OpenMendel: a cooperative programming project for statistical genetics

Hua Zhou, Janet S. Sinsheimer, Douglas M. Bates, Benjamin B. Chu, Christopher A. German, Sarah S. Ji, Kevin L. Keys, Juhyun Kim, Seyoon Ko, Gordon D. Mosher, Jeanette C. Papp, Eric M. Sobel, Jing Zhai, Jin J. Zhou, Kenneth Lange

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Statistical methods for genome-wide 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 OpenMendel project ( 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.

Original languageEnglish (US)
Pages (from-to)61-71
Number of pages11
JournalHuman Genetics
Issue number1
StatePublished - Jan 1 2020


  • Collaborative programming
  • Computational statistics
  • GWAS
  • Open source
  • Statistical genomics

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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