Bioinformatics Research Center, North Carolina State University, Raleigh, NC, United States of America.
Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, United States of America.
PLoS One. 2020 Mar 2;15(3):e0229493. doi: 10.1371/journal.pone.0229493. eCollection 2020.
It is standard practice to model site-to-site variability of substitution rates by discretizing a continuous distribution into a small number, K, of equiprobable rate categories. We demonstrate that the variance of this discretized distribution has an upper bound determined solely by the choice of K and the mean of the distribution. This bound can introduce biases into statistical inference, especially when estimating parameters governing site-to-site variability of substitution rates. Applications to two large collections of sequence alignments demonstrate that this upper bound is often reached in analyses of real data. When parameter estimation is of primary interest, additional rate categories or more flexible modeling methods should be considered.
通常的做法是通过将连续分布离散化为少数几个（K）等概率的速率类别来对站点间替换率的变异性进行建模。我们证明，这个离散化分布的方差有一个上限，仅由 K 的选择和分布的均值决定。这个界限可能会给统计推断引入偏差，尤其是在估计控制站点间替换率变异性的参数时。对两个大型序列比对集的应用表明，在分析实际数据时，通常会达到这个上限。当主要关注参数估计时，应该考虑增加更多的速率类别或更灵活的建模方法。
