Biology subjects

Kechris, K.

Publications and source records attributed to Kechris, K..

9 recordsLinked to original sources

bioRxiv2026

MetaboXcan: A multiomic framework linking genetically predicted metabolites, gene expression, and complex traits

Genetically regulated molecular phenotypes, such as gene expression and metabolites, are widely thought to mediate the effect of disease-relevant genetic loci identified by genome-wide association studies (GWAS); however, molecular mechanisms connecting genetic variation to disease remain poorly understood. While transcriptome-wide association studies (TWAS) have identified disease-associated genes, strategies that integrate the metabolome remain underexplored. Here, we introduce MetaboXcan, a framework for predicting plasma metabolite levels from genetic data and associating them with complex traits using GWAS summary statistics. We trained lasso regression models on plasma metabolite and genotype data from the Metabolic Syndrome in Men Study (METSIM); these models outperform existing genetic metabolite predictors and generalize well across independent cohorts. MetaboXcan leverages these models to perform four complementary association analyses: (i) gene-to-trait (TWAS), (ii) gene-to-metabolite (M-TWAS), (iii) metabolite-to-trait (MWAS), and (iv) gene-expression-based metabolite-to-trait associations (g-MWAS). The framework further organizes results into metabolic pathways and gene-metabolite interaction networks to facilitate biological interpretation. Applied to chronic kidney disease (CKD), MetaboXcan identified known disease risk genes (PDILT/UMOD, SPATA5L1/GATM), as well as multiple CKD-relevant metabolites (e.g., glycine, homoarginine). Our integrated multiomic analysis enables biological interpretation by revealing glycine availability-centered biochemical axes spanning oxidative stress, cellular energetics, and vascular signaling, while nominating candidate mechanisms for downstream investigation. These results validate known disease-related biology with genetic evidence and generate new hypotheses for further investigation. MetaboXcan is publicly available and broadly applicable to any GWAS phenotype.

genetics↗

bioRxiv2025

Differences in immune cell profiles around the time of islet autoimmunity seroconversion in children with and without type 1 diabetes

Seroconversion (SV) marks the initiation of islet autoimmunity (IA) and pre-clinical phase of type 1 diabetes, yet the contributions of immune cells beyond cytotoxic T cells remain unclear. We applied high-resolution immune cell-type deconvolution using peripheral blood DNA methylation data from nested case-control samples of the Diabetes Autoimmunity Study in the Young (DAISY; n=151) and The Environmental Determinants of Diabetes in the Young (TEDDY; n=166) to estimate immune cell proportions across pre-SV and SV timepoints and construct functional ratios, such as the neutrophil-to-lymphocyte ratio (NLR). Using linear models, we evaluated differences between type 1 diabetes cases and controls at pre-SV, SV, and the change across timepoints. Pre-SV, cases had higher NLR and lower CD4T/CD8T cell ratios. At SV, the combined B-CD4T-CD8T memory/naive ratio was reduced in cases. From pre-SV to SV, cases showed attenuations in NLR, B-memory/naive, and B-CD4T-CD8T memory/naive ratios. These patterns may reflect delayed or disrupted immune maturation with the persistence or expansion of naive cells or impaired transition to memory subsets following antigen exposure. Our findings highlight early shifts in innate and adaptive immune cell dynamics during type 1 diabetes pathogenesis and support immune cell ratios as potential biomarkers for risk stratification and mechanistic insight. Article HighlightsO_LIWe sought to examine immune cells around the time of IA seroconversion in children at higher risk for type 1 diabetes. C_LIO_LIWe wanted to answer whether immune cell ratio differences exist between type 1 diabetes cases and controls around IA at pre-SV, SV, and the change pre-SV to SV. C_LIO_LIWe found immune cell ratio differences between type 1 diabetes cases and controls before, during, and across SV timepoints, suggesting potential etiological and pathophysiological roles. C_LIO_LIOur findings highlight the complexity of immunodynamics around IA seroconversion and potential role for immune cell ratios in type 1 diabetes risk stratification and intervention. C_LI

immunology↗

bioRxiv2024

A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference

Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest. Author summaryMulti-omics network inference is crucial for identifying disease-specific molecular interactions across various molecular profiles, which helps understand the biological processes related to disease etiology. Traditional multi-omics integration methods focus mainly on pairwise interactions by only considering two molecular profiles at a time. This approach overlooks the complex, higher-order correlations often present in multi-omics data, especially when analyzing more than two types of -omics data and phenotypes. Higher-order correlation, by definition, refers to the simultaneous relationships among more than two types of -omics data and phenotype, providing a more complex and complete understanding of the interactions in biological systems. Our research introduces Sparse Generalized Tensor Canonical Correlation Network Analysis (SGTCCA-Net), a novel framework that effectively utilizes both higher-order and lower-order correlations for multi-omics network inference. SGTCCA-Net is adaptable for exploring diverse correlation structures within multi-omics data and is able to construct complex multi-omics networks in a two-dimensional space. This method offers a comprehensive view of molecular feature interactions with respect to complex diseases. Our simulation studies and real data experiments validate SGTCCA-Net as a potent tool for biomarker identification and uncovering biological mechanisms associated with targeted diseases.

bioinformatics↗

bioRxiv2024

PathIntegrate: Multivariate modelling approaches for pathway-based multi-omics data integration

As terabytes of multi-omics data are being generated, there is an ever-increasing need for methods facilitating the integration and interpretation of such data. Current multi-omics integration methods typically output lists, clusters, or subnetworks of molecules related to an outcome. Even with expert domain knowledge, discerning the biological processes involved is a time-consuming activity. Here we propose PathIntegrate, a method for integrating multi-omics datasets based on pathways, designed to exploit knowledge of biological systems and thus provide interpretable models for such studies. PathIntegrate employs single-sample pathway analysis to transform multi-omics datasets from the molecular to the pathway-level, and applies a predictive single-view or multi-view model to integrate the data. Model outputs include multi-omics pathways ranked by their contribution to the outcome prediction, the contribution of each omics layer, and the importance of each molecule in a pathway. Using semi-synthetic data we demonstrate the benefit of grouping molecules into pathways to detect signals in low signal-to-noise scenarios, as well as the ability of PathIntegrate to precisely identify important pathways at low effect sizes. Finally, using COPD and COVID-19 data we showcase how PathIntegrate enables convenient integration and interpretation of complex high-dimensional multi-omics datasets. The PathIntegrate Python package is available at https://github.com/cwieder/PathIntegrate. Author summaryOmics data, which provides a readout of the levels of molecules such as genes, proteins, and metabolites in a sample, is frequently generated to study biological processes and perturbations within an organism. Combining multiple omics data types can provide a more comprehensive understanding of the underlying biology, making it possible to piece together how different molecules interact. There exist many software packages designed to integrate multi-omics data, but interpreting the resulting outputs remains a challenge. Placing molecules into the context of biological pathways enables us to better understand their collective functions and understand how they may contribute to the condition under study. We have developed PathIntegrate, a pathway-based multi-omics integration tool which helps integrate and interpret multi-omics data in a single step using machine learning. By integrating data at the pathway rather than the molecular level, the relationships between molecules in pathways can be strengthened and more readily identified. PathIntegrate is demonstrated on Chronic Obstructive Pulmonary Disease and COVID-19 metabolomics, proteomics, and transcriptomics datasets, showcasing its ability to efficiently extract perturbed multi-omics pathways from large-scale datasets.

bioinformatics↗

bioRxiv2023

Matrix Linear Models for connecting metabolite composition to individual characteristics

AO_SCPLOWBSTRACTC_SCPLOWHigh-throughput metabolomics data provide a detailed molecular window into biological processes. We consider the problem of assessing how the association of metabolite levels with individual (sample) characteristics such as sex or treatment may depend on metabolite characteristics such as pathway. Typically this is one in a two-step process: In the first step we assess the association of each metabolite with individual characteristics. In the second step an enrichment analysis is performed by metabolite characteristics among significant associations. We combine the two steps using a bilinear model based on the matrix linear model (MLM) framework we have previously developed for high-throughput genetic screens. Our framework can estimate relationships in metabolites sharing known characteristics, whether categorical (such as type of lipid or pathway) or numerical (such as number of double bonds in triglycerides). We demonstrate how MLM offers flexibility and interpretability by applying our method to three metabolomic studies. We show that our approach can separate the contribution of the overlapping triglycerides characteristics, such as the number of double bonds and the number of carbon atoms. The proposed method have been implemented in the open-source Julia package, MatrixLM. Data analysis scripts with example data analyses are also available.

bioinformatics↗

bioRxiv2022

RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks

Inferring gene co-expression networks is a useful process for understanding gene regulation and pathway activity. The networks are usually undirected graphs where genes are represented as nodes and an edge represents a significant co-expression relationship. When gene-expression data from multiple conditions (e.g., treatments, tissues, strains) are available, joint estimation of networks harnessing shared information across them can significantly increase the power of analysis. In addition, examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. Condition adaptive fused graphical lasso (CFGL) is an existing method that incorporates condition specificity in a fused graphical lasso (FGL) model for estimating multiple co-expression networks. However, the current implementation of CFGL is prohibitively slow even for a moderate number of genes and can only be used for a maximum of three conditions. In this paper, we propose a fast alternative of CFGL known as rapid condition adaptive fused graphical lasso (RCFGL). In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). We use a more efficient algorithm in the iterative steps compared to CFGL, enabling faster computation and making it easily generalizable for more than three conditions. We also present a novel screening rule to determine if the full network estimation problem can be broken down into estimation of smaller disjoint sub-networks, thereby reducing the complexity further. We demonstrate the computational advantage and superior performance of our method compared to two non-condition adaptive methods, FGL and FMGL, and one condition adaptive method, CFGL in several simulation scenarios. We use RCFGL to jointly estimate the gene co-expression networks of different brain regions (conditions) using a cohort of heterogeneous stock rats. We also provide an accommodating C and Python based package that implements RCFGL. Author summaryInferring gene co-expression networks can be useful for understanding pathway activity and gene regulation. While jointly estimating co-expression networks of multiple conditions, taking into account condition specificity such as information about an edge being present only in a specific condition, substantially increases the power. In this paper, a computationally rapid condition adaptive method for jointly estimating gene co-expression networks of multiple conditions is proposed. The novelty of the method is demonstrated through a broad range of simulation studies and a real data analysis with multiple brain regions from a genetically diverse cohort of rats.

bioinformatics↗

bioRxiv2021

PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes

High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often have disjoint components, i.e. nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway which notably lessens testing power. In this paper we propose the Pathway Integrated Regression-based Kernel Association Test (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or "smoothed" graph into a score test, PaIRKAT is capable of providing more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure. Author SummaryPaIRKAT is a tool for improving testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. Our paper proposes PaIRKAT, a method for including known biological connections between high dimensional variables by representing them as edges of graphs or networks. It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or smoothing approach for managing this issue. We demonstrate the benefits of this approach through simulation studies and an application to the metabolomic data from the COPDGene study.

bioinformatics↗

bioRxiv2021

Analysis of SARS-CoV-2 Mutations Over Time Reveals Increasing Prevalence of Variants in the Spike Protein and RNA-Dependent RNA Polymerase

Amid the ongoing COVID-19 pandemic, it has become increasingly important to monitor the mutations that arise in the SARS-CoV-2 virus, to prepare public health strategies and guide the further development of vaccines and therapeutics. The spike (S) protein and the proteins comprising the RNA-Dependent RNA Polymerase (RdRP) are key vaccine and drug targets, respectively, making mutation surveillance of these proteins of great importance. Full protein sequences for the spike proteins and RNA-dependent RNA polymerase proteins were downloaded from the GISAID database, aligned, and the variants identified. Polymorphisms in the protein sequence were investigated at the protein structural level and examined longitudinally in order to identify sequence and strain variants that are emerging over time. Our analysis revealed a group of variants in the spike protein and the polymerase complex that appeared in August, and account for around five percent of the genomes analyzed up to the last week of October. A structural analysis also facilitated investigation of several unique variants in the receptor binding domain and the N-terminal domain of the spike protein, with high-frequency mutations occurring more commonly in these regions. The identification of new variants emphasizes the need for further study on the effects of these mutations and the implications of their increased prevalence, particularly as these mutations may impact vaccine or therapeutic efficacy.

bioinformatics↗

bioRxiv2018

Condition-adaptive fused graphical lasso (CFGL): an adaptive procedure for inferring condition-specific gene co-expression network

Co-expression network analysis provides useful information for studying gene regulation in biological processes. Examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. One challenge in this type of analysis is that the sample sizes in each condition are usually small, making the statistical inference of co-expression patterns highly underpowered. A joint network construction that borrows information from related structures across conditions has the potential to improve the power of the analysis.\n\nOne possible approach to constructing the co-expression network is to use the Gaussian graphical model. Though several methods are available for joint estimation of multiple graphical models, they do not fully account for the heterogeneity between samples and between co-expression patterns introduced by condition specificity. Here we develop the condition-adaptive fused graphical lasso (CFGL), a data-driven approach to incorporate condition specificity in the estimation of co-expression networks. We show that this method improves the accuracy with which networks are learned. The application of this method on a rat multi-tissue dataset and The Cancer Genome Atlas (TCGA) breast cancer dataset provides interesting biological insights. In both analyses, we identify numerous modules enriched for Gene Ontology functions and observe that the modules that are upregulated in a particular condition are often involved in condition-specific activities. Interestingly, we observe that the genes strongly associated with survival time in the TCGA dataset are less likely to be network hubs, suggesting that genes associated with cancer progression are likely to govern specific functions, rather than regulating a large number of biological processes. Additionally, we observed that the tumor-specific hub genes tend to have few shared edges with normal tissue, revealing tumor-specific regulatory mechanism.\n\nAuthor summaryGene co-expression networks provide insights into the mechanism of cellular activity and gene regulation. Condition-specific mechanisms may be identified by constructing and comparing co-expression networks of multiple conditions. We propose a novel statistical method to jointly construct co-expression networks for gene expression profiles from multiple conditions. By using a data-driven approach to capture condition-specific co-expression patterns, this method is effective in identifying both co-expression patterns that are specific to a condition and that are common across conditions. The application of this method on real datasets reveals interesting biological insights.

bioinformatics↗