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Open Access Highly Accessed Methodology article

A systematic search for SNPs/haplotypes associated with disease phenotypes using a haplotype-based stepwise procedure

Yin Yang1, Shuying Sue Li1, Jason W Chien2, Jessica Andriesen1 and Lue Ping Zhao13*

  • * Corresponding author: Lue P Zhao

  • † Equal contributors

Author Affiliations

1 Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N, Seattle, WA 98109, USA

2 Division of Clinical Research Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N, Seattle, WA 98109, USA

3 Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N, Mailstop M2-B500, Seattle, WA 98109, USA

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BMC Genetics 2008, 9:90  doi:10.1186/1471-2156-9-90

Published: 22 December 2008



Genotyping technologies enable us to genotype multiple Single Nucleotide Polymorphisms (SNPs) within selected genes/regions, providing data for haplotype association analysis. While haplotype-based association analysis is powerful for detecting untyped causal alleles in linkage-disequilibrium (LD) with neighboring SNPs/haplotypes, the inclusion of extraneous SNPs could reduce its power by increasing the number of haplotypes with each additional SNP.


Here, we propose a haplotype-based stepwise procedure (HBSP) to eliminate extraneous SNPs. To evaluate its properties, we applied HBSP to both simulated and real data, generated from a study of genetic associations of the bactericidal/permeability-increasing (BPI) gene with pulmonary function in a cohort of patients following bone marrow transplantation.


Under the null hypothesis, use of the HBSP gave results that retained the desired false positive error rates when multiple comparisons were considered. Under various alternative hypotheses, HBSP had adequate power to detect modest genetic associations in case-control studies with 500, 1,000 or 2,000 subjects. In the current application, HBSP led to the identification of two specific SNPs with a positive validation.


These results demonstrate that HBSP retains the essence of haplotype-based association analysis while improving analytic power by excluding extraneous SNPs. Minimizing the number of SNPs also enables simpler interpretation and more cost-effective applications.