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1.
Guillaume Butler-Laporte; Gundula Povysil; Jack A Kosmicki; Elizabeth T Cirulli; Theodore Drivas; Simone Furini; Chadi Saad; Axel Schmidt; Pawel Olszewski; Urszula Korotko; Mathieu Quinodoz; Elifnaz Celik; Kousik Kundu; Klaudia Walter; Junghyung Jung; Amy D Stockwell; Laura G Sloofman; Daniel M Jordan; Ryan C Thompson; Diane Del Valle Del Valle; Nicole Simons Simons; Esther Cheng Cheng; Robert Sebra Sebra; Eric E Schadt; Seunghee Schulze-Kim Shulze-Kim; Sacha Gnjatic Gnjatic; Miriam Merad Merad; Joseph D Buxbaum; Noam D Beckmann; Alexander W Charney; Bartlomiej Przychodzen; Timothy Chang; Tess D Pottinger; Ning Shang; Fabian Brand; Francesca Fava; Francesca Mari; Karolina Chwialkowska; Magdalena Niemira; Szymon Pula; J Kenneth Baillie; Alex Stuckey; Antonio Salas; Xabier Bello; Jacobo Pardo-Seco; Alberto Gomez-Carballa; Irene Rivero-Calle; Federico Martinon-Torres; Andrea Ganna; Konrad J Karczewski; Kumar Veerapen; Mathieu Bourgey; Guillaume Bourque; Robert JM Eveleigh; Vincenzo Forgetta; David Morrison; David Langlais; Mark Lathrop; Vincent Mooser; Tomoko Nakanishi; Robert Frithiof; Michael Hultstrom; Miklos Lipcsey; Yanara Marincevic-Zuniga; Jessica Nordlund; Kelly M Schiabor Barrett; William Lee; Alexandre Bolze; Simon White; Stephen Riffle; Francisco Tanudjaja; Efren Sandoval; Iva Neveux; Shaun Dabe; Nicolas Casadei; Susanne Motameny; Manal Alaamery; Salam Massadeh; Nora Aljawini; Mansour S Almutairi; Yaseen M Arab; Saleh A Alqahtan; Fawz S Al Harthi; Amal Almutairi; Fatima Alqubaishi; Sarah Alotaibi; Albandari Binowayn; Ebtehal A Alsolm; Hadeel El Bardisy; Mohammad Fawzy; - COVID-19 Host Genetics Initiative; - DeCOI Host Genetics Group; - GEN-COVID Multicenter Study (Italy); - Mount Sinai Clinical Intelligence Center; - GEN-COVID consortium (Spain); - GenOMICC Consortium; - Japan COVID-19 Task Force; - Regeneron Genetics Center; Daniel H Geschwind; Stephanie Arteaga; Alexis Stephens; Manish J Butte; Paul C Boutros; Takafumi N Yamaguchi; Shu Tao; Stefan Eng; Timothy Sanders; Paul J Tung; Michael E Broudy; Yu Pan; Alfredo Gonzalez; Nikhil Chavan; Ruth Johnson; Bogdan Pasaniuc; Brian Yaspan; Sandra Smieszek; Carlo Rivolta; Stephanie Bibert; Pierre-Yves Bochud; Maciej Dabrowski; Pawel Zawadzki; Mateusz Sypniewski; Elzbieta Kaja; Pajaree Chariyavilaskul; Voraphoj Nilaratanakul; Nattiya Hirankarn; Vorasuk Shotelersuk; Monnat Pongpanich; Chureerat Phokaew; Wanna Chetruengchai; Katsuhi Tokunaga; Masaya Sugiyama; Yosuke Kawai; Takanori Hasegawa; Tatsuhiko Naito; Ho Namkoong; Ryuya Edahiro; Akinori Kimura; Seishi Ogawa; Takanori Kanai; Koichi Fukunaga; Yukinori Okada; Seiya Imoto; Satoru Miyano; Serghei Mangul; Malak S Abedalthagafi; Hugo Zeberg; Joseph J Grzymski; Nicole L Washington; Stephan Ossowski; Kerstin U Ludwig; Eva C Schulte; Olaf Riess; Marcin Moniuszko; Miroslaw Kwasniewski; Hamdi Mbarek; Said I Ismail; Anurag Verma; David B Goldstein; Krzysztof Kiryluk; Alessandra Renieri; Manuel AR Ferreira; J Brent Richards.
Preprint in English | medRxiv | ID: ppmedrxiv-22273040

ABSTRACT

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p=5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights. Author SummaryCOVID-19 clinical outcomes vary immensely, but a patients genetic make-up is an important determinant of how they will fare against the virus. While many genetic variants commonly found in the populations were previously found to be contributing to more severe disease by the COVID-19 Host Genetics Initiative, it isnt clear if more rare variants found in less individuals could also play a role. This is important because genetic variants with the largest impact on COVID-19 severity are expected to be rarely found in the population, and these rare variants require different technologies to be studies (usually whole-exome or whole-genome sequencing). Here, we combined sequencing results from 21 cohorts across 12 countries to perform a rare variant association study. In an analysis comprising 5,085 participants with severe COVID-19 and 571,737 controls, we found that the gene for toll-like receptor 7 (TLR7) on chromosome X was an important determinant of severe COVID-19. Importantly, despite being found on a sex chromosome, this observation was consistent across both sexes.

2.
Preprint in English | medRxiv | ID: ppmedrxiv-20244798

ABSTRACT

ObjectivesTo investigate the effectiveness of hydroxychloroquine and dexamethasone on coronavirus disease (COVID-19) mortality using patient data outside of randomized trials. DesignPhenotypes derived from electronic health records were analyzed using the stability-controlled quasi-experiment (SCQE) to provide a range of possible causal effects of hydroxy-chloroquine and dexamethasone on COVID-19 mortality. Setting and participantsData from 2,007 COVID-19 positive patients hospitalized at a large university hospital system over the course of 200 days and not enrolled in randomized trials were analyzed using SCQE. For hyrdoxychloroquine, we examine a high-use cohort (n=766, days 1 to 43) and a later, low-use cohort (n=548, days 44 to 82). For dexamethasone, we examine a low-use cohort (n=614, days 44 to 101) and high-use cohort (n=622, days 102 to 200). Outcome measure14-day mortality, with a secondary outcome of 28-day mortality. ResultsHydroxycholoroquine could only have been significantly (p<0.05) beneficial if baseline mortality was at least 6.4 percentage points (55%) lower among patients in the later (low-use) than the earlier (high-use) cohort. Hydroxychloroquine instead proves significantly harmful if baseline mortality rose from one cohort to the next by just 0.3 percentage points. Dexamethasone significantly reduced mortality risk if baseline mortality in the later (high-use) cohort (days 102-200) was higher than, the same as, or up to 1.5 percentage points lower than that in the earlier (low-use) cohort (days 44-101). It could only prove significantly harmful if mortality improved from one cohort to the next by 6.8 percentage points due to other causes--an assumption implying an unlikely 84% reduction in mortality due to other causes, leaving an in-hospital mortality rate of just 1.3%. ConclusionsThe assumptions required for a beneficial effect of hydroxychloroquine on 14 day mortality are difficult to sustain, while the assumptions required for hydroxychloroquine to be harmful are difficult to reject with confidence. Dexamethasone, by contrast, was beneficial under a wide range of plausible assumptions, and was only harmful if a nearly impossible assumption is met. More broadly, the SCQE reveals what inferences can be credibly supported by evidence from non-randomized uses of experimental therapies, making it a useful tool when randomized trials have not yet produced clear evidence or to provide corroborative evidence from different populations.

3.
Preprint in English | medRxiv | ID: ppmedrxiv-20245308

ABSTRACT

Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrated a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n=18,502). We identified 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterized the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (BioVU; n=85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicated these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in BioVU, pan-UK Biobank, and Biobank Japan. Our study highlights putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response. SINGLE-SENTENCE SUMMARYLarge-scale genomic studies identify genes in the inflammation and coagulation pathways contributing to risk and symptomology of COVID-19 disease.

4.
Preprint in English | medRxiv | ID: ppmedrxiv-20145581

ABSTRACT

With the continuing coronavirus disease 2019 (COVID-19) pandemic coupled with phased reopening, it is critical to identify risk factors associated with susceptibility and severity of disease in a diverse population to help shape government policies, guide clinical decision making, and prioritize future COVID-19 research. In this retrospective case-control study, we used de-identified electronic health records (EHR) from the University of California Los Angeles (UCLA) Health System between March 9th, 2020 and June 14th, 2020 to identify risk factors for COVID-19 susceptibility (severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2) PCR test positive), inpatient admission, and severe outcomes (treatment in an intensive care unit or intubation). Of the 26,602 individuals tested by PCR for SARS-CoV-2, 992 were COVID-19 positive (3.7% of Tested), 220 were admitted in the hospital (22% of COVID-19 positive), and 77 had a severe outcome (35% of Inpatient). Consistent with previous studies, males and individuals older than 65 years old had increased risk of inpatient admission. Notably, individuals self-identifying as Hispanic or Latino constituted an increasing percentage of COVID-19 patients as disease severity escalated, comprising 24% of those testing positive, but 40% of those with a severe outcome, a disparity that remained after correcting for medical co-morbidities. Cardiovascular disease, hypertension, and renal disease were premorbid risk factors present before SARS-CoV-2 PCR testing associated with COVID-19 susceptibility. Less well-established risk factors for COVID-19 susceptibility included pre-existing dementia (odds ratio (OR) 5.2 [3.2-8.3], p=2.6 x 10-10), mental health conditions (depression OR 2.1 [1.6-2.8], p=1.1 x 10-6) and vitamin D deficiency (OR 1.8 [1.4-2.2], p=5.7 x 10-6). Renal diseases including end-stage renal disease and anemia due to chronic renal disease were the predominant premorbid risk factors for COVID-19 inpatient admission. Other less established risk factors for COVID-19 inpatient admission included previous renal transplant (OR 9.7 [2.8-39], p=3.2x10-4) and disorders of the immune system (OR 6.0 [2.3, 16], p=2.7x10-4). Prior use of oral steroid medications was associated with decreased COVID-19 positive testing risk (OR 0.61 [0.45, 0.81], p=4.3x10-4), but increased inpatient admission risk (OR 4.5 [2.3, 8.9], p=1.8x10-5). We did not observe that prior use of angiotensin converting enzyme inhibitors or angiotensin receptor blockers increased the risk of testing positive for SARS-CoV-2, being admitted to the hospital, or having a severe outcome. This study involving direct EHR extraction identified known and less well-established demographics, and prior diagnoses and medications as risk factors for COVID-19 susceptibility and inpatient admission. Knowledge of these risk factors including marked ethnic disparities observed in disease severity should guide government policies, identify at-risk populations, inform clinical decision making, and prioritize future COVID-19 research.

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