Genetical genomicsexperiments combine information on phenotypic traits, molecular markers andgene expression to study the genetic mechanisms governing variation in complextraits. Such studies can be used, for example, to estimate heritabilities ofmRNA transcript abundances, to map expression quantitative trait loci (eQTL),and to infer regulatory gene networks. Microarray experiments, however, canbe extremely costly and time consuming, which may limit sample sizes and statisticalpower. Thus it is crucial to optimize experimental designs by carefully choosingthe subjects to be assayed, and by cautiously controlling systematic factorsaffecting the system. Also, a rigorous strategy should be used for allocatingmRNA samples across slides and dye labeling, so that effects of interest arenot confounded with nuisance factors. In this presentation, we review some designsstrategies for genetical genomics studies, including the selection of individualsfor increased genetic dissimilarity and for a higher number of recombinationevents, as well as efficient microarray experiment layouts for various experimentalgoals.
