The biological distance and genetic evidence for long-range migration in the prehistoric Midwest

Lyle W. KonigsbergSusan R. Frankenberg

Goals of Paper

1. Summarize biological distance and genetic evidence for long-range migration in the prehistoric Midwest

2. Address the role of ancient DNA in answering questions about long-range migration

Previous biological distance studies

1. Buikstra (1976) – “The results of the population comparisons suggest that Middle Woodland communities involve relatively stable, long term occupations within a local region.”

2. Reichs (1984) – for Ohio and Illinois Hopewell, “Explanations involving population migrations or significant biological interaction are not indicated.”

3. Sciulli and Mahaney (1986) – “…the present results argue against the hypothesis of large-scale migrations of Hopewell populations from Illinois to Ohio.”

Previous biological distance studies, cont.

4. Konigsberg (1987) – A cowardly approach that only looked at within-site variation

5. Steadman (2001) – “…intraregional population movement was a more significant contributor to Mississippian population structure than interregional gene flow…”

6. Pennefather-O’Brien (2006) – “…biological relatedness could be one aspect of widespread participation in the phenomenon referred to as Hopewell.”

“Block o’ cheese model” (Konigsberg 1990)

After removing three northern sites and removing temporal trends

Correlation biological distance with river distance= 0.5891 (p=0.006)

Correlation biological distance with “time distance” = -0.2702 (p=0.092)

Tiles in upper 40% vector magnitude and divergence no more than 6 degrees

Konigsberg & Buikstra (1995)

Oft forgotten problems with quantitative traits

1. One completely heritable quantitative trait is only “worth” one biallelic locus (Rogers and Harpending, 1983).

2. The trace of P-1G (the “pig matrix”?) gives the equivalence in numbers of biallelic loci (Williams-Blangero and Blangero, 1989).

3. We often assume environmental variance is random with respect to population structure, but…

Benefits of aDNA (mtDNA)

1. From sequence data have one polymorphic locus (e.g., 40 haplotypes from Pete Klunk MW and Hopewell Site)

2. There is no environmental variance to be concerned with.

From Cabana, Hunley, and Kaestle (2008):Population Continuity or Replacement?

1. Unnecessarily complicated because it is couched in a statistical hypothesis testing framework rather than being framed as an estimation problem.

2. Spatial model is probably inappropriate for a river valley (Konigsberg 1987 used a finite linear stepping-stone model)

Lee (2012) Bayesian Statistics: An Introduction

“The nub of the argument here is that in drawing any conclusion from an experiment only the actual observation x made (and not other possible outcomes that might have occurred) is relevant. This is in contrast to methods, by which, for example, a null hypothesis is rejected because the probability of a value as large or larger than that actually observed is small…”

What is the migration rate estimated from aDNA data?

1. mtDNA haplogroup data sampled from an ancestral population – Bolnick’s (2005) data on 39 individuals from the Pete Klunk Middle Woodland site.

2. Comparable data from a descendant population – Raff’s (2008) data on 47 individuals from the Schild Mississippian site.

3. Assume a fixed (female) effective population size (of 50) and number of generations (30).

What is the migration rate estimated from aDNA data?, cont.

In the infinite island model:

After 30 iterations (for 30 generations) check for closeness of model Fst to actual Fst (0.0492) from aDNA and estimate m (female) = 0.15.

2 2 '1 11 1 1st stF m m F

N N

“These go to eleven.”Nigel Tufnel (1984)

Approximate Bayesian Computation (ABC)

1. Draw the migration rate from a uniform prior (0 – 1)

2. Simulate 30 generations of genetic drift (Ne = 50) and migration at the sampled migration rate.

3. If the absolute difference between the simulated Fst and the actual Fst is less than 0.0005, accept the simulated Fst as a draw from the posterior density.

And it works!

And it works! - HORRIBLY

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Migration Rate

De

nsi

ty

… because there are not enough data

0.00 0.05 0.10 0.15 0.20 0.25 0.30

02

46

8

Bootstrap FST

FST

De

nsi

ty

0.0492

Bolnick and Smith (2007)

“...gene flow did accompany the cultural exchange between Middle Woodland communities in the Ohio and Illinois Valleys...not the result of a mass population movement between Ohio and Illinois; rather, it most likely reflected the movement of a small number of individuals each generation.”

Bolnick and Smith (2007), cont.

“...the genetic data indicate migration and gene flow primarily in one direction, from Ohio to Illinois. This finding is surprising since no archaeological or morphological studies have proposed this pattern of migration, and Prufer (1964) actually interpreted the archaeological evidence as indicating the opposite...”

NemMigrate IM

Klunk to Hopewell 0.4 0.1

Hopewell to Klunk 7.0 141.0

Total 7.4 141.1

A

D

C

B

X

Pete Klunk MW(Bolnick 2005)

Hopewell Site(Mills 2003)

From LAMARCMigrants per generation

Ne m

Po

ste

rio

r D

en

sity

0 5 10 15

Hopewell KlunkKlunk Hopewell

Whither now?

1. Biological distance studies of past populations are likely to become a thing of the past if they are not integrated with aDNA analytical methods.

2. Single locus aDNA studies (mtDNA) may not have the resolution desired for some studies of interest in archaeology.

3. Need better communication between aDNA practitioners and the programmers / population geneticists who develop program packages.