Forward-time simulations of human populations with complex diseases.
Forward-time simulations of human populations with complex diseases.
复制标题
DOI：
10.1371/journal.pgen.0030047
复制
发表时间：
2007-03-23
期刊：
影响因子：
4.5
通讯作者：
Kimmel, Marek
中科院分区：
文献类型：
--
作者：
Peng, Bo;Amos, Christopher I.;Kimmel, Marek
由于个人计算机的功能日益强大，以及灵活的前向时间模拟程序（如simuPOP）的可用性，现在可以使用前向时间方法模拟复杂的人类疾病的演变。这种方法可能比合并方法更强大，因为它允许使用几乎任意的遗传和人口模型模拟一个以上的疾病易感性位点。然而，由于缺乏合适的模拟框架，这种模拟的应用受到了阻碍。例如，目前尚不清楚何时以及如何将疾病突变体（特别是那些处于纯化选择下的突变体）引入到进化的群体中，以及如何控制最后一代的疾病等位基因频率。在本文中，我们引入了一个前向时间模拟框架，使我们能够生成大型多代人口与复杂的疾病所造成的非连锁疾病易感基因座，根据特定的人口和进化特性。无关的个体，或大或小的谱系可以从所得到的人群中提取，并为广泛的研究设计和确定方法提供样本。我们展示了我们的模拟框架，使用三个例子，映射基因与情感状态，数量性状，和发病年龄的假设癌症，分别。非加性的适应度模型，人口结构，基因-基因相互作用进行了模拟。病例对照，同胞对，和大谱系样本来自模拟人口，并通过各种基因定位方法进行检查。高血压、糖尿病等复杂疾病通常是由多种疾病易感基因、环境因素以及它们之间的相互作用引起的。模拟具有复杂疾病的群体或样本是研究这些疾病可能的遗传结构和开发更有效的基因定位方法的有效方法。与传统的后向时间（合并）方法相比，基于人口的前向时间模拟更适合这项任务，因为它们可以模拟几乎任意的人口统计和遗传特征。前向时间模拟还允许研究人员基于不同的研究设计和确定方法在基因定位方法之间进行头对头比较。不幸的是，一个群体的世代进化是一个随机的过程，因此疾病等位基因的命运是不可预测的，并且没有有效的方法来控制疾病等位基因在当前世代的频率。在本文中，作者提出了一种模拟方法，避免了这些问题，使前向时间人口模拟的一个实际的解决方案，模拟复杂的疾病。
Due to the increasing power of personal computers, as well as the availability of flexible forward-time simulation programs like simuPOP, it is now possible to simulate the evolution of complex human diseases using a forward-time approach. This approach is potentially more powerful than the coalescent approach since it allows simulations of more than one disease susceptibility locus using almost arbitrary genetic and demographic models. However, the application of such simulations has been deterred by the lack of a suitable simulation framework. For example, it is not clear when and how to introduce disease mutants—especially those under purifying selection—to an evolving population, and how to control the disease allele frequencies at the last generation. In this paper, we introduce a forward-time simulation framework that allows us to generate large multi-generation populations with complex diseases caused by unlinked disease susceptibility loci, according to specified demographic and evolutionary properties. Unrelated individuals, small or large pedigrees can be drawn from the resulting population and provide samples for a wide range of study designs and ascertainment methods. We demonstrate our simulation framework using three examples that map genes associated with affection status, a quantitative trait, and the age of onset of a hypothetical cancer, respectively. Nonadditive fitness models, population structure, and gene–gene interactions are simulated. Case-control, sibpair, and large pedigree samples are drawn from the simulated populations and are examined by a variety of gene-mapping methods. Complex diseases such as hypertension and diabetes are usually caused by multiple disease-susceptibility genes, environment factors, and interactions between them. Simulating populations or samples with complex diseases is an effective approach to study the likely genetic architecture of these diseases and to develop more effective gene-mapping methods. Compared to traditional backward-time (coalescent) methods, population-based, forward-time simulations are more suitable for this task because they can simulate almost arbitrary demographic and genetic features. Forward-time simulations also allow the researcher to perform head-to-head comparisons among gene-mapping methods based on different study designs and ascertainment methods. Unfortunately, evolving a population generation by generation is a random process, so the fates of disease alleles are unpredictable and there is no effective way to control the disease allele frequency at the present generation. In this paper, the authors propose a simulation method that avoids these problems and makes forward-time population simulation a practical solution for the simulation of complex diseases.
登录
查看更多内容
影响因子：
1.4
作者：
Austerlitz, F;JungMuller, B;Gouyon, PH
通讯作者：
Gouyon, PH
影响因子：
1.5
作者：
ALBIN, RL
通讯作者：
ALBIN, RL
影响因子：
2.1
作者：
Chen, Wei-Min;Abecasis, Goncalo R.
通讯作者：
Abecasis, Goncalo R.
影响因子：
30.8
作者：
Abecasis, GR;Cherny, SS;Cardon, LR
通讯作者：
Cardon, LR
影响因子：
1.4
作者：
Coop, G;Griffiths, RC
通讯作者：
Griffiths, RC