Dehasque Marianne, Ávila-Arcos María C, Díez-Del-Molino David, Fumagalli Matteo, Guschanski Katerina, Lorenzen Eline D, Malaspinas Anna-Sapfo, Marques-Bonet Tomas, Martin Michael D, Murray Gemma G R, Papadopulos Alexander S T, Therkildsen Nina Overgaard, Wegmann Daniel, Dalén Love, Foote Andrew D
Centre for Palaeogenetics 10691 Stockholm Sweden.
Department of Bioinformatics and Genetics Swedish Museum of Natural History 10405 Stockholm Sweden.
Evol Lett. 2020 Mar 18;4(2):94-108. doi: 10.1002/evl3.165. eCollection 2020 Apr.
Evolutionary processes, including selection, can be indirectly inferred based on patterns of genomic variation among contemporary populations or species. However, this often requires unrealistic assumptions of ancestral demography and selective regimes. Sequencing ancient DNA from temporally spaced samples can inform about past selection processes, as time series data allow direct quantification of population parameters collected before, during, and after genetic changes driven by selection. In this Comment and Opinion, we advocate for the inclusion of temporal sampling and the generation of paleogenomic datasets in evolutionary biology, and highlight some of the recent advances that have yet to be broadly applied by evolutionary biologists. In doing so, we consider the expected signatures of balancing, purifying, and positive selection in time series data, and detail how this can advance our understanding of the chronology and tempo of genomic change driven by selection. However, we also recognize the limitations of such data, which can suffer from postmortem damage, fragmentation, low coverage, and typically low sample size. We therefore highlight the many assumptions and considerations associated with analyzing paleogenomic data and the assumptions associated with analytical methods.
包括选择在内的进化过程，可以基于当代种群或物种间的基因组变异模式进行间接推断。然而，这通常需要对祖先种群统计学和选择机制做出不切实际的假设。对来自不同时间样本的古DNA进行测序，可以为过去的选择过程提供信息，因为时间序列数据能够直接量化在选择驱动的基因变化之前、期间和之后收集的种群参数。在这篇评论与观点文章中，我们主张在进化生物学中纳入时间采样并生成古基因组数据集，并强调一些尚未被进化生物学家广泛应用的最新进展。在此过程中，我们考虑了时间序列数据中平衡选择、纯化选择和正选择的预期特征，并详细说明了这如何能推进我们对选择驱动的基因组变化的时间顺序和节奏的理解。然而，我们也认识到此类数据的局限性，这些数据可能会受到死后损伤、片段化、低覆盖率以及通常样本量较小的影响。因此，我们强调了与分析古基因组数据相关的诸多假设和考量以及与分析方法相关的假设。
