Power analysis for genome-wide association studies
Power analysis for genome-wide association studies
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DOI：
10.1186/1471-2156-8-58
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发表时间：
2007-08-28
期刊：
影响因子：
2.9
通讯作者：
Klein, Robert J.
中科院分区：
文献类型：
--
作者：
Klein, Robert J.
背景：全基因组关联研究是破译复杂疾病遗传学的一种很有前途的新工具。为了为这类研究选择合适的样本量和基因分型平台，需要考虑遗传模型、标签SNP选择和感兴趣的人群的功率计算。结果：全基因组关联研究的功率可以使用一组标签SNP和代表性人群中的大量基因型SNP来计算，例如通过HapMap项目获得的。正如预期的那样，功率随着样本大小和效果大小的增加而增加。功率还取决于所选的标签SNP。在某些情况下，通过在更少的SNP上对更多的个体进行基因分型比在更多的SNP上对更少的个体进行基因分型可以获得更大的权力。结论：全基因组关联研究应该经过深思熟虑的设计，基因分型平台的选择和样本量的确定要通过仔细的权力计算来确定。
Background: Genome-wide association studies are a promising new tool for deciphering the genetics of complex diseases. To choose the proper sample size and genotyping platform for such studies, power calculations that take into account genetic model, tag SNP selection, and the population of interest are required.Results: The power of genome-wide association studies can be computed using a set of tag SNPs and a large number of genotyped SNPs in a representative population, such as available through the HapMap project. As expected, power increases with increasing sample size and effect size. Power also depends on the tag SNPs selected. In some cases, more power is obtained by genotyping more individuals at fewer SNPs than fewer individuals at more SNPs.Conclusion: Genome-wide association studies should be designed thoughtfully, with the choice of genotyping platform and sample size being determined from careful power calculations.