QTL analysis of trichome-mediated insect resistance in potato

Genetic mapping of several components of a complex type of insect resistance has been undertaken as a means toward more efficient use of the valuable characteristics of a wild relative of potato. RFLP maps constructed on interspecific diploid progenies of Solanum tuberosum × S. berthaultii were used in conjunction with morphological, biochemical and biological phenotyping to identify quantitative trait loci (QTLs) contributing to trichome-mediated insect resistance. By superimposing QTL data for a wide range of phenotypes including biochemical assays, correlative and direct screens for insect resistance, and adaptation to the target environment on the genetic maps, we have addressed the organization, action and interaction of genes controlling the resistance mechanism. The outcome contributes to an understanding of the association between component traits and between desirable and undesirable features of the donor species generated in an applied breeding program. Research is proceeding toward the development of selectable markers for the introgression and transfer of this resistance among potato gene pools.     

玉米RFLP连锁图谱构建及大斑病QTL定位

玉米大斑病菌存在有生理小种分化的现象,目前5个已定名的生理小种在我国均已发现,还有一些尚未定位名的新类群也出出现,提高玉米对大斑病的抗性,只有提高数量抗性才能达到目的,为了弄清楚玉米对大斑病数量抗性的基因数目及效效应,利用抗病自交5系P138和感病自交系缩3为亲本构建了F2：3家系群体,采用RFLP标记构建了包含了124个标记的玉米RFLP连锁图,覆盖玉米基因组1999．8cM,标记间平均距离为16．5cM,定位了玉米大斑病的病斑长,病斑宽和病斑面积的QTL分别为3、3、2个,其联合贡献率分别为58．1％、71．5％和27．5％,没有检测到病斑数／叶的QTL,其表现为单基因或者寡基因控制的性状,研究结果增加了对玉米大斑病的认识,对玉米抗大斑病育种具有重要的指导意义。     

Epistatic expression of quantitative trait loci (QTL) in soybean [Glycine max (L.) Merr.] determined by QTL association with RFLP alleles

Quantitative trait values for seed oil and protein content or for maturity were measured in recombinant inbred lines (RIL) of soybean derived from a cross between two soybean cultivars: Minsoy PI 27890 and Noir 1 PI 290136. Seed oil was found to be inversely correlated to protein content. By analyzing DNA from plants with extreme phenotypes, we were able to identify quantitative trait loci (QTL) for these traits as being linked to several restriction fragment length polymorphism (RFLP) loci, including R183 for oil and protein content and R79 for maturity. Cumulative distributions of trait values were graphed for those RIL with Minsoy alleles and for those with Noir 1 alleles. As already suggested by the alleles found associated with extreme phenotypes, the distributions were consistent with an independent and additive expression of the maturity QTL linked to R79. That is, the cumulative distributions for plants with Minsoy alleles and for plants with Noir 1 alleles were similar in shape, but the entire Noir 1 curve had been shifted to later maturity dates. In contrast, the trait distributions for a locus affecting oil and protein content linked to R183 were not compatible with an additive model. These results suggest that this approach can be used for rapid identification of QTLs with epistatic expression.     

Genetic models to estimate additive and non-additive effects of marker-associated QTL using multiple regression techniques

Summary The development of molecular markers has recently raised expectations for their application in selection programs. However, some questions related to quantitative trait loci (QTL) identification are still unanswered. The objectives of this paper are (1) to develop statistical genetic models for detecting and locating on the genome multi-QTL with additive, dominance and epistatic effects using multiple linear regression analysis in the backcross and F_n generations from the cross of two inbred lines; and (2) to discuss the bias caused by linked and unlinked QTL on the genetic estimates. Non-linear models were developed for different backcross and F_n generations when both epistasis and no epistasis were assumed. Generation analysis of marked progenies is suggested as a way of increasing the number of observations for the estimates without additional cost for molecular scoring. Some groups of progenies can be created in different generations from the same scored individuals. The non-linear models were transformed into approximate multivariate linear models to which combined stepwise and standard regression analysis could be applied. Expressions for the biases of the marker classes from linked QTL were obtained when no epistasis was assumed. When epistasis was assumed, these expressions increased in complexity, and the biases were caused by both linked and unlinked QTL.     

应用二种定位法比较不同世代水稻产量性状QTL的检测结果

应用珍汕97B／密阳46的F2和重组自交系（RIL）群体,建立RFLP连锁图,检测控制稻谷产量及其5个构成因子的QTL。结果表明,具有较大加性效应者,能同时在F2和RIL群体中检测到。而且,在重组自交系群体中,发现设重复的表型鉴定与基于单株的表型鉴定,对效应较高的QTL的检测影响不大。     

Use of QTL analysis in physiological research

Quantitative trait locus (QTL) analysis is a powerful approach to map and subsequently identify genes involved in complex traits. Here we describe the basic principles and recent achievements of this method, and its application in physiological research in plants. The rapidly increasing amount of molecular and “omics” data and genetic resources and tools, in model species (Arabidopsis) and crops, will greatly support and stimulate the use of this approach in the near future. Published in Russian in Fiziologiya Rastenii, 2007, Vol. 54, No. 1, pp. 15–21. The text was submitted by the authors in English.     

Mapping multiple QTL of different effects: comparison of a simple sequential testing strategy and multiple QTL mapping

The aim of this study was to explore, by computer simulation, the mapping of QTLs in a realistic but complex situation of many (linked) QTLs with different effects, and to compare two QTL mapping methods. A novel method to dissect genetic variation on multiple chromosomes using molecular markers in backcross and F₂ populations derived from inbred lines was suggested, and its properties tested using simulations. The rationale for this sequential testing method was to explicitly test for alternative genetic models. The method consists of a series of four basic statistical tests to decide whether variance was due to a single QTL, two QTLs, multiple QTLs, or polygenes, starting with a test to detect genetic variance associated with a particular chromosome. The method was able to distinguish between different QTL configurations, in that the probability to `detect' the correct model was high, varying from 0.75 to 1. For example, for a backcross population of 200 and an overall heritability of 50%, in 78% of replicates a polygenic model was detected when that was the underlying true model. To test the method for multiple chromosomes, QTLs were simulated on 10 chromosomes, following a geometric series of allele effects, assuming positive alleles were in coupling in the founder lines For these simulations, the sequential testing method was compared to the established Multiple QTL Mapping (MQM) method. For a backcross population of 400 individuals, power to detect genetic variance was low with both methods when the heritability was 0.40. For example, the power to detect genetic variation on a chromosome on which 6 QTLs explained 12.6% of the genetic variance, was less than 60% for both methods. For a large heritability (0.90), the power of MQM to detect genetic variance and to dissect QTL configurations was generally better, due to the simultaneous fitting of markers on all chromosomes. It is concluded that when testing different QTL configurations on a single chromosome using the sequential testing procedure, regions of other chromosomes which explain a significant amount of variation should be fitted in the model of analysis. This study reinforces the need for large experiments in plants and other species if the aim of a genome scan is to dissect quantitative genetic variation.     

QTL mapping of resistance to Sporisorium reiliana in maize

We mapped and characterized quantitative trait loci (QTL) for resistance to Sporisorium reiliana. A population of 220 F₃ families produced from the cross of two European elite inbreds (D32, D145) was evaluated with two replications at a French location with high natural incidence of S. reiliana and at a Chinese location employing artificial inoculation. The 220 F₃ families were genotyped with 87 RFLP and seven SSR markers. Using composite interval mapping, we identified two different sets of 3 and 8 QTL for the French and the Chinese locations explaining 13% and 44% of respectively. Individual QTL explained up to 14% of σ^² _p. The 11 QTL mapped to eight maize chromosomes and displayed mostly additive or partial dominant gene action. Significant digenic epistatic interactions were detected for one pair of these QTL. Only a few QTL for S. reiliana were in common with QTL for resistance to Ustilago maydis and Puccinia sorghi, identified at a German location for the same population. Consequently, in our materials resistance to these three fungal pathogens of maize seems to be inherited independently. Received: 14. December 1998 / Accepted: 30 January 1999     

Combined analysis of data from two granddaughter designs: A simple strategy for QTL confirmation and increasing experimental power in dairy cattle

A joint analysis of five paternal half-sib Holstein families that were part of two different granddaughter designs (ADR- or Inra-design) was carried out for five milk production traits and somatic cell score in order to conduct a QTL confirmation study and to increase the experimental power. Data were exchanged in a coded and standardised form. The combined data set (JOINT-design) consisted of on average 231 sires per grandsire. Genetic maps were calculated for 133 markers distributed over nine chromosomes. QTL analyses were performed separately for each design and each trait. The results revealed QTL for milk production on chromosome 14, for milk yield on chromosome 5, and for fat content on chromosome 19 in both the ADR- and the Inra-design (confirmed within this study). Some QTL could only be mapped in either the ADR- or in the Inra-design (not confirmed within this study). Additional QTL previously undetected in the single designs were mapped in the JOINT-design for fat yield (chromosome 19 and 26), protein yield (chromosome 26), protein content (chromosome 5), and somatic cell score (chromosome 2 and 19) with genomewide significance. This study demonstrated the potential benefits of a combined analysis of data from different granddaughter designs.     

Mapping QTL controlling yield and yield components in a spring barley (Hordeum vulgare L.) cross using marker regression

An RFLP map constructed from 99 doubled haploid lines of a cross between two spring barley varieties (Blenheim × Kym) was used to localize quantitative trait loci (QTL) controlling grain yield and yield components by marker regression and single-marker analysis. Trials were conducted over three years. Genotype-by-year interaction was detected for plant grain weight and ear grain weight so they were analysed separately for each year. None was detected for thousand-grain weight and ear grain number so data were pooled over years. A total of eleven QTL were detected for plant grain weight over two years and fourteen for ear grain weight over three years. Seven QTL were detected for plot yield. The locus with the largest effect was on chromosome 2(2H)L and accounted for 19% of the variation in the progeny. Eight QTL were detected for thousand-grain weight and five for ear grain number. Many of the QTL detected were in comparable positions in each year. Yield and yield components were only partly correlated. Comparisons based on common RFLP markers showed that some QTL were found in positions similar to those identified in other studies. For a number of QTL the identification of linked markers provided suitable opportunities for marker-assisted selection and improvement of barley and reference markers with which to analyse the homoeologous chromosome regions of wheat and other cereals.     

Comparative QTL mapping of resistance to Ustilago maydis across four populations of European flint-maize

 We mapped and characterized quantitative trait loci (QTLs) for resistance to Ustilago maydis and investigated their consistency across different flint-maize populations. Four independent populations, comprising 280 F₃ lines (A×B^I), 120 F₅ lines (A×B^II), 131 F₄ lines (A×C) and 133 F₄ lines (C×D), were produced from four European elite flint inbreds (A, B, C, D) and genotyped at 89, 151, 104, and 122 RFLP marker loci, respectively. All F_n lines were evaluated in field trials with two replications in five German environments. Genotypic variances were highly significant for the percentage of U. maydis infected plants (UST) in all populations, and heritabilities exceeded 0.69. Between five and ten QTLs were detected in individual populations by composite interval mapping, explaining between 39% and 58% of the phenotypic variance. These 19 different QTLs were distributed over all ten chromosomes without any clustering on certain chromosomes. In most cases, gene action was dominant or overdominant. Fourteen pairs of the detected QTLs for UST displayed significant digenic epistatic interactions, but only two of them did so after arcsin √UST/100 transformation. Significant QTL× environment interactions occurred frequently. Between two to four QTLs were common between pairs of populations. Population C×D was also grown in Chartres, a location with a high U. maydis incidence. Two out of six QTLs identified for Chartres were in common with QTLs detected across five German environments for C×D. Consequently, marker-assisted or phenotypic selection based on results from natural infection seem to be suitable breeding strategies for improving the resistance of maize to U. maydis. Received: 3 July 1998 / Accepted: 24 July 1998     

QTL analysis of flower and fruit traits in sour cherry

The map locations and effects of quantitative trait loci (QTLs) were estimated for eight flower and fruit traits in sour cherry (Prunus cerasus L.) using a restriction fragment length polymorphism (RFLP) genetic linkage map constructed from a double pseudo-testcross. The mapping population consisted of 86 progeny from the cross between two sour cherry cultivars, Rheinische Schattenmorelle (RS)×Erdi Botermo (EB). The genetic linkage maps for RS and EB were 398.2 cM and 222.2 cM, respectively, with an average interval length of 9.8 cM. The RS/EB linkage map that was generated with shared segregating markers consisted of 17 linkage groups covering 272.9 cM with an average interval length of 4.8 cM. Eleven putatively significant QTLs (LOD >2.4) were detected for six characters (bloom time, ripening time, % pistil death, % pollen germination, fruit weight, and soluble solids concentration). The percentage of phenotypic variation explained by a single QTL ranged from 12.9% to 25.9%. Of the QTLs identified for the traits in which the two parents differed significantly, 50% had allelic effects opposite to those predicted from the parental phenotype. Three QTLs affecting flower traits (bloom time, % pistil death, and % pollen germination) mapped to a single linkage group, EB 1. The RFLP closest to the bloom time QTL on EB 1 was detected by a sweet cherry cDNA clone pS141 whose partial amino acid sequence was 81% identical to that of a Japanese pear stylar RNase. Received: 4 March 1999 / Accepted: 27 August 1999     

QTL analysis of potato tuberization

Quantitative trait loci (QTLs) affecting tuberization were detected in reciprocal backcrosses between Solanum tuberosum and S. berthaultii. Linkage analyses were performed between traits and RFLP alleles segregating from both the hybrid and the recurrent parent using a set of framework markers from the potato map. Eleven distinct loci on seven chromosomes were associated with variation in tuberization. Most of the loci had small effects, but a QTL explaining 27% of the variance was found on chromosome 5. More QTLs were detected while following alleles segregating from the recurrent S. tuberosum parent used to make the backcross than were detected by following alleles segregating from the hybrid parent. More than half of the alleles favoring tuberization were at least partly dominant. Tuberization was favored by an allele from S. berthaultii at 3 of the 5 QTLs detected by segregation from the hybrid parent. The additive effects of the QTLs for tuberization explained up to 53% of the phenotypic variance, and inclusion of epistatic effects increased this figure to 60%. The most common form of epistasis was that in which presence of an allele at each of 2 loci favoring tuberization was no more effective than the presence of a favorable allele at 1 of the 2 loci. The QTLs detected for tuberization traits are discussed in relationship to those previously detected for trichome-mediated insect resistance derived from the unadapted wild species.Paper number 54 of the Department of Fruit and Vegetable Science, Cornell University     

QTL analysis for aphid resistance and growth traits in apple

The rosy apple aphid (Dysaphis plantaginea), the leaf-curling aphid (Dysaphis cf. devecta) and the green apple aphid (Aphis pomi) are widespread pest insects that reduce growth of leaves, fruits and shoots in apple (Malus × domestica). Aphid control in apple orchards is generally achieved by insecticides, but alternative management options like growing resistant cultivars are needed for a more sustainable integrated pest management (IPM). A linkage map available for a segregating F₁-cross of the apple cultivars ‘Fiesta’ and ‘Discovery’ was used to investigate the genetic basis of resistance to aphids. Aphid infestation and plant growth characteristics were repeatedly assessed for the same 160 apple genotypes in three different environments and 2 consecutive years. We identified amplified fragment length polymorphism (AFLP) markers linked to quantitative trait loci (QTLs) for resistance to D. plantaginea (‘Fiesta’ linkage group 17, locus 57.7, marker E33M35–0269; heritability: 28.3%), and to D. cf. devecta (‘Fiesta’ linkage group 7, locus 4.5, marker E32M39–0195; heritability: 50.2%). Interactions between aphid species, differences in climatic conditions and the spatial distribution of aphid infestation were identified as possible factors impeding the detection of QTLs. A pedigree analysis of simple sequence repeat (SSR) marker alleles closely associated with the QTL markers revealed the presence of the alleles in other apple cultivars with reported aphid resistance (‘Wagener’, ‘Cox’s Orange Pippin’), highlighting the genetic basis and also the potential for gene pyramiding of aphid resistance in apple. Finally, significant QTLs for shoot length and stem diameter were identified, while there was no relationship between aphid resistance and plant trait QTLs. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Genetic mapping of QTL controlling tissue-culture response on chromosome 2B of wheat (Triticum aestivum L.) in relation to major genes and RFLP markers

 Three quantitative trait loci (QTL) for tissue- culture response (Tcr) were mapped on chromosome 2B of hexaploid wheat (Triticum aestivum L.) using single-chromosome recombinant lines. Tcr-B1 and Tcr-B2, affecting both green spots initiation and shoot regeneration, were mapped in relation to RFLP markers in the centromere region and on the short arm of chromosome 2B, linked to the photoperiod-response gene Ppd2. A third QTL (Tcr-B3), influencing regeneration only, was closely related to the disease resistance locus Yr7/Sr9g on the long arm of chromosome 2B. The homoeologous relationships to the tissue-culture response loci Qsr, Qcg and Shd of barley are discussed. A possible influence of the earliness per se genes of wheat and barley is suggested. Received: 30 August 1996 / Accepted: 15 November 1996     

Spurious linkage between markers in QTL mapping

Selective genotyping of extreme progeny is a powerful method to increase the information content per individual when looking for quantitative trait loci (QTLs) using molecular markers for which a map is known. However, if marker information from the selected individuals is used to construct the map of the markers, this can lead to distorted segregation of the markers that in turn can lead to the estimation of a spurious linkage between independently inherited markers. The mistaken estimation of linkage between independently inherited markers will occur when there are two (or more) independently inherited QTLs linked to two (or more) markers and the same individuals are used to estimate the map of the markers and to do the QTL estimation. The incorrect linkage occurs because in selecting individuals from the tails of the phenotypic distribution we will also be selecting certain combinations of the markers instead of obtaining a random sample of the true distribution of the marker genotypes. Analytical results are outlined and the analyses of a simulated data set illustrate the problems that could arise when data from individuals chosen by selective genotyping are incorrectly employed to construct a marker map. A strategy is proposed to remedy this problem.     

粳型超级稻品种'沈农265'穗部和穗颈维管束性状的QTL剖析

采用粳型超级稻品种'沈农265'和普通品种'丽江新团黑谷'的176株F2群体分析24个穗部和穗颈维管束数量性状位点(QTL)的结果显示,共检测到37个QTL,它们分布在水稻的第1、2、3、4、5、6、7,8,9和12号染色体上,单个QTL对性状表型贡献率在11.0%-65.0%之间,其中大于20%的有15个.这些QTL,分别在第3、4、6、9和12号染色体上.以6个QTL簇(QCR)的形式存在.QCR-3和QCR-12控制穗部和穗颈维管束性状,QCR-4a、QCR-4b、QCR-6和QCR-9控制穗部性状.在这些区域已经定位了多个控制穗部性状的QTL,说明紧密连锁或成簇分布是穗颈维管束性状和穗部性状高度相关的遗传学基础之一.     

A comprehensive analysis of QTL for abdominal fat and breast muscle weights on chicken chromosome 5 using a multivariate approach

Quantitative trait loci (QTL) influencing the weight of abdominal fat (AF) and of breast muscle (BM) were detected on chicken chromosome 5 (GGA5) using two successive F₂ crosses between two divergently selected 'Fat' and 'Lean' INRA broiler lines. Based on these results, the aim of the present study was to identify the number, location and effects of these putative QTL by performing multitrait and multi-QTL analyses of the whole available data set. Data concerned 1186 F₂ offspring produced by 10 F₁ sires and 85 F₁ dams. AF and BM traits were measured on F₂ animals at slaughter, at 8 (first cross) or 9 (second cross) weeks of age. The F₀, F₁ and F₂ birds were genotyped for 11 microsatellite markers evenly spaced along GGA5. Before QTL detection, phenotypes were adjusted for the fixed effects of sex, F₂ design, hatching group within the design, and for body weight as a covariable. Univariate analyses confirmed the QTL segregation for AF and BM on GGA5 in male offspring, but not in female offspring. Analyses of male offspring data using multitrait and linked-QTL models led us to conclude the presence of two QTL on the distal part of GGA5, each controlling one trait. Linked QTL models were applied after correction of phenotypic values for the effects of these distal QTL. Several QTL for AF and BM were then discovered in the central region of GGA5, splitting one large QTL region for AF into several distinct QTL. Neither the 'Fat' nor the 'Lean' line appeared to be fixed for any QTL genotype. These results have important implications for prospective fine mapping studies and for the identification of underlying genes and causal mutations.     

Crop model based QTL analysis across environments and QTL based estimation of time to floral induction and flowering in <Emphasis Type="Italic">Brassica oleracea</Emphasis>

Studying quantitative traits is complicated due to genotype by environment interactions. One strategy to overcome these difficulties is to combine quantitative trait loci (QTL) and ecophysiological models, e.g. by identifying QTLs for the response curves of adaptive traits to influential environmental factors. A B. oleracea DH-population segregating for time to flowering was cultivated at different temperature regimes. Composite interval mapping was carried out on the three parameters of a model describing time to flowering as a function of temperature, i.e. on the intercept and slope of the response of time to floral induction to temperature and on the duration from transition to flowering. The additive effects of QTLs detected for the parameters have been used to estimate time to floral induction and flowering in the B. oleracea DH-population. The combined QTL and crop model explained 66% of the phenotypic variation for time to floral induction and 56% of the phenotypic variation for time to flowering. Estimation of time to floral induction and flowering based on environment specific QTLs explained 61 and 41% of the phenotypic variation. Results suggest that flowering time can be predicted effectively by coupling QTL and crop models and that using crop modelling tools for QTL analysis increases the power of QTL detection.