基于单核苷酸多态性估计群体参数和重组率。
Estimation of population parameters and recombination rates from single nucleotide polymorphisms.
作者信息
Nielsen R
机构信息
Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138, USA.
出版信息
Genetics. 2000 Feb;154(2):931-42. doi: 10.1093/genetics/154.2.931.
Some general likelihood and Bayesian methods for analyzing single nucleotide polymorphisms (SNPs) are presented. First, an efficient method for estimating demographic parameters from SNPs in linkage equilibrium is derived. The method is applied in the estimation of growth rates of a human population based on 37 SNP loci. It is demonstrated how ascertainment biases, due to biased sampling of loci, can be avoided, at least in some cases, by appropriate conditioning when calculating the likelihood function. Second, a Markov chain Monte Carlo (MCMC) method for analyzing linked SNPs is developed. This method can be used for Bayesian and likelihood inference on linked SNPs. The utility of the method is illustrated by estimating recombination rates in a human data set containing 17 SNPs and 60 individuals. Both methods are based on assumptions of low mutation rates.
介绍了一些用于分析单核苷酸多态性（SNP）的一般似然法和贝叶斯方法。首先，推导了一种从处于连锁平衡的SNP估计群体参数的有效方法。该方法应用于基于37个SNP位点估计人类群体的增长率。证明了在计算似然函数时，通过适当的条件设定，至少在某些情况下可以避免由于位点抽样偏差导致的确定偏差。其次，开发了一种用于分析连锁SNP的马尔可夫链蒙特卡罗（MCMC）方法。该方法可用于对连锁SNP进行贝叶斯和似然推断。通过估计包含17个SNP和60个个体的人类数据集中的重组率来说明该方法的实用性。这两种方法都基于低突变率的假设。
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