title: Large-scale single-nucleotide polymorphism (SNP) and haplotype analyses, using dense SNP Maps, of 199 drug-related genes in 752 subjects: the analysis of the association between uncommon SNPs within haplotype blocks and the haplotypes constructed with haplotype-tagging SNPs.
abstract: 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.
authors.affiliation: Division of Genomic Medicine, Department of Advanced Biomedical Engineering and Science, and Institute of Rheumatology, Tokyo Women's Medical University, Tokyo, Japan. kamatani@ior.twmu.ac.jp
authors.forename: Naoyuki
authors.initials: N
authors.lastname: Kamatani
authors.forename: Akihiro
authors.initials: A
authors.lastname: Sekine
authors.forename: Takuya
authors.initials: T
authors.lastname: Kitamoto
authors.forename: Aritoshi
authors.initials: A
authors.lastname: Iida
authors.forename: Susumu
authors.initials: S
authors.lastname: Saito
authors.forename: Akifumi
authors.initials: A
authors.lastname: Kogame
authors.forename: Eisuke
authors.initials: E
authors.lastname: Inoue
authors.forename: Manabu
authors.initials: M
authors.lastname: Kawamoto
authors.forename: Masayoshi
authors.initials: M
authors.lastname: Harigai
authors.forename: Yusuke
authors.initials: Y
authors.lastname: Nakamura
mesh_terms: D019295:Computational Biology; D005787:Gene Frequency; D006239:Haplotypes; D006801:Humans; D010599:Pharmacokinetics; D020641:Polymorphism, Single Nucleotide