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Your search keyword '"Nilanjana Banerjee"' showing total 3 results
Start Over Author "Nilanjana Banerjee" Publisher cold spring harbor laboratory
3 results on '"Nilanjana Banerjee"'

1. Genome-wide analysis in 756,646 individuals provides first genetic evidence that ACE2 expression influences COVID-19 risk and yields genetic risk scores predictive of severe disease

Author

Marie V. Coignet, Dylan Sun, Lukas Habegger, Evan Maxwell, Daniel J. Rader, David J. Carey, Katherine A. Siminovitch, Giorgio Sirugo, Anne E. Justice, Joelle Mbatchou, Eli A. Stahl, Gonçalo R. Abecasis, Alexander H. Li, David A. Turissini, Adam E. Locke, Kristin A. Rand, Aris Baras, Joseph B. Leader, Genevieve H.L. Roberts, Ahna R. Girshick, Deepika Sharma, Nilanjana Banerjee, Fabricio S. P. Kury, John D. Overton, Shane McCarthy, Jonathan Marchini, Xiaodong Bai, Eurie L. Hong, Rouel Lanche, Amy Damask, Ashish Yadav, Raghavendran Partha, Shannon R. McCurdy, Miao Zhang, Suganthi Balasubramanian, Hyun Min Kang, Anurag Verma, Marcus B. Jones, Marylyn D. Ritchie, Colm O'Dushlaine, Manuel A. R. Ferreira, Adam J. Mansfield, Anthony Marcketta, Joshua D. Backman, Alan R. Shuldiner, Michael N. Cantor, Tooraj Mirshahi, Lee Dobbyn, Spencer C. Knight, William J Salerno, Harenda Guturu, Jeffrey G. Reid, Julie E. Horowitz, Lauren Gurski, Danny S. Park, Kyoko Watanabe, Catherine A. Ball, Asher K. Haug Baltzell, and Jack A. Kosmicki

Subjects
2019-20 coronavirus outbreak, Increased risk, Coronavirus disease 2019 (COVID-19), business.industry, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Genetic variants, MEDLINE, Severe disease, Medicine, Bioinformatics, business
Abstract

SARS-CoV-2 enters host cells by binding angiotensin-converting enzyme 2 (ACE2). Through a genome-wide association study, we show that a rare variant (MAF = 0.3%, odds ratio 0.60, P=4.5x10-13) that down-regulates ACE2 expression reduces risk of COVID-19 disease, providing human genetics support for the hypothesis that ACE2 levels influence COVID-19 risk. Further, we show that common genetic variants define a risk score that predicts severe disease among COVID-19 cases.

Published
2020
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2. A catalog of associations between rare coding variants and COVID-19 outcomes

Author

Anurag Verma, Anne E. Justice, Guillaume Butler-Laporte, E. N. Smith, Lukas Habegger, Joelle Mbatchou, Susan P. Walker, Albert Tenesa, Joseph D. Szustakowski, Konrad Rawlik, Dylan Sun, Loukas Moutsianas, Shane McCarthy, Richard H Scott, Aris Baras, Evan Maxwell, Aldo Cordova-Palomera, Tooraj Mirshahi, Dorota Pasko, David J. Carey, Sahar Esmaeli, Adam J. Mansfield, Quanli Wang, Jeffrey S. Reid, Nilanjana Banerjee, Joshua D. Backman, Athanasios Kousathanas, Ashish Yadav, Mark J. Caulfield, Alison M. Meynert, Rouel Lanche, Jack A. Kosmicki, James F. Wilson, J. Brent Richards, Heiko Runz, Gonçalo R. Abecasis, Adam E. Locke, Justin W. Davis, Mark Lathrop, Alan R. Shuldiner, Lauren Gurski, J Kenneth Baillie, Michael N. Cantor, David Goldstein, John D. Overton, Kyoko Watanabe, Amanda O'Neill, Yunfeng Huang, Jonathan Marchini, Xiaodong Bai, Krzysztof Kiryluk, Slavé Petrovski, Sean O'Keeffe, Erika Kvikstad, Anthony Marcketta, Margaret M. Parker, Giorgio Sirugo, Julie E. Horowitz, Emily Wong, Olympe Chazara, Paul Nioi, Manuel A. R. Ferreira, Sándor Szalma, Joseph B. Leader, Shareef Khalid, William J Salerno, Deepika Sharma, Tomoko Nakanishi, Marcus B. Jones, Gundula Povysil, Marylyn D. Ritchie, Colm O'Dushlaine, Xiuwen Zheng, Daniel J. Rader, Suganthi Balasubramanian, Hyun Min Kang, Yi-Pin Lai, Alexander H. Li, Xing Chen, and Erola Pairo-Castineira

Subjects
Coronavirus disease 2019 (COVID-19), business.industry, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Genome-wide association study, medicine.disease_cause, Biobank, Article, Genetic association analysis, Immunology, Multiple comparisons problem, Medicine, business, Gene, Coronavirus
Abstract

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease-19 (COVID-19), a respiratory illness that can result in hospitalization or death. We investigated associations between rare genetic variants and seven COVID-19 outcomes in 543,213 individuals, including 8,248 with COVID-19. After accounting for multiple testing, we did not identify any clear associations with rare variants either exome-wide or when specifically focusing on (i) 14 interferon pathway genes in which rare deleterious variants have been reported in severe COVID-19 patients; (ii) 167 genes located in COVID-19 GWAS risk loci; or (iii) 32 additional genes of immunologic relevance and/or therapeutic potential. Our analyses indicate there are no significant associations with rare protein-coding variants with detectable effect sizes at our current sample sizes. Analyses will be updated as additional data become available, with results publicly browsable athttps://rgc-covid19.regeneron.com.

Published
2020
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3. Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank

Author

Ida Surakka, Alexander Lopez, Lukas Habegger, Shareef Khalid, William J Salerno, Andrew Blumenfeld, Bin Ye, Jeffrey G. Reid, Ashutosh K. Pandey, Alicia Hawes, John D. Overton, Giovanni Coppola, Jonathan Marchini, Wendy K. Chung, David J. Carey, David H. Ledbetter, Kristian Hveem, Aris N. Economides, Cristopher V. Van Hout, Kavita Praveen, Cristen J. Willer, Joshua D. Backman, Anthony Marcketta, Ioanna Tachmazidou, Marcus B. Jones, O’Dushlaine Colm, John Penn, Joseph B. Leader, Leland Barnard, Daren Liu, George D. Yancopoulos, Michael N. Cantor, Suganthi Balasubramanian, Laura M. Yerges-Armstrong, Alan R. Shuldiner, Claudia Gonzaga-Jauregui, John C. Whittaker, Nilanjana Banerjee, Aris Baras, Gonçalo R. Abecasis, Claudia Schurmann, Robert A. Scott, Evan Maxwell, Matthew R. Nelson, Jeffrey Staples, Ashish Yadav, Joshua D. Hoffman, Lon R. Cardon, and Alexander H. Li

Subjects
education.field_of_study, Genotype, Population, Disease, Computational biology, Biology, education, Exome, Gene, Biobank, Exome sequencing, Loss function
Abstract

SUMMARYThe UK Biobank is a prospective study of 502,543 individuals, combining extensive phenotypic and genotypic data with streamlined access for researchers around the world. Here we describe the first tranche of large-scale exome sequence data for 49,960 study participants, revealing approximately 4 million coding variants (of which ~98.4% have frequency < 1%). The data includes 231,631 predicted loss of function variants, a >10-fold increase compared to imputed sequence for the same participants. Nearly all genes (>97%) had ≥1 predicted loss of function carrier, and most genes (>69%) had ≥10 loss of function carriers. We illustrate the power of characterizing loss of function variation in this large population through association analyses across 1,741 phenotypes. In addition to replicating a range of established associations, we discover novel loss of function variants with large effects on disease traits, including PIEZO1 on varicose veins, COL6A1 on corneal resistance, MEPE on bone density, and IQGAP2 and GMPR on blood cell traits. We further demonstrate the value of exome sequencing by surveying the prevalence of pathogenic variants of clinical significance in this population, finding that 2% of the population has a medically actionable variant. Additionally, we leverage the phenotypic data to characterize the relationship between rare BRCA1 and BRCA2 pathogenic variants and cancer risk. Exomes from the first 49,960 participants are now made accessible to the scientific community and highlight the promise offered by genomic sequencing in large-scale population-based studies.

Published
2019
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