Abstract
The genome-wide association study (GWAS) has become a routine approach for mapping disease risk loci with the advent of large-scale genotyping technologies. Multi-allelic haplotype markers can provide superior power compared with single-SNP markers in mapping disease loci. However, the application of haplotype-based analysis to GWAS is usually bottlenecked by prohibitive time cost for haplotype inference, also known as phasing. In this study, we developed an efficient approach to haplotype-based analysis in GWAS. By using a reference panel, our method accelerated the phasing process and reduced the potential bias generated by unrealistic assumptions in phasing process. The haplotype-based approach delivers great power and no type I error inflation for association studies. With only a medium-size reference panel, phasing error in our method is comparable to the genotyping error afforded by commercial genotyping solutions.
Highlights
The availability of inexpensive platforms for performing dense single nucleotide polymorphism (SNP) analysis makes it possible and affordable to conduct genome-wide association study (GWAS) of complex diseases
The benefit of including haplotypetagging SNPs, especially those based on a cluster of 2–3 SNP markers, has been well recognized after the discovery of ‘‘block-like’’ linkage disequilibrium (LD) pattern in human genome [2]
As the haplotypes observed in the reference panel is a subset of all the existing haplotypes of a natural population due to limited samples in the reference panel, we classify all the existing haplotypes into two groups, named ‘‘observed’’ and ‘‘unobserved’’ groups
Summary
The availability of inexpensive platforms for performing dense single nucleotide polymorphism (SNP) analysis makes it possible and affordable to conduct GWAS of complex diseases. 800 risk SNPs have been reported from over 600 genome-wide association studies in the past years [1]. Power to detect disease susceptibility loci is an essential consideration in the design of GWAS. Researchers have compared the power of single-SNP and haplotype-based association analysis in different genetic scenarios. Theoretical studies demonstrated that the use of multi-allelic haplotypes significantly improved the power and robustness of association studies [3]. This theoretical analysis has been well supported by association studies for many different traits. Haplotypes conferring high susceptibility were identified for schizophrenia, nicotine dependence and macular degeneration for example [4,5,6]
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