ArticleBMC bioinformatics2009
designGG: an R-package and web tool for the optimal design of genetical genomics experiments.
Article in BMC bioinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Plasticity of maternal environment-dependent expression-QTLs of tomato seeds.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Network Analysis PrioritizesG3 (Bethesda, Md.) · 2020Article
- Metabolomic analysis of tomato seed germination.Metabolomics : Official journal of the Metabolomic Society · 2017Article
- A Systems Genetics Approach Identifies Gene Regulatory Networks Associated with Fatty Acid Composition in Brassica rapa Seed.Plant physiology · 2016Article
- Pheno2Geno - High-throughput generation of genetic markers and maps from molecular phenotypes for crosses between inbred strains.BMC bioinformatics · 2015Article
- Identifying genotype-by-environment interactions in the metabolism of germinating arabidopsis seeds using generalized genetical genomics.Plant physiology · 2013Article
- The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button.BMC bioinformatics · 2010Article
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Authors and funding
6 authors.
Funding
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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 http://gbic.biol.rug.nl/designGG. 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.
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