Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility

被引：3

作者：

van Blokland, Irene, V ^{[1
,2
]}

Lanting, Pauline ^{[1
]}

Ori, Anil P. S. ^{[1
,3
]}

Vonk, Judith M. ^{[4
]}

Warmerdam, Robert C. A. ^{[1
]}

Herkert, Johanna C. ^{[1
]}

Boulogne, Floranne ^{[1
]}

Claringbould, Annique ^{[1
,5
]}

Lopera-Maya, Esteban A. ^{[1
]}

Bartels, Meike ^{[6
,7
]}

Hottenga, Jouke-Jan ^{[6
]}

Ganna, Andrea ^{[8
]}

Karjalainen, Juha ^{[8
,9
,10
]}

Hayward, Caroline ^{[11
]}

Fawns-Ritchie, Chloe ^{[12
]}

Campbell, Archie ^{[13
]}

Porteous, David ^{[13
]}

Cirulli, Elizabeth T. ^{[14
]}

Barrett, Kelly M. Schiabor ^{[14
]}

Riffle, Stephen ^{[14
]}

Bolze, Alexandre ^{[14
]}

White, Simon ^{[14
]}

Tanudjaja, Francisco ^{[14
]}

Wang, Xueqing ^{[14
]}

Ramirez, Jimmy M., III ^{[14
]}

Lim, Yan Wei ^{[14
]}

Lu, James T. ^{[14
]}

Washington, Nicole L. ^{[14
]}

de Geus, Eco J. C. ^{[6
,7
]}

Deelen, Patrick ^{[1
,15
]}

Boezen, H. Marike ^{[4
]}

Franke, Lude H. ^{[1
]}

机构：

[1] Univ Groningen, Univ Med Ctr Groningen, Dept Genet, Groningen, Netherlands

[2] Univ Groningen, Univ Med Ctr Groningen, Dept Cardiol, Groningen, Netherlands

[3] Univ Groningen, Univ Med Ctr Groningen, Dept Psychiat, Groningen, Netherlands

[4] Univ Groningen, Univ Med Ctr Groningen, Dept Epidemiol, Groningen, Netherlands

[5] EMBL, Struct Computat Biol Unit, Heidelberg, Germany

[6] Vrije Univ Amsterdam, Dept Biol Psychol, FGB, Amsterdam, Netherlands

[7] Amsterdam UMC, Amsterdam Publ Hlth Res Inst, Amsterdam, Netherlands

[8] Univ Helsinki, Inst Mol Med Finland, Helsinki, Finland

[9] Broad Inst MIT & Harvard, Cambridge, MA 02142 USA

[10] Massachusetts Gen Hosp, Analyt & Translat Genet Unit ATGU, Boston, MA 02114 USA

[11] Univ Edinburgh, Inst Genet & Mol Med, MRC Human Genet Unit, Edinburgh, Midlothian, Scotland

[12] Univ Edinburgh, Dept Psychol, Edinburgh, Midlothian, Scotland

[13] Univ Edinburgh, Ctr Genom & Expt Med, Inst Genet & Mol Med, Med Genet Sect, Edinburgh, Midlothian, Scotland

[14] Helix OpCo LLC, San Mateo, CA USA

[15] Univ Med Ctr Utrecht, Dept Genet, Utrecht, Netherlands

来源：

PLOS ONE | 2021年 / 16卷 / 08期

基金：

英国惠康基金; 英国医学研究理事会; 欧洲研究理事会;

关键词：

D O I：

10.1371/journal.pone.0255402

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.

引用

页数：18

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