Abstract
BackgroundHigh-dimensional biomolecular profiling of genetically different individuals in one or more environmental conditions is an increasingly popular strategy for exploring the functioning of complex biological systems. The optimal design of such genetical genomics experiments in a cost-efficient and effective way is not trivial.ResultsThis paper presents designGG, an R package for designing optimal genetical genomics experiments. A web implementation for designGG is available at . All software, including source code and documentation, is freely available.ConclusionDesignGG allows users to intelligently select and allocate individuals to experimental units and conditions such as drug treatment. The user can maximize the power and resolution of detecting genetic, environmental and interaction effects in a genome-wide or local mode by giving more weight to genome regions of special interest, such as previously detected phenotypic quantitative trait loci. This will help to achieve high power and more accurate estimates of the effects of interesting factors, and thus yield a more reliable biological interpretation of data. DesignGG is applicable to linkage analysis of experimental crosses, e.g. recombinant inbred lines, as well as to association analysis of natural populations.
Highlights
High-dimensional biomolecular profiling of genetically different individuals in one or more environmental conditions is an increasingly popular strategy for exploring the functioning of complex biological systems
Genetical genomics [1] has become a popular strategy for studying complex biological systems using a combination of classical genetics, biomolecular profiling and bioinformatics [2,3,4,5]
The objective of designGG is to find an optimal allocation of genetically different samples to different conditions and experimental units favoring a precise esti
Summary
DesignGG, a freely-available R package and web tool presented in this work, represents a novel tool for the researcher interested in system genetics. Http://www.biomedcentral.com/1471-2105/10/188 ful experimental design provided by designGG, limited resources, such as arrays and samples, are maximally exploited, and more accurate estimates of parameters of interest can be achieved. Requirement: R statistical software available at http:// www.r-project.org/ for the stand-alone version. DesignGG: an R-package for the optimal design of genetical genomics experiments. DesignGG aims at finding an optimal design of genetical genomics experiments which maximize the power and resolution of detecting genetic, environmental and interaction effects. This will help to achieve high power and more accurate estimates of the effects of interesting factors, and yield a more reliable biological interpretation of data.
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