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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-20167874

ABSTRACT

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is due to the high rates of transmission by individuals who are asymptomatic at the time of transmission1, 2. Frequent, widespread testing of the asymptomatic population for SARS-CoV-2 is essential to suppress viral transmission. Despite increases in testing capacity, multiple challenges remain in deploying traditional reverse transcription and quantitative PCR (RT-qPCR) tests at the scale required for population screening of asymptomatic individuals. We have developed SwabSeq, a high-throughput testing platform for SARS-CoV-2 that uses next-generation sequencing as a readout. SwabSeq employs sample-specific molecular barcodes to enable thousands of samples to be combined and simultaneously analyzed for the presence or absence of SARS-CoV-2 in a single run. Importantly, SwabSeq incorporates an in vitro RNA standard that mimics the viral amplicon, but can be distinguished by sequencing. This standard allows for end-point rather than quantitative PCR, improves quantitation, reduces requirements for automation and sample-to-sample normalization, enables purification-free detection, and gives better ability to call true negatives. After setting up SwabSeq in a high-complexity CLIA laboratory, we performed more than 80,000 tests for COVID-19 in less than two months, confirming in a real world setting that SwabSeq inexpensively delivers highly sensitive and specific results at scale, with a turn-around of less than 24 hours. Our clinical laboratory uses SwabSeq to test both nasal and saliva samples without RNA extraction, while maintaining analytical sensitivity comparable to or better than traditional RT-qPCR tests. Moving forward, SwabSeq can rapidly scale up testing to mitigate devastating spread of novel pathogens.

3.
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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