Challenges in interpreting Mendelian randomization studies with a disease as the exposure: Using COVID-19 liability studies as an exemplar

以疾病为暴露因素的孟德尔随机化研究解读面临的挑战:以新冠肺炎易感性研究为例

阅读:1

Abstract

Mendelian randomization (MR) studies using diseases as exposures are increasingly prevalent although any observed associations do not necessarily imply effect of diseases. To illustrate this challenge, we conducted a systematic review of MR studies focusing on COVID-19 consequence. We hypothesized if outcome genome-wide association studies (GWAS) were conducted before COVID-19 pandemic in late 2019, any observed associations in these studies were unlikely to be driven by COVID-19. We systematically searched PubMed, EMBASE, and MEDLINE for all MR studies published between 1 January 2019 and 20 May 2023. Inclusion criteria included MR studies which used COVID-19 as the exposure and designed to assess COVID-19's impact on health outcomes. We extracted relevant information, such as result interpretation and relevance assumption assessment. This review was registered at PROSPERO (CRD42023421079). Amongst 57 included studies, 45 studies used outcome GWAS published prior to 2019 whilst the remaining studies likely used outcome GWAS containing data collected before 2019. Relevance assumption was assessed mainly by p values. A total of 35 studies showed an association of COVID-19 liability with health outcomes. Regardless of the results, 45 studies attributed these as evidence (or lack of evidence) of COVID-19 consequence. In MR studies using disease liability as exposure, relevance assumption should consider the prevalence of the disease in the outcome GWAS in the context of 2 sample Mendelian randomization study rather than p values/F-statistic alone. Even when these are verified, these studies likely suffered from pleiotropy, making corresponding interpretation as effect of disease challenging.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。