Estimating coverage and power for genetic association studies using near-complete variation data
Estimating coverage and power for genetic association studies using near-complete variation data
复制标题
DOI：
10.1038/ng.180
复制
发表时间：
2008-07-01
期刊：
影响因子：
30.8
通讯作者：
Nickerson, Deborah A.
中科院分区：
文献类型：
--
作者：
Bhangale, Tushar R.;Rieder, Mark J.;Nickerson, Deborah A.
尽管研究表明，来自国际人类基因组单体型图计划（HapMap）的单核苷酸多态性（SNPs）为关联研究提供了有前景的覆盖度和效力，但替代变异数据集的缺乏限制了独立分析。利用在HapMap样本中重新测序的76个基因的近乎完整的变异数据，我们发现，与基于HapMap的估计相比，商业基因分型阵列对常见变异的覆盖度要低得多。我们对这些阵列在一系列疾病模型中所提供的效力进行了量化。
Although studies suggest that SNPs derived from HapMap provide promising coverage and power for association studies, the lack of alternative variation datasets limits independent analysis. Using near-complete variation data for 76 genes resequenced in HapMap samples, we find that coverage of common variation by commercial genotyping arrays is substantially lower compared to the HapMap-based estimates. We quantify the power offered by these arrays for a range of disease models.