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This work proposes an original method for disease subtyping from both longitudinal clinical variables and genetic markers via a mixture of regressions model, with logistic weights function of a potentially large number of genetic variables. In order to address these large-scale problems, variable selection is an essential step. We thus propose to discard genetic variables that may not be relevant for clustering by maximizing a penalized likelihood via a Classification Expectation Maximization algorithm. The proposed method is validated on simulations. The approach is applied to a data set from a cohort of Parkinson's disease patients. Several subtypes of the disease as well as genetic variants potentially having a role in this typology have been identified.Identifying new genetic associations in non-Mendelian complex diseases is an increasingly difficult challenge. Yet, these diseases seem to have a significant part of heritability to explain. This missing heritability could be explained by the existence of subtypes involving different genetic factors. Taking genetic information into account in clinical trials can therefore be of interest to guide the process of subtyping a complex disease. Most methods dealing with multiple sources of information rely on data transformation, with two main tendencies regarding disease subtyping in that situation: i) the clustering of clinical data followed with posterior genetic analyzes and ii) the clustering of clinical and genetic variables. Both face limitations that we propose to leverage.This work proposes an original method for disease subtyping from both longitudinal clinical variables and high-dimensionnal genetic markers via a sparse mixture of regressions model. The added value of our approach lies in its interpretability regarding two aspects. First, our model links both clinical and genetic data with regard to their respective initial nature (i. e. without transformation) and does not need post-processing to come back to the original information to interpret the subtypes. Also, it can adress large-scale problems thanks to a variable selection step to discard genetic variables that may not be relevant for subtyping.The proposed method is validated on simulations. A dataset from a cohort of Parkinson's disease patients was also analyzed. Several subtypes of the disease as well as genetic variants having potentially a role in this typology have been identified., Ce travail propose une méthode pour le sous-typage de maladies à partir de variables cliniques longitudinales et de marqueurs génétiques via un modèle de mélange de régressions, avec des poids logistiques fonctions d'un nombre potentiellement important de variables génétiques. Pour traiter ce problème de grande dimension, la sélection des variables pertinentes est essentielle. Nous proposons donc d'exclure les variables génétiques qui ne seraient pas pertinentes pour la classification en maximisant une vraisemblance pénalisée par le biais d'un algorithme Classification Expectation Maximization. La méthode proposée est validée sur des simulations. L'approche est ensuite mise en application sur des données provenant d'une cohorte de patients atteints de la maladie de Parkinson. Plusieurs sous-types de la maladie ainsi que des variantes génétiques ayant un rôle potentiel dans cette typologie sont ainsi identifiés.

This work proposes an original method for disease subtyping from both longitudinal clinical variables and genetic markers via a mixture of regressions model, with logistic weights function of a potentially large number of genetic variables. In order to address these large-scale problems, variable selection is an essential step. We thus propose to discard genetic variables that may not be relevant for clustering by maximizing a penalized likelihood via a Classification Expectation Maximization algorithm. The proposed method is validated on simulations. The approach is applied to a data set from a cohort of Parkinson's disease patients. Several subtypes of the disease as well as genetic variants potentially having a role in this typology have been identified.Identifying new genetic associations in non-Mendelian complex diseases is an increasingly difficult challenge. Yet, these diseases seem to have a significant part of heritability to explain. This missing heritability could be explained by the existence of subtypes involving different genetic factors. Taking genetic information into account in clinical trials can therefore be of interest to guide the process of subtyping a complex disease. Most methods dealing with multiple sources of information rely on data transformation, with two main tendencies regarding disease subtyping in that situation: i) the clustering of clinical data followed with posterior genetic analyzes and ii) the clustering of clinical and genetic variables. Both face limitations that we propose to leverage.This work proposes an original method for disease subtyping from both longitudinal clinical variables and high-dimensionnal genetic markers via a sparse mixture of regressions model. The added value of our approach lies in its interpretability regarding two aspects. First, our model links both clinical and genetic data with regard to their respective initial nature (i. e. without transformation) and does not need post-processing to come back to the original information to interpret the subtypes. Also, it can adress large-scale problems thanks to a variable selection step to discard genetic variables that may not be relevant for subtyping.The proposed method is validated on simulations. A dataset from a cohort of Parkinson's disease patients was also analyzed. Several subtypes of the disease as well as genetic variants having potentially a role in this typology have been identified.; Ce travail propose une méthode pour le sous-typage de maladies à partir de variables cliniques longitudinales et de marqueurs génétiques via un modèle de mélange de régressions, avec des poids logistiques fonctions d'un nombre potentiellement important de variables génétiques. Pour traiter ce problème de grande dimension, la sélection des variables pertinentes est essentielle. Nous proposons donc d'exclure les variables génétiques qui ne seraient pas pertinentes pour la classification en maximisant une vraisemblance pénalisée par le biais d'un algorithme Classification Expectation Maximization. La méthode proposée est validée sur des simulations. L'approche est ensuite mise en application sur des données provenant d'une cohorte de patients atteints de la maladie de Parkinson. Plusieurs sous-types de la maladie ainsi que des variantes génétiques ayant un rôle potentiel dans cette typologie sont ainsi identifiés.

This work proposes an original method for disease subtyping from both longitudinal clinical variables and genetic markers via a mixture of regressions model, with logistic weights function of a potentially large number of genetic variables. In order to address these large-scale problems, variable selection is an essential step. We thus propose to discard genetic variables that may not be relevant for clustering by maximizing a penalized likelihood via a Classification Expectation Maximization algorithm. The proposed method is validated on simulations. The approach is applied to a data set from a cohort of Parkinson's disease patients. Several subtypes of the disease as well as genetic variants potentially having a role in this typology have been identified.; Ce travail propose une méthode pour le sous-typage de maladies à partir de variables cliniques longitudinales et de marqueurs génétiques via un modèle de mélange de régressions, avec des poids logistiques fonctions d'un nombre potentiellement important de variables génétiques. Pour traiter ce problème de grande dimension, la sélection des variables pertinentes est essentielle. Nous proposons donc d'exclure les variables génétiques qui ne seraient pas pertinentes pour la classification en maximisant une vraisemblance pénalisée par le biais d'un algorithme Classification Expectation Maximization. La méthode proposée est validée sur des simulations. L'approche est ensuite mise en application sur des données provenant d'une cohorte de patients atteints de la maladie de Parkinson. Plusieurs sous-types de la maladie ainsi que des variantes génétiques ayant un rôle potentiel dans cette typologie sont ainsi identifiés.

International audience; High-throughput RNA-sequencing has become the gold standard method for whole-transcriptome gene expression analysis, and is widely used in numerous applications to study cell and tissue transcriptomes. It is also being increasingly used in a number of clinical applications, including expression profiling for diagnostics and alternative transcript detection. However, despite its many advantages, RNA sequencing can be challenging in some situations, for instance in cases of low input amounts or degraded RNA samples. Several protocols have been proposed to overcome these challenges, and many are available as commercial kits. In this study, we systematically test three recent commercial technologies for RNA-seq library preparation (TruSeq, SMARTer and SMARTer Ultra-Low) on human biological reference materials, using standard (1 mg), low (100 ng and 10 ng) and ultra-low (

High-throughput RNA-sequencing has become the gold standard method for whole-transcriptome gene expression analysis, and is widely used in numerous applications to study cell and tissue transcriptomes. It is also being increasingly used in a number of clinical applications, including expression profiling for diagnostics and alternative transcript detection. However, despite its many advantages, RNA sequencing can be challenging in some situations, for instance in cases of low input amounts or degraded RNA samples. Several protocols have been proposed to overcome these challenges, and many are available as commercial kits. In this study, we systematically test three recent commercial technologies for RNA-seq library preparation (TruSeq, SMARTer and SMARTer Ultra-Low) on human biological reference materials, using standard (1 mg), low (100 ng and 10 ng) and ultra-low (

This work proposes an original method for disease subtyping from both longitudinal clinical variables and genetic markers via a mixture of regressions model, with logistic weights function of a potentially large number of genetic variables. In order to address these large-scale problems, variable selection is an essential step. We thus propose to discard genetic variables that may not be relevant for clustering by maximizing a penalized likelihood via a Classification Expectation Maximization algorithm. The proposed method is validated on simulations. The approach is applied to a data set from a cohort of Parkinson's disease patients. Several subtypes of the disease as well as genetic variants potentially having a role in this typology have been identified.; Ce travail propose une méthode pour le sous-typage de maladies à partir de variables cliniques longitudinales et de marqueurs génétiques via un modèle de mélange de régressions, avec des poids logistiques fonctions d'un nombre potentiellement important de variables génétiques. Pour traiter ce problème de grande dimension, la sélection des variables pertinentes est essentielle. Nous proposons donc d'exclure les variables génétiques qui ne seraient pas pertinentes pour la classification en maximisant une vraisemblance pénalisée par le biais d'un algorithme Classification Expectation Maximization. La méthode proposée est validée sur des simulations. L'approche est ensuite mise en application sur des données provenant d'une cohorte de patients atteints de la maladie de Parkinson. Plusieurs sous-types de la maladie ainsi que des variantes génétiques ayant un rôle potentiel dans cette typologie sont ainsi identifiés.

Background A large amount of research has been devoted to the detection and investigation of epistatic interactions in genome-wide association studies (GWASs). Most of the literature focuses on low-order interactions between single-nucleotide polymorphisms (SNPs) with significant main effects. Results In this paper we propose an original approach for detecting epistasis at the gene level, without systematically filtering on significant genes. We first compute interaction variables for each gene pair by finding its Eigen-Epistasis component, defined as the linear combination of Gene SNPs having the highest correlation with the phenotype. The selection of significant effects is done using a penalized regression method based on Group Lasso controlling the False Discovery Rate. Conclusion The method is tested against two recent alternative proposals from the literature using synthetic data, and shows good performances in different settings. We demonstrate the power of our approach by detecting new gene-gene interactions on three genome-wide association studies. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1488-0) contains supplementary material, which is available to authorized users.

Figure S2. Simulation on 25 genes with fully simulated data and various simulated effects. Discoveries for the setting with numerous effects. Heatmap of the ratio of the number of times where each variable was significant to the total number of simulations for R 2=0.7 using the Wang Pathway model for the phenotype simulation with fully simulated data. We consider 25 genes with two main effects for genes 1 and 2, and four interaction effects between genes 3 and 4, genes 5 and 6, genes 7 and 8, and genes 9 and 10. (PDF 30 kb)

Figure S5. Comparison of power to detect main effects in the realistic simulation study. Power under a realistic context. The figures show the power to detect main effects of the three methods depending on R 2. (PDF 7 kb)

Figure S3. Discoveries in the realistic simulation study with R 2=0.4. Discoveries under a realistic context. Heatmaps of the ratio of the number of times where each variable was significant to the total number of simulations for R 2=0.4. (PDF 9 kb)

Figure S1. Comparison of power to detect main effects in the simplified simulation study. Power under a simplified context. The figures show the power to detect main effects of the three methods depending on R 2. (PDF 8 kb)