TY - JOUR

T1 - Inference of natural selection from ancient DNA

AU - Dehasque, Marianne

AU - avila-Arcos, Maria C.

AU - Diez-del-Molino, David

AU - Fumagalli, Matteo

AU - Guschanski, Katerina

AU - Lorenzen, Eline D.

AU - Malaspinas, Anna-Sapfo

AU - Marques-Bonet, Tomas

AU - Martin, Michael D.

AU - Murray, Gemma G. R.

AU - Papadopulos, Alexander S. T.

AU - Therkildsen, Nina Overgaard

AU - Wegmann, Daniel

AU - Dalen, Love

AU - Foote, Andrew D.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Adaptation

KW - ancient DNA

KW - natural selection

KW - paleogenomics

KW - time series

KW - BALANCING SELECTION

KW - DELETERIOUS MUTATIONS

KW - GENOME SEQUENCE

KW - MOLECULAR EVOLUTION

KW - POPULATION-GENETICS

KW - BAYESIAN-INFERENCE

KW - LINKED SELECTION

KW - RAPID EVOLUTION

KW - COAT COLOR

KW - SPECIATION

U2 - 10.1002/evl3.165

DO - 10.1002/evl3.165

M3 - Letter

C2 - 32313686

VL - 4

SP - 94

EP - 108

JO - Evolution Letters

JF - Evolution Letters

SN - 2056-3744

IS - 2

ER -