Statistical power in COVID-19 case-control host genomic study design.

Yu-Chung Lin,Jennifer D Brooks,Shelley B Bull,France Gagnon,Celia M T Greenwood,Rayjean J Hung,Jerald Lawless,Andrew D Paterson,Lei Sun,Lisa J Strug, Genetic Epidemiology Committee of the Canadian COVID Genomics Network (CanCOGeN) HostSeq Project

Genome medicine(2020)

引用 8|浏览42
暂无评分
摘要
The identification of genetic variation that directly impacts infection susceptibility to SARS-CoV-2 and disease severity of COVID-19 is an important step towards risk stratification, personalized treatment plans, therapeutic, and vaccine development and deployment. Given the importance of study design in infectious disease genetic epidemiology, we use simulation and draw on current estimates of exposure, infectivity, and test accuracy of COVID-19 to demonstrate the feasibility of detecting host genetic factors associated with susceptibility and severity in published COVID-19 study designs. We demonstrate that limited phenotypic data and exposure/infection information in the early stages of the pandemic significantly impact the ability to detect most genetic variants with moderate effect sizes, especially when studying susceptibility to SARS-CoV-2 infection. Our insights can aid in the interpretation of genetic findings emerging in the literature and guide the design of future host genetic studies.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要