MOTIVATION: Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges. RESULTS: We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer's Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits. AVAILABILITY AND IMPLEMENTATION: The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM).

With the advances in high-throughput biotechnologies, high-dimensional multi-layer omics data become increasingly available. They can provide both confirmatory and complementary information to disease risk and thus have offered unprecedented opportunities for risk prediction studies. However, the high-dimensionality and complex inter/intra-relationships among multi-omics data have brought tremendous analytical challenges. Here we present a computationally efficient penalized linear mixed model with generalized method of moments estimator (MpLMMGMM) for the prediction analysis on multi-omics data. Our method extends the widely used linear mixed model proposed for genomic risk predictions to model multi-omics data, where kernel functions are used to capture various types of predictive effects from different layers of omics data and penalty terms are introduced to reduce the impact of noise. Compared with existing penalized linear mixed models, the proposed method adopts the generalized method of moments estimator and it is much more computationally efficient. Through extensive simulation studies and the analysis of positron emission tomography imaging outcomes, we have demonstrated that MpLMMGMM can simultaneously consider a large number of variables and efficiently select those that are predictive from the corresponding omics layers. It can capture both linear and nonlinear predictive effects and achieves better prediction performance than competing methods.

MOTIVATION: Linear mixed models (LMMs) have long been the method of choice for risk prediction analysis on high-dimensional data. However, it remains computationally challenging to simultaneously model a large amount of variants that can be noise or have predictive effects of complex forms. RESULTS: In this work, we have developed a penalized LMM with generalized method of moments (pLMMGMM) estimators for prediction analysis. pLMMGMM is built within the LMM framework, where random effects are used to model the joint predictive effects from all variants within a region. Different from existing methods that focus on linear relationships and use empirical criteria for variable screening, pLMMGMM can efficiently detect regions that harbor genetic variants with both linear and non-linear predictive effects. In addition, unlike existing LMMs that can only handle a very limited number of random effects, pLMMGMM is much less computationally demanding. It can jointly consider a large number of regions and accurately detect those that are predictive. Through theoretical investigations, we have shown that our method has the selection consistency and asymptotic normality. Through extensive simulations and the analysis of PET-imaging outcomes, we have demonstrated that pLMMGMM outperformed existing models and it can accurately detect regions that harbor risk factors with various forms of predictive effects. AVAILABILITY AND IMPLEMENTATION: The R-package is available at https://github.com/XiaQiong/GMMLasso. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Building an accurate disease risk prediction model is an essential step in the modern quest for precision medicine. While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. In this work, we have developed a deep neural network (DNN) based prediction modeling framework. We first proposed a group-wise feature importance score for feature selection, where genes harboring genetic variants with both linear and non-linear effects are efficiently detected. We then designed an explainable transfer-learning based DNN method, which can directly incorporate information from feature selection and accurately capture complex predictive effects. The proposed DNN-framework is biologically interpretable, as it is built based on the selected predictive genes. It is also computationally efficient and can be applied to genome-wide data. Through extensive simulations and real data analyses, we have demonstrated that our proposed method can not only efficiently detect predictive features, but also accurately predict disease risk, as compared to many existing methods.

MOTIVATION: Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance. RESULTS: We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer's Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive. AVAILABILITYAND IMPLEMENTATION: The R-package is available at https://github.com/yhai943/BLMM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The use of human genome discoveries and other established factors to build an accurate risk prediction model is an essential step toward precision medicine. While multi-layer high-dimensional omics data provide unprecedented data resources for prediction studies, their corresponding analytical methods are much less developed. RESULTS: We present a multi-kernel penalized linear mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard linear mixed models widely used in genomic risk prediction, for multi-omics data analysis. MKpLMM can capture not only the predictive effects from each layer of omics data but also their interactions via using multiple kernel functions. It adopts a data-driven approach to select predictive regions as well as predictive layers of omics data, and achieves robust selection performance. Through extensive simulation studies, the analyses of PET-imaging outcomes from the Alzheimer's Disease Neuroimaging Initiative study, and the analyses of 64 drug responses, we demonstrate that MKpLMM consistently outperforms competing methods in phenotype prediction. AVAILABILITY AND IMPLEMENTATION: The R-package is available at https://github.com/YaluWen/OmicPred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The emerging multilayer omics data provide unprecedented opportunities for detecting biomarkers that are associated with complex diseases at various molecular levels. However, the high-dimensionality of multiomics data and the complex disease etiologies have brought tremendous analytical challenges. RESULTS: We developed a U-statistics-based non-parametric framework for the association analysis of multilayer omics data, where consensus and permutation-based weighting schemes are developed to account for various types of disease models. Our proposed method is flexible for analyzing different types of outcomes as it makes no assumptions about their distributions. Moreover, it explicitly accounts for various types of underlying disease models through weighting schemes and thus provides robust performance against them. Through extensive simulations and the application to dataset obtained from the Alzheimer's Disease Neuroimaging Initiatives, we demonstrated that our method outperformed the commonly used kernel regression-based methods. AVAILABILITY AND IMPLEMENTATION: The R-package is available at https://github.com/YaluWen/Uomic. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: DNA methylation is an important epigenetic modification that has essential role in gene regulation, cell differentiation and cancer development. Bisulfite sequencing is a widely used technique to obtain genome-wide DNA methylation profiles, and one of the key tasks of analyzing bisulfite sequencing data is to detect differentially methylated regions (DMRs) among samples under different treatment conditions. Although numerous tools have been proposed to detect differentially methylated single CpG site (DMC) between samples, methods for direct DMR detection, especially for complex study designs, are largely limited. RESULTS: We present a new software, GetisDMR, for direct DMR detection. We use beta-binomial regression to model the whole-genome bisulfite sequencing data, where variations in methylation levels and confounding effects have been accounted for. We employ a region-wise test statistic, which is derived from local Getis-Ord statistics and considers the spatial correlation between nearby CpG sites, to detect DMRs. Unlike existing methods, that attempt to infer DMRs from DMCs based on empirical criteria, we provide statistical inference for direct DMR detection. Through extensive simulations and an application to two mouse datasets, we demonstrate that GetisDMR achieves better sensitivities, positive predictive values, more exact locations and better agreement of DMRs with current biological knowledge. AVAILABILITY AND IMPLEMENTATION: It is available at https://github.com/DMU-lilab/GetisDMR CONTACTS: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.