Including sampling and phenotyping costs into the optimization of two stage designs for genomewide association studies
Including sampling and phenotyping costs into the optimization of two stage designs for genomewide association studies
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DOI：
10.1002/gepi.20245
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发表时间：
2007-12-01
影响因子：
2.1
通讯作者：
Schaefer, Helmut
中科院分区：
文献类型：
--
作者：
Mueller, Hans-Helge;Pahl, Roman;Schaefer, Helmut
我们提出了优化的两阶段设计全基因组病例对照关联研究，使用假设检验范式。为了节省基因分型成本，仅在子样品中对完整的标记物组进行基因分型（阶段1）。在第11阶段，然后在剩余的子样品中对最有希望的标记进行基因分型。在最近的出版物中，提出了两个阶段的设计，最大限度地减少了总的基因分型成本。为了实现全面的设计优化，我们还将采样成本纳入成本函数和设计优化中。由此产生的最佳设计显着不同于那些优化的基因分型成本（部分优化的设计），并实现了相当大的进一步降低成本。与部分优化设计相比，完全优化两阶段设计具有更高的第一阶段样本比例。此外，在一阶段设计中增加样本量是必要的，以补偿由于部分基因分型而导致的功效损失，对于完全优化的两阶段设计来说，这一点不太明显。此外，我们解决的情况下，调查人员有兴趣获得尽可能多的信息，但在预算方面受到限制。在这方面，我们开发了两级设计，在一定的成本约束下最大限度地提高功率。
We propose optimized two-stage designs for genome-wide case-control association studies, using a hypothesis testing paradigm. To save genotyping costs, the complete marker set is genotyped in a sub-sample only (stage 1). On stage 11, the most promising markers are then genotyped in the remaining sub-sample. In recent publications, two-stage designs were proposed which minimize the overall genotyping costs. To achieve full design optimization, we additionally include sampling costs into both the cost function and the design optimization. The resulting optimal designs differ markedly from those optimized for genotyping costs only (partially optimized designs), and achieve considerable further cost reductions. Compared with partially optimized designs, fully optimized two-stage designs have higher first-stage sample proportion. Furthermore, the increment of the sample size over the one-stage design, which is necessary in two-stage designs in order to compensate for the loss of power due to partial genotyping, is less pronounced for fully optimized two-stage designs. In addition, we address the scenario where the investigator is interested to gain as much information as possible, however is restricted in terms of a budget. In that we develop two-stage designs that maximize the power under a certain cost constraint.