Pe'er Itsik, de Bakker Paul I W, Maller Julian, Yelensky Roman, Altshuler David, Daly Mark J
Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
Nat Genet. 2006 Jun;38(6):663-7. doi: 10.1038/ng1816. Epub 2006 May 21.
Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome, and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction of common variants captured by 25-100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of putative causal alleles to which it is correlated.
新兴技术首次使得同时对数十万单核苷酸多态性（SNP）进行基因分型成为可能，从而推动了全基因组关联研究。利用国际人类基因组单体型图计划（International HapMap Project）的经验性基因分型数据，我们评估了三种全基因组基因分型阵列上所包含的SNP集合在多大程度上能够捕获全基因组中的常见SNP，并且我们发现，大多数常见SNP要么被这些产品直接捕获，要么通过连锁不平衡被捕获。我们探索了利用人类基因组单体型图数据来提高使用这些固定标记集进行关联研究功效的分析策略，结果表明，在关联分析中有限地纳入特定单倍型检验能够将捕获的常见变异比例提高25%至100%。最后，我们引入了一种贝叶斯关联分析方法，即通过权衡每个统计检验的似然性来反映与其相关的假定因果等位基因的数量。
