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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.

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.

Some important and interesting topics in the newly emerging disciplines of Statistical genomics andbioinformatics have been discussed briefly in relation to plants with possible references to fruit crops. This paper is therefore divided into two parts relating to the two disciplines, respectively. In the first part, mapping of quantitative trait loci (QTL), association mapping, mapping of gene expression transcripts (eQTL), marker-assisted selection, and a systems approach to quantitative genetics have been dealt with. In the second part, generation of databases, annotation, annotated sequence databases, and sequence similarity search have been described.

Some important and interesting topics in the newly emerging disciplines of Statistical genomics andbioinformatics have been discussed briefly in relation to plants with possible references to fruit crops. This paper is therefore divided into two parts relating to the two disciplines, respectively. In the first part, mapping of quantitative trait loci (QTL), association mapping, mapping of gene expression transcripts (eQTL), marker-assisted selection, and a systems approach to quantitative genetics have been dealt with. In the second part, generation of databases, annotation, annotated sequence databases, and sequence similarity search have been described.