Genetically predicted effects of COVID-19 on 2272 traits: exploring through a phenome-wide Mendelian randomization study.

IF 3.6 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Junyu Zhou, Yi Ge, Jing Yu, Yu Zhang
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引用次数: 0

Abstract

Background: The COVID-19 pandemic has significantly impacted global health, making it essential to understand its genetic effects on various traits.

Method: Leveraging the extensive FinnGen dataset comprising 500 000 individuals, we performed a Mendelian randomization (MR) phenome-wide association study. COVID-19-related phenotypes obtained from the COVID-19 Host Genetics Initiative GWAS (release 7). We employed four distinct approaches, including MR-Egger, weighted median, random-effect inverse variance weighted (IVW), and weighted mode, to conduct the MR analysis.

Results: Two hundred fifty-five potential causal effects of COVID-19 were observed for a diverse range of outcomes using the IVW method, including cardiovascular disorders, respiratory conditions, autoimmune diseases, and metabolic disorders. Apart from a few that can be classified as "other traits," the majority of the traits are disease-related traits. We have also identified 31 traits, wherein all four distinct MR analyses yielded a P-value of less than 0.05. Only one trait remained statistically significant after multiple testing correction using the conservative Bonferroni threshold (P < 2.2E-5).

Conclusions: This phenome-wide MR study provides valuable insights into the genetically predicted effects of COVID-19 on a comprehensive range of traits. The identified associations contribute to our understanding of the complex interplay between the impact of the post-COVID-19 era on healthcare and may have implications for the development of targeted therapeutic strategies and public health interventions. Key messages What is already known on this topic - COVID-19 has a high mortality rate, and patients often have many sequelae, including myocarditis, acute respiratory distress syndrome, and neurological and hematologic complications. What this study adds Most of the current relevant studies lack large-scale phenotype-group ranging Mendelian randomization (MR) studies on the outcome of COVID-19 due to their small sample sizes. Therefore, this study performed a full phenotypic group MR analysis in the FinnGen dataset to investigate the relationship between COVID-19 and thousands of outcome variables. How this study might affect research, practice or policy- The study identified a set of traits that are strongly associated with genetic susceptibility to the long-term effects of COVID-19.

2019冠状病毒病对2272个性状的遗传预测影响:通过全现象孟德尔随机化研究进行探索。
背景:COVID-19大流行严重影响了全球健康,因此了解其对各种性状的遗传影响至关重要。方法:利用广泛的FinnGen数据集,包括500,000个个体,我们进行了孟德尔随机化(MR)全现象关联研究。从COVID-19宿主遗传学倡议GWAS(第7版)获得的COVID-19相关表型。我们采用了四种不同的方法,包括MR- egger、加权中位数、随机效应逆方差加权(IVW)和加权模式,进行MR分析。结果:使用IVW方法观察了255种COVID-19的潜在因果效应,包括心血管疾病、呼吸系统疾病、自身免疫性疾病和代谢紊乱。除了少数可以归类为“其他特征”的特征外,大多数特征都是与疾病相关的特征。我们还鉴定了31个性状,其中所有四种不同的MR分析产生的p值小于0.05。在使用保守的Bonferroni阈值进行多次测试校正后,只有一个性状仍然具有统计学意义(P)。结论:这项全现象范围的MR研究为COVID-19对一系列性状的遗传预测影响提供了有价值的见解。已确定的关联有助于我们理解后covid -19时代对医疗保健的影响之间复杂的相互作用,并可能对制定有针对性的治疗策略和公共卫生干预措施产生影响。关于这一主题的已知情况- COVID-19死亡率高,患者通常有许多后遗症,包括心肌炎、急性呼吸窘迫综合征以及神经和血液系统并发症。由于样本量小,目前大多数相关研究缺乏对COVID-19结局的大规模表型组范围的孟德尔随机化(MR)研究。因此,本研究在FinnGen数据集中进行了全表型组MR分析,以调查COVID-19与数千个结果变量之间的关系。这项研究如何影响研究、实践或政策——该研究确定了一系列与COVID-19长期影响的遗传易感性密切相关的特征。
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来源期刊
Postgraduate Medical Journal
Postgraduate Medical Journal 医学-医学:内科
CiteScore
8.50
自引率
2.00%
发文量
131
审稿时长
2.5 months
期刊介绍: Postgraduate Medical Journal is a peer reviewed journal published on behalf of the Fellowship of Postgraduate Medicine. The journal aims to support junior doctors and their teachers and contribute to the continuing professional development of all doctors by publishing papers on a wide range of topics relevant to the practicing clinician and teacher. Papers published in PMJ include those that focus on core competencies; that describe current practice and new developments in all branches of medicine; that describe relevance and impact of translational research on clinical practice; that provide background relevant to examinations; and papers on medical education and medical education research. PMJ supports CPD by providing the opportunity for doctors to publish many types of articles including original clinical research; reviews; quality improvement reports; editorials, and correspondence on clinical matters.
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