Analyses and comparison of imputation-based association methods.
Analyses and comparison of imputation-based association methods.
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DOI：
10.1371/journal.pone.0010827
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发表时间：
2010-05-26
期刊：
影响因子：
3.7
通讯作者：
Deng HW
中科院分区：
文献类型：
--
作者：
Pei YF;Zhang L;Li J;Deng HW
基因型插补方法在恢复非分型基因型数据方面已变得越来越流行。估算基因型的一个重要应用是测试疾病的遗传关联。基于插补的关联测试可以提供超出仅对类型标记 SNP 进行测试所提供的额外见解。已经提出了各种有效的基于插补的关联测试。然而，他们的表现受到多种遗传因素的影响，而这些因素尚未得到充分研究。在本研究中，我们使用模拟和真实数据集，研究了非类型因果 SNP 的 LD、MAF 和插补准确率对七种流行的基于插补的关联方法（包括 MACH2qtl/dat、SNPTEST、ProbABEL、Beagle、Plink、BIMBAM 和 SNPMStat）性能的影响。我们还旨在提供方法之间的全面比较。结果表明：1)．基于插补的关联测试可以在中、高 LD 水平下增强信号并提高功效，并且随着 LD 水平的加强，功效的提高也随之增加； 2) 在中高LD水平下，非分型因果SNP的MAF越高，功效越大； 3）。低LD水平下，高插补准确率并不能保证功效的提高； 4).在方法中，MACH2qtl/dat、ProbABEL 和 SNPTEST 表现相似，并且始终优于其他方法。我们的结果有助于指导实际应用中基于插补的关联检验的选择。
Genotype imputation methods have become increasingly popular for recovering untyped genotype data. An important application with imputed genotypes is to test genetic association for diseases. Imputation-based association test can provide additional insight beyond what is provided by testing on typed tagging SNPs only. A variety of effective imputation-based association tests have been proposed. However, their performances are affected by a variety of genetic factors, which have not been well studied. In this study, using both simulated and real data sets, we investigated the effects of LD, MAF of untyped causal SNP and imputation accuracy rate on the performances of seven popular imputation-based association methods, including MACH2qtl/dat, SNPTEST, ProbABEL, Beagle, Plink, BIMBAM and SNPMStat. We also aimed to provide a comprehensive comparison among methods. Results show that: 1). imputation-based association tests can boost signals and improve power under medium and high LD levels, with the power improvement increasing with strengthening LD level; 2) the power increases with higher MAF of untyped causal SNPs under medium to high LD level; 3). under low LD level, a high imputation accuracy rate cannot guarantee an improvement of power; 4). among methods, MACH2qtl/dat, ProbABEL and SNPTEST perform similarly and they consistently outperform other methods. Our results are helpful in guiding the choice of imputation-based association test in practical application.
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影响因子：
2.9
作者：
Marquard V;Beckmann L;Heid IM;Lamina C;Chang-Claude J
通讯作者：
Chang-Claude J
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30.8
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Marchini, Jonathan;Howie, Bryan;Donnelly, Peter
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Donnelly, Peter
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4.5
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Guan, Yongtao;Stephens, Matthew
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64.8
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通讯作者：
Skol, Andrew
影响因子：
4.7
作者：
DAFNY, N;PELLIS, NR
通讯作者：
PELLIS, NR