Epistasis within disease-related genes (gene-gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene-gene interactions; however, some gene-gene interactions may require identification through multiple contingency table measures. In this study, a multiobjective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene-gene interactions. Two contingency table measures, namely the correct classification rate and normalized mutual information, were selected to design the fitness functions in MODEMDR. The characteristics of multiobjective optimization enable MODEMDR to use multiple measures to efficiently and synchronously detect significant gene-gene interactions within a reasonable time frame. Epistatic models with and without marginal effects under various parameter settings (heritability and minor allele frequencies) were used to assess existing methods by comparing the detection success rates of gene-gene interactions. The results of the simulation datasets show that MODEMDR is superior to existing methods. Moreover, a large dataset obtained from the Wellcome Trust Case Control Consortium was used to assess MODEMDR. MODEMDR exhibited efficiency in identifying significant gene-gene interactions in genome-wide association studies.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5634479 | PMC |
| http://dx.doi.org/10.1038/s41598-017-12773-x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens.
Hum Genomics
December 2024
Department of Clinical Neurosciences, 'Carol Davila' University of Medicine and Pharmacy, Bucharest, Romania.
Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models.
View Article and Find Full Text PDFGWAS-Significant Loci and Uterine Fibroids Risk: Analysis of Associations, Gene-Gene and Gene-Environmental Interactions.
Front Biosci (Schol Ed)
December 2024
Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 305041 Kursk, Russia.
Background: Uterine fibroids (UF) is the most common benign tumour of the female reproductive system. We investigated the joint contribution of genome-wide association studies (GWAS)-significant loci and environment-associated risk factors to the UF risk, along with epistatic interactions between single nucleotide polymorphisms (SNPs).
Methods: DNA samples from 737 hospitalised patients with UF and 451 controls were genotyped using probe-based PCR for seven common GWAS SNPs: rs117245733 , rs547025 rs2456181 , rs7907606 , , rs58415480 , rs7986407 , and rs72709458 .
Polymorphism in Genes Encoding HSP40 Family Proteins is Associated with Ischemic Stroke Risk and Brain Infarct Size: A Pilot Study.
J Integr Neurosci
December 2024
Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 305041 Kursk, Russia.
Background: Heat shock proteins (HSPs) play a critical role in the molecular mechanisms of ischemic stroke (IS). A possible role for HSP40 family proteins in atherosclerosis progression has already been revealed; however, to date, molecular genetic studies on the involvement of genes encoding proteins of the HSP40 family in IS have not yet been carried out.
Aim: We sought to determine whether nine single nucleotide polymorphisms (SNPs) in genes encoding HSP40 family proteins (, , , , and ) are associated with the risk and clinical features of IS.
Mapping the gene space at single-cell resolution with gene signal pattern analysis.
Nat Comput Sci
December 2024
Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, USA.
In single-cell sequencing analysis, several computational methods have been developed to map the cellular state space, but little has been done to map or create embeddings of the gene space. Here we formulate the gene embedding problem, design tasks with simulated single-cell data to evaluate representations, and establish ten relevant baselines. We then present a graph signal processing approach, called gene signal pattern analysis (GSPA), that learns rich gene representations from single-cell data using a dictionary of diffusion wavelets on the cell-cell graph.
View Article and Find Full Text PDFscGraph2Vec: a deep generative model for gene embedding augmented by graph neural network and single-cell omics data.
Gigascience
January 2024
National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China.
Background: Exploring the cellular processes of genes from the aspects of biological networks is of great interest to understanding the properties of complex diseases and biological systems. Biological networks, such as protein-protein interaction networks and gene regulatory networks, provide insights into the molecular basis of cellular processes and often form functional clusters in different tissue and disease contexts.
Results: We present scGraph2Vec, a deep learning framework for generating informative gene embeddings.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!