Benjamin F Voight1. 1. Department of Pharmacology and Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19143, USA Department of Pharmacology and Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19143, USA.
Abstract
UNLABELLED: I present MR_predictor, a simulation engine designed to guide the development and interpretation of statistical tests of causality between phenotypes using genetic instruments. MR_predictor provides a framework to model either individual traits or complex scenarios where multiple phenotypes are correlated or dependent on each other. Crucially, MR_predictor can incorporate the effects of multiple biallelic loci (linked or unlinked) contributing genotypic variability to one or more simulated phenotypes. The software has a range of options for sample generation, and output files generated by MR_predictor port into commonly used analysis tools (e.g. PLINK, R), facilitating analyses germane for Mendelian Randomization studies. Benchmarks for speed and power calculations for summary statistic-based Mendelian Randomization analyses are presented and compared with analytical expectation. AVAILABILITY AND IMPLEMENTATION: The simulation engine is implemented in PERL, and the associated scripts can be downloaded from github.com, and online documentation, tutorial and example datasets are available at http://coruscant.itmat.upenn.edu/mr_predictor.
UNLABELLED: I present MR_predictor, a simulation engine designed to guide the development and interpretation of statistical tests of causality between phenotypes using genetic instruments. MR_predictor provides a framework to model either individual traits or complex scenarios where multiple phenotypes are correlated or dependent on each other. Crucially, MR_predictor can incorporate the effects of multiple biallelic loci (linked or unlinked) contributing genotypic variability to one or more simulated phenotypes. The software has a range of options for sample generation, and output files generated by MR_predictor port into commonly used analysis tools (e.g. PLINK, R), facilitating analyses germane for Mendelian Randomization studies. Benchmarks for speed and power calculations for summary statistic-based Mendelian Randomization analyses are presented and compared with analytical expectation. AVAILABILITY AND IMPLEMENTATION: The simulation engine is implemented in PERL, and the associated scripts can be downloaded from github.com, and online documentation, tutorial and example datasets are available at http://coruscant.itmat.upenn.edu/mr_predictor.
Authors: Rachael C Aikens; Wei Zhao; Danish Saleheen; Muredach P Reilly; Stephen E Epstein; Emmi Tikkanen; Veikko Salomaa; Benjamin F Voight Journal: Diabetes Date: 2016-10-04 Impact factor: 9.461
Authors: Tanya Keenan; Wei Zhao; Asif Rasheed; Weang K Ho; Rainer Malik; Janine F Felix; Robin Young; Nabi Shah; Maria Samuel; Nasir Sheikh; Megan L Mucksavage; Omar Shah; Jin Li; Michael Morley; Annika Laser; Nadeem Hayat Mallick; Khan Shah Zaman; Mohammad Ishaq; Syed Zahed Rasheed; Fazal-Ur-Rehman Memon; Faisal Ahmed; Bashir Hanif; Muhammad Shakir Lakhani; Muhammad Fahim; Madiha Ishaq; Naresh Kumar Shardha; Naveeduddin Ahmed; Khalid Mahmood; Waseem Iqbal; Saba Akhtar; Rabia Raheel; Christopher J O'Donnell; Christian Hengstenberg; Winifred März; Sekar Kathiresan; Nilesh Samani; Anuj Goel; Jemma C Hopewell; John Chambers; Yu-Ching Cheng; Pankaj Sharma; Qiong Yang; Jonathan Rosand; Giorgio B Boncoraglio; Shahana Urooj Kazmi; Hakon Hakonarson; Anna Köttgen; Andreas Kalogeropoulos; Philippe Frossard; Ayeesha Kamal; Martin Dichgans; Thomas Cappola; Muredach P Reilly; John Danesh; Daniel J Rader; Benjamin F Voight; Danish Saleheen Journal: J Am Coll Cardiol Date: 2016-02-02 Impact factor: 24.094
Authors: Kelsey E Johnson; Katherine M Siewert; Derek Klarin; Scott M Damrauer; Kyong-Mi Chang; Philip S Tsao; Themistocles L Assimes; Kara N Maxwell; Benjamin F Voight Journal: PLoS Med Date: 2020-09-11 Impact factor: 11.069