Association of the C allele of rs479200 in the EGLN1 gene with COVID-19 severity in Indian population: a novel finding

IF 3.8 3区 医学 Q2 GENETICS & HEREDITY Human Genomics Pub Date : 2024-01-30 DOI:10.1186/s40246-024-00572-1
Renuka Harit, Sajal De, Piyoosh Kumar Singh, Deepika Kashyap, Manish Kumar, Dibakar Sahu, Chander Prakash Yadav, Mradul Mohan, Vineeta Singh, Ram Singh Tomar, Kailash C. Pandey, Kapil Vashisht
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Abstract

The present study investigated two single nucleotide polymorphisms (SNPs)—rs479200 and rs516651 in the host EGLN1/PHD2 gene for their association with COVID-19 severity. A retrospective cohort of 158 COVID-19 patients from the Indian population (March 2020 to June 2021) was enrolled. Notably, the frequency of C allele (0.664) was twofold higher than T allele (0.336) in severe COVID-19 patients. Here, we report a novel finding that the C allele of rs479200 in the EGLN1 gene imparts a high risk of severe COVID-19 (odds ratio—6.214 (1.84–20.99) p = 0.003; 9.421 (2.019–43.957) p = 0.004), in additive inheritance model (adjusted and unadjusted, respectively).
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印度人群中 EGLN1 基因 rs479200 的 C 等位基因与 COVID-19 严重程度的关系:一项新发现
本研究调查了宿主EGLN1/PHD2基因中的两个单核苷酸多态性(SNPs)--rs479200和rs516651与COVID-19严重程度的关系。研究人员对来自印度人群(2020 年 3 月至 2021 年 6 月)的 158 名 COVID-19 患者进行了回顾性队列研究。值得注意的是,在严重的 COVID-19 患者中,C 等位基因的频率(0.664)是 T 等位基因(0.336)的两倍。在此,我们报告了一项新发现,即 EGLN1 基因中 rs479200 的 C 等位基因在加性遗传模型中(分别为调整和未调整)会带来严重 COVID-19 的高风险(几率比-6.214 (1.84-20.99) p = 0.003; 9.421 (2.019-43.957) p = 0.004)。
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来源期刊
Human Genomics
Human Genomics GENETICS & HEREDITY-
CiteScore
6.00
自引率
2.20%
发文量
55
审稿时长
11 weeks
期刊介绍: Human Genomics is a peer-reviewed, open access, online journal that focuses on the application of genomic analysis in all aspects of human health and disease, as well as genomic analysis of drug efficacy and safety, and comparative genomics. Topics covered by the journal include, but are not limited to: pharmacogenomics, genome-wide association studies, genome-wide sequencing, exome sequencing, next-generation deep-sequencing, functional genomics, epigenomics, translational genomics, expression profiling, proteomics, bioinformatics, animal models, statistical genetics, genetic epidemiology, human population genetics and comparative genomics.
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