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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    ABSTRACT: Utilization of the natural genetic variation in traditional breeding programs remains a major challenge in crop plants. The identification of candidate genes underlying, or associated with, phenotypic trait QTLs is desired for effective marker assisted breeding. With the advent of high throughput -omics technologies, screening of entire populations for association of gene expression with targeted traits is becoming feasible but remains costly. Here we present the identification of novel candidate genes for different potato tuber quality traits by employing a pooling approach reducing the number of hybridizations needed. Extreme genotypes for a quantitative trait are collected and the RNA from contrasting bulks is then profiled with the aim of finding differentially expressed genes. We have successfully implemented the pooling strategy for potato quality traits and identified candidate genes associated with potato tuber flesh color and tuber cooking type. Elevated expression level of a dominant allele of the beta-carotene hydroxylase (bch) gene was associated with yellow flesh color through mapping of the gene under a major QTL for flesh color on chromosome 3. For a second trait, a candidate gene with homology to a tyrosine-lysine rich protein (TLRP) was identified based on allele specificity of the probe on the microarray. TLRP was mapped on chromosome 9 in close proximity to a QTL for potato cooking type strengthening its significance as a candidate gene. Furthermore, we have performed a profiling experiment targeting a polygenic trait, by pooling individual genotypes based both on phenotypic and marker data, allowing the identification of candidate genes associated with the two different linkage groups. A pooling approach for RNA-profiling with the aim of identifying novel candidate genes associated with tuber quality traits was successfully implemented. The identified candidate genes for tuber flesh color (bch) and cooking type (tlrp) can provide useful markers for breeding schemes in the future. Strengths and limitations of the approach are discussed.
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    ABSTRACT: 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. 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. 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.
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