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Table S1: Phenotypes associated with single R161Q and R200W VHL germline mutations are different from the phenotype associated with the combined mutations. Table S2: VHL mutations associated with polycythemia are different from the VHL mutations involved in severe von Hippel-Lindau disease. Table S3: Members of the family carrying the VHL-R200W+R161Q mutations present normal hematological values. Table S4: Intra-domain hydrogen bonds are severely destabilized by the mutations. Table S5: The double mutant reduced the inter-domain mobility compared to the single one. Table S6: The pVHL/HIF-2α direct target genes are deregulated with a significantly ascending gradient by the pVHL mutants of increased severity. Table S7: The target genes of the pVHL/HIF-2α axis that are deregulated in renal cell carcinoma play a role in oncogenic pathways. Figure S1: Germline mutations in the VHL gene are associated with distinct phenotypes. Figure S2: (A) The crystallographic structure of pVHL contains a seven-stranded β-sandwich connected to four α-helices by a flexible linker. (B) The R200W mutation dramatically weakens hydrogen bonds established in the wild-type form between Arg200 and Glu134, and between Arg197 and Arg120. (C) The R161Q mutation indirectly influences crucial stabilizing interactions involving primarily Arg 167. Figure S3: VHL mRNA expression is equivalent in 786.O cells transduced by the different VHL inducible lentiviral constructs.

Supplementary Material and Methods and Legend of Supplementary Tables and Figures

The cumulative stack-up of geometric variations in mechanical systems can be modeled summing and intersecting sets of constraints. These constraints derive from tolerance zones or from contact restrictions between parts. The advantage of this approach is its robustness for treating any kind of mechanisms, including the over-constrained ones. However, the sum of constraints, which must be computed when simulating the accumulation of defects in serial joints, is a very time-consuming operation. In previous papers, we proposed to virtually limit the degrees of freedom of the toleranced features and joints turning the polyhedra into polytopes to avoid manipulating unbounded objects. Even though this approach enables to process the whole mechanism, it also introduces bounding or cap facets which increase the complexity of the operand sets after each operation until becoming far too significant. In this work, we introduce algorithms summing, intersecting and testing inclusions. As they operate on sets of constraints using unbounded polyhedral objects, we identify the smaller sub-space in which the projection of these operands are bounded sets. Calculating the sum in this sub-space allows reducing the operands complexity significantly and consequently the computational time. Then, checking the final inclusion informs us not only about the compliance of the mechanism tolerances with respect to the functional specification but also to quantify how far we are from this target. Finally prismatic polyhedra integrate ISO and contacts specifications in a very natural way and are able to perform a full kinematic analysis of the mechanism. After presenting the geometric properties on which this approach rely, we demonstrate it on an industrial case. Then we compare the computation times, prove the robustness of the new method and show how to quantify the functional condition compliance with respect to a given set of tolerances.

A new method for the analysis of time to ankylosis complication on a dataset of replanted teeth is proposed. In this context of left-censored, interval-censored and right-censored data, a Cox model with piecewise constant baseline hazard is introduced. Estimation is carried out with the expectation maximisation (EM) algorithm by treating the true event times as unobserved variables. This estimation procedure is shown to produce a block diagonal Hessian matrix of the baseline parameters. Taking advantage of this interesting feature in the EM algorithm, a [Image: see text] penalised likelihood method is implemented in order to automatically determine the number and locations of the cuts of the baseline hazard. This procedure allows to detect specific areas of time where patients are at greater risks for ankylosis. The method can be directly extended to the inclusion of exact observations and to a cure fraction. Theoretical results are obtained which allow to derive statistical inference of the model parameters from asymptotic likelihood theory. Through simulation studies, the penalisation technique is shown to provide a good fit of the baseline hazard and precise estimations of the resulting regression parameters.

We focus here on the distribution of the random count N of a regular expression in a multi-state random sequence generated by a heterogenous Markov source. We first briefly recall how classical Markov chain embedding techniques allow reducing the problem to the count of specific transitions in a (heterogenous) order 1 Markov chain over a deterministic finite automaton state space. From this result we derive the expression of both the mgf/pgf of N as well as the factorial and non-factorial moments of N. We then introduce the notion of evidence-based constraints in this context. Following the classical forward/backward algorithm in hidden Markov models, we provide explicit recursions allowing to compute the mgf/pgf of N under the evidence constraint. All the results presented are illustrated with a toy example.

INTRODUCTION Hereditary transthyretin (ATTRv) amyloidosis is of autosomal dominant transmission, caused by a spectrum of mutations in the transthyretin (TTR) gene. The ATTRV30M (p.Val50Met) is the most frequent substitution in Europe. Northern Sweden is a known cluster for ATTRV30M amyloidosis patients due to high prevalence of the mutation rate, with homozygous cases. First symptoms occur generally during the 6th decade. Previous studies reported low penetrance in this area and possible anticipation in families. In order to refine our knowledge of the genetic aspects, penetrance and factors that influence the disease's risk, we performed a comprehensive study of ATTRV30M families in Sweden. METHODS To assess anticipation, well-established age at onset (AO) was compared in all informative parent-offspring pairs and in subgroups, after excluding ascertainment biases. Penetrance was estimated using a non-parametric method that enables to study covariates' effect on the disease's risk. RESULTS We analysed 114 ATTRV30M Swedish families, including 12 homozygous individuals. Among 131 parent-offspring pairs, we found an average anticipation of 11.7 [Standard Deviation (SD) =10.03] years, higher in case of maternal transmission (mean ± SD = 13.7 ± 8.4 years), compared to paternal transmission (mean ± SD = 7.9 ± 11.5 years, p

In epidemiological or demographic studies, with variable age at onset, a typical quantity of interest is the incidence of a disease (for example the cancer incidence). In these studies, the individuals are usually highly heterogeneous in terms of dates of birth (the cohort) and with respect to the calendar time (the period) and appropriate estimation methods are needed. In this article a new estimation method is presented which extends classical age-period-cohort analysis by allowing interactions between age, period and cohort effects. We introduce a bidimensional regularized estimate of the hazard rate where a penalty is introduced on the likelihood of the model. This penalty can be designed either to smooth the hazard rate or to enforce consecutive values of the hazard to be equal, leading to a parsimonious representation of the hazard rate. In the latter case, we make use of an iterative penalized likelihood scheme to approximate the L 0 norm, which makes the computation tractable. The method is evaluated on simulated data and applied on breast cancer survival data from the SEER program.

International audience

Although bariatric surgery is proven to sustain weight loss in morbidly obese patients, long-term adverse effects have yet to be fully characterized. This study compared the long-term consequences of two common forms of bariatric surgery: one-anastomosis gastric bypass (OAGB) and Roux-en-Y Gastric Bypass (RYGB) in a preclinical rat model. We evaluated the influence of biliopancreatic limb (BPL) length, malabsorption, and bile acid (BA) reflux on esogastric mucosa. After 30 weeks of follow-up, Wistar rats operated on RYGB, OAGB with a short BPL (15 cm, OAGB-15), or a long BPL (35 cm, OAGB-35), and unoperated rats exhibit no cases of esogastric cancer, metaplasia, dysplasia, or Barrett’s esophagus. Compared to RYGB, OAGB-35 rats presented higher rate of esophagitis, fundic gastritis and perianastomotic foveolar hyperplasia. OAGB-35 rats also revealed the greatest weight loss and malabsorption. On the contrary, BA concentrations were the highest in the residual gastric pouch of OAGB-15 rats. Yet, no association could be established between the esogastric lesions and malabsorption, weight loss, or gastric bile acid concentrations. In conclusion, RYGB results in a better long-term outcome than OAGB, as chronic signs of biliary reflux or reactional gastritis were reported post-OAGB even after reducing the BPL length in a preclinical rat model.

International audience; In this article we suggest a new statistical approach considering survival heterogeneity as a breakpoint model in an ordered sequence of time to event variables. The survival responses need to be ordered according to a numerical covariate. Our esti- mation method will aim at detecting heterogeneity that could arise through the or- dering covariate. We formally introduce our model as a constrained Hidden Markov Model (HMM) where the hidden states are the unknown segmentation (breakpoint locations) and the observed states are the survival responses. We derive an efficient Expectation-Maximization (EM) framework for maximizing the likelihood of this model for a wide range of baseline hazard forms (parametrics or nonparametric). The posterior distribution of the breakpoints is also derived and the selection of the number of segments using penalized likelihood criterion is discussed. The performance of our survival breakpoint model is finally illustrated on a diabetes dataset where the observed survival times are ordered according to the calendar time of disease onset.

SummaryBackgroundWestern Cambodia is the epicentre of Plasmodium falciparum multidrug resistance and is facing high rates of dihydroartemisinin–piperaquine treatment failures. Genetic tools to detect the multidrug-resistant parasites are needed. Artemisinin resistance can be tracked using the K13 molecular marker, but no marker exists for piperaquine resistance. We aimed to identify genetic markers of piperaquine resistance and study their association with dihydroartemisinin–piperaquine treatment failures.MethodsWe obtained blood samples from Cambodian patients infected with P falciparum and treated with dihydroartemisinin–piperaquine. Patients were followed up for 42 days during the years 2009–15. We established in-vitro and ex-vivo susceptibility profiles for a subset using piperaquine survival assays. We determined whole-genome sequences by Illumina paired-reads sequencing, copy number variations by qPCR, RNA concentrations by qRT-PCR, and protein concentrations by immunoblotting. Fisher’s exact and non-parametric Wilcoxon rank-sum tests were used to identify significant differences in single-nucleotide polymorphisms or copy number variants, respectively, for differential distribution between piperaquine-resistant and piperaquine-sensitive parasite lines.FindingsWhole-genome exon sequence analysis of 31 culture-adapted parasite lines associated amplification of the plasmepsin 2–plasmepsin 3 gene cluster with in-vitro piperaquine resistance. Ex-vivo piperaquine survival assay profiles of 134 isolates correlated with plasmepsin 2 gene copy number. In 725 patients treated with dihydroartemisinin–piperaquine, multicopy plasmepsin 2 in the sample collected before treatment was associated with an adjusted hazard ratio (aHR) for treatment failure of 20·4 (95% CI 9·1–45·5, p

In a survival analysis context, we suggest a new method to estimate the piecewise constant hazard rate model. The method provides an automatic procedure to find the number and location of cut points and to estimate the hazard on each cut interval. Estimation is performed through a penalized likelihood using an adaptive ridge procedure. A bootstrap procedure is proposed in order to derive valid statistical inference taking both into account the variability of the estimate and the variability in the choice of the cut points. The new method is applied both to simulated data and to the Mayo Clinic trial on primary biliary cirrhosis. The algorithm implementation is seen to work well and to be of practical relevance.

When considering a genetic disease with variable age at onset (e.g., familial amyloid neuropathy, cancers), computing the individual risk of the disease based on family history (FH) is of critical interest for both clinicians and patients. Such a risk is very challenging to compute because 1 the genotype X of the individual of interest is in general unknown, 2 the posterior distribution PX∣FH,T>t changes with t (T is the age at disease onset for the targeted individual), and 3 the competing risk of death is not negligible. In this work, we present modeling of this problem using a Bayesian network mixed with (right-censored) survival outcomes where hazard rates only depend on the genotype of each individual. We explain how belief propagation can be used to obtain posterior distribution of genotypes given the FH and how to obtain a time-dependent posterior hazard rate for any individual in the pedigree. Finally, we use this posterior hazard rate to compute individual risk, with or without the competing risk of death. Our method is illustrated using the Claus-Easton model for breast cancer. The competing risk of death is derived from the national French registry.

In this paper, we provide the ML (Maximum Likelihood) and the REML (REstricted ML) criteria for consistently estimating multivariate linear mixed-effects models with arbitrary correlation structure between the random effects across dimensions, but independent (and possibly heteroscedastic) residuals. By factorizing the random effects covariance matrix, we provide an explicit expression of the profiled deviance through a reparameterization of the model. This strategy can be viewed as the generalization of the estimation procedure used by Douglas Bates and his co-authors in the context of the fitting of one-dimensional linear mixed-effects models. Beside its robustness regarding the starting points, the approach enables a numerically consistent estimate of the random effects covariance matrix while classical alternatives such as the EM algorithm are usually non-consistent. In a simulation study, we compare the estimates obtained from the present method with the EM algorithm-based estimates. We finally apply the method to a study of an immune response to Malaria in Benin.

BACKGROUND: Substantial evidence indicates that cytophilic IgG responses to Plasmodium falciparum merozoite antigens play a role in protection from malaria. The specific targets mediating immunity remain unclear. Evaluating antibody responses in infants naturally-exposed to malaria will allow to better understand the establishment of anti-malarial immunity and to contribute to a vaccine development by identifying the most appropriate merozoite candidate antigens.METHODS: The study was based on parasitological and clinical active follow-up of infants from birth to 18 months of age conducted in the Tori Bossito area of southern Benin. For 399 infants, plasma levels of cytophilic IgG antibodies with specificity for five asexual stage malaria vaccine candidate antigens were determined by ELISA in infants' peripheral blood at 6, 9, 12 and 15 months of age. Multivariate mixed logistic model was used to investigate the association between antibody levels and anti-malarial protection in the trimester following the IgG quantification. Moreover, the concentrations of merozoite antigen-specific IgG were compared between a group of infants apparently able to control asymptomatic malaria infection (CAIG) and a group of infants with no control of malaria infection (Control group (NCIG)). Protective effect of antibodies was also assessed after 15 months of malaria exposure with a Cox regression model adjusted on environmental risk.RESULTS: Cytophilic IgG responses to AMA1, MSP1, MSP2-3D7, MSP2-FC27, MSP3 and GLURP R2 were associated with increasing malarial infection risk in univariate analysis. The multivariate mixed model showed that IgG1 and IgG3 to AMA1 were associated with an increased risk of malarial infection. However infants from CAIG (n = 53) had significantly higher AMA1-, MSP2-FC27-, MSP3-specific IgG1 and AMA1-, MSP1-, MSP2-FC27-, MSP3 and GLURP-R2-specific IgG3 than those from NCIG (n = 183). The latter IgG responses were not associated with protection against clinical malaria in the whole cohort when protective effect is assessed after 15 months of malaria exposition.CONCLUSION: In this cohort, merozoite antigen-specific cytophilic IgG levels represent a marker of malaria exposure in infants from 6 to 18 months of age. However, infants with resolution of asymptomatic infection (CAIG) seem to have acquired naturally immunity against P. falciparum. This observation is encouraging in the context of the development of multitarget P. falciparum vaccines.

International audience; For many data analysis tasks, obtaining the causal relationships between interacting objects is of crucial interest. Here, the case of modelling causal relationships via causal orderings is considered. Triplet ordering preferences are used to perform Monte Carlo sampling of the posterior causal orderings originating from the analysis of experiments involving observation as well as, usually few, interventions, like knockouts in case of gene expression. The performance of this sampling approach is compared to a previously used sampling via pairwise ordering preference as well as to the sampling of the full posterior distribution. This is performed for artificially generated causal, i.e. directed acyclic graphs (DAGs) with a scale-free structure, i.e. a power-law distribution for the out-degree. The sampling using the triplets ordering turns out to be superior to both other approaches, similar to our previous work, where the less-realistic case of Erdős-R nyi random graphs was considered.

Detecting gene-environment (G × E) interactions in the context of genome-wide association studies (GWAS) is a challenging problem since standard methods generally present a lack of power. An additional difficulty arises from the fact that the causal exposure is seldom observed and only a proxy of this exposure is observed. This leads to an additional drop in terms of power and it explains the failure of standard methods in detecting interactions, even very strong ones. In this article, we consider the latent exposure as a source of heterogeneity and we propose a new powerful method, named “Breakpoint Model for Logistic Regression” (BMLR), based on a breakpoint model, in order to detect G × E interactions when causal exposure is unobserved. First, the BMLR method is compared to the ordered-subset analysis for case-control method, which has been developed for the same purpose, through simulations. This highlights the ability of BMLR to detect the heterogeneity, and therefore, to detect interaction with latent exposure. Finally, the BMLR method is compared to standard methods, such as Plink, to perform a GWAS on a published realistic benchmark.

We aimed to determine if children born to mothers with placental malaria are more susceptible to malaria and remain at higher risk between birth and 18 months. From a public health point of view, protecting children born to infected placenta remains a priority.Children born to mothers with placental malaria (PM) have been described as more susceptible to the occurrence of a first malaria infection. However, whether or not these children remain more at risk during infancy has never been explored. We aimed to determine if children born to mothers with PM are more susceptible to malaria and remain at higher risk between birth and 18 months. Five hundred fifty children were followed up weekly with control of temperature and, if > 37.5A degrees C, both a rapid diagnostic test for malaria and a thick blood smear were performed. Taking into account environmental risk of infection, the relationship between occurrences of malaria attacks from birth to 18 months was modeled using Cox models for recurrent events. PM is not associated with an overall susceptibility to malaria but only with the delay of occurrence of the first malaria attack. Children born from mothers with PM tend to have an increased risk for the first malaria attack (hazard ratio [HR] = 1.33; P = .048) but not for subsequent ones (HR = 0.9; P = .46). Children who experienced 1 malaria attack were strongly at risk to develop subsequent infections independent of placental infection and environmental exposure. These results are consistent with the existence of an individual susceptibility to malaria unrelated to PM. From a public health point of view, protecting children born to infected placenta remains a priority, but seems insufficient to account for other frail children for whom a biomarker of frailty needs to be found.

International audience; Causal network inference is an important methodological challenge in biology as well as other areas of application. Although several causal network inference methods have been proposed in recent years, they are typically applicable for only a small number of genes, due to the large number of parameters to be estimated and the limited number of biological repli-cates available. In this work, we consider the specific case of transcriptomic studies made up of both observational and interventional data in which a single gene of biological interest is knocked out. We focus on a marginal causal estimation approach, based on the framework of Gaussian directed acyclic graphs, to infer causal relationships between the knocked-out gene and a large set of other genes. In a simulation study, we found that our proposed method accurately differentiates between downstream causal relationships and those that are upstream or simply associative. It also enables an estimation of the total causal effects between the gene of interest and the remaining genes. Our method performed very similarly to a classical differential analysis for experiments with a relatively large number of biological replicates, but has the advantage of providing a formal causal interpretation. Our proposed marginal causal approach is computationally efficient and may be applied to several thousands of genes simultaneously. In addition, it may help highlight subsets of genes of interest for a more thorough subsequent causal network inference. The method is implemented in an R package called MarginalCausality (available on GitHub).

International audience; Significant variability of phenotype and age of onset are well known in TTR-FAP associated to a wide spectrum of pathogenic TTR variants, among which Val30Met is the most frequent [1,2]. Recently, new therapeutic options became available that should be administered from the very onset of symptoms. In this context, the knowledge of the risk of being symptomatic for mutation carriers (penetrance) is essential to adjust the follow-up of carriers and for patientmanagement [3,4]. This study aims to refine penetrance estimates in the main pathogenic variants encountered in ourTTR-FAP population using a newly developed non parametric approach named NPSE for Non-Parametric Survival Estimate.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is also available via DOI:10.1186/s12874-016-0129-z Background: The understanding of changes in temporal processes related to human carcinogenesis is limited. One approach for prospective functional genomic studies is to compile trajectories of differential expression of genes, based on measurements from many case-control pairs. We propose a new statistical method that does not assume any parametric shape for the gene trajectories. Methods: The trajectory of a gene is defined as the curve representing the changes in gene expression levels in the blood as a function of time to cancer diagnosis. In a nested case–control design it consists of differences in gene expression levels between cases and controls. Genes can be grouped into curve groups, each curve group corresponding to genes with a similar development over time. The proposed new statistical approach is based on a set of hypothesis testing that can determine whether or not there is development in gene expression levels over time, and whether this development varies among different strata. Curve group analysis may reveal significant differences in gene expression levels over time among the different strata considered. This new method was applied as a “proof of concept” to breast cancer in the Norwegian Women and Cancer (NOWAC) postgenome cohort, using blood samples collected prospectively that were specifically preserved for transcriptomic analyses (PAX tube). Cohort members diagnosed with invasive breast cancer through 2009 were identified through linkage to the Cancer Registry of Norway, and for each case a random control from the postgenome cohort was also selected, matched by birth year and time of blood sampling, to create a case-control pair. After exclusions, 441 case-control pairs were available for analyses, in which we considered strata of lymph node status at time of diagnosis and time of diagnosis with respect to breast cancer screening visits. Results: The development of gene expression levels in the NOWAC postgenome cohort varied in the last years before breast cancer diagnosis, and this development differed by lymph node status and participation in the Norwegian Breast Cancer Screening Program. The differences among the investigated strata appeared larger in the year before breast cancer diagnosis compared to earlier years. Conclusions: This approach shows good properties in term of statistical power and type 1 error under minimal assumptions. When applied to a real data set it was able to discriminate between groups of genes with non-linear similar patterns before diagnosis.

Introduction Un microsatellite est une sequence d’ADN composee de la repetition de un a six nucleotides. L’instabilite des microsatellites (MSI) se retrouve dans 15 % des cancers colorectaux (CRC), de l’estomac, de l’endometre et plus rarement dans d’autres localisations (ovaires, voies excretrices urinaires, intestin grele, sein, poumon, prostate, etc.). Elle resulte de mutations deleteres dans les genes du systeme de reparation des mesappariements de l’ADN (« MisMatch Repair », MMR). Chez certains patients, ces mutations MMR sont hereditaires, les predisposant a la survenue de cancers MSI de localisations tres diverses et de maniere precoce au cours de la vie (syndrome de Lynch). La detection de ce syndrome est fondamentale pour adapter la prise en charge des patients (conseil genetique, orientation therapeutique). La premiere source d’information pour la detection de ce syndrome est l’histoire familiale de cancer, avec, en fonction de ceux qui sont disponibles, les resultats de tests realises sur l’ADN tumoral (test MSI, IHC pour etude de l’expression des proteines MMR) comme sur l’ADN germinal (recherche de mutations MMR constitutionnelles par sequencage). Methodes Avec l’augmentation des donnees biologiques et moleculaires, le developpement d’un modele prenant en compte simultanement l’ensemble de ces donnees de maniere la plus exhaustive possible devient indispensable pour accompagner la demarche diagnostique et orienter la decision therapeutique. Nous faisons l’hypothese qu’un tel modele permettrait de mieux preciser le risque de syndrome de Lynch chez les nombreux patients pour lesquels un diagnostic de certitude ne peut etre pose a l’heure actuelle (difficulte d’identification de VUS – « Variants of Uncertain Significance » – sur les genes MMR notamment). Pour repondre a ce defi, nous proposons un modele mendelien integratif base sur : – l’histoire familiale de cancer (colon, endometre et autres) ; – les donnees cliniques individuelles des membres de la famille (type de cancer, profil de l’ADN tumoral, IHC des proteines MMR) ; – les donnees relatives au variant (sequencage de l’ADN constitutionnel, tests fonctionnels, tests in silico, bases de donnees). Notre modele est implemente sous la forme d’un reseau bayesien combinant la genetique mendelienne et les durees de survie. Les incidences par localisation et sexe pour les non-porteurs sont celles de la population francaise (InVS, 2017), et les incidences relatives pour les porteurs sont celles du modele MMRpro (Chen et al., 2006). L’inference exacte (exploration exhaustive de l’ensemble des genotypes non observes) repose sur la version la plus recente de l’algorithme d’Elston-Stewart (Totir et al., 2009) et permet ainsi le calcul simultane des risques pour l’ensemble des individus de la famille. Resultats Notre modele integratif calcule : – la probabilite (Lynch ou non) associee aux VUS observes ; – la probabilite individuelle de predisposition ; – le risque individuel tumoral par localisation (avec risque competitif avec la mort). Nous validons notre modele et le comparons a MMRpro, PREM1,2,6 et MMRpredict sur un ensemble de familles de patients CRC-MSI pris en charge a l’hopital Saint-Antoine. Conclusion Les perspectives de ce travail incluent le calcul de risques plus complexes (risque familial, modeles multi-etats, etc.), une estimation fine des incidences par gene et localisation, l’adaptation du modele a d’autres syndromes (ex. sein/ovaire et BRCA).

International audience; In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

To our knowledge, effects of age, placental malaria infection, infections during follow-up, nutritional habits, sickle-cell trait and individual exposure to Anopheles bites were never explored together in a study focusing on the acquisition of malaria antibody responses among infants living in endemic areas.Five hundred and sixty-seven Beninese infants were weekly followed-up from birth to 18 months of age. Immunoglobulin G (IgG), IgG1 and IgG3 specific for 5 malaria antigens were measured every 3 months. A linear mixed model was used to analyze the effect of each variable on the acquisition of antimalarial antibodies in 6-to18-month old infants in univariate and multivariate analyses. Placental malaria, nutrition intakes and sickle-cell trait did not influence the infant antibody levels to P. falciparum antigens. In contrary, age, malaria antibody levels at birth, previous and present malaria infections as well as exposure to Anopheles bites were significantly associated with the natural acquisition of malaria antibodies in 6-to18-month old Beninese infants. This study highlighted inescapable factors to consider simultaneously in an immuno-epidemiological study or a vaccine trial in early life.

Summary In the framework of case-control studies many different test statistics are available to measure the association of a marker with a given disease. Nevertheless, choosing one particular statistic can lead to very different conclusions. In the absence of a consensus for this choice, a tempting option is to evaluate the power of these different statistics prior to make any decision. We review the available methods dedicated to power computation and assess their respective reliability in treating a wide range of tests on a wide range of alternative models. Considering Monte-Carlo, non-central chi-square and Delta-Method estimates, we evaluate empirical, asymptotic and numerical approaches. Additionally we introduce the use of the Delta-Method, extended to order 2, intended to provide better results than the traditional order-1 Delta-Method. Supplementary data can be found at: http://stat.genopole.cnrs.fr/software/dm2.

Penalized selection criteria like AIC or BIC are among the most popular methods for variable selection. Their theoretical properties have been studied intensively and are well understood, but making use of them in case of high-dimensional data is difficult due to the non-convex optimization problem induced by L0 penalties. An elegant solution to this problem is provided by the multi-step adaptive lasso, where iteratively weighted lasso problems are solved, whose weights are updated in such a way that the procedure converges towards selection with L0 penalties. In this paper we introduce an adaptive ridge procedure (AR) which mimics the adaptive lasso, but is based on weighted Ridge problems. After introducing AR its theoretical properties are studied in the particular case of orthogonal linear regression. For the non-orthogonal case extensive simulations are performed to assess the performance of AR. In case of Poisson regression and logistic regression it is illustrated how the iterative procedure of AR can be combined with iterative maximization procedures. The paper ends with an efficient implementation of AR in the context of least-squares segmentation.

In this paper we propose a method which can infer causal links. In particular we focus on the difficulty of high dimensional cases. We begin by introducing a recent model that we will modify to improve performance. We add a skeleton and a Ridge penalty. We finish with some numerical simulations.; Nous proposons dans ce papier une méthode permettant de déceler les liens causaux dans une hypothèse graphe acyclique dirigé (DAG). En particulier, nous nous focalisons sur les difficultés du cas en grande dimension. Nous commençons par introduire une méthode récente d’inférence causale, que nous modifierons pour en améliorer les performances. Nous y ajouterons la possibilité d’inclure un squelette ainsi qu’une pénalisation Ridge. Nous finirons sur quelques simulations numériques.

Published version also available at http://dx.doi.org/10.1016/j.mehy.2015.07.006 Traditionally, the prospective design has been chosen for risk factor analyses of lifestyle and cancer using mainly estimation by survival analysis methods. With new technologies, epidemiologists can expand their prospective studies to include functional genomics given either as transcriptomics, mRNA and microRNA, or epigenetics in blood or other biological materials. The novel functional analyses should not be assessed using classical survival analyses since the main goal is not risk estimation, but the analysis of functional genomics as part of the dynamic carcinogenic process over time, i.e., a ‘‘processual’’ approach. In the risk factor model, time to event is analysed as a function of exposure variables known at start of follow-up (fixed covariates) or changing over the follow-up period (time-dependent covariates). In the processual model, transcriptomics or epigenetics is considered as functions of time and exposures. The success of this novel approach depends on the development of new statistical methods with the capacity of describing and analysing the time-dependent curves or trajectories for tens of thousands of genes simultaneously. This approach also focuses on multilevel or integrative analyses introducing novel statistical methods in epidemiology. The processual approach as part of systems epidemiology might represent in a near future an alternative to human in vitro studies using human biological material for understanding the mechanisms and pathways involved in carcinogenesis.

Association studies are traditionally performed in the case-control framework. As a first step in the analysis process, comparing allele frequencies using the Pearson’s chi-square statistic is often invoked. However such an approach assumes the independence of alleles under the hypothesis of no association, which may not always be the case. Consequently this method introduces a bias that deviates the expected type I error-rate. In this article we first propose an unbiased and exact test as an alternative to the biased allelic test. Available data require to perform thousands of such tests so we focused on its fast execution. Since the biased allelic test is still widely used in the community, we illustrate its pitfalls in the context of genome-wide association studies and particularly in the case of low-level tests. Finally, we compare the unbiased and exact test with the Cochran-Armitage test for trend and show it perfoms similarly in terms of power. The fast, unbiased and exact allelic test code is available in R, C++ and Perl at: http://stat.genopole.cnrs.fr/software/fueatest.

e13005 Background: Most of the BRCA1/2 and PALB2 analyses performed in patients with a personal and familial history (FH) of breast/ovarian cancer are negative. In this context, there is no targeted analysis available to identify relatives at high risk (carrier of the causal hypothetical mutated gene). Thus, the recommendations of surveillance to all relatives are based only on the FH of cancer. At the Institut Curie we assess the degree of severity of the FH by calculating the probability of carrying a single autosomal dominant susceptibility gene without taking into account genotyping results but using the LINKAGE software with the parameters of the Claus-Easton model (1993). This reliable but old tool has a slow console interactive interface (roughly 15 min of clinicians time for each assessed family). Methods: We rewrite the model as a Bayesian network combining Mendelian genetics with classical survival analysis. All probabilistic computations are performed through belief propagation (sum-product algorithm) over real numbers or polynomials. The programming language is C++. Results: pCarrier is a modern and efficient implementation of the Claus-Easton model for clinicians with the following features: 1) fast non-interactive command line ( < 1s to process a previously registered family) combined with a graphical user-friendly interface; 2) computations dealing with monozygotic twins or loops without any human intervention (ex: loop breaking); 3) choice between the original discretized model (individual FH into 14 classes) and its continuous version (piecewise constant hazard); 4) marginal individual carrier distribution for all individuals in the pedigree; 5) joint distribution of the number of carriers among any group of individuals (ex: in the entire family, among the living only). Conclusions: pCarrier is experimented since January 2017 in the Institut Curie as a replacement of the existing tool and appears so far to be dramatically faster and easier to use for the clinicians, highly reliable (consistence with the existing tool), and able to provide new useful decision-making probabilities using the distribution of the number of carriers.

The classic model of tumor suppression implies that malignant transformation requires full “two-hit” inactivation of a tumor-suppressor gene. However, more recent work in mice has led to the proposal of a “continuum” model that involves more fluid concepts such as gene dosage-sensitivity and tissue specificity. Mutations in the tumor-suppressor gene von Hippel-Lindau (VHL) are associated with a complex spectrum of conditions. Homozygotes or compound heterozygotes for the R200W germline mutation in VHL have Chuvash polycythemia, whereas heterozygous carriers are free of disease. Individuals with classic, heterozygous VHL mutations have VHL disease and are at high risk of multiple tumors (e.g., CNS hemangioblastomas, pheochromocytoma, and renal cell carcinoma). We report here an atypical family bearing two VHL gene mutations in cis (R200W and R161Q), together with phenotypic analysis, structural modeling, functional, and transcriptomic studies of these mutants in comparison with classical mutants involved in the different VHL phenotypes. We demonstrate that the complex pattern of disease manifestations observed in VHL syndrome is perfectly correlated with a gradient of VHL protein (pVHL) dysfunction in hypoxia signaling pathways. Thus, by studying naturally occurring familial mutations, our work validates in humans the “continuum” model of tumor suppression. Cancer Res; 74(22); 6554–64. ©2014 AACR.

In this paper, we propose an approach, based on the stochastic expectation maximization (SEM) algorithm and Gibbs sampling, to deal with the problem caused by censoring in the response of a hierarchical random intercept models. We compared our approach with the existing methods via real data sets as well as simulations. Results showed that our approach outperformed other approaches in terms of estimation accuracy and computing efficiency.

In this paper, we consider the Integrated Completed Likelihood (ICL) as a useful criterion for estimating the number of changes in the underlying distribution of data, specifically in problems where detecting the precise location of these changes is the main goal. The exact computation of the ICL requires O(Kn(2)) operations (with K the number of segments and n the number of data-points) which is prohibitive in many practical situations with large sequences of data. We describe a framework to estimate the ICL with O(K(2)n) complexity. Our approach is general in the sense that it can accommodate any given model distribution. We checked the run-time and validity of our approach on simulated data and demonstrate its good performance when analyzing real Next-Generation Sequencing (NGS) data using a negative binomial model. Our method is implemented in the R package postCP and available on the CRAN repository.

An important step in systems biology is to improve our knowledge of how genes causally interact with one another. A few approaches have been proposed for the estimation of causal effects among genes, either based on observational data alone or requiring a very precise intervention design with one knock-out experiment for each gene. We recently suggested a more flexible algorithm, using a Markov chain Monte Carlo algorithm and the Mallows ranking model, that can analyze any intervention design, including partial or multiple knock-outs, using the framework of Gaussian Bayesian networks. We previously demonstrated the superior performance of this algorithm in comparison to alternative methods, although it can be computationally expensive to implement. The aim of this paper is to propose an alternative approach taking advantage of node pair ordering preferences to sample the posterior distribution according to the Babington-Smith ranking distribution. This novel algorithm proved, both in a simulation study and on the DREAM4 challenge data, to attain estimation of the causal effects as accurate as the MCMC-Mallows approach with a highly improved computational efficiency, being at least 100 times faster. We also tested our algorithm on the Rosetta Compendium dataset with more contrasted results. We nevertheless anticipate that our new approach might be very useful for practical biological applications.

Differences between plant varieties are based on phenotypic observations, which are both space and time consuming. Moreover, the phenotypic data result from the combined effects of genotype and environment. On the contrary, molecular data are easier to obtain and give a direct access to the genotype. In order to save experimental trials and to concentrate efforts on the relevant comparisons between varieties, the relationship between phenotypic and genetic distances is studied. It appears that the classical genetic distances based on molecular data are not appropriate for predicting phenotypic distances. In the linear model framework, we define a new pseudo genetic distance, which is a prediction of the phenotypic one. The distribution of this distance given the pseudo genetic distance is established. Statistical properties of the predicted distance are derived when the parameters of the model are either given or estimated. We finally apply these results to distinguishing between 144 maize lines. This cas...