| Autor: |
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, On behalf of the Genetic Epidemiology Committee of the Canadian COVID Genomics Network (CanCOGeN) HostSeq Project |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Genome Medicine, Vol 12, Iss 1, Pp 1-8 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
1756-994X |
| DOI: |
10.1186/s13073-020-00818-2 |
| Popis: |
Abstract 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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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