Dataset Information

Regulatory Noncoding and Predicted Pathogenic Coding Variants of CCR5 Predispose to Severe COVID-19.

ABSTRACT: Genome-wide association studies (GWAS) found locus 3p21.31 associated with severe COVID-19. CCR5 resides at the same locus and, given its known biological role in other infection diseases, we investigated if common noncoding and rare coding variants, affecting CCR5, can predispose to severe COVID-19. We combined single nucleotide polymorphisms (SNPs) that met the suggestive significance level (P ≤ 1 × 10^-5) at the 3p21.31 locus in public GWAS datasets (6406 COVID-19 hospitalized patients and 902,088 controls) with gene expression data from 208 lung tissues, Hi-C, and Chip-seq data. Through whole exome sequencing (WES), we explored rare coding variants in 147 severe COVID-19 patients. We identified three SNPs (rs9845542, rs12639314, and rs35951367) associated with severe COVID-19 whose risk alleles correlated with low CCR5 expression in lung tissues. The rs35951367 resided in a CTFC binding site that interacts with CCR5 gene in lung tissues and was confirmed to be associated with severe COVID-19 in two independent datasets. We also identified a rare coding variant (rs34418657) associated with the risk of developing severe COVID-19. Our results suggest a biological role of CCR5 in the progression of COVID-19 as common and rare genetic variants can increase the risk of developing severe COVID-19 by affecting the functions of CCR5.

SUBMITTER: Cantalupo S

PROVIDER: S-EPMC8161088 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Regulatory Noncoding and Predicted Pathogenic Coding Variants of CCR5 Predispose to Severe COVID-19.

Cantalupo Sueva S Lasorsa Vito Alessandro VA Russo Roberta R Andolfo Immacolata I D'Alterio Giuseppe G Rosato Barbara Eleni BE Frisso Giulia G Abete Pasquale P Cassese Gian Marco GM Servillo Giuseppe G Gentile Ivan I Piscopo Carmelo C Della Monica Matteo M Fiorentino Giuseppe G Russo Giuseppe G Cerino Pellegrino P Buonerba Carlo C Pierri Biancamaria B Zollo Massimo M Iolascon Achille A Capasso Mario M

International journal of molecular sciences 20210520 10

Genome-wide association studies (GWAS) found locus 3p21.31 associated with severe COVID-19. CCR5 resides at the same locus and, given its known biological role in other infection diseases, we investigated if common noncoding and rare coding variants, affecting CCR5, can predispose to severe COVID-19. We combined single nucleotide polymorphisms (SNPs) that met the suggestive significance level (P ≤ 1 × 10-5) at the 3p21.31 locus in public GWAS datasets (6406 COVID- ...[more]

PMID: 34065289

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Project description:With the advent of the post genomic era, the research for the genetic mechanism of the diseases has found to be increasingly depended on the studies of the genes, the gene-networks and gene-protein interaction networks. To explore gene expression and regulation, the researchers have carried out many studies on transcription factors and their binding sites (TFBSs). Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene mutation and the occurrence of the disease. It has been demonstrated that based on the deep learning methods, the performances of the prediction for the functions of the noncoding variants are outperforming than those of the conventional methods. The research on the prediction for functions of Single Nucleotide Polymorphisms (SNPs) is expected to uncover the mechanism of the gene mutation affection on traits and diseases of human beings.We reviewed the conventional TFBSs identification methods from different perspectives. As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model performance measure et al. And then we summarized the techniques that usually used in finding out the functional noncoding variants from de novo sequence.Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases.

Dataset Information

Regulatory Noncoding and Predicted Pathogenic Coding Variants of CCR5 Predispose to Severe COVID-19.

Publications

Regulatory Noncoding and Predicted Pathogenic Coding Variants of <i>CCR5</i> Predispose to Severe COVID-19.

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