The Experts below are selected from a list of 121854 Experts worldwide ranked by ideXlab platform

Oliver Stegle - One of the best experts on this subject based on the ideXlab platform.

  • a Linear Mixed Model approach to study multivariate gene environment interactions
    Nature Genetics, 2019
    Co-Authors: Rachel Moore, Francesco Paolo Casale, Marc Jan Bonder, Danilo Horta, Lude Franke, Ines Barroso, Oliver Stegle
    Abstract:

    Different exposures, including diet, physical activity, or external conditions can contribute to genotype–environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured Linear Mixed Model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our Model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our Model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.

  • a Linear Mixed Model approach to study multivariate gene environment interactions
    bioRxiv, 2018
    Co-Authors: Rachel Moore, Francesco Paolo Casale, Marc Jan Bonder, Danilo Horta, Lude Franke, Ines Barroso, Oliver Stegle
    Abstract:

    Different environmental factors, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (GxE). Although high-dimensional environmental data are increasingly available, and multiple environments have been implicated with GxE at the same loci, multi-environment tests for GxE are not established. Such joint analyses can increase power to detect GxE and improve the interpretation of these effects. Here, we propose the structured Linear Mixed Model (StructLMM), a computationally efficient method to test for and characterize loci that interact with multiple environments. After validating our Model using simulations, we apply StructLMM to body mass index in UK Biobank, where our method detects previously known and novel GxE signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.

Matthew Stephens - One of the best experts on this subject based on the ideXlab platform.

Sheng Luo - One of the best experts on this subject based on the ideXlab platform.

  • joint Modeling of multiple repeated measures and survival data using multidimensional latent trait Linear Mixed Model
    Statistical Methods in Medical Research, 2019
    Co-Authors: Jue Wang, Sheng Luo
    Abstract:

    Impairment caused by Amyotrophic lateral sclerosis (ALS) is multidimensional (e.g. bulbar, fine motor, gross motor) and progressive. Its multidimensional nature precludes a single outcome to measure disease progression. Clinical trials of ALS use multiple longitudinal outcomes to assess the treatment effects on overall improvement. A terminal event such as death or dropout can stop the follow-up process. Moreover, the time to the terminal event may be dependent on the multivariate longitudinal measurements. In this article, we develop a joint Model consisting of a multidimensional latent trait Linear Mixed Model (MLTLMM) for the multiple longitudinal outcomes, and a proportional hazards Model with piecewise constant baseline hazard for the event time data. Shared random effects are used to link together two Models. The Model inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation implemented in Stan language. Our proposed Model is evaluated by simulation studies and is applied to the Ceftriaxone study, a motivating clinical trial assessing the effect of ceftriaxone on ALS patients.

  • multidimensional latent trait Linear Mixed Model an application in clinical studies with multivariate longitudinal outcomes
    Statistics in Medicine, 2017
    Co-Authors: Jue Wang, Sheng Luo
    Abstract:

    Multilevel item response theory (MLIRT) Models have been widely used to analyze the multivariate longitudinal data of Mixed types (e.g., categorical and continuous) in clinical studies. The MLIRT Models often have unidimensional assumption, that is, the multiple outcomes are clinical manifestations of a univariate latent variable. However, the unidimensional assumption may be unrealistic because some diseases may be heterogeneous and characterized by multiple impaired domains with variable clinical symptoms and disease progressions. We relax this assumption and propose a multidimensional latent trait Linear Mixed Model (MLTLMM) to allow multiple latent variables and within-item multidimensionality (one outcome can be a manifestation of more than one latent variable). We conduct extensive simulation studies to assess the unidimensional MLIRT Model and the proposed MLTLMM Model. The simulation studies suggest that the MLTLMM Model outperforms unidimensional Model when the multivariate longitudinal outcomes are manifested by multiple latent variables. The proposed Model is applied to two motivating studies of amyotrophic lateral sclerosis: a clinical trial of ceftriaxone and the Pooled Resource Open-Access ALS Clinical Trials database. Copyright © 2017 John Wiley & Sons, Ltd.

Rachel Moore - One of the best experts on this subject based on the ideXlab platform.

  • a Linear Mixed Model approach to study multivariate gene environment interactions
    Nature Genetics, 2019
    Co-Authors: Rachel Moore, Francesco Paolo Casale, Marc Jan Bonder, Danilo Horta, Lude Franke, Ines Barroso, Oliver Stegle
    Abstract:

    Different exposures, including diet, physical activity, or external conditions can contribute to genotype–environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured Linear Mixed Model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our Model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our Model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.

  • a Linear Mixed Model approach to study multivariate gene environment interactions
    bioRxiv, 2018
    Co-Authors: Rachel Moore, Francesco Paolo Casale, Marc Jan Bonder, Danilo Horta, Lude Franke, Ines Barroso, Oliver Stegle
    Abstract:

    Different environmental factors, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (GxE). Although high-dimensional environmental data are increasingly available, and multiple environments have been implicated with GxE at the same loci, multi-environment tests for GxE are not established. Such joint analyses can increase power to detect GxE and improve the interpretation of these effects. Here, we propose the structured Linear Mixed Model (StructLMM), a computationally efficient method to test for and characterize loci that interact with multiple environments. After validating our Model using simulations, we apply StructLMM to body mass index in UK Biobank, where our method detects previously known and novel GxE signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.

Xiang Zhou - One of the best experts on this subject based on the ideXlab platform.