Improvement of effectiveness and durability of disease resistance in crops most often relies on the use of quantitative resistance, with the hypothesis that a wide range of quantitative resistance factors (QTL) makes the overcoming of the resistance by the pathogen more difficult. For an optimum use of these QTL in effective and durable strategies of resistance deployment, there is a need to precisely know their localization but also their stability/specificity and their allelic effects in various genetic backgrounds. Stem canker caused by the fungus Leptosphaeria maculans is one of the most important diseases in oilseed rape. In this Brassica napus- L. maculans pathosystem, QTL were previously identified by linkage analysis using populations derived from biparental crosses that were analyzed separately. In this study, we explored new quantitative resistance factors using a multi-cross connected design derived from four resistant lines crossed with a single susceptible line. Independent and connected mapping analyses revealed to be complementary to get an overview of QTL organization. We validated different QTL across different years and genetic backgrounds and identified novel QTL which had not yet been mapped. Population-common and population-specific QTL were identified. Knowledge of QTL organization and effects should help in the rational choice of relevant factors in breeding resistant genotypes to be integrated with other control means such as cultural practices and rotations for durable management of the disease.

Consumers demand healthy and palatable meat, both factors being affected by fat composition. However, red meat has relatively high concentration of saturated fatty acids and low concentration of the beneficial polyunsaturated fatty acids. To select animals prone to produce particular fat types, it is necessary to identify the genes influencing muscle lipid composition. This paper describes an association study in which a large panel of candidate genes involved in adipogenesis, lipid metabolism and energy homoeostasis was tested for effects on fat composition in 15 European cattle breeds. Sixteen genes were found to have significant effects on different lipid traits, and among these, CFL1 and MYOZ1 were found to have large effects on the ratio of 18:2/18:3, CRI1 on the amount of neutral adrenic acid (22:4 n-6), MMP1 on docosahexaenoic acid (22:6 n-3) and conjugated linoleic acid, PLTP on the ratio of n-6:n-3 and IGF2R on flavour. Several genes - ALDH2, CHRNE, CRHR2, DGAT1, IGFBP3, NEB, SOCS2, SUSP1, TCF12 and FOXO1 - also were found to be associated with both lipid and organoleptic traits although with smaller effect. The results presented here help in understanding the genetic and biochemical background underlying variations in fatty acid composition and flavour in beef.

Variations in meat quality traits are under complex genetic control and improvement has been hampered by the difficulty in their measurement. Several QTL have been reported for different meat quality related traits, but few genes have been described which explain large amounts of the phenotypic variation. The use of single nucleotide polymorphism (SNP) marker panels with predictive value for carcass traits have been evaluated for cattle and SNP are commercially available even though their predictive accuracy may be low in different breeds. To identify new molecular markers for meat quality, an association study was performed in 15 breeds of cattle using 389 SNP belonging to 206 candidate genes known to be involved in muscle development, metabolism and structure. Fifty-four SNP belonging to 20 different genes were found associated with different growth, carcass and meat quality traits. Some of them were novel associations and other were replications of known associations. Among the former, the gene-network associated with the calpain/calpastatin system was shown to be associated with meat texture, although small effects are found for the examined polymorphisms. Novel associations also included SNP in AANAT which was associated with collagen (P=0.006), CAST with fatty acid muscle composition (P=0.00003), CYP1A1 with juiciness (P=0.0005), DGAT2 with physical traits (P=0.0009) and lipid content (P=0.01) in muscle, MADH3 with the myofibrilar fragmentation index (MFI) (P=0.01), NEB with weight (P=0.00009), PCSK1 with juiciness (P=0.002), PLOD3 with carcass performance (P=0.0009) and fatty acids (P=0.04), and PGAM2 and VIM with post-mortem maturation (P=0.00008 and 0.000005, respectively). These data provide a starting point to investigate the complex gene-networks underlying economically important traits which are of importance to the beef industry for the improvement of production efficiency and meat quality.

Advancements in genotyping are rapidly decreasing marker costs and increasing marker density. This opens new possibilities for mapping quantitative trait loci (QTL), in particular by combining linkage disequilibrium information and linkage analysis (LDLA). In this study, we compared different approaches to detect QTL for four traits of agronomical importance in two large multi-parental datasets of maize (Zea mays L.) of 895 and 928 testcross progenies composed of 7 and 21 biparental families, respectively, and genotyped with 491 markers. We compared to traditional linkage-based methods two LDLA models relying on the dense genotyping of parental lines with 17,728 SNP: one based on a clustering approach of parental line segments into ancestral alleles and one based on single marker information. The two LDLA models generally identified more QTL (60 and 52 QTL in total) than classical linkage models (49 and 44 QTL in total). However, they performed inconsistently over datasets and traits suggesting that a compromise must be found between the reduction of allele number for increasing statistical power and the adequacy of the model to potentially complex allelic variation. For some QTL, the model exclusively based on linkage analysis, which assumed that each parental line carried a different QTL allele, was able to capture remaining variation not explained by LDLA models. These complementarities between models clearly suggest that the different QTL mapping approaches must be considered to capture the different levels of allelic variation at QTL involved in complex traits.

Among the several linkage disequilibrium measures known to capture different features of the non-independence between alleles at different loci, the most commonly used for diallelic loci is the r(2) measure. In the present study, we tackled the problem of the bias of r(2) estimate, which results from the sample structure and/or the relatedness between genotyped individuals. We derived two novel linkage disequilibrium measures for diallelic loci that are both extensions of the usual r(2) measure. The first one, r(S)(2), uses the population structure matrix, which consists of information about the origins of each individual and the admixture proportions of each individual genome. The second one, r(V)(2), includes the kinship matrix into the calculation. These two corrections can be applied together in order to correct for both biases and are defined either on phased or unphased genotypes.We proved that these novel measures are linked to the power of association tests under the mixed linear model including structure and kinship corrections. We validated them on simulated data and applied them to real data sets collected on Vitis vinifera plants. Our results clearly showed the usefulness of the two corrected r(2) measures, which actually captured 'true' linkage disequilibrium unlike the usual r(2) measure.

Understanding the genetic bases underlying heterosis is a major issue in maize (Zea mays L.). We extended the North Carolina design III (NCIII) by using three populations of recombinant inbred lines derived from three parental lines belonging to different heterotic pools, crossed with each parental line to obtain nine families of hybrids. A total of 1253 hybrids were evaluated for grain moisture, silking date, plant height, and grain yield. Quantitative trait loci (QTL) mapping was carried out on the six families obtained from crosses to parental lines following the “classical” NCIII method and with a multiparental connected model on the global design, adding the three families obtained from crosses to the nonparental line. Results of the QTL detection highlighted that most of the QTL detected for grain yield displayed apparent overdominance effects and limited differences between heterozygous genotypes, whereas for grain moisture predominance of additive effects was observed. For plant height and silking date results were intermediate. Except for grain yield, most of the QTL identified showed significant additive-by-additive epistatic interactions. High correlation observed between heterosis and the heterozygosity of hybrids at markers confirms the complex genetic basis and the role of dominance in heterosis. An important proportion of QTL detected were located close to the centromeres. We hypothesized that the lower recombination in these regions favors the detection of (i) linked QTL in repulsion phase, leading to apparent overdominance for heterotic traits and (ii) linked QTL in coupling phase, reinforcing apparent additive effects of linked QTL for the other traits.

A quantitative trait locus (QTL) analysis designed for a multi-parent population was carried out and tested in oil palm ( Elaeis guineensis Jacq.), which is a diploid cross-fertilising perennial species. A new extension of the MCQTL package was especially designed for crosses between heterozygous parents. The algorithm, which is now available for any allogamous species, was used to perform and compare two types of QTL search for small size families, within-family analysis and across-family analysis, using data from a 2 × 2 complete factorial mating experiment involving four parents from three selected gene pools. A consensus genetic map of the factorial design was produced using 251 microsatellite loci, the locus of the Sh major gene controlling fruit shell presence, and an AFLP marker of that gene. A set of 76 QTLs involved in 24 quantitative phenotypic traits was identified. A comparison of the QTL detection results showed that the across-family analysis proved to be efficient due to the interconnected families, but the family size issue is just partially solved. The identification of QTL markers for small progeny numbers and for marker-assisted selection strategies is discussed.

Quantitative trait loci (QTL) detection experiments have often been restricted to large biallelic populations. Use of connected multiparental crosses has been proposed to increase the genetic variability addressed and to test for epistatic interactions between QTL and the genetic background. We present here the results of a QTL detection performed on six connected F(2) populations of 150 F(2:3) families each, derived from four maize inbreds and evaluated for three traits of agronomic interest. The QTL detection was carried out by composite interval mapping on each population separately, then on the global design either by taking into account the connections between populations or not. Epistatic interactions between loci and with the genetic background were tested. Taking into account the connections between populations increased the number of QTL detected and the accuracy of QTL position estimates. We detected many epistatic interactions, particularly for grain yield QTL (R(2) increase of 9.6%). Use of connections for the QTL detection also allowed a global ranking of alleles at each QTL. Allelic relationships and epistasis both contribute to the lack of consistency for QTL positions observed among populations, in addition to the limited power of the tests. The potential benefit of assembling favorable alleles by marker-assisted selection are discussed.

SUMMARYBreeding programs face the challenge of integrating information from genomics and from quantitative trait loci (QTL) analysis in order to identify genomic sequences controlling the variation of important traits. Despite the development of integrative databases, building a consensus map of genes, QTL and other loci gathered from multiple maps remains a manual and tedious task. Nevertheless, this is a critical step to reveal co-locations between genes and QTL. Another important matter is to determine whether QTL linked to same traits or related ones is detected in independent experiments and located in the same region, and represents a single locus or not. Statistical tools such as meta-analysis can be used to answer this question. BioMercator has been developed to automate map compilation and QTL meta-analysis, and to visualize co-locations between genes and QTL through a graphical interface.AVAILABILITYAvailable upon request (http://moulon/~bioinfo/BioMercator/). Free of charge for academic use.

We present a maximum likelihood method for mapping quantitative trait loci that uses linkage disequilibrium information from single and multiple markers. We made paired comparisons between analyses using a single marker, two markers and six markers. We also compared the method to single marker regression analysis under several scenarios using simulated data. In general, our method outperformed regression (smaller mean square error and confidence intervals of location estimate) for quantitative trait loci with dominance effects. In addition, the method provides estimates of the frequency and additive and dominance effects of the quantitative trait locus.

Understanding the genetic basis of heterosis in a given species is a major issue for both evolutionary research and applied purposes. In order to map QTLs involved in heterosis in maize and to estimate their effect, we evaluated the hybrids between three connected RIL populations and the three initial parental inbreds, yielding a total of 1278 hybrids. A specific data analysis approach using multilocus models was developed using MCQTL software. Thirteen regions with significant effect on variation were detected for grain yield and 11 for grain moisture at harvest. These two traits display different genetic effects: only few significant dominance effects could be detected for moisture, whereas all yield QTLs displayed such effects. Furthermore, most yield QTLs displayed apparent overdominance effects.

Bacterial wilt caused by the soilborne bacterium Ralstonia solanacearum attacks hundreds of plant species, including many agriculturally important crops. Natural resistance to this disease has been found in some species and is usually inherited as a polygenic trait. In tomato, a model crop plant, genetic analysis previously revealed the involvement of several QTL (quantitative trait loci) controlling resistance and, in all of these studies with different strains of the pathogen, loci on chromosome 6 played the predominant role in controlling this trait. Using quantitative data collected from a greenhouse test F3 population, we identified a new locus on chromosome 12 that appears to be active specifically against a race 1 biovar 3 Pss4 bacterial strain endemic to Taiwan. Chromosome 6 still contributes significantly to the control of the resistance, and weaker associations of the trait to other regions of the genome are observed. These results are discussed in the context of current molecular knowledge about the strain specificity of disease resistance genes.

Ralstonia solanacearum is a soil-borne bacterium that causes the serious disease known as bacterial wilt in many plant species. In tomato, several QTL controlling resistance have been found, but in different studies, markers spanning a large region of chromosome 6 showed strong association with the resistance. By using two different approaches to analyze the data from a field test F3 population, we show that at least two separate loci approximately 30 cM apart on this chromosome are most likely involved in the resistance. First, a temporal analysis of the progression of symptoms reveals a distal locus early in the development of the disease. As the disease progresses, the maximum LOD peak observed shifts toward the proximal end of the chromosome, obscuring the distal locus. Second, although classical interval mapping could only detect the presence of one locus, a statistical "two-QTL model" test, specifically adapted for the resolution of linked QTL, strongly supported the hypothesis for the presence of two loci. These results are discussed in the context of current molecular knowledge about disease resistance genes on chromosome 6 and observations made by tomato breeders during the production of bacterial wilt-resistant varieties.

The aim of this paper was to compare different methods for testing the presence of one versus multiple QTLs on a same chromosome. We describe different methods that have partially been taken out of the literature. We perform simulations covering different situations to compare the power of these methods for detecting more than one QTL. None of the tests considered appear to be similar; that is, the first-type error depends on the value of the parameters concerning the first QTL. The method starting with a two-QTL model is the most powerful in many situations.

Statistical methods for the detection of genes influencing quantitative trait (QTLs) with the aid of genetic markers are well developed for the analysis of a single trait. In practice, many experimental data contain observations on multiple correlated traits and methods that permit joint analysis of all traits are now required. Generalization of the maximum likelihood method to a multitrait analysis is a good approach, but the increase in complexity, due to the number of parameters to be estimated simultaneously, could restrain its practical use when the number of traits is large. We propose an alternative method based on two separate steps. The first step is to estimate the (co)variance matrix of the traits and use this estimate to obtain the canonical variables associated to the traits. The second step is to apply a single-trait maximum likelihood method to each of the canonical variables and to combine the results. Working in a local asymptotic framework for the effects of the putative pleiotropic QTL, i.e., for a pleiotropic QTL whose effect is too small to be detected with certainty, we prove that the combined analysis with canonical variables is asymptotically equivalent to a multitrait maximum likehood analysis. A threshold for the mapping of the pleiotropic QTL is also given. The probability of detecting a QTL is not always increased by the addition of more correlated traits. As an example, a theoretical comparison between the power of a multitrait analysis with two variables and the power of a single-trait analysis is presented. Experimental data collected to study the polygenic resistance of tomato plants to bacterial wilt are used to illustrate the combined analysis with canonical variables.

SummaryA Bayesian approach to map Quantitative Trait Loci (QTL) is compared to the flanking markers regression method (asymptotically equivalent to the traditional Interval Mapping method) using simulated backcross data. The main part consists of a comparison of the properties for a one QTL model. The Bayesian approach gives less biases and more accurate estimates of the genetic effect and significance thresholds close to χ2 quantiles when the number of individuals and markers become large. The behaviour of both approaches in the case of two linked QTL is mentioned.ZusammenfassungEigenschaften eines BAYES Ansatz zur QTL Identifikation im Vergleich zur flankierende Marker Regressions‐Methode.Der Bayes Ansatz zur Kartierung von Quantitativen Merkmals Loci (QTL) wurde mittels simulierter Rückkreuzungsdaten mit der Flankierenden Marker Regressionsmethode (asymptotisch äquivalent mit der traditionellen Intervall Markierung) verglichen. Es werden hauptsächlich die Eigenschaften für ein ein‐QTL Modell geprüft. Der Bayes Ansatz ergibt weniger verzerrte und genauere Schätz‐werte der genetischen Wirkung und Signifikanz Schwellen nahe den χ2 Quantilen bei großen Individuen und Marker Zahlen. Die Eigenschaften beider Ansätze im Falle von zwei gekoppelten QTL werden erwähnt.

Three linkage maps of the genome of the microhymenopteran Trichogramma brassicae were constructed from the analysis of segregation of random amplified polymorphic DNA markers in three F2 populations. These populations were composed of the haploid male progeny of several virgin F1 females, which resulted from the breeding of four parental lines that were nearly fixed for different random amplified polymorphic DNA markers and that were polymorphic for longevity and fecundity characters. As the order of markers common to the three mapping populations was found to be well conserved, a composite linkage map was constructed. Eighty-four markers were organized into five linkage groups and two pairs. The mean interval between two markers was 17.7 cM, and the map spanned 1330 cM.

Alfalfa (Medicago sativa L.) is a forage legume of world-wide importance whose both allogamous and autotetraploid nature maximizes the genetic diversity within natural and cultivated populations. This genetic diversity makes difficult the discrimination between two related populations. We analyzed this genetic diversity by screening DNA from individual plants of eight cultivated and natural populations of M. sativa and M.  falcata using the RAPD method. A high level of genetic variation was found within and between populations. Using five primers, 64 intense bands were scored as present or absent across all populations. Most of the loci were revealed to be highly polymorphic whereas very few population-specific polymorphisms were identified. From these observations, we adopted a method based on the Roger’s genetic distance between populations using the observed frequency of bands to discriminate populations pairwise. Except for one case, the between-population distances were all significantly different from zero. We have also determined the minimal number of bands and individuals required to test for the significance of between-population distances.

The confidence interval for the map location of quantitative trait loci (QTL) is a very important quantity for geneticists. The one LOD support interval has been proved to be a biased confidence interval. Moreover the distribution of the LOD score has been shown to depend on the value of the QTL effect, which is why the LOD score cannot be used to build an unbiased confidence interval when the value of the QTL effect is unknown. A new confidence interval based on a maximun likelihood ratio test and using statistics whose asymptotic distribution does not depend on the QTL effect, has been proposed and proved to lead to an asymptotically similar confidence interval. The major difficulty of this method is the computation of the correct threshold for the maximum likelihood ratio test. An approximation for the threshold is proposed in this paper. When the value of the QTL effect is known, an unbiased confidence region could be built using the LOD score. A simulation study is carried out to compare the average length of this region, which is unobtainable for an unknown value of the QTL effect, to the average length of the asymptotically similar confidence interval. It shows that the required property of similarity does not increase the confidence interval length significantly for QTL having a small effect, and leads to an increase of about 5cM length for a 1 Morgan chromosome when the value of the QTL is great. An empirical symmetrical confidence interval could be constructed with the empirical distribution function of the maximun likelihood estimation for the QTL position. The simulation study shows that, when a QTL is detected, the average length of the asymptotically similar confidence interval could be half the length of the empirical symmetrical one. This great difference can be explained by the fact that the asymptotically similar confidence interval is dependent of the interval mapping test of detection whereas the empirical symmetrical one is independent of the actual data.

This paper is concerned with the construction of a confidence interval for the quantitative trait loci (QTL) location parameter in an F-2 population. As usual for QTLs having small effects, this method is developed in the local asymptotic framework. A maximum likelihood ratio test is built up from statistics whose asymptotic distribution under the null hypothesis does not depend on the nuisance parameters. Simulations show that our confidence interval gives the correct probability of containing the true location of the QTL. An example using experimental data from an F-2 progeny of maize is presented to illustrate this methodology.