Analyses and comparison of accuracy of different genotype imputation methods.

Analyses and comparison of accuracy of different genotype imputation methods.
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DOI:
10.1371/journal.pone.0003551
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发表时间:
2008
期刊:
影响因子:
3.7
通讯作者:
Deng, Hong-Wen
Deng, Hong-Wen
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Pei, Yu-Fang;Li, Jian;Zhang, Lei;Papasian, Christopher J.;Deng, Hong-Wen

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遗传关联分析的能力经常受到缺失基因型数据的影响,这导致缺乏重要的发现,例如,in silico硅片replication复制studies研究.一种解决方案是基于已知的连锁不平衡(LD)关系,从分型的侧翼标记估算未分型的SNP。几种插补方法是可用的,它们在关联研究中的有用性已被证明,但影响其相对性能的准确性的因素尚未得到系统的研究。因此,我们调查和比较了五种流行的基因型插补方法,MACH,IMPUTE,fastPHASE,PLINK和Beagle的性能,以评估和比较影响插补准确率(AR)的因素的影响。我们的研究结果表明,一个未分型的标记物,更强的LD和较低的MAF产生更好的AR的所有五种方法。我们还观察到,参考样品中更多数量的单倍型导致MACH、IMPUTE、PLINK和Beagle的AR更高,但对fastPHASE的AR几乎没有影响。一般而言,MACH和IMPUTE产生了相似的结果,并且这两种方法始终优于fastPHASE、PLINK和Beagle。本研究对基因型数据缺失情况下关联分析中插补方法的应用具有指导意义。
The power of genetic association analyses is often compromised by missing genotypic data which contributes to lack of significant findings, e.g., in in silico replication studies. One solution is to impute untyped SNPs from typed flanking markers, based on known linkage disequilibrium (LD) relationships. Several imputation methods are available and their usefulness in association studies has been demonstrated, but factors affecting their relative performance in accuracy have not been systematically investigated. Therefore, we investigated and compared the performance of five popular genotype imputation methods, MACH, IMPUTE, fastPHASE, PLINK and Beagle, to assess and compare the effects of factors that affect imputation accuracy rates (ARs). Our results showed that a stronger LD and a lower MAF for an untyped marker produced better ARs for all the five methods. We also observed that a greater number of haplotypes in the reference sample resulted in higher ARs for MACH, IMPUTE, PLINK and Beagle, but had little influence on the ARs for fastPHASE. In general, MACH and IMPUTE produced similar results and these two methods consistently outperformed fastPHASE, PLINK and Beagle. Our study is helpful in guiding application of imputation methods in association analyses when genotype data are missing.
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期刊: NATURE GENETICS
影响因子: 30.8
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