Inference of natural selection from ancient DNA

Area of Science:

• Evolutionary Biology
• Genomics
• Paleogenetics

Background:

• Inferring evolutionary selection from genomic variation often relies on unsupported assumptions about past population demographics and selection pressures.
• Current methods struggle to accurately reconstruct the dynamics of genetic changes driven by selection.

Purpose of the Study:

• To advocate for the integration of temporal sampling and paleogenomic data generation in evolutionary biology research.
• To highlight recent advances in paleogenomic analysis applicable to evolutionary studies.
• To explore the utility of time-series data for understanding selection signatures.

Main Methods:

• Analysis of genomic variation in temporally spaced ancient DNA (aDNA) samples.
• Quantification of population parameters from time-series data.
• Examination of expected signatures of balancing, purifying, and positive selection in temporal datasets.

Main Results:

• Temporal sampling in paleogenomics allows direct quantification of population parameters, providing a clearer picture of selection.
• Time-series data can reveal the chronology and tempo of genomic changes driven by selection.
• Paleogenomic data analysis requires careful consideration of limitations like DNA damage, fragmentation, low coverage, and small sample sizes.

Conclusions:

• Paleogenomic data, despite limitations, offers a powerful approach to study past evolutionary selection directly.
• Integrating temporal sampling and paleogenomics can significantly advance our understanding of evolutionary processes.
• Further development and application of analytical methods for paleogenomic data are crucial.