Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan 250012, PR China.
BMC Genet. 2010 Jan 26;11:6. doi: 10.1186/1471-2156-11-6.
Genetic association study is currently the primary vehicle for identification and characterization of disease-predisposing variant(s) which usually involves multiple single-nucleotide polymorphisms (SNPs) available. However, SNP-wise association tests raise concerns over multiple testing. Haplotype-based methods have the advantage of being able to account for correlations between neighbouring SNPs, yet assuming Hardy-Weinberg equilibrium (HWE) and potentially large number degrees of freedom can harm its statistical power and robustness. Approaches based on principal component analysis (PCA) are preferable in this regard but their performance varies with methods of extracting principal components (PCs).
PCA-based bootstrap confidence interval test (PCA-BCIT), which directly uses the PC scores to assess gene-disease association, was developed and evaluated for three ways of extracting PCs, i.e., cases only(CAES), controls only(COES) and cases and controls combined(CES). Extraction of PCs with COES is preferred to that with CAES and CES. Performance of the test was examined via simulations as well as analyses on data of rheumatoid arthritis and heroin addiction, which maintains nominal level under null hypothesis and showed comparable performance with permutation test.
PCA-BCIT is a valid and powerful method for assessing gene-disease association involving multiple SNPs.
遗传关联研究目前是鉴定和描述疾病易感变异的主要手段，通常涉及多个可用的单核苷酸多态性（SNP）。然而，SNP 关联测试引发了对多重检验的关注。基于单倍型的方法具有能够解释相邻 SNP 之间相关性的优势，但假设 Hardy-Weinberg 平衡（HWE）和潜在的大量自由度可能会损害其统计功效和稳健性。基于主成分分析（PCA）的方法在这方面更可取，但它们的性能因提取主成分（PC）的方法而异。
开发了基于 PCA 的引导置信区间检验（PCA-BCIT），该检验直接使用 PC 分数来评估基因与疾病的关联，并针对提取 PC 的三种方法进行了评估，即仅病例（CAES）、仅对照（COES）和病例和对照合并（CES）。与 CAES 和 CES 相比，COES 提取 PC 的效果更好。通过模拟以及对类风湿关节炎和海洛因成瘾数据的分析来检验检验的性能，该检验在零假设下保持了名义水平，并表现出与置换检验相当的性能。
PCA-BCIT 是一种有效且强大的方法，可用于评估涉及多个 SNP 的基因与疾病的关联。
