genetic variation in north africa and eurasia: neolithic demic diffusion vs. paleolithic...

AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 95:137-154 (1994)

Genetic Variation in North Africa and Eurasia: Neolithic Demic Diffusion vs. Paleolithic Colonisation

GUIDO BARBUJANI, ANDREA PILASTRO, SILVIA DE DOMENICO, AND COLIN RENFREW Dipartimento di Scienze Statistiche, Uniuersita di Bologna, I-40126 Bologna, Italy (G.B.); Dipartimento di Biologia, Uniuersita’ di Padoua, I-35121 Padoua, Italy (G.B., A.P., S.D.D.); Department of Archaeology, University of Cambridge, Cambridge CB2 302 , UK (C.R.)

KEY WORDS transmission, Spatial autocorrelation

Gene frequencies, Languages, Gene flow, Cultural

ABSTRACT The hypothesis that both genetic and linguistic similarities among Eurasian and North African populations are due to demic diffusion of neolithic farmers is tested against a wide database of allele frequencies. Demic diffusion of farming and languages from the Near East should have determined clines in areas defined by linguistic criteria; the alternative hypothesis of cultural transmission does not predict clines. Spatial autocorrelation analysis shows significant gradients in three of the four linguistic families supposedly affected by neolithic demic diffusion; the Afroasiatic family is the exception. Many such gradients are not observed when populations are jointly analyzed, regardless of linguistic classification. This is incompatible with the hypothesis that major cultural transformations in Eurasia (diffusion of related languages and spread of agriculture) took place without major demographic changes. The model of demic diffusion seems therefore to provide a mechanism explaining coevolution of linguistic and biological traits in much of the Old World. Archaeological, linguistic, and genetic evidence agree in suggesting a multidirectional process of gene flow from the Near East in the neolithic. However, the possibility should be envisaged that some allele frequency patterns can predate the neolithic and depend on the initial spread of Homo sapiens sapiens from Africa into Eurasia. o 1994 Wiley-Liss, Inc.

The transition from a hunter-gatherer economy to one of food production (the neolithic transition) was, in many regions of the world, one of the most momentous episodes in human history. It was associated in many cases with a very significant population increase (Hassan, 1973). Two contrasting models have been put forward for the incep- tion of a farming economy in the Near East and in Europe in areas beyond the nuclear zone (Harlan, 19711, where the initial domestication of the basic food plants and live- stock took place.

The first model, one of demic diffusion (Ammerman and Cavalli-Sforza, 1973, 1984), is based on the observation that when technological advances make food supplies

abundant, population sizes increase. To support greater numbers of individuals, the population will then need to expand into new territories. If expansion is accompanied by limited admixture with previous residents, and if the new technologies are not passed to them, the immigrants and their descendants will be able to further increase in numbers and spread into adjacent areas. In the course of time, this will bring their genes to distant locations. A combination of population growth and gradual dispersal

Received February 16,1993; accepted March 21,1994. Address reprint requests to Guido Barbujani, Dipartimento di

Biologia, via Trieste 75, 1-35121 Padova, Italy.

0 1994 WILEY-LISS. INC

G. BARBUJANI ET AL 138

may have caused parallel cultural and genetic transformations in the neolithic. The technique of farming is then supposed to have been carried to new areas by the local movements of peasants from the farming region of high population density to adjacent areas of lower density where farming was not yet practised. In this way, the new farming technique may have spread across the land mass together with the population de- scended from the original farmers in the nuclear zone.

In the alternative, acculturation model (Zvelebil and Zvelebil, 1988), the indigenous population outside the nuclear area ac- quires the farming techniques, along with the plant and animal domesticates, by con- tact. The indigenous population may have increased, without significant population input from the first farmers of the nuclear zone or their descendants.

Under the demic diffusion model, different phenomena of evolutionary relevance may have taken place, depending on whether or not the regions into which early farmers moved were already populated. If the expansion of farmers led to contacts with other groups, a certain degree of admixture presumably ensued. If, on the contrary, early farmers expanded into unoccupied areas, or displaced without admixture the previous residents, founder effects presumably occurred. In both cases, gradients of allele frequencies are to be expected (Sgaramella- Zonta and Cavalli-Sforza, 1973; Menozzi et al., 1978; Rendine et al., 1986; Cavalli- Sforza et al., 1993; for a theoretical treat- ment, see Endler, 1977). On the contrary, spatial structuring of allele frequencies is not predicted by an acculturation model, or by a model in which gene flow is not accompanied by population growth (Menozzi et al., 1978; Cavalli-Sforza et al., 1993).

The dates of origin of farming at various localities in Europe, as estimated from the archaeological record, correlate with the frequencies of several alleles in current Euro- pean populations (Menozzi et al., 1978; Sokal and Menozzi, 1982; Sokal et al., 1991). This strongly suggests that the introduction of agriculture and animal breeding was not the result simply of cultural transmission but in fact involved significant population

movements, very possibly in one or the other form of demic diffusion. Accordingly, a large fraction of the allele frequency gradients currently observed in Europe (Sokal et al., 1989a, 1992; Piazza, 1993) are interpreted as a consequence of the spread of alleles of Near Eastern origin. But little is known to date on the other regions bordering on the Near East.

Under the demic diffusion model, the dispersing farmers should have propagated their language or its derivative into the new territories, resulting in large-scale linguistic similarities. But if, on the contrary, farming spread mostly by acculturation, no major linguistic changes need be envisaged, although language change can occur in such cases without any significant movement of population (Ehret, 1988). The existence of large-scale language families whose distribution intersects with the nuclear farming area in the Near East gives support, along with other arguments, to the hypothesis that dispersing early farmers from the Near East brought with them not only the techniques of farming and the basic plant and animal domesticates, but also the proto-language ancestral to the languages of the families in question (Cavalli-Sforza, 1988; Ren- frew, 1991). This has been argued for Europe and the Indoeuropean language family (Renfrew, 1987); for north Africa and the Afroasiatic languages; for southern Iran, India, and Pakistan (the hypothetical original distribution of the Elamo-Dravidian languages); and possibly for Turkestan and central Asia (the early Altaic languages). Such a hypothesis may cast light on two puz- zling aspects of the large-scale structure of human populations. First, the clines described at various loci extend far beyond the limits of agricultural dispersal in Europe (Piazza et al., 1981; Piazza and Menozzi, 1983; Barbujani, 1987a, 1988; Cavalli- Sforza et al., 1993); second, large genetic differences are often observed at linguistic boundaries (Barbujani and Sokal, 1990; Barbujani et al., 1990, 1994; Guglielmino et al., 1990; Aguirre et al., 1991; Bertranpetit and Cavalli-Sforza, 1991).

The model of neolithic demic diffusion sets out to explain linguistic affinities of populations without resorting to undocumented

GENETIC VARIATION IN NORTH AFRICA AND EURASIA 139

migrations between them. Cavalli-Sforza and Renfrew envisage a gradual, multidirectional spread of neolithic farmers from the nuclear area in three (Cavalli-Sforza, 1988) or four (Renfrew, 1991) episodes. In each episode, the language of the first farmers re- placed preexisting languages in the area in question, giving rise to a distinct language family: Indo-European, Afroasiatic, Elamo- Dravidian, and [in Renfrew’s (1991) view1 Altaic, respectively. For a theoretical outline of how a process of this kind may occur, see Sherratt and Sherratt (1988).

This proposal is in itself strengthened by the recognition of deep similarities among the four language families in question (Dol- gopolsky, 1987; Kaiser and Shevoroshkin, 1988). The hypothetical language notionally ancestral to all of them has been termed Nostratic [the term Eurasiatic, proposed by Greenberg (19871, entails a comparable but different classification (Ruhlen, 1991, 1992)l. The Nostratic hypothesis thus classes the four language families in question within a larger, Nostratic, macrofamily. It suggests on linguistic grounds that the original homeland for the proto-Nostratic language will have been in the Near East, namely, in what, on quite independent archaeological arguments, has been identified as the area from which early farming spread.

The Nostratic hypothesis is not a necessary component of the view presented here, that the current distribution of these major language families is in large measure due to the dissemination of relevant proto-languages by demic diffusion. On the other hand, the geographical implications of our argument would lend some measure of support to the Nostratic view. Although the extent and significance of the relationships among the relevant language families (Mor- purgo-Davies, 1989; Nocentini, 19931, and between languages and genes (Bateman et al., 19901, is still controversial, the idea of multidirectional demic diffusion in the neolithic can offer a clue to understanding current patterns of genetic variation in Eurasia and North Africa. In turn, the identification of clines within certain groups of linguistically related populations would provide biological support for the Nostratic hypothesis.

To test the hypothesis of demic diffusion of proto-Nostratic speakers, or NDD hypothesis, we studied genetic variation in groups of populations postulated to derive from a common ancestor living in the Near East (Ren- frew, 1991). In a companion paper we show that these groups display larger genetic variances than comparable groups, and that their allele frequencies correlate with their geographic distance from the Near East (Barbujani and Pilastro, 1993). Here we em- ploy spatial autocorrelation statistics to summarise geographic variation of allele frequencies, and we discuss the results ob- tained in the light of other biological, linguistic, and archaeological evidence.

DETAILS ON THE NDD MODEL The four episodes of agricultural dispersal

associated with farming origin in the Near East, as hypothesized by Renfrew, are seen in Figure 1. Such a dispersal was proposed as the main, certainly not the only, phenomenon resulting in the current levels of population differentiation. During the neolithic transition and after its end [dated in Europe at approximately 5,000 years ago (Renfrew, 198711, other migratory movements took place and language replacement may have occurred for other reasons. Such processes certainly modified the simple and parallel patterns of linguistic and genetic variation expected under demic diffusion. But still, if the neolithic transition was important in de- termining the genetic population structure of a large section of the Old World, we expect to be able to recognize at least some of its consequences.

Before attempting to do so, it was necessary to correctly classify, with respect to the hypothesis being tested, some populations that adopted a new language independently of demic diffusion. Indeed, episodes of language replacement accompanied by limited genetic change [as in the three models listed by Renfrew (1989, 1992)], can decouple biological and linguistic evolution (Cavalli- Sforza et al., 1992).

Historical data on three comparatively recent episodes of language replacement (the spread of Indoeuropean languages in Asia, of the Turkic subgroup of Altaic languages in Western Asia, and of Arabic in North Af-

Fig.

1.

Four

wav

es o

f N

eolit

hic

agri

cult

ural

dis

pers

al, a

s pr

opos

ed b

y R

enfr

ew (

1991

). F

or t

he p

ur-

pose

s of

thi

s pa

per,

Ind

oeur

opea

n sp

eake

rs o

f Asi

a an

d E

lam

o-D

ravi

dian

spe

aker

s bel

ong

to th

e sa

me

grou

p. L

angu

ages

cod

ed a

s fol

low

s: IE

, Ind

oeur

opea

n (E

urop

e); A

A, A

froa

siat

ic;

ID, I

ndoe

urop

ean

(Asi

a)

and

Ela

mo-

Dra

vidi

an; A

L, A

ltaic

.


rica) suggest that they did not entail major changes in the biological buildup of the populations involved (Renfrew, 1987, 1991). These were not cases of demic diffusion, but of elite dominance (see Renfrew, 1987), associated with gene flow only to a very limited extent. Instead of trying to guess the relative proportions of former residents and recent immigrants in each locality at the end of each episode, we chose to treat these as purely cultural transformations. Negligible demographic changes were assumed to have occurred while the languages changed. This is certainly an overly simplified assumption, but it is conservative. If the NDD model proves compatible with the data, that will be despite this assumption, certainly not because of it.

Accordingly, all current speakers of In- doeuropean or Elamo-Dravidian languages from Iran and the Indian subcontinent were considered to derive from farmers who im- migrated along the Southeastern dispersal route (Fig. 1). In this way we assume that when Elamo-Dravidian languages were re- placed by Indoeuropean around 2,000 BC, allele frequencies were only marginally affected. Similarly, Turkic speakers of Anato- lia were considered as descendants of indigenous farmers who adopted Turkic only in the early second millennium AD; hence, they were not pooled with the other Altaic speakers. Finally, Arabic speakers of North Africa were excluded from analysis when- ever a sample of Berber speakers was available at the same locality; but when this was not the case, their allele frequencies contributed to the Afroasiatic dataset. In addition, recent immigrants (e.g., Jews in Europe, Parsi in India) were excluded from analysis if they could not be assigned to their place of origin.

In this way, 13 groups of populations could be defined (Fig. 2). We shall refer to them by a two-letter code throughout this paper. The first group, NE, includes all populations of the Near East, regardless of the language they currently speak. For the purposes of this study, and in agreement with the most recent hypotheses on the area of origin of agriculture (Renfrew, 1991), the Near East extends up to 50 degrees of longitude East. These populations are considered to derive in their genetic makeup from local

neolithic populations that did not emigrate and underwent only very limited admixture.

Under the NDD model, each of the following groups represents the descendants of a group of neolithic farmers who dispersed along one of the four routes of Figure 1 (Ren- frew, 1991): IE, Indoeuropeans of Europe; ID, Indoeuropeans of Asia and Elamo-Dra- vidians; AA, Afroasiatic speakers; AL, Al- taic speakers, excluding Turkic speakers of Anatolia. These groups will be jointly called NDD groups; the NDD model predicts allele frequency gradients within them, radiating away from the Near East.

Eight additional groups include populations not belonging to the Nostratic macrofamily, or belonging to it but not considered to owe their language to a process of demic diffusion from the Near East (Cavalli- Sforza, 1988; Renfrew, 1991). They are, respectively, speakers of Caucasian (CA), Uralic (UR), Sinotibetan (ST), Austric (AU), Chukchi-Kamchatkan (CK), and Eskimo- Aleut (ES) languages, plus two linguistic isolates, namely, the Basques (BA) and Hunza, the latter speaking Burushaski (BU). Some of these groups were used for comparison in a previous study (Barbujani and Pilastro, 1993) and will be jointly re- ferred to as other linguistic families.

MATERIALS AND METHODS Data

A database of Eurasian allele frequencies, previously including data on eight markers (Barbujani et al., 1990), was updated. Infor- mation was added on seven other markers and on African Afroasiatic-speaking populations. For haptoglobins, group-specific component, Duffy and Kell blood groups, we incorporated all data we could find through an extensive survey of the literature published between 1960 and 1991, including compila- tions of human allele frequencies (Mourant et al., 1976; Tills et al., 1983; Roychoudhury and Nei, 1988). Conversely, data on ABO, Rh, and MN blood groups were incorporated only for populations that were already present in the database having been typed for at least another marker, except for Afroasiatic speakers, of which we included all samples we could find.

Fig.

2.

Lan

guag

e fa

mili

es in

Eur

asia

and

Nor

th A

fric

a (R

uhle

n, 1

987)

. Lan

guag

es c

oded

as

follo

ws:

B

A, B

asqu

e; I

E, I

ndoe

urop

ean

(Eur

ope)

; AA

, Afr

oasi

atic

; CA

, Cau

casi

an; U

R, U

ralic

; ID

, Ind

oeur

opea

n (A

sia)

and

Elam

o-D

ravi

dian

; AL,

Alta

ic;

BU

, Bur

usha

ski;

ST, S

ino-

Tibe

tan;

AU

, Aus

tric

; ES,

Esk

imo-

A

leut

; CK

, Chu

kchi

-Kam

chat

kan.

The

put

ativ

e ar

ea of

orig

in o

f agr

icul

ture

(whe

re p

opul

atio

ns d

efin

ed

as N

E a

re lo

cate

d) is

in b

lack

.

143 GENETIC VARIATION IN NORTH AFRICA AND EURASIA

TABLE 1. Numbers of samples considered

Locus1 Populationgroup ABO Rh MN Kell Duffy Hp Gc GLO ESD PGM ACP AK ADA PGD G W

Indoeuropean of Europe

Indoeuropean of Asia and Elamo-Dravidian

Afroasiatic Altaic Near East Uralic Caucasic Sinotibetan Austric Totals

109 75 89

39 35 41

33 31 37 33 25 33 14 14 16 12 9 17 10 9 8 24 17 16 18 10 16

292 225 274

100 60

21 23

30 24 12 9 12 10 17 16

7 5 17 12 14 14

230 173

152 93 69

48 31 20

30 16 9 41 22 15 15 8 7 17 17 7 6 4 7

24 13 13 23 23 10

356 226 157

84

48

5 18 6 7 7

13 16

205 -

92

45

12 39 10 12 3

16 20

249 -

92

38

20 28 10 9 8

12 11

228 __

15 68 73

45 25 38

19 6 20 13 15 30 6 11 9

12 10 9 3 3 3

11 10 11 13 13 28

197 161 225

46

10

4 18 1 5 2 8

12 106

‘Loci abbreviated as follows: Haptoglobins, Hp; group-specific component, Gc; glyoxalase I, GLO; esterase D, ESD; phosphoglucomutase, PGM; acid phosphatase, ACP; adenylate kinase, AK; adenosine dearninase, ADA phosphogluconate dehydrogenase, PGD; glutamic-pyruvic transami- nase. GPT

Linguistic classification Each record of the updated database con-

tained allele frequencies for one locus, the sample size, and the geographic coordinates of the sampling locality. Following Ruhlen (19911, each population was then assigned a language family code, unless it belonged to the Near East. Overall, 222 allele frequencies were available for the CA, CK, ES, BA, and BU groups. These data were insuffi- cient for the statistical analysis described below within each group and had to be discarded. In addition, samples were discarded when their linguistic identity could not be defined with certainty. The database analysed includes 3,304 records (Table 1).

Spatial autocorrelation analysis Spatial autocorrelation statistics (Sokal

and Oden, 1978) summarize spatial patterns of genetic similarity. An arbitrary number of distance classes is defined for each set of data, and an autocorrelation coef- ficient (Moran’s I in the present study) is calculated within each of them, represent- ing the average level of genetic similarity between the pairs of populations separated by that distance. In this study, eight distance classes were generally employed (de- tailed in the caption to Table 21, but for a few markers and groups the distribution of sampling localities was such that different numbers of classes, or different class limits, had to be chosen.

In large samples, Moran’s I values range from +1 (for perfect identity of all pairs of values in a distance class: positive autocorrelation) to - 1 (when all pairs of values fall on opposite sides of the average: negative autocorrelation). Under a randomization hypothesis, the value expected in the absence of autocorrelation is close to 0. Criteria for assessing the significance of autocorrelation statistics are in Sokal and Oden (1978) and Oden (1984).

Rare alleles were not considered. In addition, at each locus one polymorphic allele was discarded to reduce the effects of the negative correlations among allele frequencies. Overall, 19 independent alleles were analysed.

The set of autocorrelation coefficients calculated for one allele (correlogrum) allows an objective definition of the spatial modes of variation shown by that allele. Schemati- cally, the following classes of correlograms were recognised here (Sokal, 1979; Sokal and Wartenberg, 1981; Sokal et al., 1989a). All of them, except class 1, had to be overall significant by the Bonferroni criterion (Oden, 1984).

1. Nonsignificant: levels of genetic variation statistically compatible with chance, no spatial structure apparent.

2. Cline: a decreasing series of coefficients, from positive in the first distance classes, to negative at large distances. Clines represent the strongest genetic evi-

144 G. BARBUJANI ET AL

TABLE 2. Autocorrelation coefficients (Moran's 1) for 19 alleles in seven population groups

Distance class Allele 1 2 3 4 5 6 7 8 P

Indoeuropean of Europe and Near East ABO-IA .43 .30 ABO-ZB .61 .36 Rh-D .51 .41 MN-M .22 .05 Kell-K .04 .oo Duffy-a .17 .07 HP' .10 - .09 Gc' .21 -.01 GLO' .63 .ll ESD' .30 .ll PGM' .38 .03 A C P .40 .26 ACp6 .41 .16 AK' .37 .04 ADA' .33 .26 PGD" .08 .17 P G P .25 .12 GPT' .07 -.15 G P F -.01 -.09

Indoeuropean of Asia, Elamo-Dravidian, ABO-ZA .62 .16 ABO-ZB .29 .28 Rh-D .32 51

Kell-K -.30 .17 Duffy-a .22 .28 HP' .66 .41 Gc' .ll .02 GLO' .ll .08 ESD' .47 50 PGM' .27 .10 A C P .oo .08 ACPb .07 .27 AK' .33 .18

PGIP .20 .14 P G P .21 .15

ABO-ZA .89 .25 ABO-IB .37 .28 Rh-D -.02 -.lo MN-M .35 .12 Kell-K .78 - .33 Duffy-a .70 .24 HP' .26 .32 Gc' -.13 .17 GLO' .07 .49 PGM' .43 -.17 A C P .36 .40 A C P .38 .44 AK' .73 .28 ADA1 .20 - .06 PGD" .16 - .24 P G P .16 p.24

MN-M .ll p.25

ADA' -.32 -.04

Afroasiatic and Near East

Altaic and Near East ABO-ZA .75 .13 ABO-ZB .26 .40 Rh-D .87 .61 MN-M .31 .18 Kell-K .45 .40 Duffy-a .64 .68 HP' .36 .13 Gc' .22 .28 GLO' 1.00 1.00 ESD' 5 2 .55 PGM' 5 5 .45

.04

.23

.28

.04 -.03

.01

.03

.07

.05

.06

.01

.06

.09 -.12

.16

.04

.12

.01

.02

.12

.44

.37

.06 p.03

.12

.42 -.07

.01

.44 -.lo -.05

.03

.40

.06

.03

.03

.13

and Ne

.15

.03

.29 -.09 .31 .23 .09

-.02 .lo .09 .10 .25

-.12 .14 .14

-.13 .13 .61 .32 .07 .68 .13

-.22 .86 .62 .38

-.20 .07 .10 .03

-.01 -.03 -.03 -.03 -.03

.03

.08 -.lo -.07 -.12

.16 -.05

.04 -.05 -.05

.01

.15

.25

'ar East

-.04 - .07 -.20

5 6 p.18 -.lo

.08

.03

.03

.10

.02 -.02 - .08 -.07

.oo

.20

.22

.17 -.lo - .35

.19

-.13 -.02 - .09

.06

.01

.12 -.01 - .09 p.19 -.03 - .08 -.05 - .07

.08

.05 - .03 - .04

.01

.02

-.02 .05 .20 .09

p.09 -.08

.26 -.06

.19 -.03

.12 -.01

.08

.21 p.07 - .28

.28

.02

.01 -.06

.08

.01 -.08 -.03

.55 -.33 -.23 -.15 -.04 -.04 - .25 -.04 - .24 -.09 -.21 -.19 -.14 .05 -.02 .10 -.02 .10

-.06 .08 .27 .08 .68 .59 .37 .06 .42 .18 .80 1.00 .21 -.11 .04 -.17 .23 -.05 .60 .12 .12 .08

.08 .02 - .07 -.78 -.17 .43

.04 -.17

.02 -.07 -.06 p.20 .01 -.01

-.09 .07 -.05 - .30 -.lo -.30

.01 -.25 -.05 -.12 -.07 -.lo

.05 - . lo - .09 - .63 -.03 -.11 - .08 -.32 -.01 .09 p.05 .15

.03 -.l8 -.08 -.41 -.08 - .42

.oo -.01 - .09 .07

.10 -.06 - .02 -.28

.oo .03

.12 .02 -.14 -.61 -.06 - . lo

.02 -.21 -.02 -.30 -.19 -.27

. 00 -.04

.08 -.06

.06 -.05

-.05 -.31 -.32 -.08 -.09 -.lo

.04 -.18

.01 -.03

p.08 - .42 .05

-.14

-.18 -.18

-.19 -.02

.71 -.38

.59

.02 -32

-.27 -.75

.17

- .03

-.31

-.03 -.03

-.17 -.12 -.21 -.18

.17 - .96 -.31

-.84

.39

-.16 -1.00 -.64 - .34 -.07 -.08 -.06

.10 -.09 -.31 - .35 -.49 - .44

.09 -1.00 -.15 -.45

-.19 .21

-.34 -.06 -.04 -.19 -.a

.oo -.35 -.41 -.11

.03 -.12 - .26

.03

.02

.01

-.07 .08 .01

- .35 . 00

- 3 4 .~

-57

-.07

-.lo -.lo -.12 -.23 -.87 -.16 -.41

-.06

-.47

0.00016 0.00016 0.00016 0.00016

ns 0.0008 0.008 0.008 0.00016 0.00016 0.00016 0.00016 0.00016 0.00016 0.00016 0.00016 0.00016

ns ns

0.00016 0.00016 0.00016

ns ns ns

0.00016 ns

0.0016 0.00016

ns ns

0.00016 0.00016

ns 0.008 0.016

0.00016 0.00016

ns 0.00016 0.00016 0.0001 0.00016 0.0016

ns ns

0.00025 0.00025 0.05

ns ns ns

0.0008 0.00016 0.00016 0.00016 0.00016 0.00014 0.00016

ns 0.00014 0.00012 0.00016

Continued


TABLE 2. Autocorrelation coefficients (Moran's I) for 19 alleles in seven population groups (continued)

Distance class ~

Allele 1

A C P ACPb AK' ADA' PGD" PGD" GPT' G P F

Sinotibetan ABO-I* ABO-IB Rh-D MN-M Kell-K HP' PGM'

ABO-IA A B O - I ~ MNM Duffy-a

Gc' ESD' PGM' PGD" P G P

Austric

HP'

Uralic MN-M Kell-K Duffy-a

Gc' HP'

.19 27 .56 .70 .32 .30

-.17 -.17

.05

.15 -.61 -.66

.13 1.00 2 3

.13

.17 - 2 2 -.07

2 7 .46

-21 .10

-24

.12

.61 5 0 .31 .85

2 3 4 5

.01

.15

.35

.78

.60

.42 -.07 -.06

.37

.57 2 5 .88 .46 .40

-.09 -.lo

.08

.21

.51

.68

.46

.40

.05

.05

21 .15 -.03 .38 -.05 -.31

-.16 2 0 .03 -25 .18 .01 -.lo 2 5 -.01

.89 .68 2 9 2 4 -.06 .01

-.67 - 2 0

.03 -.13

.40

.18

.49

.01

.03 -.lo

-.31 -.19 .16 -.04 2 6 -20

-.06 -.16 .06 .03 .oo .32

-.62 .68 .31 .ll .38 .35

-.14 .13

.03 -.06 -.02

.ll .lo .oo

.07 -50 2 8 -.09 -.02 .32

.53 2 4 -.02

-.05 .26 .05 .23 .22 .21

-.03 -.03

-.04 -.31 - .09 -.18

-10 -.40 -.15

-.19 .22 .33 .07 .13

- .04 -.02 -.05

.39

.03

.07 -.35

.28 -.03

6

-.04 .16

-.61 -.51 - .32 -.37

~

-.06 .08

-.18 .06

-.19 - .50 -.35

.05

.06 -21

.08

.05 - .21 -.18 -.06 -.05 -.02

.02 - .08 -.14

.03 p.87 -1.00

7

-.lo -.03 -.77 -21 -.18 -.11

__

-.16 .15 .09

- 2 2 -.19 - 5 0

.ll

.08 - 2 8 -.19 -.44 -.34 -21 -.09

.02 <:-.12

.07

- 6 2 P.70

-1.00 - .99

8 P

-.19 - .31

-.65 - .36 -.30

-.07 -.32

.12 -.41

-1.00

-.09 -24 -.45 -.07

-.84 - .77

-1.00

0.0008 0.00016 0.00014 0.00016 0.00016 0.00016

ns ns

ns ns ns ns ns

0.00014 ns

ns ns ns ns

0.00016 ns ns ns

0.00016 ns

ns 0.008 0.00014 0.008 n.nnni2

~

Significant autocorrelation coefficients are in boldtype. Pare upper limits of Bonferroni probabilities for the whole correlogram (Oden, 1984). Upper distance class limits are as follows: 1: 300, 2: 600, 3: 1,00", 4: 1,500, 5: 2,000, 6: 3,000, 7: 4,000, 8:.8,000 km; due to the distribution of sampling localities, for some alleles different numbers of classes had to be chosen. For seven classes, upper class limits are as follows: 1: 500,Z: 1,000,3: 1,500,4: 3,000,5: 4,000,6: 6,000, 7: 8,000 km; for six classes, upper class limits are as follows: 1: 500,Z: 1,000,3: 2,000,4: 3,000,5: 5,000, 6: 8,000 km; for five classes, upper class limits are as follows: 1: 500, 2: 1,000, 3: 1,500, 4 3,000, and 5: 8,000 km. Upper class limits for Sinotibetans are as follows: 1: 300,Z: 600, 3: 1,000, 4: 1,500, 5: 2,000, 6: 3,000, 7: 4,000 km.

dence for demic diffusion of early farmers in Europe (Menozzi et al., 1978; Ammerman and Cavalli-Sforza, 1984).

3. Long-distance differentiation: largest negative autocorrelation at the largest distances. When near populations resemble each other genetically, then a long-distance differentiation pattern is just a cline whose central part shows a somewhat irregular variation and will be classified as a cline in the following sections of this paper. We shall speak of long-distance differentiation only in the absence of positive autocorrelation at short distances.

4. Isolation by distance: a decrease of genetic similarity with distance, from positive to insignificant (Barbujani, 1987b). This is expected when only drift and short-range dispersal affect populations (Kimura and Weiss, 1964; Morton et al., 1968, 1971),

within distances not much greater than individual dispersal distances (Yasuda and Morton, 1969).

5 . Depression: highest negative autocorrelation in one of the central classes, genetic differences at the extremes of the range either smaller or nonsignificant.

6. Intrusion: highest positive autocorrelation in one of the intermediate distance classes, genetic differences at the extremes of the range insignificant.

Only clines were considered consistent with the demic diffusion model. This is certainly a conservative choice with respect to the hypothesis being tested. First of all, a certain initial allele-frequency differential is necessary for any migratory process to determine a cline (see Sokal et al., 1989b); even under demic diffusion, clines can be

G. BAREXJJANI ET AL. 146

established only if the genetic differences between the expanding and the recipient populations are larger than drift variances. In addition, drift and sampling variances add random noise to the autocorrelation patterns (Slatkin and Arter, 1991), and clinal distribution of allele frequencies may some- times evolve into, or be described as, long- distance differentiation patterns. These patterns were not considered to support the NDD model. Basically, in this way we re- garded as compatible with the NDD model only the patterns that by no means could be determined by random divergence of allele frequencies.

REFORMULATING THE NDD MODEL For the purposes of this paper, the NDD

model may now be restated as follows. Alle- les of Near Eastern origin spread in the neolithic, following four major routes. As a consequence, one should observe clinal or quasi- clinal autocorrelation patterns, when each of the IE, ID, AA, and AL population groups is analysed together with the NE populations; the last mentioned represent the current closest relatives of the populations that underwent demic diffusion. Clinal or quasi- clinal patterns should be uncommon in the other linguistic families, unless other demic diffusion processes took place there.

Joint analysis of the NDD groups with the Near Eastern populations is necessary to ensure that the clines detected, if any, are really consistent with the spread of individuals from the Near East. Indeed, a different statistical technique described several clines in which the genetic differences with the Near East increased at decreasing distances from there, among ST, CA, and UR speakers (Barbujani and Pilastro, 1993).

RESULTS The spatial autocorrelation coefficients

calculated are in Table 2; significances are two tailed. As expected, significant spatial structure is the rule rather than the exception. This is partly due to the large size of the regions considered, with populations at the extremes of the range being often separated by several thousand kilometers. Ran- dom genetic divergence (and negative autocorrelation) is to be expected when distances

are so large. On the other hand, especially among the NDD language families, many alleles have similar frequencies in spatially close localities, as shown by the positive autocorrelation in the first distance class. This is the likely consequence of isolation by distance, causing a decline of population resemblance over limited distances (Yasuda and Morton, 1969; Wijsman and Cavalli- Sforza, 1984), as is to be expected under a wide range of evolutionary conditions. Ex- ceptions are ST- and AU-speaking populations, where even at small distances genetic variation appears generally random.

Among NDD language families, most patterns (56/71) significantly depart from randomness, as assessed by Bonferroni criterion (Oden, 1984). Clines are observed for 9 out of 19 alleles in the IE group, 7 of 17 in ID, 3 of 16 in AA, and 12 of 19 in AL (Table 3). An example of the relationship between longitude and allele frequencies is shown in Figure 3, where a clinal decrease of the frequency of the ADA2 allele with distance from the Near East is evident in two linguistic groups. When the frequencies of this allele were studied regardless of linguistic classification, the overall pattern did not appear clinal (Barbujani, 1987a) because of the opposite trends among ID and AL speaking populations. Among the other families, conversely, there is some evidence of patterns compatible with demic diffusion processes in the UR, but not in the ST and AU groups.

Statistically perfect, or nearly so, clines prevail for IE speakers (nine alleles), whereas a discontinuous decrease of autocorrelation with distance is common in the AL group (eight alleles), perhaps reflecting a more irregular distribution of sampling localities. In the ID group, both kinds of clinal patterns are observed. The loci showing gradients differ among language families. Among the 15 alleles showing at least one significant gradient in the NDD groups, only for ESD is variation clinal and significant in all the groups tested.

Although a discussion of the possible adaptive values of these polymorphisms would be out of place here, this does not suggest that gene-frequency gradients are due to selection affecting specific alleles in specific environments, but rather it points to

GENETIC VARIATION IN NORTH AFRICA AND EURASIA 147 TABLE 3. A summary of spatial autocorrelation results-Nostratic demic diffusion groups

Population group

of Europe Asia, Elamo-Dravidian, Afroasiatic Altaic and Near East

Indoeuropean Indoeuropean of

Allele and Near East and Near East and Near East

ABO-I* ABO-IB Rh-D MN-M Kell-K

Duffya HP'

Gc' GLO' ESD' PGM' ACP" ACPb AK' ADA' PGD" PGD' GFT1 GPTZ

Depression Cline Cline Cline ns

Depression Isolation by

distance Depression Depression Cline Cline Cline Cline Depression Cline Depression Cline ns ns

Cline Cline Cline ns ns

ns Cline

ns Cline Cline ns ns Depression Cline ns Depression Depression - -

Depression Depression ns Cline Isolation by

distance Depression Cline

Long dist. diff. ns

ns Depression Depression Cline ns ns ns

-

Cline Cline Cline Cline Long dist. diff.

Cline Depression

ns Cline Cline Cline Long dist. d Z . Long dist. diff. Cline Cline Cline Cline ns na

gene flow processes affecting all the genome, and evident only at certain loci (Slat- kin, 1985,1987).

The spatial patterns among AU speakers do not depart from randomness for any of the 11 alleles tested (summarized in Table 4). Only the distribution of Hp variants among ST speakers corresponds to the ex- pectations of a model of demic diffusion. A greater number of significant spatial patterns is detected among UR speakers, where, however, only five loci could be tested.

DISCUSSION Spatial autocorrelation analysis among

speakers of languages whose distribution is here attributed to the NDD process reveals a strong patterning of genetic variation. Ge- netic variation appears largely consistent with a process of multidirectional demic diffusion: Nearly half of the alleles studied show clinal patterns. This seems remark- able because, as mentioned earlier, gene flow can determine a cline only in the pres- ence of a substantial initial allele frequency difference between populations. Computer simulations suggest that such a difference could be close to 40% (Sokal et al., 1989b), that is, larger than that observed at most

loci between current black and white populations (see Roychoudhury and Nei, 1988). Therefore, under the NDD model, clines could not occur in more than a limited fraction of the genome, and even less so in the absence of a population expansion process.

A n alternative explanation for the gradients observed, still consistent with demic diffusion, is a series of founder effects occurring in a phase of population expansion not accompanied by admixture, but followed by local gene flow. The genetic consequences of such a process have not been modelled in detail, but empirical studies in natural populations whose history is known show gradients that were doubtless determined in this way (Easteal, 1985, 1988). Computer simulations (unpublished results by Barbujani, Sokal, and Oden) confirm that some allele frequency gradients in Europe are compatible with founder effects during a population expansion. At the present stage, therefore, a role of founder effects cannot be ruled out, but it remains largely to be explored.

In the NDD language families, eight significant clines are apparent at the AK, ADA, PGD, and GPT loci, which did not show clinal variation in a study where populations were jointly analysed by spatial autocorrelation methods, regardless of linguistic classi-

148 G. BARBUJANI ET AL.

0.25

0.20

0.15

0.10

0.05

0.00

0 00

@y ""0

0 0

$ * 0

0

A

A A

A A

A

AAA 2 A A

A A A A

A

A AA

1 1 I I

0 50 100 150

Longitude

Fig. 3. Plot of ADA' allele frequencies in the Near East (solid stars), and in the IE (open circles), ID (open triangles), AA (solid circles), and AL (solid triangles) groups. Clinal variation consistent with the spread of the ADA' allele from the Near East is evident among IE and AL speakers.

TABLE 4. A summary of spatial autocorrelation resu l t sa ther language families

Population group Allele Sinotibetan Austrie Uralic

ABO-I* ns ABO-IB ns Rh-D ns I@-M ns Kell-K ns Duff' -

HPl Gc' ESD' - PGM' ns PGD" - PGD" -

Cline -

ns ns

ns -

-

ns Long dist. diff. Isol&ion by dist.

ns ns

ns Long dist. diff.

- - - ns

Cline Cline Cline Cline - - - -

fication (Barbujani, 1987a). The patterns shown by AL-speaking populations differ little from those observed among IE and ID, thus supporting the view whereby Altaic languages should be included among those that were propagated by demic diffusion (Renfrew, 1991).

Although it is impossible exactly to quan-

tify the departure from a model not including major population movements from the Near East, these results are incompatible with both random variation and pure isolation by distance, that is, the two likely mi- croevolutionary scenarios associated with cultural diffusion of farming from the nuclear zones.

Previous studies showed that genetic variation is larger in the NDD groups than in four other families and in the Near East, as should be expected if the former evolved through incomplete admixture between genetically heterogeneous populations. Also, a direct proportionality was demonstrated between genetic distances and geographic distances from the putative place of origin of farming, the Near East (Barbujani and Pi- lastro, 1993). These findings, along with the evidence here provided by spatial autocorrelation analysis, correspond to the expecta- tions of a model whereby the current patterns of genetic and linguistic resemblance largely reflect the centrifugal spread of neo-

149 GENETIC VARIATION IN NORTH AFRICA AND EURASIA

lithic farmers from the Near East, or the NDD model.

The evidence supporting the NDD model is very strong for the IE, ID, and AL groups, less so for AA speakers. These populations also showed the least marked, although significant, correlation between genetic differences and geographic distances from the Levant (Barbujani and Pilastro, 1993). Methodological reasons, namely, the limited number of data points available and their irregular distribution in space, may have contributed to conceal a significant genetic structure. However, this argument applies to the ST and AU populations as well and, furthermore, cannot be tested. Therefore, based both on this spatial autocorrelation analysis and on the results of a comparison of genetic and geographic distances, there seems to be only scant evidence for demic diffusion in the area where AA languages are currently spoken.

According to Ruhlen (personal communi- cations, cited in Renfrew, 19911, AA languages spread from Africa into Asia, and not vice versa. The few clines observed in this and in the previous study, although significant, would then be due to phenomena other than neolithic demic diffusion. One such phenomenon could be the expansion of Ar- abs, in historical times. That expansion, however, is unlikely to have caused the clines here described, since few Arabic- speaking populations of North Africa were considered. The vast majority of African samples in the AA group, on the contrary, spoke Berber or Cushitic. A drawback of this was that most AA populations in this study occupy marginal zones in the AA-speaking region. These zones may have been affected only marginally by demic diffusion, and their inhabitant’s genetic pool may include a large fraction of genes coming from previous Paleolithic residents. In addition, somatic characteristics and allele frequencies at the Rh, Gm, and HLA loci among Cushitic speakers suggest substantial Caucasoid admixture (Excoffier et al., 1987).

Accordingly, a t least three views on the diffusion of AA languages are consistent with the results of this study: (1) AA languages spread through demic diffusion in the neolithic, but the populations speaking

these languages underwent major demographic transformations, and the clines resulting from demic diffusion are now apparent only at a limited set of loci; (2) AA languages spread from Africa by a yet-to- define demographic process that led to the establishment of a limited number of gradients; (3) AA languages spread mostly through cultural contacts, either from Africa or from Asia, and the clines identified in the AA-speaking area are due to microevolu- tionary phenomena that have nothing to do with linguistic evolution.

Two papers that we were not aware of in the phase of development of this study suggest that views (2) and (3) may be more likely than (1). According to Starostin’s (1990) glottochronological calculations, under the assumption of a constant rate of linguistic divergence, Proto-Afroasiatic should have separated about 15,000 years ago from the other Nostratic proto-languages, that is, much earlier than posited by the NDD model. Conversely, the estimated times of separation among Proto-Indoeuropean, Proto-Elamo-Dravidian, and Proto-Altaic agree with the dates of demic diffusion estimated by archaeologists (Starostin, 1990). Recent linguistic findings, therefore, show a surprising agreement with the results of our analysis of genetic variation.

Other genetic observations are consistent as well. Mitochondria1 DNA data show evidence of two population expansions, in Eu- ropeans, and in Arabs and sub-Saharan Af- ricans, respectively, which occurred recently relative to the whole history of Homo sapiens sapiens (Templeton, 1993). The large genetic differences between these groups suggest that the two expansions have been largely independent.

Among the linguistic families other than AA, IE, ID, and AL, most patterns of genetic variation appear to be random; UR speakers are a possible exception. The quasi-clinal patterns observed among them, however, may largely reflect East-West differences between populations that share common an- cestors (whose existence is documented by linguistic similarities), but who then evolved in virtual independence, being separated at present by five to eight thousand kilometers; indeed, genetic distances be-

G. BARBUJANI ET AL. 150

tween the UR and the NE groups do not correlate with the respective geographic distances (Barbujani and Pilastro, 1993). The few clinal patterns observed in a former study among AU speakers (Barbujani and Pilastro, 1993) are not confirmed by this spatial autocorrelation analysis.

These findings are in contrast with what is observed in other continents. Native American populations are markedly differ- entiated, but only in North America do they show large-scale genetic structure (Suarez et al., 1985; Schurr et al., 1990; ORourke et al., 1992; Torroni et al., 1992). In addition, with very few exceptions (Barrantes et al., 1990), genetic and linguistic differences appear largely uncorrelated (Chakraborty, 1976; Chakraborty et al., 1976; Salzano et al., 1977; Murillo et al. 19771, or negatively correlated (Spuhler, 1972), which does not suggest coevolution of biological traits and languages. The overall picture emerging is one in which isolating mechanisms have caused founder effects and favoured random genetic drift, with processes of gene flow playing a comparatively minor evolutionary role (see also Cavalli-Sforza et al., 1993). In sub-Saharan Africa, conversely, genetic variation appears spatially random at the few loci studied on a sufficient scale, but genetic and linguistic distances correlate (Excoffier et al., 1987,19911, which points to long-distance displacement of linguistically related groups. Eurasia is clearly the conti- nent where the tightest relationships are evident among different kinds of population markers, genetic and cultural, and between them and geography.

Is the NDD model the only possible explanation for these relationships? Local gene flow is expected to generate only the patterns that we classified as isolation by distance (see Wijsman and Cavalli-Sforza, 1984; Barbujani, 1987a). To determine a significant autocorrelation structure over such a vast area, massive demographic phenomena (or, less likely, adaptive pressures) appear necessary. However, no single recent, archaeologically or historically documented evolutionary process, seems to have had the potential for establishing clines over entire continents. Some or all Eurasian populations have been affected by other demo-

graphic processes, such as long-range migration (Gimbutas, 1979; Anthony, 1986), dispersal not associated with the origin of agriculture (Sokal, 19911, and local extinc- tions and recolonizations, all of them poten- tially affecting genetic andor linguistic diversity. However, it would be difficult to explain how processes such as these, independently occurring in distinct areas, could eventually yield such a strong correspon- dence between patterns of linguistic and biological variation on a continental scale. Unless one evolutionary pressure predom- inated, and largely determined both genetic and linguistic differentiation, languages and allele frequencies should be only poorly associated. It seems reasonable to conclude that phenomena occurring after the neolithic may well account for the genetic characteristics of specific populations (see e.g., Cavalli-Sforza and Piazza, 1993), or for clines at few individual loci, but the overall population structure of Eurasia has proba- bly been determined in the neolithic, or earlier.

The possibility should then be considered that the clines here described originated earlier than 10,000 years ago. Despite some controversies in the interpretation of single pieces of evidence (Excoffier and Langaney, 1989; Maddison, 1991; Templeton, 1992), genetic and fossil data show some agreement in indicating that Homo sapiens sapiens originated in Africa (Wainscoat et al., 1986; Cann et al., 1987; Stringer and An- drews, 1988; Rouhani, 1989; Vigilant et al., 1991; Livingstone, 1992; Templeton, 1993) and was present in the Near East approximately 90,000 years ago (Valladas et al., 1988). This means that two groups of humans dispersed from the Levant at different times, along similar routes. The first group was composed of Paleolithic hunter-gatherers coming north from Africa, who colonized Eurasia between 90 and 20 thousmd years ago (Renfrew, 1992a); the second group was composed by the first neolithic farmers, who started expanding in Eurasia roughly 10 thousand years ago (Renfrew, 1987,1992a). Both dispersal waves could have left a per- sistent mark in the genetic structure of contemporary populations, in the form of clines. The first wave could have done that through


a series of founder effects, the second wave either through the same mechanism or because of admixture with the populations en- countered during demic diffusion, or by a mixture of the two.

Schematically, there seem to be two classes of evidence in favour of a Paleolithic origin of the gradients we described:

1. Some gradients encompass the area where U R languages are spoken, which also belong to the Nostratic macrofamily (Kaiser and Shevoroshkin, 1988), but are not considered to have undergone neolithic demic diffusion.

2. Evidence of gradients is weakest (if still significant under certain statistical criteria; Barbujani and Pilastro, 1993) among AA. Some implications of this finding have already been discussed. The clines observed nowadays at the MN, Hp, and AK loci in AA-speaking populations may then depend on processes preceding neolithic demic diffusion.

On the contrary, three types of consider- ations support a neolithic origin of the clines here described:

1. If these clines are not due to the farmers’ dispersal, an alternative explanation should be found for the parallelism between patterns of genetic and linguistic variation, demonstrated by many authors in the Old World (Cavalli-Sforza et al., 1988, 1992; Sokal, 1988; see also Barbujani, 1991 and Renfrew, 1992b), including Africa (Excoffier et al., 1987). A Paleolithic origin of present- time language families, paralleling the initial dispersal of Homo sapiens sapiens, seems highly unlikely. Actually, critics of the models of coevolution between language and genes support a more recent origin of current language families (see, e.g., Cole- man, 1988, for Indoeuropean, and Cal- laghan, 1990).

2. The populations speaking ST and AU languages do not belong to the clines, in agreement with the NDD model, and in contrast with the likely consequences of the spread of hunter-gatherers in Asia, which

cannot have been affected by linguistic bar- riers established much later.

3. As stated earlier, there is ample archaeological evidence of cultural diffusion processes in the neolithic, which does not prove demic diffusion, but is perfectly consistent with it (Ammerman and Cavalli-Sforza, 1984; Renfrew, 1987, and references therein).

Of course, it is possible that both paleolithic and neolithic processes determined the strong patterning of genetic variation that is evident in contemporary populations of the NDD groups. Neolithic demic diffusion may have both reinforced some genetic effects of previous Paleolithic migrations and may have contributed to concealing others.

As Langaney et al. (1992) pointed out, rec- onciling human phylogeny with archaeological and linguistic evidence is no easy goal, and one that no single study is likely to achieve. At this stage, it may be worthwhile to reconsider the NDD model, which we had to put forward in a certainly oversimplified way, to allow testing of its predictions.

If the AA-speaking populations are considered not to have expanded through demic diffusion in the neolithic, one of the two main biological objections to the NDD model is removed. Genetic data are then fully compatible with demic diffusion in three areas of Eurasia, regardless of the statistical technique employed for the analysis. These areas represent linguistic units, which further corroborates the already abundant evidence for coevolution of biological and cultural traits (Sokal, 1988; Cavalli-Sforza et al., 1988,1992). Whether or not this multidirectional demic diffusion accounts for the gen- eral pattern of linguistic relatedness in Eur- asia is open to some doubt. Indeed, not all language families comprised in the Nos- tratic macrofamily may owe their current distribution to neolithic demic diffusion. At present, a strong case can be made for parallel diffusion of farming and language among IE, AL, and ID speakers, by means of a process that involved major demographic changes (and not purely cultural transformations).

152 G. BARBUJANI ET AL

The results of this study are therefore consistent with the view that current biological and linguistic characteristics of most Eur- asian populations largely depend on a single dispersal phenomenon. Neolithic farmers diffusing from the Near East presumably brought their languages and their genes into new territories, thus establishing conti- nentwide clines that often end at language- family boundaries. This pattern is still rec- ognizable despite successive processes of population subdivision, drift, and gene flow that locally altered it. The status of Afroasi- atic-speaking populations will need further studies to be defined, but linguistic and genetic evidence agree in suggesting that their evolutionary history might have been different.

ACKNOWLEDGMENTS We thank Robert R. Sokal, Luca Cavalli-

Sforza, Laurent Excoffier, and Michael Turelli for stimulating discussion across the years. Funds for this study were provided by the Italian Ministry for the University and Scientific Research (MURST).

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