A systematic review and functional bioinformatics analysis of genes associated with Crohn’s disease identify more than 120 related genes

Garza-Hernandez, Debora; Sepúlveda-Villegas, Maricruz; Garcia-Pelaez, José; Aguirre-Gamboa, Raúl; Lakatos, László; Estrada, Karol; Martínez-Vazquez, Manuel; Treviño, Víctor

doi:10.1186/s12864-022-08491-y

Cited by 10 publications

(10 citation statements)

References 110 publications

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“…[ SNX20 ]: SNX20 interacts with the cytoplasmic domain of PSGL1 and cycles PSGL1 into endosomes. SNX20 gene was added manually as one of the 126 significant genes associated with CD in a systematic review and functional analysis of a gene set potentially related to CD [46]. Besides, to increases the power of GWAS studies, Abegaz et al studied the impact of confounding in gene–gene interaction (epistasis) detection.…”

Section: Resultsmentioning

confidence: 99%

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Bian¹,

Li²,

Leung³

et al. 2023

Preprint

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Transcriptome-wide association study (TWAS) is an emerging model leveraging gene expressions to direct genotype-phenotype association mapping. A key component in TWAS is the prediction of gene expressions; and many statistical approaches have been developed along this line. However, a problem is that many genes have low expression heritability, limiting the performance of any predictive model. In this work, hypothesizing that appropriate denoising may improve the quality of expression data (including heritability), we propose AE-TWAS, which adds a transformation step before conducting standard TWAS. The transformation is composed of two steps by first splitting the whole transcriptome into co-expression networks (modules) and then using autoencoder (AE) to reconstruct the transcriptome data within each module. This transformation removes noise (including nonlinear ones) from the transcriptome data, paving the path for downstream TWAS. We applied AE-TWAS to the GTEx whole blood transcriptome data and GWAS data of five human diseases, showing two inspiring properties of AE-TWAS: (1) After transformation, the transcriptome data enjoy higher expression heritability at the low-heritability spectrum and possess higher connectivity within the modules. (2) The transferred transcriptome indeed enables better performance of TWAS; and moreover, the newly formed highly connected genes (i.e., hub genes) are more functionally relevant to diseases, evidenced by their functional annotations and overlap with TWAS hits. Taking together, we show that autoencoder transformation produces better transcriptome, which in turn enables improved expression-assisted genotype-phenotype association mapping. The impact of this work may be beyond the field of gene mapping: AE can be deemed as a nonlinear extension of principal component analysis (PCA) that is used for removing artifacts in expression data routinely. As such, this work may inspire more expression-based applications to be carried out after an appropriate AE-transformation, unlocking the use of AE-denoised transcriptome in many fields.

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Section: Resultsmentioning

confidence: 99%

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Bian¹,

Li²,

Leung³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, intensive research has been underway to identify the causes of CD. Among the numerous approaches that have been taken so far, the unbiased, high-throughput techniques used to analyze DNA, RNA, and proteins are becoming increasingly popular [8][9][10][11][12]. As with any other multifactorial diseases (e.g., cardiovascular disease and diabetes), it is difficult to pinpoint the exact mechanisms underlying CD.…”

Section: Introductionmentioning

confidence: 99%

CrohnDB: A Web Database for Expression Profiling of Protein-Coding and Long Non-Coding RNA Genes in Crohn Disease

et al. 2023

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Crohn disease (CD) is a type of inflammatory bowel disease that causes inflammation in the digestive tract. Cases of CD are increasing worldwide, calling for more research to elucidate the pathogenesis of CD. For this purpose, the usage of the RNA-sequencing (RNA-seq) technique is increasingly appreciated, as it captures RNA expression patterns at a particular time point in a high-throughput manner. Although many RNA-seq datasets are generated from CD patients and compared to those of healthy donors, most of these datasets are analyzed only for protein-coding genes, leaving non-coding RNAs (ncRNAs) undiscovered. Long non-coding RNAs (lncRNAs) are any ncRNAs that are longer than 200 nucleotides. Interest in studying lncRNAs is increasing rapidly, as lncRNAs bind other macromolecules (DNA, RNA, and/or proteins) to finetune signaling pathways. To fill the gap in knowledge about lncRNAs in CD, we performed secondary analysis of published RNA-seq data of CD patients compared to healthy donors to identify lncRNA genes and their expression changes. To further facilitate lncRNA research in CD, we built a web database, CrohnDB, to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in CD patients compared to healthy donors.

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“…5 In genome-wide association studies to date, more than 120 related genes have been identified for CD, 67 of which were found to be differentially expressed for both CD and UC patients compared to non-IBD individuals. 6 These biological underpinnings have connected IBD to an array of comorbidities including asthma 7 and diabetes, 8 which are also characterized by polygenic architectures and complex environmental interactions related to immune activation. Additionally, many of the genes most strongly associated with IBD diagnosis are active in host-microbe interactions (such as pathogen-associated molecular pattern recognition, inflammatory responses, and phagocytic processes), including toll-like receptor (TLR) 4, TLR9, nucleotide binding oligomerization domain containing 2 (NOD2), interleukin-23 receptor, and tumor necrosis factor.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, many of the genes most strongly associated with IBD diagnosis are active in host-microbe interactions (such as pathogen-associated molecular pattern recognition, inflammatory responses, and phagocytic processes), including toll-like receptor (TLR) 4, TLR9, nucleotide binding oligomerization domain containing 2 (NOD2), interleukin-23 receptor, and tumor necrosis factor. 6 Multiple environmental risk factors for IBD have been identified, including smoking, excess body fat, 9 urban living, 10 antibiotic exposure, 10 and adverse childhood events. 11 On the other hand, studies have highlighted protective environmental factors including Helicobacter pylori infection, 12 breastfeeding, 9 and adequate vitamin D status.…”

Section: Introductionmentioning

confidence: 99%

Poly-omic risk scores predict inflammatory bowel disease diagnosis

Arehart

Sterrett

Garris

et al. 2022

Preprint

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Inflammatory Bowel Disease (IBD) is characterized by complex etiology and a disrupted gut microbiota. The substantial non-genetic variance for Crohn's disease and ulcerative colitis (≥25%) suggests that both genetic and environmental factors contribute to IBD development. We aim to extend the framework of genomic studies by examining gut characteristics that are affected by both genetic and environmental factors. Specifically, we train models and validate their accuracy using data that quantifies the microbiota, their transcripts, and the metabolites present in the gut. The IBD Multi-omics Database from the Human Microbiome Project 2 provided 1,785 repeated samples for 131 individuals (103 cases, 27 controls) across multiple -omics layers including metagenomics, metatranscriptomics, viromics, and metabolomics. After splitting the subjects into training and validation groups, we used mixed effects least absolute shrinkage and selection operator (LASSO) regression to determine the most relevant features for each -omic layer. These features, along with demographic covariates, were incorporated into a polygenic risk score framework to generate four separate -omic-level prediction scores. All four -omic-level scores were then combined into a final regression to assess the relative importance of individual -omics and the added benefits when considered together. Our models identified several species, pathways, and metabolites known to be associated with IBD risk. Individually, metabolomics and viromics based scores were more predictive than metagenomics or metatranscriptomics based scores, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke's R2 of 0.46 and an AUC of 0.80 [95% CI: 0.63, 0.98].

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A systematic review and functional bioinformatics analysis of genes associated with Crohn’s disease identify more than 120 related genes

Cited by 10 publications

References 110 publications

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

CrohnDB: A Web Database for Expression Profiling of Protein-Coding and Long Non-Coding RNA Genes in Crohn Disease

Poly-omic risk scores predict inflammatory bowel disease diagnosis

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