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Ena-DATASET-KNIH-30-03-2021-02:19:01:943-332 - samples

ABSTRACT: SMC08 h3k4me1 ChIP-Seq paired end data

PROVIDER: EGAD00001007346 | EGA |

REPOSITORIES: EGA

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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing coronavirus disease 2019 (COVID-19) was announced as an outbreak by the World Health Organization (WHO) in January 2020 and as a pandemic in March 2020. The majority of infected individuals have experienced no or only mild symptoms, ranging from fully asymptomatic cases to mild pneumonic disease. However, a minority of infected individuals develop severe respiratory symptoms. The objective of this study was to identify susceptible HLA alleles and clinical markers that can be used in risk prediction model for the early identification of severe COVID-19 among hospitalized COVID-19 patients. A total of 137 patients with mild COVID-19 (mCOVID-19) and 53 patients with severe COVID-19 (sCOVID-19) were recruited from the Center Hospital of the National Center for Global Health and Medicine (NCGM), Tokyo, Japan for the period of February-August 2020. High-resolution sequencing-based typing for eight HLA genes was performed using next-generation sequencing. In the HLA association studies, HLA-A*11:01:01:01 [Pc = 0.013, OR = 2.26 (1.27-3.91)] and HLA-C*12:02:02:01-HLA-B*52:01:01:02 [Pc = 0.020, OR = 2.25 (1.24-3.92)] were found to be significantly associated with the severity of COVID-19. After multivariate analysis controlling for other confounding factors and comorbidities, HLA-A*11:01:01:01 [P = 3.34E-03, OR = 3.41 (1.50-7.73)], age at diagnosis [P = 1.29E-02, OR = 1.04 (1.01-1.07)] and sex at birth [P = 8.88E-03, OR = 2.92 (1.31-6.54)] remained significant. The area under the curve of the risk prediction model utilizing HLA-A*11:01:01:01, age at diagnosis, and sex at birth was 0.772, with sensitivity of 0.715 and specificity of 0.717. To the best of our knowledge, this is the first article that describes associations of HLA alleles with COVID-19 at the 4-field (highest) resolution level. Early identification of potential sCOVID-19 could help clinicians prioritize medical utility and significantly decrease mortality from COVID-19.

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Ena-DATASET-KNIH-30-03-2021-02:19:01:943-332 - samples

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