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针对三联体和无关个体的大型数据集进行基因型填充和单倍型相位推断的统一方法。

A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals.

作者信息

Browning Brian L, Browning Sharon R

机构信息

Department of Statistics, University of Auckland, Auckland 1142, New Zealand.

出版信息

Am J Hum Genet. 2009 Feb;84(2):210-23. doi: 10.1016/j.ajhg.2009.01.005. Epub 2009 Feb 5.

Abstract

We present methods for imputing data for ungenotyped markers and for inferring haplotype phase in large data sets of unrelated individuals and parent-offspring trios. Our methods make use of known haplotype phase when it is available, and our methods are computationally efficient so that the full information in large reference panels with thousands of individuals is utilized. We demonstrate that substantial gains in imputation accuracy accrue with increasingly large reference panel sizes, particularly when imputing low-frequency variants, and that unphased reference panels can provide highly accurate genotype imputation. We place our methodology in a unified framework that enables the simultaneous use of unphased and phased data from trios and unrelated individuals in a single analysis. For unrelated individuals, our imputation methods produce well-calibrated posterior genotype probabilities and highly accurate allele-frequency estimates. For trios, our haplotype-inference method is four orders of magnitude faster than the gold-standard PHASE program and has excellent accuracy. Our methods enable genotype imputation to be performed with unphased trio or unrelated reference panels, thus accounting for haplotype-phase uncertainty in the reference panel. We present a useful measure of imputation accuracy, allelic R(2), and show that this measure can be estimated accurately from posterior genotype probabilities. Our methods are implemented in version 3.0 of the BEAGLE software package.

摘要

我们提出了为未分型标记估算数据以及在无关个体和亲子三联体的大型数据集中推断单倍型相位的方法。我们的方法在有可用的已知单倍型相位时会加以利用,并且计算效率高,从而能够利用包含数千个体的大型参考面板中的全部信息。我们证明,随着参考面板规模越来越大,尤其是在估算低频变异时,插补准确性会大幅提高,而且未分型的参考面板也能提供高度准确的基因型插补。我们将我们的方法置于一个统一框架中,该框架能够在单一分析中同时使用来自三联体和无关个体的未分型和分型数据。对于无关个体,我们的插补方法能产生校准良好的后验基因型概率和高度准确的等位基因频率估计值。对于三联体,我们的单倍型推断方法比金标准PHASE程序快四个数量级,并且具有出色的准确性。我们的方法能够使用未分型的三联体或无关参考面板进行基因型插补,从而考虑到参考面板中的单倍型相位不确定性。我们提出了一种有用的插补准确性度量方法,即等位基因R(2),并表明该度量方法可以从后验基因型概率中准确估计出来。我们的方法在BEAGLE软件包的3.0版本中实现。

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