Kamatani Naoyuki, Sekine Akihiro, Kitamoto Takuya, Iida Aritoshi, Saito Susumu, Kogame Akifumi, Inoue Eisuke, Kawamoto Manabu, Harigai Masayoshi, Nakamura Yusuke
Division of Genomic Medicine, Department of Advanced Biomedical Engineering and Science, and Institute of Rheumatology, Tokyo Women's Medical University, Tokyo, Japan.
Am J Hum Genet. 2004 Aug;75(2):190-203. doi: 10.1086/422853. Epub 2004 Jun 16.
To optimize the strategies for population-based pharmacogenetic studies, we extensively analyzed single-nucleotide polymorphisms (SNPs) and haplotypes in 199 drug-related genes, through use of 4,190 SNPs in 752 control subjects. Drug-related genes, like other genes, have a haplotype-block structure, and a few haplotype-tagging SNPs (htSNPs) could represent most of the major haplotypes constructed with common SNPs in a block. Because our data included 860 uncommon (frequency <0.1) SNPs with frequencies that were accurately estimated, we analyzed the relationship between haplotypes and uncommon SNPs within the blocks (549 SNPs). We inferred haplotype frequencies through use of the data from all htSNPs and one of the uncommon SNPs within a block and calculated four joint probabilities for the haplotypes. We show that, irrespective of the minor-allele frequency of an uncommon SNP, the majority (mean +/- SD frequency 0.943+/-0.117) of the minor alleles were assigned to a single haplotype tagged by htSNPs if the uncommon SNP was within the block. These results support the hypothesis that recombinations occur only infrequently within blocks. The proportion of a single haplotype tagged by htSNPs to which the minor alleles of an uncommon SNP were assigned was positively correlated with the minor-allele frequency when the frequency was <0.03 (P<.000001; n=233 [Spearman's rank correlation coefficient]). The results of simulation studies suggested that haplotype analysis using htSNPs may be useful in the detection of uncommon SNPs associated with phenotypes if the frequencies of the SNPs are higher in affected than in control populations, the SNPs are within the blocks, and the frequencies of the SNPs are >0.03.
为优化基于人群的药物遗传学研究策略,我们通过对752名对照受试者使用4190个单核苷酸多态性(SNP),广泛分析了199个药物相关基因中的单核苷酸多态性和单倍型。与其他基因一样,药物相关基因具有单倍型块结构,少数单倍型标签SNP(htSNP)可代表由一个块中的常见SNP构建的大多数主要单倍型。由于我们的数据包含860个罕见(频率<0.1)SNP,其频率得到了准确估计,因此我们分析了块内(549个SNP)单倍型与罕见SNP之间的关系。我们利用来自一个块内所有htSNP和一个罕见SNP的数据推断单倍型频率,并计算单倍型的四个联合概率。我们发现,无论罕见SNP的次要等位基因频率如何,如果罕见SNP在块内,大多数(平均±标准差频率0.943±0.117)次要等位基因被分配到由htSNP标记的单个单倍型中。这些结果支持了块内重组仅偶尔发生的假设。当罕见SNP的频率<0.03时,被htSNP标记的单个单倍型中分配了罕见SNP次要等位基因的比例与次要等位基因频率呈正相关(P<0.000001;n = 233[Spearman等级相关系数])。模拟研究结果表明,如果SNP在受影响人群中的频率高于对照人群、SNP在块内且SNP频率>0.03,使用htSNP进行单倍型分析可能有助于检测与表型相关的罕见SNP。