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通过样本中杂合位点数量的分布来检测等位基因的非随机关联。

Detection of nonrandom association of alleles from the distribution of the number of heterozygous loci in a sample.

作者信息

Chakraborty R

出版信息

Genetics. 1984 Nov;108(3):719-31. doi: 10.1093/genetics/108.3.719.

Abstract

The distribution of the number of heterozygous loci in two randomly chosen gametes or in a random diploid zygote provides information regarding the nonrandom association of alleles among different genetic loci. Two alternative statistics may be employed for detection of nonrandom association of genes of different loci when observations are made on these distributions: observed variance of the number of heterozygous loci (s2k) and a goodness-of-fit criterion (X2) to contrast the observed distribution with that expected under the hypothesis of random association of genes. It is shown, by simulation, that s2k is statistically more efficient than X2 to detect a given extent of nonrandom association. Asymptotic normality of s2k is justified, and X2 is shown to follow a chi-square (chi 2) distribution with partial loss of degrees of freedom arising because of estimation of parameters from the marginal gene frequency data. Whenever direct evaluations of linkage disequilibrium values are possible, tests based on maximum likelihood estimators of linkage disequilibria require a smaller sample size (number of zygotes or gametes) to detect a given level of nonrandom association in comparison with that required if such tests are conducted on the basis of s2k. Summarization of multilocus genotype (or haplotype) data, into the different number of heterozygous loci classes, thus, amounts to appreciable loss of information.

摘要

在两个随机选择的配子或一个随机的二倍体合子中杂合位点数量的分布,提供了有关不同基因位点上等位基因非随机关联的信息。当对这些分布进行观察时,可以采用两种替代统计量来检测不同位点基因的非随机关联:杂合位点数量的观察方差(s2k)和一个拟合优度标准(X2),以将观察到的分布与基因随机关联假设下预期的分布进行对比。通过模拟表明,在检测给定程度的非随机关联时,s2k在统计上比X2更有效。s2k的渐近正态性是合理的,并且X2被证明遵循卡方(chi 2)分布,由于从边际基因频率数据估计参数,自由度会部分损失。只要能够直接评估连锁不平衡值,与基于s2k进行此类测试相比,基于连锁不平衡的最大似然估计量的测试检测给定水平的非随机关联所需的样本量(合子或配子数量)更小。因此,将多位点基因型(或单倍型)数据汇总到不同数量的杂合位点类别中,相当于信息的大量损失。

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