Abstract

Genome-wide association studies (GWAS) simultaneously investigating hundreds of thousands of single nucleotide polymorphisms (SNP) have become a powerful tool in the investigation of new disease susceptibility loci. Haplotypes are sometimes thought to be superior to SNPs and are promising in genetic association analyses. The application of genome-wide haplotype analysis, however, is hindered by the complexity of haplotypes themselves and sophistication in computation. We systematically analyzed the haplotype effects for breast cancer risk among 5,761 African American women (3,016 cases and 2,745 controls) using a sliding window approach on the genome-wide scale. Three regions on chromosomes 1, 4 and 18 exhibited moderate haplotype effects. Furthermore, among 21 breast cancer susceptibility loci previously established in European populations, 10p15 and 14q24 are likely to harbor novel haplotype effects. We also proposed a heuristic of determining the significance level and the effective number of independent tests by the permutation analysis on chromosome 22 data. It suggests that the effective number was approximately half of the total (7,794 out of 15,645), thus the half number could serve as a quick reference to evaluating genome-wide significance if a similar sliding window approach of haplotype analysis is adopted in similar populations using similar genotype density.

Highlights

  • Genome-wide association studies (GWAS) have been demonstrated to have the power to detect modest to small effects of genetic variants with various common diseases [1]

  • D’ and r2 are useful in capturing the linkage disequilibrium (LD) pattern between a pair of markers, they are hardly to be extended to higher order of dependency among markers

  • We implemented a genome-wide haplotype association analysis searching for breast cancer risk susceptibility loci in African American women

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Summary

Introduction

Genome-wide association studies (GWAS) have been demonstrated to have the power to detect modest to small effects of genetic variants with various common diseases [1]. Haplotype analysis has become a prominent example of multilocus genetic association studies and has assisted in finding new disease susceptibility loci [3,4,5,6,7,8]. Haplotypes consist of SNPs or other genetic markers on the same chromosome that are inherited together with little contemporary recombination [9]. Haplotype information may aid GWAS in identifying new marker-phenotype associations for several reasons [10]. Haplotype analysis has been reported to be superior to analysis based on individual SNPs by simulation [13] and empirical studies [14,15]

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