A new method for Quantitative Trait Loci Detection

We consider the likelihood ratio test (LRT) process related to the test of the absence of QTL on the interval $[0,T]$ representing a chromosome (a QTL denotes a quantitative trait locus, i.e. a gene with quantitative effect on a trait). We give the asymptotic distribution of this LRT process under the general alternative that there exist $m$ QTL on $[0,T]$. This theoretical result allows us to propose to estimate the number of QTL and their positions using the LASSO. Our method does not require the choice of cofactors contrary to Composite Interval Mapping (CIM). Besides, our method is not affected by interactions

Mean number and correlation function of critical points of isotropic Gaussian fields

Let X = {X(t) : t ∈ R N } be an isotropic Gaussian random field with real values. In a first part we study the mean number of critical points of X with index k using random matrices tools. We obtain an exact expression for the probability density of the eigenvalue of rank k of a N-GOE matrix. We deduce some exact expressions for the mean number of critical points with a given index. In a second part we study attraction or repulsion between these critical points. A measure is the correlation function. We prove attraction between critical points when N > 2, neutrality for N = 2 and repulsion for N = 1. The attraction between critical points that occurs when the dimension is greater than two is due to critical points with adjacent indexes. A strong repulsion between maxima and minima is observed. The correlation function between maxima (or minima) depends on the dimension of the ambient space

The SgLasso and its cousins for selective genotyping and extreme sampling: application to association studies and genomic selection

We introduce a new variable selection method, called SgLasso, that handles extreme data, and suitable when the correlation between regressors is known. It is appropriate in genomics since once the genetic map has been built, the correlation is perfectly known. Besides, we prove that the signal to noise ratio is largely increased by considering the extremes. Our method relies on the construction of a specific statistical test, a transformation of the data and by the knowledge of the correlation between regressors. This new technique is inspired by stochastic processes arising from statistical genetics. Our approach and existing methods are compared for simulated and real data, and the results point to the validity of our approach.. Nous introduisons une nouvelle méthode de selection de variables, nommée SgLasso, qui prend en compte les données extrêmes. Notre méthode est basée sur la construction d'un test statistique spécifique, une transformation des données et par la connaissance de la corrélation entre régresseurs. Cela s'avère approprié en génomique car une fois la carte génétique construite, cette corrélation est parfaitement connue. Cette nouvelle technique est inspirée des processus stochastiques en provenance de la statistique génétique. Nous prouvons que le rapport signal bruit est largement augmenté en considérant les extrêmes. Notre approche ainsi que les méthodes existantes sont comparées sur données simulées et réelles. Ceci valide notre nouvelle approche

Processus de tests de rapports de vraisemblance pour la détection de QTL

International audienceOn considère le processus de tests de rapport de vraisemblance (LRT) en référence au test d'absence de QTL sur un intervalle [0,T] représentant un chromosome (QTL désigne un gène à effet quantitatif). On étudie la distribution asymptotique du processus de LRT sous l'hypothèse nulle d'absence de QTL sur [0,T], et sous l'alternative générale qu'il existe m QTL sur [0,T]. On suggère d'estimer le nombre de QTL, leurs positions, et leurs effets par vraisemblance pénalisée. Les résultats seront généralisés au cas où les individus sont structurés en familles

Likelihood Ratio Test process for Quantitative Trait Loci detection.

We consider the likelihood ratio test (LRT) process related to the test of the absence of QTL on the interval [0,T] representing a chromosome (a QTL denotes a quantitative trait locus, i.e. a gene with quantitative effect on a trait). We give the asymptotic distribution of this LRT process under the null hypothesis that there is no QTL on [0,T] and under the general alternative that there exist m QTL on [0,T]. We propose to estimate the number of QTL, their positions and their effects by penalized likelihood. Our results are extended to the case where individuals are structured into families

Threshold and power for Quantitative Trait Locus detection

We propose several new methods to calculate threshold and power for Quantitative Trait Locus (QTL) detection. They are based on asymptotic theoretical results presented in Rabier et al. (2009) . The asymptotic validity is checked by simulations. The methods proposed are fast and easy to implement. A comparison of power between a multiple testing procedure and a global test has been realized, showing far better performances of the global test for the detection of a QTL

Likelihood Ratio Test process for Quantitative Trait Locus detection

International audienceWe consider the likelihood ratio test (LRT) process related to the test of the absence of QTL (a QTL denotes a quantitative trait locus, i.e. a gene with quantitative effect on a trait) on the interval [0,T] representing a chromosome. The observation is the trait and the composition of the genome at some locations called ''markers''. We give the asymptotic distribution of this LRT process under the null hypothesis that there is no QTL on [0,T] and under local alternatives with a QTL at t* on [0,T]. We show that the LRT is asymptotically the square of some Gaussian process. We give a description of this process as an '' non-linear interpolated and normalized process ''. We propose a simple method to calculate the maximum of the LRT process using only statistics on markers and their ratio. This gives a new method to calculate thresholds for QTL detection

Logistic modeling of summer expression of esca symptoms in tolerant and susceptible cultivars in Bordeaux vineyards

The seasonal dynamics of esca leaf symptom development were monitored and modelled over 10 years (from 2004 to 2006, 2012 to 2014, and 2018 to 2021) in eleven vineyards near Bordeaux (France) and on five cultivars, including three susceptible and two tolerant Field observations performed once or twice a week from the end of May to mid-September confirmed i) the evolution over time of esca leaf symptoms, ii) the presence under the bark of a discolored xylem longitudinal stripe with nonfunctional vessels, and iii) a gradual increase in the number of symptomatic plants within each vineyard. Of the three models tested, nonlinear logistic regression was the best fitting curve, showing a clear and systematic progressive sigmoidal pattern of cumulative esca leaf symptom observations regardless of ‘vineyard*year’ situation. Relationships with climatic data confirmed that all periods of symptom expression corresponded to the warmest and driest period of each vegetative season. Examinations of key dates corresponding to four threshold levels of cumulative incidence of leaf symptomatic vines [S1 (first observed symptoms), S10 %, S50 % and S90 %] showed that tolerant cultivars (Merlot noir and Malbec) generally developed leaf symptoms later than susceptible cultivars (Cabernet-Sauvignon, Cabernet franc, and Sauvignon blanc). A variance analysis and a principal component analysis (PCA) confirmed that compared to susceptible cultivars, tolerant cultivars were associated with increased temperature sums above 10 °C from 1st January, reaching the same symptom thresholds S1 and S10 % and with more cumulative rainfall at the S1 stage. Overall, this study reveals the key role of temperature as a triggering factor for esca symptom expression in relation to fungal activity. The results indicate that the S10 % stage can be used as a discriminant variable to separate cultivars according to their susceptibility. Finally, logistic modelling can be used as a descriptive and analytical tool to study the seasonal dynamics of esca

New 8-nitroquinolinone derivative displaying submicromolar in vitro activities against both Trypanosoma brucei and cruzi

International audienceAn antikinetoplastid pharmacomodulation study was conducted at position 6 of the 8-nitroquinolin-2(1H)-one pharmacophore. Fifteen new derivatives were synthesized and evaluated in vitro against L. infantum, T. brucei brucei, and T. cruzi, in parallel with a cytotoxicity assay on the human HepG2 cell line. A potent and selective 6-bromo-substituted antitrypanosomal derivative 12 was revealed, presenting EC50 values of 12 and 500 nM on T. b. brucei trypomastigotes and T. cruzi amastigotes respectively, in comparison with four reference drugs (30 nM ≤ EC50 ≤ 13 μM). Moreover, compound 12 was not genotoxic in the comet assay and showed high in vitro microsomal stability (half life >40 min) as well as favorable pharmacokinetic behavior in the mouse after oral administration. Finally, molecule 12 (E° = −0.37 V/NHE) was shown to be bioactivated by type 1 nitroreductases, in both Leishmania and Trypanosoma, and appears to be a good candidate to search for novel antitrypanosomal lead compounds

Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication)

Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set