Biology subjects

Lewinger, J. P.

Publications and source records attributed to Lewinger, J. P..

4 recordsLinked to original sources

bioRxiv2024

Pathway Polygenic Risk Scores (pPRS) for the Analysis of Gene-environment Interaction

A polygenic risk score (PRS) is used to quantify the combined disease risk of many genetic variants. For complex human traits there is interest in determining whether the PRS modifies, i.e. interacts with, important environmental (E) risk factors. Detection of a PRS by environment (PRS x E) interaction may provide clues to underlying biology and can be useful in developing targeted prevention strategies for modifiable risk factors. The standard PRS may include a subset of variants that interact with E but a much larger subset of variants that affect disease without regard to E. This latter subset will water down the underlying signal in former subset, leading to reduced power to detect PRS x E interaction. We explore the use of pathway-defined PRS (pPRS) scores, using state of the art tools to annotate subsets of variants to genomic pathways. We demonstrate via simulation that testing targeted pPRS x E interaction can yield substantially greater power than testing overall PRS x E interaction. We also analyze a large study (N=78,253) of colorectal cancer (CRC) where E = non-steroidal anti-inflammatory drugs (NSAIDs), a well-established protective exposure. While no evidence of overall PRS x NSAIDs interaction (p=0.41) is observed, a significant pPRS x NSAIDs interaction (p=0.0003) is identified based on SNPs within the TGF-{beta} / gonadotropin releasing hormone receptor (GRHR) pathway. NSAIDS is protective (OR=0.84) for those at the 5th percentile of the TGF-{beta}/GRHR pPRS (low genetic risk, OR), but significantly more protective (OR=0.70) for those at the 95th percentile (high genetic risk). From a biological perspective, this suggests that NSAIDs may act to reduce CRC risk specifically through genes in these pathways. From a population health perspective, our result suggests that focusing on genes within these pathways may be effective at identifying those for whom NSAIDs-based CRC-prevention efforts may be most effective. Author SummaryThe identification of polygenic risk score (PRS) by environment (PRSxE) interactions may provide clues to underlying biology and facilitate targeted disease prevention strategies. The standard approach to computing a PRS likely includes many variants that affect disease without regard to E, reducing power to detect PRS x E interactions. We utilize gene annotation tools to develop pathway-based PRS (pPRS) scores and show by simulation studies that testing pPRS x E interaction can yield substantially greater power than testing PRS x E, while also integrating biological knowledge into the analysis. We apply our method to a large study of colorectal cancer to identify a significant pPRS x NSAIDs interaction (p=0.0003) based on SNPs within the TGF-{beta} / gonadotropin releasing hormone receptor (GRHR) pathway. Our findings suggest that focusing on genetic susceptibility within biologically informed pathways may be more sensitive for identifying exposures that can be considered as part of a precision prevention approach.

genetics↗

bioRxiv2024

A Regularized Cox Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies

BackgroundAssociated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes. MethodsA hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent. ResultsIn a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level. ConclusionsThe proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

bioinformatics↗

bioRxiv2022

A novel significance-based approach to two-stage hypothesis testing for discovery of gene-environment interactions

Two-step tests for gene-environment (G x E) interactions exploit marginal SNP effects to improve the power of a genome-wide interaction scan (GWIS). They combine a screening step based on marginal effects used to bin SNPs for weighted hypothesis testing in the second step to deliver greater power over single-step tests while preserving the genome-wide type I error. However, the presence of many SNPs with detectable marginal effects on the trait of interest can reduce power by displacing true interactions with weaker marginal effects and by adding to the number of tests that need to be corrected for multiple testing. We introduce a new significance-based allocation into bins for step 2 G x E testing that overcomes the displacement issue and propose a computationally efficient approach to account for multiple testing within bins. Simulation results demonstrate that these simple improvements can provide substantially greater power than current methods under several scenarios. An application to a multi-study collaboration for understanding colorectal cancer (CRC) reveals a GxSex interaction located within the SMAD7 gene.

genomics↗

bioRxiv2020

Incorporating Prior Knowledge into Regularized Regression

MotivationAssociated with genomic features like gene expression, methylation, and genotypes, used in statistical modeling of health outcomes, there is a rich set of meta-features like functional annotations, pathway information, and knowledge from previous studies, that can be used post-hoc to facilitate the interpretation of a model. However, using this meta-feature information a-priori rather than post-hoc can yield improved prediction performance as well as enhanced model interpretation. ResultsWe propose a new penalized regression approach that allows a-priori integration of external meta-features. The method extends LASSO regression by incorporating individualized penalty parameters for each regression coefficient. The penalty parameters are in turn modeled as a log-linear function of the meta-features and are estimated from the data using an approximate empirical Bayes approach. Optimization of the marginal likelihood on which the empirical Bayes estimation is based is performed using a fast and stable majorization-minimization procedure. Through simulations, we show that the proposed regression with individualized penalties can outperform the standard LASSO in terms of both parameters estimation and prediction performance when the external data is informative. We further demonstrate our approach with applications to gene expression studies of bone density and breast cancer. Availability and implementationThe methods have been implemented in the R package xtune freely available for download from CRAN.

bioinformatics↗