Division of Cancer Epidemiology and Genetics, NCI, Rockville, Maryland 20892, USA.
Genet Epidemiol. 2009 Dec;33(8):700-9. doi: 10.1002/gepi.20422.
It is increasingly recognized that pathway analyses-a joint test of association between the outcome and a group of single nucleotide polymorphisms (SNPs) within a biological pathway-could potentially complement single-SNP analysis and provide additional insights for the genetic architecture of complex diseases. Building upon existing P-value combining methods, we propose a class of highly flexible pathway analysis approaches based on an adaptive rank truncated product statistic that can effectively combine evidence of associations over different SNPs and genes within a pathway. The statistical significance of the pathway-level test statistics is evaluated using a highly efficient permutation algorithm that remains computationally feasible irrespective of the size of the pathway and complexity of the underlying test statistics for summarizing SNP- and gene-level associations. We demonstrate through simulation studies that a gene-based analysis that treats the underlying genes, as opposed to the underlying SNPs, as the basic units for hypothesis testing, is a very robust and powerful approach to pathway-based association testing. We also illustrate the advantage of the proposed methods using a study of the association between the nicotinic receptor pathway and cigarette smoking behaviors.
越来越多的人认识到，通路分析——对一个生物学通路中的结局和一组单核苷酸多态性（SNP）之间的关联进行联合检验——可能会补充单 SNP 分析，并为复杂疾病的遗传结构提供更多的见解。在现有的 P 值合并方法的基础上，我们提出了一类基于自适应秩截断积统计量的高度灵活的通路分析方法，该方法可以有效地整合通路内不同 SNP 和基因之间关联的证据。通过一种高效的置换算法来评估通路水平的检验统计量的统计学意义，这种算法在计算上仍然是可行的，无论通路的大小和概括 SNP 水平和基因水平关联的基本检验统计量的复杂程度如何。我们通过模拟研究表明，一种以基因为基础的分析方法，将潜在的基因而非潜在的 SNP 作为假设检验的基本单位，是一种非常稳健和强大的基于通路的关联检验方法。我们还通过对烟碱受体通路与吸烟行为之间关联的研究说明了所提出方法的优势。
