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

Investigating population histories by

genetic methods

Three different approaches:

1. Ancient-DNA

2. Present day genes

3. Genetic simulations

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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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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

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The Reconstruction of a Stone Age dwelling at Kierikki, Finland

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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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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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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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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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)

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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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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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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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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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!

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The Reconstruction of a Stone Age village

at Saarijärvi, Finland

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).

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Thank You!

Wulffmorgenthaler 21 February 2008

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