Inference of natural selection from ancient DNA

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.

doi:10.1002/evl3.165

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Inference of natural selection from ancient DNA

Dehasque, Marianne Centre for Palaeogenetics 10691 Stockholm Sweden - Department of Bioinformatics and Genetics, Swedish Museum of Natural History 10405 Stockholm Sweden - Department of Zoology, Stockholm University 10691 Stockholm Sweden
Ávila‐Arcos, María C. International Laboratory for Human Genome Research (LIIGH)UNAM Juriquilla Queretaro 76230 Mexico
Díez‐del‐Molino, David Centre for Palaeogenetics 10691 Stockholm Sweden - Department of Zoology, Stockholm University 10691 Stockholm Sweden
Fumagalli, Matteo Department of Life Sciences, Silwood Park Campus, Imperial College London Ascot SL5 7PY United Kingdom
Guschanski, Katerina Animal Ecology, Department of Ecology and Genetics, Science for Life Laboratory, Uppsala University 75236 Uppsala Sweden
Lorenzen, Eline D. Globe Institute, University of Copenhagen DK‐1350 Copenhagen Denmark
Malaspinas, Anna‐Sapfo Department of Computational Biology, University of Lausanne 1015 Lausanne Switzerland - SIB Swiss Institute of Bioinformatics 1015 Lausanne Switzerland
Marques‐Bonet, Tomas Institut de Biologia Evolutiva(CSIC‐Universitat Pompeu Fabra), Parc de Recerca Biomèdica de Barcelona Barcelona Spain - National Centre for Genomic Analysis—Centre for Genomic Regulation, Barcelona Institute of Science and Technology 08028 Barcelona Spain - Institucio Catalana de Recerca i Estudis Avançats 08010 Barcelona Spain - Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona Cerdanyola del Vallès Spain
Martin, Michael D. Department of Natural History, NTNU University Museum, Norwegian University of Science and Technology (NTNU) Trondheim Norway
Murray, Gemma G. R. Department of Veterinary Medicine, University of Cambridge Cambridge CB2 1TN United Kingdom
Papadopulos, Alexander S. T. Molecular Ecology and Fisheries Genetics Laboratory, School of Biological Sciences, Bangor University Bangor LL57 2UW United Kingdom
Therkildsen, Nina Overgaard Department of Natural Resources, Cornell University Ithaca New York 14850
Wegmann, Daniel Department of Biology, Université de Fribourg 1700 Fribourg Switzerland - Swiss Institute of Bioinformatics Fribourg Switzerland
Dalén, Love Centre for Palaeogenetics 10691 Stockholm Sweden - Department of Bioinformatics and Genetics, Swedish Museum of Natural History 10405 Stockholm Sweden
Foote, Andrew D. Molecular Ecology and Fisheries Genetics Laboratory, School of Biological Sciences, Bangor University Bangor LL57 2UW United Kingdom

01.04.2020

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Evolution Letters. - 2020, vol. 4, no. 2, p. 94–108

English 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.

Faculty

Faculté des sciences et de médecine

Department

Département de Biologie

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