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1. An ancestry‐based approach for detecting interactions

   https://doi.org/10.1002/gepi.22087  

   Park, Danny S. ; Eskin, Itamar ; Kang, Eun Yong ; Gamazon, Eric R. ; Eng, Celeste ; Gignoux, Christopher R. ; Galanter, Joshua M. ; Burchard, Esteban ; Ye, Chun J. ; Aschard, Hugues ; et al ( November 2017 , Genetic Epidemiology)

   Epistasis and gene‐environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene‐environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.

   In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry, defined as the proportion of ancestry derived from each ancestral population (e.g., the fraction of European/African ancestry in African Americans), in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, respectively, identifying nine interactions that were significant at. We show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for lowP‐values ().

   We show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.

    

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2. A Synthetic Data Integration Framework to Leverage External Summary-Level Information from Heterogeneous Populations

   https://doi.org/10.1111/biom.13852  

   Gu, Tian ; Taylor, Jeremy Michael George ; Mukherjee, Bhramar ( March 2023 , Biometrics)

   There is a growing need for flexible general frameworks that integrate individual-level data with external summary information for improved statistical inference. External information relevant for a risk prediction model may come in multiple forms, through regression coefficient estimates or predicted values of the outcome variable. Different external models may use different sets of predictors and the algorithm they used to predict the outcome Y given these predictors may or may not be known. The underlying populations corresponding to each external model may be different from each other and from the internal study population. Motivated by a prostate cancer risk prediction problem where novel biomarkers are measured only in the internal study, this paper proposes an imputation-based methodology, where the goal is to fit a target regression model with all available predictors in the internal study while utilizing summary information from external models that may have used only a subset of the predictors. The method allows for heterogeneity of covariate effects across the external populations. The proposed approach generates synthetic outcome data in each external population, uses stacked multiple imputation to create a long dataset with complete covariate information. The final analysis of the stacked imputed data is conducted by weighted regression. This flexible and unified approach can improve statistical efficiency of the estimated coefficients in the internal study, improve predictions by utilizing even partial information available from models that use a subset of the full set of covariates used in the internal study, and provide statistical inference for the external population with potentially different covariate effects from the internal population.

    

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3. Bayesian Combinatorial Multi-Study Factor Analysis

   Grabski, Isabella N ; De Vito, Roberta ; Trippa, Lorenzo ; Parmigiani, Giovanni ( July 2020 , ArXivorg)

   Analyzing multiple studies allows leveraging data from a range of sources and populations, but until recently, there have been limited methodologies to approach the joint unsupervised analysis of multiple high-dimensional studies. A recent method, Bayesian Multi-Study Factor Analysis (BMSFA), identifies latent factors common to all studies, as well as latent factors specific to individual studies. However, BMSFA does not allow for partially shared factors, i.e. latent factors shared by more than one but less than all studies. We extend BMSFA by introducing a new method, Tetris, for Bayesian combinatorial multi-study factor analysis, which identifies latent factors that can be shared by any combination of studies. We model the subsets of studies that share latent factors with an Indian Buffet Process. We test our method with an extensive range of simulations, and showcase its utility not only in dimension reduction but also in covariance estimation. Finally, we apply Tetris to high-dimensional gene expression datasets to identify patterns in breast cancer gene expression, both within and across known classes defined by germline mutations. 

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4. Multivariate log‐contrast regression with sub‐compositional predictors: Testing the association between preterm infants' gut microbiome and neurobehavioral outcomes

   https://doi.org/10.1002/sim.9273  

   Liu, Xiaokang ; Cong, Xiaomei ; Li, Gen ; Maas, Kendra ; Chen, Kun ( December 2021 , Statistics in Medicine)

   To link a clinical outcome with compositional predictors in microbiome analysis, the linear log‐contrast model is a popular choice, and the inference procedure for assessing the significance of each covariate is also available. However, with the existence of multiple potentially interrelated outcomes and the information of the taxonomic hierarchy of bacteria, a multivariate analysis method that considers the group structure of compositional covariates and an accompanying group inference method are still lacking. Motivated by a study for identifying the microbes in the gut microbiome of preterm infants that impact their later neurobehavioral outcomes, we formulate a constrained integrative multi‐view regression. The neurobehavioral scores form multivariate responses, the log‐transformed sub‐compositional microbiome data form multi‐view feature matrices, and a set of linear constraints on their corresponding sub‐coefficient matrices ensures the sub‐compositional nature. We assume all the sub‐coefficient matrices are possible of low‐rank to enable joint selection and inference of sub‐compositions/views. We propose a scaled composite nuclear norm penalization approach for model estimation and develop a hypothesis testing procedure through de‐biasing to assess the significance of different views. Simulation studies confirm the effectiveness of the proposed procedure. We apply the method to the preterm infant study, and the identified microbes are mostly consistent with existing studies and biological understandings.

    

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5. Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data

   Wang, Minjie ; Allen, Genevera I. ( January 2021 , Journal of Machine Learning Research)

   In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data view. While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that enjoys strong empirical performance and inherits the mathematical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Optimization (iGecco) method employs different convex distances, losses, or divergences for each of the different data views with a joint convex fusion penalty that leads to common groups. Additionally, integrating mixed multi-view data is often challenging when each data source is high-dimensional. To perform feature selection in such scenarios, we develop an adaptive shifted group-lasso penalty that selects features by shrinking them towards their loss-specific centers. Our so-called iGecco+ approach selects features from each data view that are best for determining the groups, often leading to improved integrative clustering. To solve our problem, we develop a new type of generalized multi-block ADMM algorithm using sub-problem approximations that more efficiently fits our model for big data sets. Through a series of numerical experiments and real data examples on text mining and genomics, we show that iGecco+ achieves superior empirical performance for high-dimensional mixed multi-view data. 

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