Leveraging the HapMap correlation structure in association studies.

TitleLeveraging the HapMap correlation structure in association studies.
Publication TypeJournal Article
Year of Publication2007
AuthorsZaitlen N, Kang H M, Eskin E, Halperin E
JournalAm J Hum Genet
Date Published2007 Apr
KeywordsDatabases, Genetic, Gene Frequency, Genetic Markers, Genomics, Genotype, Haplotypes, Humans, Models, Genetic, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, Software

Recent high-throughput genotyping technologies, such as the Affymetrix 500k array and the Illumina HumanHap 550 beadchip, have driven down the costs of association studies and have enabled the measurement of single-nucleotide polymorphism (SNP) allele frequency differences between case and control populations on a genomewide scale. A key aspect in the efficiency of association studies is the notion of "indirect association," where only a subset of SNPs are collected to serve as proxies for the uncollected SNPs, taking advantage of the correlation structure between SNPs. Recently, a new class of methods for indirect association, multimarker methods, has been proposed. Although the multimarker methods are a considerable advancement, current methods do not fully take advantage of the correlation structure between SNPs and their multimarker proxies. In this article, we propose a novel multimarker indirect-association method, WHAP, that is based on a weighted sum of the haplotype frequency differences. In contrast to traditional indirect-association methods, we show analytically that there is a considerable gain in power achieved by our method compared with both single-marker and multimarker tests, as well as traditional haplotype-based tests. Our results are supported by empirical evaluation across the HapMap reference panel data sets, and a software implementation for the Affymetrix 500k and Illumina HumanHap 550 chips is available for download.

PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/17357074?dopt=Abstract
Alternate TitleAm. J. Hum. Genet.
PubMed ID17357074