Linkage Disequilibrium Grouping of Single Nucleotide Polymorphisms (SNPs) Reflecting Haplotype Phylogeny for Efficient Selection of Tag SNPs

Author:

Takeuchi Fumihiko12,Yanai Kazuyuki3,Morii Toshiyuki3,Ishinaga Yuji3,Taniguchi-Yanai Keiko3,Nagano Shinobu3,Kato Norihiro3

Affiliation:

1. Department of Medical Ecology and Informatics, Research Institute, International Medical Center of Japan, Shinjuku, Tokyo, 162-8655, Japan

2. Department of Infection Control Science, Juntendo University, Bunkyo, Tokyo, 113-8421, Japan

3. Department of Gene Diagnostics and Therapeutics, Research Institute, International Medical Center of Japan, Shinjuku, Tokyo, 162-8655, Japan

Abstract

Abstract Single nucleotide polymorphisms (SNPs) have been proposed to be grouped into haplotype blocks harboring a limited number of haplotypes. Within each block, the portion of haplotypes is expected to be tagged by a selected subset of SNPs; however, none of the proposed selection algorithms have been definitive. To address this issue, we developed a tag SNP selection algorithm based on grouping of SNPs by the linkage disequilibrium (LD) coefficient r2 and examined five genes in three ethnic populations—the Japanese, African Americans, and Caucasians. Additionally, we investigated ethnic diversity by characterizing 979 SNPs distributed throughout the genome. Our algorithm could spare 60% of SNPs required for genotyping and limit the imprecision in allele-frequency estimation of nontag SNPs to 2% on average. We discovered the presence of a mosaic pattern of LD plots within a conventionally inferred haplotype block. This emerged because multiple groups of SNPs with strong intragroup LD were mingled in their physical positions. The pattern of LD plots showed some similarity, but the details of tag SNPs were not entirely concordant among three populations. Consequently, our algorithm utilizing LD grouping allows selection of a more faithful set of tag SNPs than do previous algorithms utilizing haplotype blocks.

Publisher

Oxford University Press (OUP)

Subject

Genetics

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