Genes & Society Argumenta Project. Argumenta lecture: The past hidden in our genes.

1. The past hidden in our genes
Combining archaeological and genetic methodology:
Prehistoric population bottlenecks in Finland
Tarja Sundell
tarja.sundell@helsinki.fi
2.9.2015

2. Investigating population histories by
genetic methods
Three different approaches:
1. Ancient-DNA
2. Present day genes
3. Genetic simulations
Sundell 2015

3. Two different simulation approaches
 Forward simulations:
To be defined in the beginning:
e.g. founder population,
subpopulations, birth rate,
mortality rate, migration probabilities
 Coalescent simulations:
The simulation starts with the chromosomes and variation observed in
present day population and these chromosomes are simulated
backwards. The simulation is run until the most recent common
ancestor (MRCA)
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4. Forward simulation
 Can be used to create entire
virtual populations
 Founder population created in
the beginning
 Populations are simulated
through their entire histories
 Simulated populations are
analyzed and the results
compared with real world data
 a simplified model
Sundell 2015
The Reconstruction of a Stone Age dwelling at Kierikki, Finland

5. Simulating population histories
 The effects of different population history scenarios on the present day
gene pool can be studied by population simulations.
 The question we want to answer: When the simulation is run with a putative
demographic model, does it produce the same amount of genetic variation
that can be seen today?
The Lilja family at Kiuruvesi in1930
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6. Population simulations in this study
 Carried out with the population genetic simulation environment simuPOP
 Individual genetic inheritance in the simulations is ruled by similar
evolutionary forces as in real life:
 transmitted from generation to generation
 prone to mutate
 frequencies drift by change
 Characteristic demographic processes added:
 birth and mortality rate
 migration between subpopulations
 population growth and decline
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7. Evidence for a prehistoric population bottleneck
 A profound difference in the
number of dwelling sites
in the Stone Age vs.
Early Metal Period
 A remarkable change in the
number of stone artefacts
and stone artefact groups
 The reduced genetic
diversity in the present day
population, especially in Y
chromosome
 The specific ‘Finnish
Disease Heritage (FDH)’
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8. Population bottleneck
 Population bottleneck:
An event in which a
considerable part of the
population is prevented
from reproduction 
population decreases
in size
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9. Quantitative analysis of the Stone Artefact Database
(Sundell et al. 2014 Antiquity)
The number of stone artefacts found in Mesolithic and
Neolithic archaeological contexts. The proportions of
typologically long-lasting artefacts are depicted by the
lighter shade of colour in the columns.
M1= Pioneering Stage (8850-8000 BC)
M2= Ancylus Mesolithic (8000-6800 BC)
M3= Litorina Mesolithic (6800-5100 BC)
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The number of stone artefact types found in Mesolithic
and Neolithic archaeological contexts. The proportions
of typologically long-lasting artefacts types are depicted
by the lighter shade of colour in the columns.
N1= Early Neolithic (5100-4000 BC)
N2= Middle Neolithic (4000-2800 BC)
N3= Late Neolithic (2800-1900/1800 BC)

10. The spatial distribution of stone artefacts
(Sundell et al. 2014 Antiquity)
Early Neolithic (N1-period) Middle Neolithic (N2-period) Late Neolithic (N3-period)
Intensity (posterior density) of stone artefacts from the:
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11. The specific ‘Finnish Disease Heritage’
 36 genetic diseases or disorders that are
significantly more common in people whose
ancestors were ethnic Finns
 Most of the gene defects are autosomal
recessive (32). Some of the defects cause the
child to die already in the fetal stage, others at
an early age.
 These diseases have wider distributions in the
world, but due to founder effects/ bottlenecks
and genetic isolation they are more common in
Finns
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12. 24 different simulation scenarios
Scenario Population size
first
2000 years
Bottleneck size at
4100-3800 BP
Internal
migration
between
subpopulations
Migration
waves (TCW
and CW)
Constant gene
flow
A1 stable 1000 no - -
A2 stable 200 no - -
B1 stable 1000 yes - -
B2 stable 200 yes - -
C1 fluctuating 1000 no - -
C2 fluctuating 200 no - -
D1 fluctuating 1000 yes - -
D2 fluctuating 200 yes - -
E1 stable 1000 no small temperate
E2 stable 200 no small temperate
F1 stable 1000 yes small temperate
F2 stable 200 yes small temperate
G1 fluctuating 1000 no small temperate
G2 fluctuating 200 no small temperate
H1 fluctuating 1000 yes small temperate
H2 fluctuating 200 yes small temperate
I1 stable 1000 no temperate small
I2 stable 200 no temperate small
J1 stable 1000 yes temperate small
J2 stable 200 yes temperate small
K1 fluctuating 1000 no temperate small
K2 fluctuating 200 no temperate small
L1 fluctuating 1000 yes temperate small
L2 fluctuating 200 yes temperate small
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13. Results
 Archaeological and genetic evidence,
together with the stone artefact analyses,
indicate that there has been
at least one Neolithic bottleneck in Finland
 the simulation scenarios with a moderate constant migration from
neighbouring populations produce genetic diversity measures similar to
those observed in present day Finnish population. Consistently, the
scenarios without migration induce considerable deviation from these
measures
 female-specific higher migration rate, compared to a gender-neutral
migration rate, brings the simulated genetic diversity closer to the observed
contemporary genetic diversity in Finland
 Our simulations also showed that a tight prehistoric bottleneck can still have
a noticeable effect on genetic diversity even today, after thousands of years.
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14. Conclusions
 Different branches of science, such as archaeology and genetics, provide
independent reflections of the same past. Combining them produces a more
complete understanding of prehistoric population events!
Sundell 2015
The Reconstruction of a Stone Age village
at Saarijärvi, Finland

15. References
 Helama, S. et al. 2013. A chronology of climatic downturns through the mid- and late- Holocene: tracing the distant effects of
explosive eruptions from palaeoclimatic and historical evidence in northern Europe. Polar Research.
 Oinonen, M. et al. 2014. Event reconstruction through Bayesian chronology: Massive mid-Holocene lake-burst triggered
large-scale ecological and cultural change. The Holocene. Vol. 24(11) 1419-1427.
 Peng, B. & Kimmel, M. 2005. SimuPOP: a forward-time population genetics simulation environment. Bioinformatics, 21(18):
3686-3687.
 SPSS Inc. 2009. PASW Statistics for Windows, Version 18.0. Chicago.
 Sundell, T. et al. 2014. Archaeology, genetics and a population bottleneck in prehistoric Finland. Antiquity, volume: 88, 342.
1132-1147.
 Sundell T. et al. 2013. Retracing Prehistoric Population Events in Finland Using Simulation. In Earl, G., Sly, T., Chrysanthi,
A., Murrieta-Flores, P., Papadopoulos, C., Romanowska, I. & Wheatley, D. (eds.). Archaeology in the Digital Era. Papers
from the 40th Annual Conference of Computer Applications and Quantitative Methods in Archaeology (CAA). 93-104.
 Sundell , T. & Kammonen, J. 2015. A Density-Based Simulation Approach for Evaluating Prehistoric Population Fluctuations
in Finland. Papers from the 42th Annual Conference of Computer Applications and Quantitative Methods in Archaeology
(CAA).
Sundell 2015

16. Thank You!
Wulffmorgenthaler 21 February 2008
Sundell 2015