Detecting gene-gene interactions from GWAS using diffusion kernel principal components

From Silos to Synthesis: A comprehensive review of domain adaptation strategies for multi-source data integration in healthcare

BACKGROUND

The integration of data from diverse sources is not only crucial for addressing data scarcity in health informatics but also enables the use of complementary information from multiple datasets. However, the isolated nature of data collected from disparate sources (referred to as 'Silos') presents significant challenges in multi-source data integration due to inherent heterogeneity and differences in data structures, formats, and standards. Domain adaptation emerges as a key framework to transition from 'Silos' to 'Synthesis' by measuring and mitigating such discrepancies, enabling uniform representation and harmonization of multi-source data.

METHODS

This study explores different approaches to healthcare data integration, highlighting the challenges associated with each type and discussing both general-purpose and healthcare-specific adaptation methods. We examine key research challenges and evaluate leading domain adaptation approaches, demonstrating their effectiveness and limitations in advancing healthcare data integration.

RESULTS

The findings highlight the potential of domain adaptation methods to significantly improve healthcare data integration while laying a foundation for future research.

CONCLUSION

Current research often lacks a comprehensive analysis of how domain adaptation can effectively address the challenges associated with integrating multi-source and multi-modal healthcare datasets. This study serves as a valuable resource for healthcare professionals and researchers, providing guidance on leveraging domain adaptation techniques to mitigate domain discrepancies in healthcare data integration.

Understanding and applying gene-environment interactions: a guide for nutrition professionals with an emphasis on integration in African research settings

Noncommunicable diseases (NCDs) are influenced by the interplay between genetics and environmental exposures, particularly diet. However, many healthcare professionals, including nutritionists and dietitians, have limited genetic background and, therefore, they may lack understanding of gene-environment interactions (GxEs) studies. Even researchers deeply involved in nutrition studies, but with a focus elsewhere, can struggle to interpret, evaluate, and conduct GxE studies. There is an urgent need to study African populations that bear a heavy burden of NCDs, demonstrate unique genetic variability, and have cultural practices resulting in distinctive environmental exposures compared with Europeans or Americans, who are studied more. Although diverse and rapidly changing environments, as well as the high genetic variability of Africans and difference in linkage disequilibrium (ie, certain gene variants are inherited together more often than expected by chance), provide unparalleled potential to investigate the omics fields, only a small percentage of studies come from Africa. Furthermore, research evidence lags behind the practices of companies offering genetic testing for personalized medicine and nutrition. We need to generate more evidence on GxEs that also considers continental African populations to be able to prevent unethical practices and enable tailored treatments. This review aims to introduce nutrition professionals to genetics terms and valid methods to investigate GxEs and their challenges, and proposes ways to improve quality and reproducibility. The review also provides insight into the potential contributions of nutrigenetics and nutrigenomics to the healthcare sphere, addresses direct-to-consumer genetic testing, and concludes by offering insights into the field's future, including advanced technologies like artificial intelligence and machine learning.

MR-GGI: accurate inference of gene-gene interactions using Mendelian randomization

BACKGROUND

Researchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the popular approaches for understanding these processes, requiring accurate identification of interactions among the genes to establish the gene regulatory network. Advances in genome-wide association studies and expression quantitative trait loci studies have led to a wealth of genomic data, facilitating more accurate inference of gene-gene interactions. However, unknown confounding factors may influence these interactions, making their interpretation complicated. Mendelian randomization (MR) has emerged as a valuable tool for causal inference in genetics, addressing confounding effects by estimating causal relationships using instrumental variables. In this paper, we propose a new statistical method, MR-GGI, for accurately inferring gene-gene interactions using Mendelian randomization.

RESULTS

MR-GGI applies one gene as the exposure and another as the outcome, using causal cis-single-nucleotide polymorphisms as instrumental variables in the inverse-variance weighted MR model. Through simulations, we have demonstrated MR-GGI's ability to control type 1 error and maintain statistical power despite confounding effects. MR-GGI performed the best when compared to other methods using the F1 score on the DREAM5 dataset. Additionally, when applied to yeast genomic data, MR-GGI successfully identified six clusters. Through gene ontology analysis, we have confirmed that each cluster in our study performs distinct functional roles by gathering genes with specific functions.

CONCLUSION

These findings demonstrate that MR-GGI accurately inferences gene-gene interactions despite the confounding effects in real biological environments.

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

Introduction: Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Methods: Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. Results: We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. The clustering derived from netMUG achieved an adjusted Rand index of 1 with respect to the synthesized true labels. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these subgroups. Discussion: netMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

Multi-view data offer advantages over single-view data for characterizing individuals, which is crucial in precision medicine toward personalized prevention, diagnosis, or treatment follow-up. Here, we develop a network-guided multi-view clustering framework named netMUG to identify actionable subgroups of individuals. This pipeline first adopts sparse multiple canonical correlation analysis to select multi-view features possibly informed by extraneous data, which are then used to construct individual-specific networks (ISNs). Finally, the individual subtypes are automatically derived by hierarchical clustering on these network representations. We applied netMUG to a dataset containing genomic data and facial images to obtain BMI-informed multi-view strata and showed how it could be used for a refined obesity characterization. Benchmark analysis of netMUG on synthetic data with known strata of individuals indicated its superior performance compared with both baseline and benchmark methods for multi-view clustering. In addition, the real-data analysis revealed subgroups strongly linked to BMI and genetic and facial determinants of these classes. NetMUG provides a powerful strategy, exploiting individual-specific networks to identify meaningful and actionable strata. Moreover, the implementation is easy to generalize to accommodate heterogeneous data sources or highlight data structures.