Article, 2020
Inference of natural selection from ancient DNA
Evolution Letters,
ISSN
2056-3744,
Volume 4,
2,
Pages 94-108,
10.1002/evl3.165
Contributors
Dehasque, Marianne
0000-0002-4640-8306
[1]
[2]
[3]
Ávila-Arcos, María C
0000-0003-1691-1696
[4]
Díez-Del-Molino, David
0000-0002-9701-5940
[2]
[3]
Fumagalli, Matteo
0000-0002-4084-2953
[5]
Guschanski, Katerina
0000-0002-8493-5457
[6]
Lorenzen, Eline Deirdre
0000-0002-6353-2819
[7]
Malaspinas, Anna-Sapfo
0000-0003-1001-7511
[8]
[9]
Marques-Bonet, Tomas
0000-0002-5597-3075
[10]
[11]
[12]
[13]
Martin, Michael David
0000-0002-2010-5139
[14]
Murray, Gemma G R
0000-0002-9531-1711
[15]
Papadopulos, Alexander S T
0000-0001-6589-754X
[16]
Therkildsen, Nina Overgaard
0000-0002-6591-591X
[17]
Wegmann, Daniel
0000-0003-2866-6739
[9]
[18]
Dalén, Love
0000-0001-8270-7613
[1]
[3]
Foote, Andrew David
0000-0001-7384-1634
[16]
Affiliations
- [1]
Swedish Museum of Natural History
[NORA names:
Sweden; Europe, EU; Nordic; OECD];
- [2]
Stockholm University
[NORA names:
Sweden; Europe, EU; Nordic; OECD];
- [3]
Centre for Palaeogenetics
[NORA names:
Sweden; Europe, EU; Nordic; OECD];
- [4]
International Laboratory for Human Genome Research (LIIGH), UNAM Juriquilla, Queretaro, 76230, Mexico
[NORA names:
Mexico; America, Central; OECD];
- [5]
Imperial College London
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
(... more)
- [6]
Uppsala University
[NORA names:
Sweden; Europe, EU; Nordic; OECD];
- [7]
University of Copenhagen
[NORA names:
KU University of Copenhagen;
University; Denmark; Europe, EU; Nordic; OECD];
- [8]
University of Lausanne
[NORA names:
Switzerland; Europe, Non-EU; OECD];
- [9]
Swiss Institute of Bioinformatics
[NORA names:
Switzerland; Europe, Non-EU; OECD];
- [10]
Barcelona Institute for Science and Technology
[NORA names:
Spain; Europe, EU; OECD];
- [11]
Institució Catalana de Recerca i Estudis Avançats
[NORA names:
Spain; Europe, EU; OECD];
- [12]
Institut Català de Paleontologia Miquel Crusafont
[NORA names:
Spain; Europe, EU; OECD];
- [13]
Institute of Evolutionary Biology
[NORA names:
Spain; Europe, EU; OECD];
- [14]
Norwegian University of Science and Technology
[NORA names:
Norway; Europe, Non-EU; Nordic; OECD];
- [15]
University of Cambridge
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
- [16]
Bangor University
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
- [17]
Cornell University
[NORA names:
United States; America, North; OECD];
- [18]
University of Fribourg
[NORA names:
Switzerland; Europe, Non-EU; OECD]
(less)
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.
Keywords
DNA,
analytical method,
ancestral demography,
ancient DNA,
assumptions,
biologists,
biology,
changes,
chronology,
comments,
considerations,
contemporary populations,
coverage,
damage,
data,
dataset,
demography,
evolutionary biologists,
evolutionary biology,
evolutionary process,
fragments,
generation,
genetic changes,
genomic changes,
genomic variation,
inclusion,
inference,
inference of natural selection,
limitations,
low coverage,
method,
natural selection,
opinion,
paleogenomic data,
parameters,
patterns,
patterns of genomic variation,
population,
population parameters,
positive selection,
postmortem,
postmortem damage,
process,
purifying,
quantification,
sample size,
samples,
selection,
selection process,
sequencing ancient DNA,
series data,
signature,
size,
species,
tempo,
temporal sampling,
time,
time series data,
variation
Funders
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