Optimal Design of Genetic Studies of Gene Expression With Two-Color Microarrays in Outbred Crosses

Roslin Institute (Edinburgh), University of Edinburgh, Roslin Biocentre, Roslin, Midlothian, United Kingdom.
Genetics (Impact Factor: 5.96). 10/2008; 180(3):1691-8. DOI: 10.1534/genetics.108.090308
Source: PubMed


Combining global gene-expression profiling and genetic analysis of natural allelic variation (genetical genomics) has great potential in dissecting the genetic pathways underlying complex phenotypes. Efficient use of microarrays is paramount in experimental design as the cost of conducting this type of study is high. For those organisms where recombinant inbred lines are available for mapping, the "distant pair design" maximizes the number of informative contrasts over all marker loci. Here, we describe an extension of this design, named the "optimal pair design," for use with F2 crosses between outbred lines. The performance of this design is investigated by simulation and compared to several other two-color microarray designs. We show that, for a given number of microarrays, the optimal pair design outperforms all other designs considered for detection of expression quantitative trait loci (eQTL) with additive effects by linkage analysis. We also discuss the suitability of this design for outbred crosses in organisms with large genomes and for detection of dominance.

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    • "Genetic variations underlying these QTLs such as duplications, indels or SNPs, can influence different aspects of cell biology by affecting gene transcription and protein activity on many different levels both directly and indirectly. Large scale transcriptomics analysis of segregating populations or recombinant inbred lines can reveal associations between gene expression and the quantitative trait data or QTLs but is relatively costly when screening whole populations over multiple years and different time points or tissue types [10-12,35]. By using a pooling approach we can rapidly screen a segregating potato population for the association of variance in gene expression with quantitative trait data. "
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