ArticleEvolution letters2020
Inference of natural selection from ancient DNA.
Article in Evolution letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The practice and promise of temporal genomics for measuring evolutionary responses to global change.Molecular ecology resources · 2025Pooled it
- Harnessing macrogenomics to study changing marine ecosystems under global climate change.Proceedings. Biological sciences · 2026Review
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.PLoS computational biology · 2026Article
- Article
- Insights into human adaptation from ancient DNA.Nature genetics · 2026Review
- Popformer: Learning general signatures of positive selection with a self-supervised transformer.bioRxiv : the preprint server for biology · 2026Article
- Urea transporter evolution: deep conservation, recent adaptation in mammals, and maintenance of the Jka/Jkb polymorphism in the past ~ 110,000 years.Biology direct · 2026Article
- Assessing Ancient DNA Sampling Strategies for Natural Selection Inference in Humans Using Allele Frequency Time Series Data.Genome biology and evolution · 2026Article
- Evidence of long-term purging of mutation burden in killer whale Orcinus orca genomes.Heredity · 2025Article
- The Genomic Basis of the Svalbard Reindeer's Adaptation to an Extreme Arctic Environment.Genome biology and evolution · 2025Article
- A novel expectation-maximization approach to infer general diploid selection from time-series genetic data.PLoS genetics · 2025Article
- Ancient DNA suggests a historical demographic decline and genetic erosion in the Atlantic bluefin tuna.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- A novel expectation-maximization approach to infer general diploid selection from time-series genetic data.bioRxiv : the preprint server for biology · 2025Article
- Lineage frequency time series reveal elevated levels of genetic drift in SARS-CoV-2 transmission in England.PLoS pathogens · 2024Article
- Predicting Functional Consequences of Recent Natural Selection in Britain.Molecular biology and evolution · 2024Article
- Elevated genetic risk for multiple sclerosis emerged in steppe pastoralist populations.Nature · 2024Article
- Unveiling recent and ongoing adaptive selection in human populations.PLoS biology · 2024Article
- Evolutionary predictions for a parasite metapopulation: Modelling salmon louse resistance to pest controls in aquaculture.Evolutionary applications · 2023Article
- Predicting functional consequences of recent natural selection in Britain.bioRxiv : the preprint server for biology · 2023Article
- Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data.Molecular biology and evolution · 2023Article
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15 authors.
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Abstract
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.
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