Online Program

Open-Source Statistical Genetic Analysis via the OpenMendel Project (308510)

Ben Chu, UCLA 
Chris German, UCLA 
Sarah Ji, UCLA 
Juhyun Kim, UCLA 
Kenneth Lange, UCLA 
Jeanette Papp, UCLA 
*Janet Sinsheimer, UCLA 
Eric Sobel, UCLA 
Hua Zhou, UCLA 
Jin Zhou, University of Arizona 
Xinkai Zhou, UCLA 

Keywords: Computational statistics, genetics and genomics, open-source software, collaborative research

The size and scope of modern genetic and genomic data present challenges to statistical analyses. Although gains in computing power, e.g., through cloud computing, alleviate some of the burden, better algorithmic designs are also required. The OpenMendel Project is a multidisciplinary, collaborative project of computational statisticians and genetic epidemiologists. The goal of the project is to provide reproducible analyses that scale to big data and to foster better communication between computer scientists, statisticians, human geneticists and clinicians. Using the modern computing language Julia and Jupyter notebooks, the OpenMendel Project is designed to allow researchers and students interested in genetic analysis, the ability to contribute to state-of-the-art, open-source statistical genetic software development, without being experts in computing. We will present an overview of the OpenMendel Project and then feature as examples several recently developed modules such as Iterative Hard Thresholding for genome-wide association studies (GWAS) and ordinal multinomial regression for GWAS.