Crohn's Disease Dataset Research Articles

Exploration of key ferroptosis-related genes and immune infiltration in Crohn’s disease using bioinformatics

Crohn's disease (CD) is a type of inflammatory bowel disease (IBD) that manifests mainly as chronic inflammation in different parts of the gastrointestinal tract, and its incidence has come to be increasing in recent years. Ferroptosis, a novel type of programmed cell death, it seems the role of ferroptosis-related biomarkers in CD has not been mentioned. Thus, the role of ferroptosis in CD and its relationship with immune infiltration were explored in this study. The CD dataset was downloaded from the Gene Expression Omnibus database. The validated ferroptosis genes (FRGs) were retrieved from the public FerrDb database. The gene expression matrix of the CD dataset was analyzed with the “limma” package in R language to obtain differentially expressed genes (DEGs) between diseased and healthy samples. Then, intersecting genes between DEGs and FRGs were identified as differentially expressed ferroptosis-associated genes (DE-FRGs). Protein–protein interaction (PPI) network analysis and visualization were carried out with STRING and Cytoscape, and key CD ferroptosis-related genes (CD-FRGs) were identified along with their Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using the clusterProfiler package. Immune cell infiltration was analyzed with CIBERSORT. The correlation between key CD-FRGs and immune-infiltrated cells in CD was studied by Spearman's correlation method. A total of 37 DE-FRGs and 6 key CD-FRGs (CAV1, CD44, HIF1A, IFNG, TIMP1 and TLR4) were identified. GO and KEGG functional analysis indicated these genes enrichment in programmed cell death and apoptotic process, HIF-1 signaling pathway and IBD. Infiltration matrix analysis of immune cells showed abundant T cells CD4 memory activated, M1 macrophages, M2 macrophages, Mast cells activated and Neutrophils in CD intestinal tissues. The 6 key CD-FRGs were correlated with immune-infiltrated cells in CD based on correlation analysis. Taken together, immune cells with abnormal infiltration can be implicated in CD due to ferroptosis. This study identified 6 key CD-FRGs that may be key biomarkers of ferroptosis in CD; they include CAV1, CD44, HIF1A, IFNG, TIMP1 and TLR4. These findings suggest that the immune response is critical in CD caused by ferroptosis through the interaction between key CD-FRGs and immune infiltrating cells.

Advanced machine learning model for predicting Crohn's disease with enhanced ant colony optimization

Changes in human lifestyles have led to a dramatic increase in the incidence of Crohn's disease worldwide. Predicting the activity and remission of Crohn's disease has become an urgent research problem. In addition, the influence of each attribute in the test sample on the prediction results and the interpretability of the model still deserves further investigation. Therefore, in this paper, we proposed a wrapper feature selection classification model based on a combination of the improved ant colony optimization algorithm and the kernel extreme learning machine, called bIACOR-KELM-FS. IACOR introduces an evasive strategy and astrophysics strategy to balance the exploration and exploitation phases of the algorithm and enhance its optimization capabilities. The optimization capability of the proposed IACOR was validated on the IEEE CEC2017 benchmark test function. And the prediction was performed on Crohn's disease dataset. The results of the quantitative analysis showed that the prediction accuracy of bIACOR-KELM-FS for predicting the activity and remission of Crohn's disease reached 98.98%. The analysis of important attributes improved the interpretability of the model and provided a reference for the diagnosis of Crohn's disease. Therefore, the proposed model is considered a promising adjunctive diagnostic method for Crohn's disease.

Identification of Potential Genes and Critical Pathways in Postoperative Recurrence of Crohn's Disease by Machine Learning And WGCNA Network Analysis.

Crohn's disease (CD) is a chronic idiopathic inflammatory bowel disease affecting the entire gastrointestinal tract from the mouth to the anus. These patients often experience a period of symptomatic relapse and remission. A 20 - 30% symptomatic recurrence rate is reported in the first year after surgery, with a 10% increase each subsequent year. Thus, surgery is done only to relieve symptoms and not for the complete cure of the disease. The determinants and the genetic factors of this disease recurrence are also not well-defined. Therefore, enhanced diagnostic efficiency and prognostic outcome are critical for confronting CD recurrence. We analysed ileal mucosa samples collected from neo-terminal ileum six months after surgery (M6=121 samples) from Crohn's disease dataset (GSE186582). The primary aim of this study is to identify the potential genes and critical pathways in post-operative recurrence of Crohn's disease. We combined the differential gene expression analysis with Recursive feature elimination (RFE), a machine learning approach to get five critical genes for the postoperative recurrence of Crohn's disease. The features (genes) selected by different methods were validated using five binary classifiers for recurrence and remission samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbor (KNN) with 10-fold cross-validation. We also performed weighted gene co-expression network analysis (WGCNA) to select specific modules and feature genes associated with Crohn's disease postoperative recurrence, smoking, and biological sex. Combined with other biological interpretations, including Gene Ontology (GO) analysis, pathway enrichment, and protein-protein interaction (PPI) network analysis, our current study sheds light on the in-depth research of CD diagnosis and prognosis in postoperative recurrence. PLOD2, ZNF165, BOK, CX3CR1, and ARMCX4, are the important genes identified from the machine learning approach. These genes are reported to be involved in the viral protein interaction with cytokine and cytokine receptors, lysine degradation, and apoptosis. They are also linked with various cellular and molecular functions such as Peptidyl-lysine hydroxylation, Central nervous system maturation, G protein-coupled chemoattractant receptor activity, BCL-2 homology (BH) domain binding, Gliogenesis and negative regulation of mitochondrial depolarization. WGCNA identified a gene co-expression module that was primarily involved in mitochondrial translational elongation, mitochondrial translational termination, mitochondrial translation, mitochondrial respiratory chain complex, mRNA splicing via spliceosome pathways, etc.; Both the analysis result emphasizes that the mitochondrial depolarization pathway is linked with CD recurrence leading to oxidative stress in promoting inflammation in CD patients. These key genes serve as the novel diagnostic biomarker for the postoperative recurrence of Crohn's disease. Thus, among other treatment options present until now, these biomarkers would provide success in both diagnosis and prognosis, aiming for a long-lasting remission to prevent further complications in CD.

Parallelization of Logic Regression Analysis on SNP-SNP Interactions of a Crohn's Disease Dataset Model

SNP-SNP interactions have been recognized to be basically important for understanding genetic causes of complex disease traits. Logic regression is an effective methods for identifying SNP-SNP interactions associated with risk of complex disease. However, identifying SNP-SNP interactions are computationally challenging and may take hours, weeks and months to complete. Although parallel computing is a powerful method to accelerate computing time, it is arduous for users to apply this method to logic regression analyses of SNP-SNP interactions because it requires advanced programming skills to correctly partition and distribute data, control and monitor tasks across multi-core CPUs or several computers, and merge output files. In this paper, we present a novel R-library called SNPInt to automatically speed up analyses of SNP-SNP interactions of genome-wide association (GWA) studies using parallel computing without the advanced programming skills. The Crohn’s disease GWA studies dataset from the Wellcome Trust Case Control Consortium (WTCCC) that includes 4,680 individuals with 500,000 SNPs’ genotypes was analyzed using logic regression on a computer cluster to evaluate SNPInt performance. The results from SNPInt with any number of CPUs are the same as the results from non-parallel approach, and SNPInt library quite accelerated the logic regression analysis. For instance, with two hundred genes and twenty permutation rounds, the computing time was continuously decreased from 7.3 days to only 0.9 day when SNPInt applied eight CPUs. Executing analyses of SNP-SNP interactions using the SNPInt library is an effective way to boost performance, and simplify the parallelization of analyses of SNP-SNP interactions.

Pathway-based Genome-wide Association Studies Reveal the Association Between Growth Factor Activity and Inflammatory Bowel Disease.

The inflammatory bowel diseases known as Crohn's disease (CD) and ulcerative colitis (UC) are related autoimmune conditions with a complex etiology composed of genetic and environmental factors. Genetic studies have revealed 200 susceptibility loci for inflammatory bowel diseases, but these only account for a small fraction of the genetic heritability of the disease. We employed pathway-based approaches to identify genes that cooperatively make contributions to the genetic etiology of CD. We exploited the largest CD dataset (20,000 cases + 28,000 controls) and UC dataset (17,000 cases + 33,500 controls) to date. We conducted a meta-analysis of 5 CD cohorts of European ancestry using 3 pathway-based approaches and further performed replication studies in an independent cohort genotyped on the Immunochip and in another pediatric cohort of European ancestry. Similar meta-analysis was performed for UC cohorts. In addition to the multiple immune-related pathways that have been implicated in the genetic etiology of inflammatory bowel diseases before, we found significant associations involving genes in growth factor signaling for CD. This result was replicated in 2 independent cohorts of European ancestry. This association with growth factor activity is not unique to CD. We found a similar significant association with UC cohorts. Our findings suggest that genes involved in pathways of growth factor signaling may make joint contributions to the etiology of CD and UC, providing novel insight into the genetic mechanisms of these diseases.

Comparisons of seven algorithms for pathway analysis using the WTCCC Crohn's Disease dataset

BackgroundThough rooted in genomic expression studies, pathway analysis for genome-wide association studies (GWAS) has gained increasing popularity, since it has the potential to discover hidden disease pathogenic mechanisms by combining statistical methods with biological knowledge. Generally, algorithms or programs proposed recently can be categorized by different types of input data, null hypothesis or counts of analysis stages. Due to complexity caused by SNP, gene and pathway relationships, re-sampling strategies like permutation are always utilized to derive an empirical distribution for test statistics for evaluating the significance of candidate pathways. However, evaluation of these algorithms on real GWAS datasets and real biological pathway databases needs to be addressed before we apply them widely with confidence.FindingsTwo algorithms which use summary statistics from GWAS as input were implemented in KGG, a novel and user-friendly software tool for GWAS pathway analysis. Comparisons of these two algorithms as well as the other five selected algorithms were conducted by analyzing the WTCCC Crohn's Disease dataset utilizing the MsigDB canonical pathways. As a result of using permutation to obtain empirical p-value, most of these methods could control Type I error rate well, although some are conservative. However, the methods varied greatly in terms of power and running time, with the PLINK truncated set-based test being the most powerful and KGG being the fastest.ConclusionsRaw data-based algorithms, such as those implemented in PLINK, are preferable for GWAS pathway analysis as long as computational capacity is available. It may be worthwhile to apply two or more pathway analysis algorithms on the same GWAS dataset, since the methods differ greatly in their outputs and might provide complementary findings for the studied complex disease.

Pseudosibship methods in the case‐parents design

Recent evidence suggests that complex traits are likely determined by multiple loci, each of which contributes a weak to moderate individual effect. Although extensive literature exists on multilocus analysis of unrelated subjects, there are relatively fewer strategies for jointly analyzing multiple loci using family data. Here we address this issue by evaluating two pseudosibship methods: the 1:1 matching, which matches each affected offspring to the pseudosibling formed by the alleles not transmitted to the affected offspring, and the exhaustive matching, which matches each affected offspring to the pseudosiblings formed by all the other possible combinations of parental alleles. We prove that the two matching strategies use exactly and approximately the same amount of information from data under additive and multiplicative genetic models, respectively. Using numerical calculations under a variety of models and testing assumptions, we show that compared with the exhaustive matching, the 1:1 matching has comparable asymptotic power in detecting multiplicative/additive effects in single-locus analysis and main effects in multilocus analysis, and it allows association testing of multiple linked loci. These results pave the way for many existing multilocus analysis methods developed for the case-control (or matched case-control) design to be applied to case-parents data with minor modifications. As an example, with the 1:1 matching, we applied an L1 regularized regression to a Crohn's disease dataset. Using the multiple loci selected in our approach, we obtained an order-of-magnitude decrease in p-value and an 18.9% increase in prediction accuracy when compared with using the most significant individual locus.

Contrasting linkage disequilibrium as a multilocus family-based association test

Linkage disequilibrium (LD) of genetic loci is routinely estimated and graphically illustrated in genetic association studies. It has been suggested that the information in LD is also useful for association mapping and genetic association can be detected by comparing LD patterns between cases and controls. Here, we extend this idea to analyze case-parents data by comparing LD patterns between transmitted and nontransmitted genotypes. We provide the condition when contrasting LD is valid for testing gene-gene interactions. A permutation procedure is given to assess statistical significance. One advantage of our proposed methods is that haplotype information is not required. Thus, the implementation of our methods is straightforward and the resulted tests are free from potential bias caused by assumptions made to estimate haplotypes in silico. Since our test statistics use pairwise LD measurements, they are less affected by missing data than many other multilocus methods. With simulated data, we demonstrate that examining LD patterns of case-parents data is a useful multilocus association mapping strategy and it complements existing association mapping methods. The application of our methods to a Crohn's disease data set shows that our methods can detect multilocus association that might be missed by other association methods. Our permutation procedure can also be modified to allow multiple offspring from a family to be analyzed.

Identification of association between disease and multiple markers via sparse partial least-squares regression

Although genome-wide association studies have led to the identifications of hundreds of genes underlying dozens of traits in recent years, most published studies have primarily used single marker-based analysis. Intuitively, more information may be utilized when multiple markers are jointly analyzed. Therefore, many methods have been proposed in the literature for association analysis between traits and multiple markers. Among these methods, simulation and real data analyses have shown that it is often more effective to reduce the dimensionality of the markers in a region through principal components analysis of all the markers first, and then to perform association analysis between traits and those principal components that account for most of the genetic variations in the region. However, one major limitation of this approach is that the principal components are derived purely from marker genotypes, without consideration of their relevance to traits. Furthermore, these components are constructed as linear combinations of all the markers even when only a limited number are potentially relevant to traits. In this manuscript, we propose the use of sparse partial least-squares regression to derive the components that are linear combinations of only relevant markers. This approach is able to use information from both traits and marker genotypes. Extensive simulations and real data analyses on a Crohn's disease data set suggest the superiority of this approach over existing methods.

The interaction index, a novel information-theoretic metric for prioritizing interacting genetic variations and environmental factors

We developed an information-theoretic metric called the Interaction Index for prioritizing genetic variations and environmental variables for follow-up in detailed sequencing studies. The Interaction Index was found to be effective for prioritizing the genetic and environmental variables involved in GEI for a diverse range of simulated data sets. The metric was also evaluated for a 103-SNP Crohn's disease dataset and a simulated data set containing 9187 SNPs and multiple covariates that was modeled on a rheumatoid arthritis data set. Our results demonstrate that the Interaction Index algorithm is effective and efficient for prioritizing interacting variables for a diverse range of epidemiologic data sets containing complex combinations of direct effects, multiple GGI and GEI.

AMBIENCE: A Novel Approach and Efficient Algorithm for Identifying Informative Genetic and Environmental Associations With Complex Phenotypes

We developed a computationally efficient algorithm AMBIENCE, for identifying the informative variables involved in gene-gene (GGI) and gene-environment interactions (GEI) that are associated with disease phenotypes. The AMBIENCE algorithm uses a novel information theoretic metric called phenotype-associated information (PAI) to search for combinations of genetic variants and environmental variables associated with the disease phenotype. The PAI-based AMBIENCE algorithm effectively and efficiently detected GEI in simulated data sets of varying size and complexity, including the 10K simulated rheumatoid arthritis data set from Genetic Analysis Workshop 15. The method was also successfully used to detect GGI in a Crohn's disease data set. The performance of the AMBIENCE algorithm was compared to the multifactor dimensionality reduction (MDR), generalized MDR (GMDR), and pedigree disequilibrium test (PDT) methods. Furthermore, we assessed the computational speed of AMBIENCE for detecting GGI and GEI for data sets varying in size from 100 to 10(5) variables. Our results demonstrate that the AMBIENCE information theoretic algorithm is useful for analyzing a diverse range of epidemiologic data sets containing evidence for GGI and GEI.

Genetic Analysis of Innate Immunity in Crohn's Disease and Ulcerative Colitis Identifies Two Susceptibility Loci Harboring CARD9 and IL18RAP

The two main phenotypes of inflammatory bowel disease (IBD)--Crohn's disease (CD) and ulcerative colitis (UC)--are chronic intestinal inflammatory disorders with a complex genetic background. Using a three-stage design, we performed a functional candidate-gene analysis of innate immune pathway in IBD. In phase I, we typed 354 SNPs from 85 innate immunity genes in 520 Dutch IBD patients (284 CD, 236 UC) and 808 controls. In phase II, ten autosomal SNPs showing association at p < 0.006 in phase I were replicated in a second cohort of 545 IBD patients (326 CD, 219 UC) and 360 controls. In phase III, four SNPs with p < 0.01 in the combined phase I and phase II analysis were genotyped in an additional 786 IBD samples (452 CD, 334 UC) and 768 independent controls. Joint analysis of 1851 IBD patients (1062 CD, 789 UC) and 1936 controls demonstrated strong association to the IL18RAP rs917997 SNP for both CD and UC (p(IBD) 1.9 x 10(-8); OR 1.35). Association in CD is independently supported by the Crohn's disease dataset of the Wellcome Trust Case Control Consortium (imputed SNP rs917997, p = 9.19 x 10(-4)). In addition, an association of the CARD9 rs10870077 SNP to CD and UC was observed (p(IBD) = 3.25 x 10(-5); OR 1.21). Both genes are located in extended haplotype blocks on 2q11-2q12 and 9q34.3, respectively. Our results indicate two IBD loci and further support the importance of the innate immune system in the predisposition to both CD and UC.

Information-Theoretic Metrics for Visualizing Gene-Environment Interactions

The purpose of our work was to develop heuristics for visualizing and interpreting gene-environment interactions (GEIs) and to assess the dependence of candidate visualization metrics on biological and study-design factors. Two information-theoretic metrics, the k-way interaction information (KWII) and the total correlation information (TCI), were investigated. The effectiveness of the KWII and TCI to detect GEIs in a diverse range of simulated data sets and a Crohn disease data set was assessed. The sensitivity of the KWII and TCI spectra to biological and study-design variables was determined. Head-to-head comparisons with the relevance-chain, multifactor dimensionality reduction, and the pedigree disequilibrium test (PDT) methods were obtained. The KWII and TCI spectra, which are graphical summaries of the KWII and TCI for each subset of environmental and genotype variables, were found to detect each known GEI in the simulated data sets. The patterns in the KWII and TCI spectra were informative for factors such as case-control misassignment, locus heterogeneity, allele frequencies, and linkage disequilibrium. The KWII and TCI spectra were found to have excellent sensitivity for identifying the key disease-associated genetic variations in the Crohn disease data set. In head-to-head comparisons with the relevance-chain, multifactor dimensionality reduction, and PDT methods, the results from visual interpretation of the KWII and TCI spectra performed satisfactorily. The KWII and TCI are promising metrics for visualizing GEIs. They are capable of detecting interactions among numerous single-nucleotide polymorphisms and environmental variables for a diverse range of GEI models.