Model averaging in linkage analysis.
Model averaging in linkage analysis.
复制标题
连锁分析中的模型平均。
DOI：
10.1002/ajmg.b.30256
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Matthysse,Steven
中科院分区：
文献类型：
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作者：
Matthysse,Steven
遗传连锁分析的方法传统上分为“模型依赖”和“模型独立”，但可能有一个中间类，其中广泛的可能的模型被认为是一个参数的家庭有用的地方。可以使用经验贝叶斯先验对模型空间进行平均，根据模型与流行病学数据的拟合优度对模型进行加权，例如疾病在人群中和一级亲属中的频率（以及多效性情况下与其他性状的相关性）。对于高维空间的平均，马尔可夫链蒙特卡罗（MCMC）有很大的吸引力，但它有一个近乎致命的缺陷：在大多数情况下，它不可能提供严格的充分条件来允许用户安全地得出链已经收敛的结论。克服收敛问题的一种方法，如果不是解决它的话，取决于详细平衡原则的简单应用。如果链的起始点具有均衡分布，则随后的每个点也具有均衡分布。第一个点根据目标分布通过拒绝抽样来选择，并且随后的点通过MCMC过程来选择，该MCMC过程将目标分布作为其平衡分布。使用经验贝叶斯先验的模型平均需要在参数空间中的许多点处快速估计似然性。在参数空间上的随机游走开始之前构造符号多项式，以使随机游走的每一步的实际似然计算非常快。功率分析在一个说明性的情况下进行说明。© 2006 Wiley利斯公司
Methods for genetic linkage analysis are traditionally divided into “model‐dependent” and “model‐independent,” but there may be a useful place for an intermediate class, in which a broad range of possible models is considered as a parametric family. It is possible to average over model space with an empirical Bayes prior that weights models according to their goodness of fit to epidemiologic data, such as the frequency of the disease in the population and in first‐degree relatives (and correlations with other traits in the pleiotropic case). For averaging over high‐dimensional spaces, Markov chain Monte Carlo (MCMC) has great appeal, but it has a near‐fatal flaw: it is not possible, in most cases, to provide rigorous sufficient conditions to permit the user safely to conclude that the chain has converged. A way of overcoming the convergence problem, if not of solving it, rests on a simple application of the principle of detailed balance. If the starting point of the chain has the equilibrium distribution, so will every subsequent point. The first point is chosen according to the target distribution by rejection sampling, and subsequent points by an MCMC process that has the target distribution as its equilibrium distribution. Model averaging with an empirical Bayes prior requires rapid estimation of likelihoods at many points in parameter space. Symbolic polynomials are constructed before the random walk over parameter space begins, to make the actual likelihood computations at each step of the random walk very fast. Power analysis in an illustrative case is described. © 2006 Wiley‐Liss, Inc.