Accelerating haplotype-based genome-wide association study using perfect phylogeny and phase-known reference data.
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 o...
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2011-01-01
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doaj-c2312ed721e8402b851fb91e3e36496b2020-11-25T01:24:02ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-0167e2209710.1371/journal.pone.0022097Accelerating haplotype-based genome-wide association study using perfect phylogeny and phase-known reference data.Yungang HeCong LiChristopher I AmosMomiao XiongHua LingLi JinThe 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.http://europepmc.org/articles/PMC3137625?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yungang He Cong Li Christopher I Amos Momiao Xiong Hua Ling Li Jin |
spellingShingle |
Yungang He Cong Li Christopher I Amos Momiao Xiong Hua Ling Li Jin Accelerating haplotype-based genome-wide association study using perfect phylogeny and phase-known reference data. PLoS ONE |
author_facet |
Yungang He Cong Li Christopher I Amos Momiao Xiong Hua Ling Li Jin |
author_sort |
Yungang He |
title |
Accelerating haplotype-based genome-wide association study using perfect phylogeny and phase-known reference data. |
title_short |
Accelerating haplotype-based genome-wide association study using perfect phylogeny and phase-known reference data. |
title_full |
Accelerating haplotype-based genome-wide association study using perfect phylogeny and phase-known reference data. |
title_fullStr |
Accelerating haplotype-based genome-wide association study using perfect phylogeny and phase-known reference data. |
title_full_unstemmed |
Accelerating haplotype-based genome-wide association study using perfect phylogeny and phase-known reference data. |
title_sort |
accelerating haplotype-based genome-wide association study using perfect phylogeny and phase-known reference data. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2011-01-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC3137625?pdf=render |
work_keys_str_mv |
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1725119224410537984 |