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首页> 外文期刊>Heredity: An International Journal of Genetics >Estimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci
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Estimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci

机译:使用数千个位点之间的连锁不平衡估计非模型物种的当代有效种群规模

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Contemporary effective population size (N-e) can be estimated using linkage disequilibrium (LD) observed across pairs of loci presumed to be selectively neutral and unlinked. This method has been commonly applied to data sets containing 10-100 loci to inform conservation and study population demography. Performance of these N-e estimates could be improved by incorporating data from thousands of loci. However, these thousands of loci exist on a limited number of chromosomes, ensuring that some fraction will be physically linked. Linked loci have elevated LD due to limited recombination, which if not accounted for can cause N-e estimates to be downwardly biased. Here, we present results from coalescent and forward simulations designed to evaluate the bias of LD-based N-e estimates ((N) over cap (e)). Contrary to common perceptions, increasing the number of loci does not increase the magnitude of linkage. Although we show it is possible to identify some pairs of loci that produce unusually large r(2) values, simply removing large r(2) values is not a reliable way to eliminate bias. Fortunately, the magnitude of bias in (N) over cap (e) is strongly and negatively correlated with the process of recombination, including the number of chromosomes and their length, and this relationship provides a general way to adjust for bias. Additionally, we show that with thousands of loci, precision of (N) over cap (e) is much lower than expected based on the assumption that each pair of loci provides completely independent information.
机译:可以使用在假设为选择性中性和未连锁的基因对对上观察到的连锁不平衡(LD)来估计当代有效种群规模(N-e)。此方法已普遍应用于包含10-100个基因座的数据集,以告知保护和研究人口统计数据。通过合并来自数千个基因座的数据,可以改善这些N-e估计的性能。但是,这数千个基因座存在于有限数量的染色体上,从而确保某些部分将物理连接。由于有限的重组,连锁基因座的LD升高,如果不加以考虑,则可能导致N-e估计值向下偏倚。在这里,我们介绍了旨在评估基于LD的N-e估计值((N)超过上限(e))的偏差的合并和正向模拟的结果。与通常的看法相反,增加基因座的数量并不会增加连锁的程度。尽管我们表明可以识别出产生异常大的r(2)值的一些基因座对,但是简单地删除大的r(2)值并不是消除偏差的可靠方法。幸运的是,(e)上方的(N)中的偏倚大小与重组过程(包括染色体数及其长度)密切相关且呈负相关,并且这种关系提供了一种调整偏见的一般方法。此外,我们显示,在成千上万个基因座的基础上,(N)相对于上限(e)的精度远低于基于每对基因座提供完全独立信息的假设所预期的精度。

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