Optimal design and analysis of genetic studies on gene expression
Optimal design and analysis of genetic studies on gene expression
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DOI：
10.1534/genetics.105.047001
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发表时间：
2006-03-01
期刊：
影响因子：
3.3
通讯作者：
Jansen, RC
中科院分区：
文献类型：
--
作者：
Fu, JY;Jansen, RC
分离群体中基因表达的全基因组谱分析有可能确定自然等位基因变异的调节后果。这类研究的成本很高，而且需要尽可能有效地利用微阵列和人口资源。我们表明，目前的研究需要通过一种新的双色微阵列设计来显着改进。我们的“远对设计”描述了两倍于阵列数量的个体，对不同基因组的个体进行共杂交，如果需要的话，给予已知调控位点更多的权重，从而最大限度地将表达变异分解为调控因子。它还可以利用大量种群（大于可用微阵列数量的两倍）作为有用的资源，从中选择最不相似的个体对。在实际基因组大小的计算机模拟中，我们的方法比其他策略确定了更多的调控因子，并且在拟南芥的应用中获得了类似的有希望的结果。我们的结果将有助于未来基因表达研究的设计和分析，并将有助于揭示更多的基因调控网络。
Whole-genome profiling of gene expression in a segregating population has the potential to identify the regulatory consequences of natural allelic variation. Costs of such studies are high and require that resources-microarrays and population-are used as efficiently as possible. We show that current studies call be improved significantly by a new design for two-color microarrays. Our "distant pair design" profiles twice as many individuals as there are arrays, cohybridizes individuals With dissimilar genomes, gives more weight to known regulatory loci if wished, and therewith maximizes the power for decomposing expression variation into regulatory factors. It can also exploit a large population (larger than twice the number of available microarrays) as a useful resource to select the most dissimilar pairs of individuals from. Cur approach identifies more regulatory factors than alternative strategies do in computer simulations for realistic genome sizes,and similar promising results are obtained in an application on Arabidopsis thaliana. Our results will aid the design and analysis of future studies on gene expression and will help to shed more light on gene regulatory networks.