OPENMENDEL: a cooperative programming project for statistical genetics.
Hua ZhouJanet S SinsheimerDouglas M BatesBenjamin B ChuChristopher A GermanSarah S JiKevin L KeysJuhyun KimSeyoon KoGordon D MosherJeanette C PappEric M SobelJing ZhaiJin J ZhouKenneth LangePublished in: Human genetics (2019)
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 (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.
Keyphrases
- big data
- quality improvement
- artificial intelligence
- machine learning
- genome wide
- copy number
- electronic health record
- data analysis
- genome wide association
- deep learning
- risk factors
- healthcare
- palliative care
- gene expression
- randomized controlled trial
- dna methylation
- systematic review
- drug delivery
- current status
- case control