Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies
Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies
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DOI：
10.1038/ng1706
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发表时间：
2006-02-01
期刊：
影响因子：
30.8
通讯作者：
Boehnke, M
中科院分区：
文献类型：
--
作者：
Skol, AD;Scott, LJ;Boehnke, M
全基因组关联是一种很有前途的方法，以确定常见的遗传变异，易患人类疾病（1 - 4）。由于对数千名受试者的数十万个标记进行基因分型的成本很高，全基因组关联研究通常遵循阶段设计，其中在第1阶段中对大量标记进行一部分可用样本（pi（样本））的基因分型，然后在第2阶段中对剩余样本进行一部分这些标记（p样本）的基因分型。分析这种两阶段数据的标准策略是将第2阶段视为重复研究，并将重点放在单独考虑第2阶段数据时达到统计学显著性的发现上（2）。我们证明，联合分析两个阶段的数据的替代策略几乎总是导致检测遗传关联的能力增加，尽管需要使用更严格的显著性水平，即使两个阶段之间的效应大小不同。我们建议对所有两阶段全基因组关联研究进行联合分析，特别是当相对较大比例的样本在第1阶段进行基因分型（pi（样本）>= 0.30），并且相对较大比例的标记在第2阶段被选择用于随访（pi（标记）>= 0.01）时。
Genome-wide association is a promising approach to identify common genetic variants that predispose to human disease(1-4). Because of the high cost of genotyping hundreds of thousands of markers on thousands of subjects, genome-wide association studies often follow a staged design in which a proportion (pi(samples)) of the available samples are genotyped on a large number of markers in stage 1, and a proportion ( psamples) of these markers are later followed up by genotyping them on the remaining samples in stage 2. The standard strategy for analyzing such two-stage data is to view stage 2 as a replication study and focus on findings that reach statistical significance when stage 2 data are considered alone(2). We demonstrate that the alternative strategy of jointly analyzing the data from both stages almost always results in increased power to detect genetic association, despite the need to use more stringent significance levels, even when effect sizes differ between the two stages. We recommend joint analysis for all two-stage genome-wide association studies, especially when a relatively large proportion of the samples are genotyped in stage 1 (pi(samples) >= 0.30), and a relatively large proportion of markers are selected for follow-up in stage 2 (pi(markers) >= 0.01).