Department of Computational Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
PLoS One. 2011;6(7):e22097. doi: 10.1371/journal.pone.0022097. Epub 2011 Jul 15.
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.
全基因组关联研究(GWAS)随着大规模基因分型技术的出现,已成为一种用于定位疾病风险基因座的常规方法。与单 SNP 标记相比,多等位基因单倍型标记在定位疾病基因座方面具有更高的功效。然而,基于单倍型的分析在 GWAS 中的应用通常受到单倍型推断(也称为相位)的高时间成本的限制。在这项研究中,我们开发了一种用于 GWAS 中基于单倍型的分析的有效方法。通过使用参考面板,我们的方法加速了相位过程,并减少了相位过程中不切实际的假设产生的潜在偏差。基于单倍型的方法为关联研究提供了强大的功效,并且没有Ⅰ型错误膨胀。仅使用中等大小的参考面板,我们方法中的相位误差与商业基因分型解决方案提供的基因分型误差相当。