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

BACKGROUND: Crohn's disease is one of the two categories of inflammatory bowel diseases that affect the gastrointestinal tract. The heritability estimate has been reported to be 0.75. Several genes linked to Crohn's disease risk have been identified using a plethora of strategies such as linkage-based studies, candidate gene association studies, and lately through genome-wide association studies (GWAS). Nevertheless, to our knowledge, a compendium of all the genes that have been associated with CD is lacking. METHODS: We conducted functional analyses of a gene set generated from a systematic review where genes potentially related to CD found in the literature were analyzed and classified depending on the genetic evidence reported and putative biological function. For this, we retrieved and analyzed 2496 abstracts comprising 1067 human genes plus 22 publications regarding 133 genes from GWAS Catalog. Then, each gene was curated and categorized according to the type of evidence associated with Crohn's disease. RESULTS: We identified 126 genes associated with Crohn's disease risk by specific experiments. Additionally, 71 genes were recognized associated through GWAS alone, 18 to treatment response, 41 to disease complications, and 81 to related diseases. Bioinformatic analysis of the 126 genes supports their importance in Crohn's disease and highlights genes associated with specific aspects such as symptoms, drugs, and comorbidities. Importantly, most genes were not included in commercial genetic panels suggesting that Crohn's disease is genetically underdiagnosed. CONCLUSIONS: We identified a total of 126 genes from PubMed and 71 from GWAS that showed evidence of association to diagnosis, 18 to treatment response, and 41 to disease complications in Crohn's disease. This prioritized gene catalog can be explored at http://victortrevino.bioinformatics.mx/CrohnDisease .

Multivariate genome-wide association study models to improve prediction of Crohn's disease risk and identification of potential novel variants.

BACKGROUND: Crohn's disease (CD) is a type of inflammatory bowel disease (IBD) that affects the gastrointestinal tract with diverse symptoms. At present, genome-wide association studies (GWAS) has discovered more than 140 genetic loci associated with CD from several datasets. Using the usual univariate GWAS methods, researchers have discovered common variants with small effects. Univariate methods assume independence among the variants that miss subtle combinatorial signals. Multivariate approaches have improved risk prediction and have complemented univariate methods for elucidating the etiology of complex traits and potential novel associations. However, the current multivariate models for CD have been assessed for three datasets (published from 2006 to 2008) under unrelated methodological settings showing a broad performance spectrum. Notably, these multivariate studies do not analyze potential novel variants. Here, we aimed to perform a robust multivariate analysis of a CD dataset different from the one commonly used, and we used the information yielded by the models to identify whether the generated models could provide additional information about the potential novel variants of CD. METHODS: Therefore, we compared different multivariate methods and models, LASSO (least absolute shrinkage and selection operator), XGBoost, random forest (RF), Bootstrap stage-wise model selection (BSWiMS), and LDpred, using a strict random subsampling approach to predict the CD risk using a recent GWAS dataset, United Kingdom IBD IBD Genetics Consortium (UKIBDGC), made available in 2017, that had not been used for CD prediction studies. In addition, we assessed the effect of common strategies by increasing and decreasing the number of single-nucleotide polymorphism (SNP) markers (using genotype imputation and linkage disequilibrium (LD)-clumping). RESULTS: We found that the LDpred model without any imputation was the best model among all the tested models for predicting the CD risk (area under the receiver operating characteristic curve (AUROC) = 0.667 ± 0.024) in this dataset. We validated the best models using a second dataset (National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) IBD Genetics Consortium, which was previously used in CD prediction studies) in which LDpred was also the best method with a similar performance (AUROC = 0.634 ± 0.009). Based on the importance of the variants yielded by the multivariate models, we identified an unnoticed region within chromosome 6, tagged by SNP rs4945943; this region was close to the gene MARCKS, which appeared to contribute to CD risk. CONCLUSIONS: This research is the first multivariate prediction analysis applied to the UKIBDGC dataset. Our robust multivariate setting analysis enabled us to identify a potential variant that contributed to the CD risk. Multivariate methods are valuable tools for identifying genes that contribute to disease risk.

A systematic review of genes affecting mitochondrial processes in cancer.

Malignant conversion of cancer cells requires efficient mitochondria reprogramming orchestrated by hundreds of genes. The transformation includes increased energy demand, biosynthesis of precursors, and reactive oxygen species needed to accelerate cell growth, proliferation, and survival. Reprogramming involves complex gene alterations that have not been methodically curated. Therefore, we systematically analyzed the literature of cancer-related genes in mitochondria. Through the analysis of >2500 PubMed abstracts and >1600 human genes, we identified 228 genes showing clear roles in cancer. Each gene was classified according to their homeostatic function, together with the pathological transitions that contribute to specific cancer hallmarks. The potential clinical relevance of these hallmarks and genes is discussed by representative examples and validated by detecting differences in gene expression levels across 16 different types of cancer. A compendium, including the gene functions and alterations underpinning cancer progression, can be explored at http://bioinformatica.mty.itesm.mx/MitoCancer.