Forward-time simulation of realistic samples for genome-wide association studies.
Forward-time simulation of realistic samples for genome-wide association studies.
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DOI：
10.1186/1471-2105-11-442
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发表时间：
2010-09-01
影响因子：
3
通讯作者：
Amos CI
中科院分区：
文献类型：
--
作者：
Peng B;Amos CI
前向时间模拟在模拟复杂人类疾病的遗传样本方面具有独特的优势，因为它们可以紧密地模拟携带这些疾病的人类群体的进化。然而，一些方法和计算的限制，阻止了这种模拟方法的力量，从充分利用现有的前向时间模拟方法。使用通用的前向时间群体遗传学模拟环境，我们开发了一种前向时间模拟方法，可用于模拟全基因组关联研究的现实样本。我们通过比较模拟样本与真实的数据来检验这种模拟方法的性质，并使用四个例子来证明其广泛的适用性，包括模拟由多种相互作用的遗传和环境因素引起的疾病的病例对照样本，模拟受疾病易感等位基因影响的三个家庭，以及对由最近的人口混合产生的结构化人口的模拟。我们的算法模拟了与人类基因组的复杂结构非常相似的种群，同时允许引入自然选择的信号。由于它的灵活性，以产生不同类型的样本与任意疾病或数量性状模型，这种模拟方法可以模拟现实的样本，以评估各种各样的统计基因定位方法的性能，全基因组关联研究。
Forward-time simulations have unique advantages in power and flexibility for the simulation of genetic samples of complex human diseases because they can closely mimic the evolution of human populations carrying these diseases. However, a number of methodological and computational constraints have prevented the power of this simulation method from being fully explored in existing forward-time simulation methods. Using a general-purpose forward-time population genetics simulation environment, we developed a forward-time simulation method that can be used to simulate realistic samples for genome-wide association studies. We examined the properties of this simulation method by comparing simulated samples with real data and demonstrated its wide applicability using four examples, including a simulation of case-control samples with a disease caused by multiple interacting genetic and environmental factors, a simulation of trio families affected by a disease-predisposing allele that had been subjected to either slow or rapid selective sweep, and a simulation of a structured population resulting from recent population admixture. Our algorithm simulates populations that closely resemble the complex structure of the human genome, while allows the introduction of signals of natural selection. Because of its flexibility to generate different types of samples with arbitrary disease or quantitative trait models, this simulation method can simulate realistic samples to evaluate the performance of a wide variety of statistical gene mapping methods for genome-wide association studies.
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影响因子：
30.8
作者：
Marchini, Jonathan;Howie, Bryan;Donnelly, Peter
通讯作者：
Donnelly, Peter
影响因子：
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作者：
Carvajal-Rodríguez A
通讯作者：
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影响因子：
3
作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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