Lin S
Department of Statistics, Ohio State University, Columbus 43210, USA.
Biometrics. 1996 Dec;52(4):1417-27.
Multipoint linkage analysis is being performed routinely in medical genetic studies to localize disease genes. This likelihood-based method is very computationally intensive. Exact computations are thus formidable for problems with large number of genetic markers and complex pedigrees. This paper proposes a Monte Carlo method to estimate the required likelihoods. The space of multilocus genotypes is sampled using a hybrid algorithm with a mixture of Gibbs samplers and Metropolis jumping kernels. These samples are essentially realizations of a Markov chain, and are distributed approximately according to the conditional genotype distribution given the observed phenotypic data. We present a simulation study with several eight-point analyses to demonstrate the feasibility of the current method.
多点连锁分析在医学遗传学研究中被常规用于定位疾病基因。这种基于似然性的方法计算量极大。因此，对于具有大量遗传标记和复杂家系的问题，精确计算是一项艰巨的任务。本文提出了一种蒙特卡罗方法来估计所需的似然性。使用一种混合算法对多位点基因型空间进行采样，该算法混合了吉布斯采样器和梅特罗波利斯跳跃核。这些样本本质上是马尔可夫链的实现，并且根据给定观察到的表型数据的条件基因型分布近似分布。我们进行了一项包含多个八点分析的模拟研究，以证明当前方法的可行性。
