The genetic consequences of selection in natural populations
The genetic consequences of selection in natural populations
复制标题
DOI：
10.1111/mec.13559
复制
发表时间：
2016-04
影响因子：
4.9
通讯作者：
Timothy J. Thurman;R. Barrett
中科院分区：
文献类型：
--
作者：
Timothy J. Thurman;R. Barrett
选择系数s量化了作用于遗传变异体的选择强度。尽管这个参数对群体遗传模型至关重要，但直到最近，我们对自然群体中s的值知之甚少。随着世纪后期分子遗传学技术的发展和随后的测序技术，生物学家现在能够识别遗传变异并将其与生物体适应性直接联系起来。我们回顾了文献发表的估计自然选择作用在遗传水平上，发现超过3000估计选择系数从79项研究。选择系数大致呈指数分布，这表明选择在遗传水平上的影响一般较弱，但偶尔也会相当强。我们使用非参数统计和正式的随机效应Meta分析来确定选择如何在生物学和方法学类别中变化。当在较短的时间尺度上测量时，选择会更强，对于测量单代内选择的研究，s的平均幅度最大。我们的分析发现，当考虑选择如何随遗传尺度而变化时，SNPs或单倍型），这表明需要进一步的研究。除了这些定量的结论，我们强调在计算，解释和报告选择系数的关键问题，并为未来的研究提供建议。
The selection coefficient, s, quantifies the strength of selection acting on a genetic variant. Despite this parameter's central importance to population genetic models, until recently we have known relatively little about the value of s in natural populations. With the development of molecular genetic techniques in the late 20th century and the sequencing technologies that followed, biologists are now able to identify genetic variants and directly relate them to organismal fitness. We reviewed the literature for published estimates of natural selection acting at the genetic level and found over 3000 estimates of selection coefficients from 79 studies. Selection coefficients were roughly exponentially distributed, suggesting that the impact of selection at the genetic level is generally weak but can occasionally be quite strong. We used both nonparametric statistics and formal random‐effects meta‐analysis to determine how selection varies across biological and methodological categories. Selection was stronger when measured over shorter timescales, with the mean magnitude of s greatest for studies that measured selection within a single generation. Our analyses found conflicting trends when considering how selection varies with the genetic scale (e.g., SNPs or haplotypes) at which it is measured, suggesting a need for further research. Besides these quantitative conclusions, we highlight key issues in the calculation, interpretation, and reporting of selection coefficients and provide recommendations for future research.