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Int J Mol Sci ; 22(10)2021 May 20.
Article in English | MEDLINE | ID: covidwho-1244036


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.

COVID-19/genetics , COVID-19/metabolism , Genetic Predisposition to Disease , Receptors, CCR5/genetics , Receptors, CCR5/metabolism , Alleles , Bronchi/metabolism , Bronchi/pathology , Bronchi/virology , COVID-19/physiopathology , Chromosomes, Human/genetics , Cohort Studies , Computational Biology , Databases, Genetic , Genome-Wide Association Study , Genotype , Humans , Lung/metabolism , Lung/pathology , Lung/virology , Polymorphism, Single Nucleotide , Whole Exome Sequencing
Hum Genomics ; 14(1): 36, 2020 10 09.
Article in English | MEDLINE | ID: covidwho-841486


INTRODUCTION: The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection. METHODS: We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age-matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification. RESULTS: We found that the XGBoost classifier could differentiate between the two classes at a significant level (p = 2 · 10-11) as measured against a randomized control and (p = 3 · 10-14) as measured against the expected value of a random guessing algorithm (AUC = 0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test. CONCLUSION: Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.

Algorithms , Betacoronavirus/isolation & purification , Chromosome Aberrations , Chromosomes, Human/genetics , Coronavirus Infections/diagnosis , Machine Learning , Pneumonia, Viral/diagnosis , Severity of Illness Index , Betacoronavirus/genetics , COVID-19 , Case-Control Studies , Coronavirus Infections/epidemiology , Coronavirus Infections/genetics , Coronavirus Infections/virology , Datasets as Topic , Humans , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/genetics , Pneumonia, Viral/virology , Risk Factors , SARS-CoV-2