Li Mingyao, Li Chun, Guan Weihua
Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA.
Eur J Hum Genet. 2008 May;16(5):635-43. doi: 10.1038/sj.ejhg.5202007. Epub 2008 Feb 6.
Genome-wide association (GWA) studies for complex human diseases are now feasible. Many GWA studies rely on commercial SNP chips, for which a common evaluation criterion is global coverage of the genome. Although providing an overall evaluation of an SNP chip, the global coverage does not tell us how the coverage varies across the genome, an important feature that should be taken into consideration, as coverage variation often results in power variation and potentially biased search in subsequent association analysis. To achieve a fuller understanding of SNP chip coverage, we conducted detailed evaluation of coverage, including (1) a map of local coverage - calculated over small consecutive genomic regions and (2) gene coverage - calculated for each known gene in the genome. These evaluations can reveal the degree of variation of each SNP chip in covering the genome and can facilitate SNP chip comparisons at a finer scale.
针对复杂人类疾病的全基因组关联（GWA）研究现在已切实可行。许多GWA研究依赖商业SNP芯片，对于此类芯片，一个常见的评估标准是基因组的全局覆盖度。尽管全局覆盖度能对SNP芯片进行整体评估，但它并未告知我们覆盖度在整个基因组中是如何变化的，而这是一个应予以考虑的重要特征，因为覆盖度的变化往往会导致后续关联分析中的效能变化以及潜在的偏差性搜索。为了更全面地了解SNP芯片的覆盖情况，我们对覆盖度进行了详细评估，包括：（1）局部覆盖度图谱——在连续的小基因组区域上计算得出；以及（2）基因覆盖度——针对基因组中的每个已知基因计算得出。这些评估能够揭示每个SNP芯片在覆盖基因组方面的变化程度，并且能够在更精细的尺度上促进SNP芯片的比较。
