Estimation of individual admixture: Analytical and study design considerations
Estimation of individual admixture: Analytical and study design considerations
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DOI：
10.1002/gepi.20064
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发表时间：
2005-05-01
影响因子：
2.1
通讯作者：
Risch, NJ
中科院分区：
文献类型：
--
作者：
Tang, H;Peng, J;Risch, NJ
混合个体的基因组代表来自不同祖先的等位基因的混合物。在美国，两个最大的少数民族群体，非洲裔美国人和西班牙裔美国人，都是混合的。在个体水平上理解混合比例（个体混合物，或IA）对于在这些群体中进行病例对照关联研究的群体遗传学家和流行病学家都是有价值的。在这里，我们提出了一个扩展以前描述的频率（最大似然或ML）的方法来估计个人的混合物，允许祖先等位基因频率的不确定性。我们将这种方法与先前基于部分似然的方法以及最近描述的贝叶斯MCMC方法进行比较。与现有的部分ML方法相比，我们的全ML方法具有更高的鲁棒性。模拟还表明，这种频率估计器实现了与贝叶斯方法相似的效率，通过均方误差标准测量，但只需要一小部分计算时间来产生点估计，从而允许进行广泛的分析（例如，模拟）不可能通过贝叶斯方法。我们的模拟结果表明，包括祖先人口或其代理人在分析中所需的任何IA估计方法，以获得合理的结果。Genet.流行病学28：289-301，2005. (c)2005 Wiley-Liss，Inc.
The genome of an admixed individual represents a mixture of alleles from different ancestries. In the United States, the two largest minority groups, African-Americans and Hispanics, are both admixed. An understanding of the admixture proportion at an individual level (individual admixture, or IA) is valuable for both population geneticists and epidemiologists who conduct case-control association studies in these groups. Here we present an extension of a previously described frequentist (maximum likelihood or ML) approach to estimate individual admixture that allows for uncertainty in ancestral allele frequencies. We compare this approach both to prior partial likelihood based methods as well as more recently described Bayesian MCMC methods. Our full ML method demonstrates increased robustness when compared to an existing partial ML approach. Simulations also suggest that this frequentist estimator achieves similar efficiency, measured by the mean squared error criterion, as Bayesian methods but requires just a fraction of the computational time to produce point estimates, allowing for extensive analysis (e.g., simulations) not possible by Bayesian methods. Our simulation results demonstrate that inclusion of ancestral populations or their surrogates in the analysis is required by any method of IA estimation to obtain reasonable results. Genet. Epidemiol. 28:289-301, 2005. (c) 2005 Wiley-Liss, Inc.