论文标题
从SNP等位基因频率估算协方差结构
Estimation of the covariance structure from SNP allele frequencies
论文作者
论文摘要
我们提出了两个新的统计数据，即V和S，以将相关人群的人口历史与SNP频率数据相关。如果种群与树相关，我们通过理论手段以及模拟表明，与标准统计相比，新统计数据可以正确识别树的根部，例如F2统计量的观察到的矩阵（人群对之间的距离）。统计V是通过平均所有SNP（类似于标准统计数据）获得的。它的期望是观察到的种群SNP频率的真正协方差矩阵，被带有相同条目的矩阵所抵消。相比之下，统计量s被放置在贝叶斯环境中，并通过对成对的SNP进行平均而获得，因此每个SNP仅使用一次。因此，它利用了成对SNP的联合分布。 此外，我们还提供了许多有关新旧统计数据及其相互关系的新型数学结果。
We propose two new statistics, V and S, to disentangle the population history of related populations from SNP frequency data. If the populations are related by a tree, we show by theoretical means as well as by simulation that the new statistics are able to identify the root of a tree correctly, in contrast to standard statistics, such as the observed matrix of F2-statistics (distances between pairs of populations). The statistic V is obtained by averaging over all SNPs (similar to standard statistics). Its expectation is the true covariance matrix of the observed population SNP frequencies, offset by a matrix with identical entries. In contrast, the statistic S is put in a Bayesian context and is obtained by averaging over pairs of SNPs, such that each SNP is only used once. It thus makes use of the joint distribution of pairs of SNPs. In addition, we provide a number of novel mathematical results about old and new statistics, and their mutual relationship.
