% of the total phenotypic variation. Days to silking and anthesis–silking interval (ASI) QTL were located on chromosomes 6, 7 and 9. A comparison of the obtained results with the published SLB resistance QTL studies suggested that the detected bins 9.03/02 and 8.03/8.02 are the hot spots for SLB resistance whereas novel QTL were identified in bins 3.08 and 8.01/8.04. The linked markers are being utilized for marker‐assisted mobilization of QTL conferring resistance to SLB in elite maize backgrounds. Fine mapping of identified QTL will facilitate identification of candidate genes underlying SLB resistance.     

Mapping of quantitative trait loci for kernel row number in maize across seven environments

Genetic factors controlling quantitative inheritance of grain yield and its components have been intensively investigated during recent decades using diverse populations in maize (Zea mays L.). Notwithstanding this, quantitative trait loci (QTL) for kernel row number (KRN) with large and consistent effect have not been identified. In this study, a linkage map of 150 simple sequence repeat (SSR) loci was constructed by using a population of 500 F2 individuals derived from a cross between elite inbreds Ye478 and Dan340. The linkage map spanned a total of 1478 cM with an average interval of 10.0 cM. A total of 397 F2:3 lines were evaluated across seven diverse environments for mapping QTL for KRN. Some QTL for grain yield and its components had previously been confirmed with this population across environments. A total of 13 QTL for KRN were identified, with each QTL explaining from 3.0 to 17.9% of phenotypic variance. The gene action for KRN was mainly additive to partial dominance. A large-effect QTL (qkrn7) with partial dominance effect accounting for 17.9% of the phenotypic variation for KRN was identified on chromosome 7 near marker umc1865 with consistent gene effect across seven diverse environments. This study has laid a foundation for map-based cloning of this major QTL and for developing molecular markers for marker-assisted selection of high KRN.     

The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel

In one of the longest-running experiments in biology, researchers at the University of Illinois have selected for altered composition of the maize kernel since 1896. Here we use an association study to infer the genetic basis of dramatic changes that occurred in response to selection for changes in oil concentration. The study population was produced by a cross between the high- and low-selection lines at generation 70, followed by 10 generations of random mating and the derivation of 500 lines by selfing. These lines were genotyped for 488 genetic markers and the oil concentration was evaluated in replicated field trials. Three methods of analysis were tested in simulations for ability to detect quantitative trait loci (QTL). The most effective method was model selection in multiple regression. This method detected approximately 50 QTL accounting for approximately 50% of the genetic variance, suggesting that >50 QTL are involved. The QTL effect estimates are small and largely additive. About 20% of the QTL have negative effects (i.e., not predicted by the parental difference), which is consistent with hitchhiking and small population size during selection. The large number of QTL detected accounts for the smooth and sustained response to selection throughout the twentieth century.     

QTL mapping of yield and fiber traits based on a four-way cross population in <Emphasis Type="Italic">Gossypium hirsutum</Emphasis> L.

Four-way cross (4WC) involving four different inbred lines frequently appears in the cotton breeding programs. However, linkage analysis and quantitative trait loci (QTL) mapping with molecular markers in cotton has largely been applied to populations derived from a cross between two inbred lines, and few results of QTL dissection were conducted in a 4WC population. In this study, an attempt was made to construct a linkage map and identify QTL for yield and fiber quality traits in 4WC derived from four different inbred lines in Gossypium hirsutum L. A linkage map was constructed with 285 SSR loci and one morphological locus, covering 2113.3 cM, approximately 42% of the total recombination length of the cotton genome. A total of 31 QTL with 5.1–25.8% of the total phenotypic variance explained were detected. Twenty-four common QTL across environments showed high stability, and six QTL were environment-specific. Several genomic segments affecting multiple traits were identified. The advantage of QTL mapping using a 4WC were discussed. This study presents the first example of QTL mapping using a 4WC population in upland cotton. The results presented here will enhance the understanding of the genetic basis of yield and fiber quality traits and enable further marker-assisted selection in cultivar populations in upland cotton.     

Quantitative trait loci for resistance to pre-harvest sprouting in US hard white winter wheat Rio Blanco

Pre-harvest sprouting (PHS) of wheat is a major problem that severely limits the end-use quality of flour in many wheat-growing areas worldwide. To identify quantitative trait loci (QTLs) for PHS resistance, a population of 171 recombinant inbred lines (RILs) was developed from the cross between PHS-resistant white wheat cultivar Rio Blanco and PHS-susceptible white wheat breeding line NW97S186. The population was evaluated for PHS in three greenhouse experiments and one field experiment. After 1,430 pairs of simple sequence repeat (SSR) primers were screened between the two parents and two bulks, 112 polymorphic markers between two bulks were used to screen the RILs. One major QTL, QPhs.pseru-3AS, was identified in the distal region of chromosome 3AS and explained up to 41.0% of the total phenotypic variation in three greenhouse experiments. One minor QTL, QPhs.pseru-2B.1, was detected in the 2005 and 2006 experiments and for the means over the greenhouse experiments, and explained 5.0-6.4% of phenotypic variation. Another minor QTL, QPhs.pseru-2B.2, was detected in only one greenhouse experiment and explained 4.5% of phenotypic variation for PHS resistance. In another RIL population developed from the cross of Rio Blanco/NW97S078, QPhs.pseru-3AS was significant for all three greenhouse experiments and the means over all greenhouse experiments and explained up to 58.0% of phenotypic variation. Because Rio Blanco is a popular parent used in many hard winter wheat breeding programs, SSR markers linked to the QTLs have potential for use in high-throughput marker-assisted selection of wheat cultivars with improved PHS resistance as well as fine mapping and map-based cloning of the major QTL QPhs.pseru-3AS.