Reference number : GC-2017-Q1-03421, amendment number : , agreement type : G, recipient legal name : Xu, Wei (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : The technological advances in next generation sequencing have enabled researchers to unveil the wide variability in microbial communities and their relationships with different diseases. Therefore, it is becoming critical to understand both environmental and host genetic factors that impact the composition of the microbiome. However, robust and powerful methods in this area are underdeveloped due to the complexity of microbiome sequencing data, which includes: a) microbial taxa data are usually grouped into operational taxonomic units (OTUs) and these counts are often highly skewed, over-dispersed, and zero inflated, b) OTU counts within a taxonomic hierarchical cluster are often highly correlated, but this multivariate nature is usually ignored, c) the study designs often involve repeated measures taken from related family members, thus inducing temporal and familial correlations. In this proposal, I will develop powerful bioinformatics, statistical, and computational methods to overcome these challenges. Specifically, I propose to use the latent variable (LV) methodology to jointly model multiple taxa from hierarchical taxonomic clusters within a longitudinal family study framework. The LV framework represents the underlying conceptual traits of the cluster and explains the correlations among different taxa. To address the over-dispersed and zero inflated features of the taxa counts, I will apply both zero-inflated and hurdle models on the multivariate OTU outcomes. 
The LV inference will be constructed based on a Bayesian framework with samplings from the posterior distribution obtained using Markov Chain Monte Carlo (MCMC) algorithms. A Bayesian model selection algorithm will be developed to choose the optimal models for a particular dataset. I will incorporate dimensionality reduction methodologies on the genetic factors so that the genetic association signals can be identified from genome-wide data. I will also explore gene-gene (GxG), and gene-environment (GxE) interactions on the microbiome data. High-efficiency computational algorithms will be developed using C++, and computational software will be implemented within a user-friendly interface which will be distributed to the microbiome research community. In addition, a standardized analytic pipeline for modeling and analysis of microbiome data will be constructed and tested by simulations. Sample size estimation and power analysis based on both theoretical deduction and empirical results will also be provided to allow design of future studies. This proposal will help standardize and optimize future research on modifiable environmental risk factors, as well as genetic factors, for microbiome sequencing studies. This research program will advance large-scale microbiome sequencing analytic technologies for Canadian and international genetics and computational biology researcher community., program purpose french : The technological advances in next generation sequencing have enabled researchers to unveil the wide variability in microbial communities and their relationships with different diseases. Therefore, it is becoming critical to understand both environmental and host genetic factors that impact the composition of the microbiome. However, robust and powerful methods in this area are underdeveloped due to the complexity of microbiome sequencing data, which includes: a) microbial taxa data are usually grouped into operational taxonomic units (OTUs) and these counts are often highly skewed, over-dispersed, and zero inflated, b) OTU counts within a taxonomic hierarchical cluster are often highly correlated, but this multivariate nature is usually ignored, c) the study designs often involve repeated measures taken from related family members, thus inducing temporal and familial correlations. In this proposal, I will develop powerful bioinformatics, statistical, and computational methods to overcome these challenges. Specifically, I propose to use the latent variable (LV) methodology to jointly model multiple taxa from hierarchical taxonomic clusters within a longitudinal family study framework. The LV framework represents the underlying conceptual traits of the cluster and explains the correlations among different taxa. To address the over-dispersed and zero inflated features of the taxa counts, I will apply both zero-inflated and hurdle models on the multivariate OTU outcomes. 
The LV inference will be constructed based on a Bayesian framework with samplings from the posterior distribution obtained using Markov Chain Monte Carlo (MCMC) algorithms. A Bayesian model selection algorithm will be developed to choose the optimal models for a particular dataset. I will incorporate dimensionality reduction methodologies on the genetic factors so that the genetic association signals can be identified from genome-wide data. I will also explore gene-gene (GxG), and gene-environment (GxE) interactions on the microbiome data. High-efficiency computational algorithms will be developed using C++, and computational software will be implemented within a user-friendly interface which will be distributed to the microbiome research community. In addition, a standardized analytic pipeline for modeling and analysis of microbiome data will be constructed and tested by simulations. Sample size estimation and power analysis based on both theoretical deduction and empirical results will also be provided to allow design of future studies. This proposal will help standardize and optimize future research on modifiable environmental risk factors, as well as genetic factors, for microbiome sequencing studies. This research program will advance large-scale microbiome sequencing analytic technologies for Canadian and international genetics and computational biology researcher community., agreement title english : Methodology Development and Implementation for Microbiome Sequencing Data: Hierarchical Modeling on Clustered Taxa Counts with Repeated Measures, agreement title french : Methodology Development and Implementation for Microbiome Sequencing Data: Hierarchical Modeling on Clustered Taxa Counts with Repeated Measures, agreement number : RGPIN, agreement value : 140000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada