Danielle Rasooly
Harvard University
18 Papers
5 Citations
Danielle Rasooly is an academic researcher from Harvard University. The author has contributed to research in topics: Medicine & Population. The author has an hindex of 2, co-authored 5 publications. Previous affiliations of Danielle Rasooly include Boston Children's Hospital.
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Papers
Conducting a Reproducible Mendelian Randomization Analysis Using the R Analytic Statistical Environment
Danielle Rasooly,Chirag J. Patel +1 more
TL;DR: A straightforward protocol for using summary-level data to perform Mendelian randomization and guidance for utilizing available software is provided.
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Comparisons of Polyexposure, Polygenic, and Clinical Risk Scores in Risk Prediction of Type 2 Diabetes
Yixuan He,Chirag M. Lakhani,Danielle Rasooly,Danielle Rasooly,Arjun K. Manrai,Arjun K. Manrai,Ioanna Tzoulaki,Chirag J. Patel +7 more
TL;DR: In this article, a polyexposure score (PXS) for type 2 diabetes (T2D) incorporating 12 nongenetic exposures and examine whether a PXS and/or a polygenic risk score (PGS) improves diabetes prediction beyond traditional clinical risk factors.
Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure
Danielle Rasooly,Gina M. Peloso,Alexandre C. Pereira,Hesamaddin Torabi Dashti,Claudia Giambartolomei,Eleanor Wheeler,Nay Aung,B. Ferolito,Maik Pietzner,Eric Farber-Eger,Quinn S. Wells,Nicole M Kosik,Liam Gaziano,Daniel Posner,A. Patrícia Bento,Qin Hui,Chang Liu,Krishna G. Aragam,Zeyuan Wang,Brian Charest,Jennifer E. Huffman,Peter W.F. Wilson,L. Phillips,John C. Whittaker,Patricia B. Munroe,Steffen E. Petersen,Kelly Cho,Andrew R. Leach,María Paula Magariños,J. Michael Gaziano,Claudia Langenberg,Yan V. Sun,Jacob Joseph,Juan P. Casas +33 more
TL;DR: A large-scale meta-analysis of heart failure genome-wide association studies (GWAS) consisting of over 90,000 heart failure cases and more than 1 million control individuals of European ancestry is conducted to uncover novel genetic determinants for heart failure.
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Bayesian Genetic Colocalization Test of Two Traits Using coloc
TL;DR: In this article , the authors present an easy and straightforward protocol for conducting a Bayesian test for colocalization of two traits using the 'coloc' package in R with summary-level results derived from GWAS and eQTL studies.
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Genome-wide association analysis and Mendelian randomization proteomics identify novel protein biomarkers and drug targets for primary prevention of heart failure
Danielle Rasooly,Gina M. Peloso,Alexandre C. Pereira,Hamed Dashti,Claudia Giambartolomei,Eleanor Wheeler,Nweni Aung,B. Ferolito,Mike Pietzner,Nicole M Kosik,Liam Gaziano,Daniel Posner,A. Patrícia Bento,Qin Hui,C. Liu,Krishna G. Aragam,Z. Wang,Brian Charest,Jennifer E. Huffman,Peter W.F. Wilson,L. Phillips,John C. Whittaker,Patricia B. Munroe,S. Petersen,Kelly Cho,Andrew R. Leach,María Paula Magariños,John Michael Gaziano,VA Million Veteran Program,Claudia Langenberg,Yi Sun,Jacob Joseph,Juan P. Casas +32 more
TL;DR: A large-scale meta-analysis of heart failure genome-wide association studies (GWAS) and MR-proteomics identified seven proteins as potential targets for interventions to be used in primary prevention of HF and identified 10 additional putatively causal genes for HF.