MRC Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Cardiff, UK.
Biol Psychiatry. 2011 Jul 15;70(2):198-203. doi: 10.1016/j.biopsych.2011.01.034. Epub 2011 Apr 9.
Given that genome-wide association studies (GWAS) of psychiatric disorders have identified only a small number of convincingly associated variants (single nucleotide polymorphism [SNP]), there is interest in seeking additional evidence for associated variants with tests of gene-gene interaction. Comprehensive pair-wise single SNP-SNP interaction analysis is computationally intensive, and the penalty for multiple testing is severe, given the number of interactions possible. Aiming to minimize these statistical and computational burdens, we have explored approaches to prioritize SNPs for interaction analyses.
Primary interaction analyses were performed with the Wellcome Trust Case-Control Consortium bipolar disorder GWAS (1868 cases, 2938 control subjects). Replication analyses were performed with the Genetic Association Information Network bipolar disorder dataset (1001 cases, 1033 control subjects). The SNPs were prioritized for interaction analysis that showed evidence for association that surpassed a number of nominally significant thresholds, are within genome-wide significant genes, or are within genes that are functionally related.
For no set of prioritized SNPs did we obtain evidence to support the hypothesis that the selection strategy identified pairs of variants that were enriched for true (statistical) interactions.
The SNPs prioritized according to a number of criteria do not have a raised prior probability for significant interaction that is detectable in samples of this size. We argue that the use of significance levels reflecting only the number of tests performed, as is now widely accepted for single SNP analysis, does not offer an appropriate degree of protection against the potential for GWAS studies to generate an enormous number of false positive interactions.
鉴于精神疾病的全基因组关联研究（GWAS）仅鉴定出少数令人信服的相关变体（单核苷酸多态性[SNP]），因此人们有兴趣通过基因-基因相互作用的检验来寻找其他相关变体的证据。全面的两两单 SNP-SNP 相互作用分析计算量很大，并且由于可能存在的相互作用数量众多，因此多重检验的惩罚非常严重。为了最大限度地减少这些统计和计算负担，我们探索了针对相互作用分析进行 SNP 优先级排序的方法。
使用惠康信托基金会病例对照联盟双相情感障碍 GWAS（1868 例，2938 例对照）进行主要的相互作用分析。使用遗传关联信息网络双相情感障碍数据集（1001 例病例，1033 例对照）进行复制分析。对表现出超过多个名义显著阈值的关联证据、位于全基因组显著基因内或位于功能相关基因内的 SNP 进行相互作用分析的优先级排序。
对于没有一组优先级 SNP，我们没有证据支持这样的假设，即选择策略确定了对真实（统计）相互作用富集的变体对。
根据多个标准进行优先级排序的 SNP 并没有提高在这种规模的样本中可检测到的显著相互作用的先验概率。我们认为，现在广泛用于单 SNP 分析的仅反映测试数量的显著性水平并不能提供适当程度的保护，以防止 GWAS 研究产生大量假阳性相互作用的可能性。
