Statistical methods for genomewide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDELproject (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.
@article{arxiv.1902.05189,
title = {OPENMENDEL: A Cooperative Programming Project for Statistical Genetics},
author = {Hua Zhou and Janet S. Sinsheimer and Christopher A. German and Sarah S. Ji and Douglas M. Bates and Benjamin B. Chu and Kevin L. Keys and Juhyun Kim and Seyoon Ko and Gordon D. Mosher and Jeanette C. Papp and Eric M. Sobel and Jing Zhai and Jin J. Zhou and Kenneth Lange},
journal= {arXiv preprint arXiv:1902.05189},
year = {2019}
}