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DesignGG: an R-package and web tool for the optimal design of genetical genomics experiments

By Y. Li, M. Swertz, G. Vera, J.Y. Fu, R. Breitling and R. Jansen


BACKGROUND: 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. The optimal design of such genetical genomics experiments in a cost-efficient and effective way is not trivial. RESULTS: This 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. CONCLUSION: DesignGG 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

Topics: QH301, QH426
Publisher: BioMed Central
Year: 2009
OAI identifier:
Provided by: Enlighten

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  2. Appasani K: Experimental Design for Gene Expression Analysis. doi
  3. (2001). Churchill GA: Experimental design for gene expression microarrays. Biostatistics doi
  4. (2008). de Koning DJ: Optimal design of genetic studies of gene expression with two-color microarrays in outbred crosses. Genetics doi
  5. (2002). Design issues for cDNA microarray experiments. Nat Rev Genet
  6. (2005). Experimental design for three-color and four-color gene expression microarrays. Bioinformatics doi
  7. (2002). Fundamentals of experimental design for cDNA microarrays. Nat Genet doi
  8. (2001). Genetical genomics: the added value from segregation. Trends Genet doi
  9. (2005). khanin R: Near-optimal designs for dual-channel microarray studies. Applied Statistics doi
  10. (2005). Kruglyak L: The landscape of genetic complexity across 5,700 gene expression traits in yeast. doi
  11. (2006). Mapping determinants of gene expression plasticity by genetical genomics in C. elegans. PLoS Genet doi
  12. (2008). RC: Generalizing genetical genomics: getting added value from environmental perturbation. Trends Genet doi
  13. (2004). RC: Molecular Genetics Information System (MOLGENIS): alternatives in developing local experimental genomics databases. Bioinformatics doi
  14. (2006). RC: Optimal design and analysis of genetic studies on gene expression. Genetics doi
  15. (2006). Rosa AJ: Review of microarray experimental design strategies for genetical genomics studies. Physiol Genomics doi
  16. (2008). Sieberts SK, et al.: Variations in DNA elucidate molecular networks that cause disease. Nature doi
  17. Simulated annealing for near-optimal dual-channel microarray designs. doi
  18. (1947). The design of experiments. 4th edition. Edinburgh: Oliver and Boyd;
  19. The R Project for Statistical Computing []
  20. (2005). Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'. Nat Genet doi

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