genetic variation in north amerindian populations: association with sociocultural complexity

AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 67:217-232 (1985)

Genetic Variation in North Amerindian Populations: The Geography of Gene Frequencies

BRIAN K. SUAREZ, JILL D. CROUSE, AND DENNIS H. O’ROURKE Departments of Psychiatry (B.K.S., J.D.C., D. H. O R . ) and Genetics (B.K.S.), Washington Uniuersity School of Medicine, The Jewish Hospital of St. Louis, St. Louis, Missouri 63110 Department of Anthropology D.H. O’R.), University of Utah, Salt Lake City, Utah 84112

KEY WORDS Maps, Migration, Principal component analysis

Amerindian, Gene frequencies, Image processing,

ABSTRACT Ten-level synthetic gene frequency maps derived from a principal component analysis of seven polymorphic loci are displayed for a large sample of North Amerindian populations. These maps are useful for assessing population affinities over broad geographical regions and perhaps, as others have argued, for inferring recent migrations. The influence of European admixture is investigated by deleting highly admixed populations and regenerating the maps. In broad outline the resultant geographic patterning, while appearing more homogeneous, preserves many features of the maps that include the highly admixed samples-especially with respect to the Eskimohon- Eskimo dichotomy. Further, in an effort to evaluate how varying the number of display levels affects patterning as well as interpretation, the maps were replotted at 5 and 20 levels. The 5-level maps are found to accentuate differences between the full data set and the less admixed data set, while the 20- level maps tend to obscure these differences.

Native American and Australian populations constitute natural experiments that allow physical anthropologists and population geneticists alike the unique opportunity to observe the effects of migration, selection, and drift. In both cases, migrating bands en- countered large uninhabited land masses. In terms of human history, the American and Australian migrations are remarkable for their recency. Nonetheless, sufficient time has passed to allow a great deal of microevo- lutionary differentiation. A conventional approach for assessing genetic variation on a continental level is to construct maps showing the geographic distribution of individual allele frequencies. Such maps have been used primarily as a tool to aid in the search for the remnants of two evolutionary forces; namely, selection and migration (Mourant, 1954). Indeed, the striking similarity in the geographic distribution of endemic malaria with that of sickle cell anemia constituted powerful evidence that selective forces were at work (Allison, 1954).

Recent advances in computer graphics have made possible the display of detailed surfaces. When coupled with multivariate sta- tistical techniques, it has become feasible to display complex surfaces that summarize the information available a t many different loci. Optimal techniques for the construction and testing of synthetic gene frequency maps are still being developed (Piazza et al., 1981b); however, a number of provocative findings have already been reported. For instance, Menozzi et al. (1978) have used data from 38 independent alleles to produce synthetic gene frequency maps of Europe and the Near East and have interpreted their surfaces as evidence that the spread of agriculture in Eu- rope was accomplished by the movement of Near Eastern Neolithic farmers rather than by the direct diffusion of farming technology (see also Sokal and Menozzi, 1982). On a larger scale Piazza et al. (1980, 1981a,b) have

Received December 13, 1983; revised March 5, 1985; accepted March 14,1985.

0 1985 ALAN R. LISS. INC

218 B.K. SUAREZ, J.D. CROUSE, AND D.H. O’ROURKE

constructed synthetic gene frequency maps for the entire world, which they interpret as supporting the hypothesis that modern hom- inid populations spread from an origin in Southern Asia. Moreover, these maps sug- gest that while most genetic variation is as- sociated with longitude, some genetic systems may reflect environmental selective pressures, since their frequencies covary with latitude (which may be taken as a crude in- dex of climatic variation).

The purpose of the present study is to examine the geographic patterning of genetic variation in a sample of native North Amer- indian populations. It is hoped that by in- specting maps of synthetic gene frequencies, testable hypotheses regarding the evolutionary mechanisms responsible for the observed patterning will be suggested. Particular at- tention will be paid to the effects of European admixture and the possibility of making in- ferences regarding precontact migrations from present day patterning.

MATERIALS AND METHODS

In order to assemble a data base from which to generate the synthetic maps of North Amerindian populations, it became necessary to establish fairly rigid inclusiodexclu- sion criteria. An optimal sample would consist of a large series of geographically well dispersed populations. Ideally, the sample size of each population would be sufficient to guard against wide sampling error, and, under the best of circumstances, each population would be typed for the same genetic systems using identical reagents and labora- tory procedures. The criteria finally chosen represent an expedient compromise between the desire to base the maps on as many “unique” populations as possible and the desire to include as many genetic systems as possible.

In order to be considered for inclusion in the present data base, the population samples had to have been minimally assessed for three blood group systems: ABO, RH, and MN. One hundred eighteen samples met this criterion. For these populations gene frequencies were then recorded for the following red cell and serum protein systems: Ss, P, D a y , Diego, haptoglobin, Kidd, Kell, and transferrin. The latter two systems proved to be essentially monomorphic in those groups that were typed for them and these systems were subsequently deleted from the data file.

Additionally haptoglobin and the Kidd blood groups were found to have been typed in too few populations to be useful for map construction. Accordingly, these systems also were dropped.

After deleting these four systems from the data base, population samples were kept or excluded according to the following criteria:

1. Iffewer than 20 individuals were marker typed, the sample was deleted.

2. If gene frequency estimates were un- available for three or four of the following systems (Ss, P, FY, DI), the group was excluded.

3. When two or more reports of the same population were available, we retained the sample with the least missing data. 4. When two or more samples were compa-

rable with respect to missing data, the report with the largest sample size was retained.

Although these criteria seem straightfor- ward, their application proved difficult in some individual cases. Thus, while we sought to include only a single representative from each population, an appropriate working def- inition of “population” was difficult to formulate. To illustrate, there was little dif- ficulty applying the criteria to the Pima. Of the two available reports, the data of Brown et al. (1958) fail criterion 3 above when com- pared to the data of Matson et al. (1968). Moreover, the Pima are a relatively well de- fined population. But what of the Maya? Mayan is at once a designation for members of a particular macrolanguage stock as well as the name of particular groups within this language family. We have taken the prag- matic approach of including groups who are referred to, either by themselves or by other investigators, by a unique name-a task com- plicated by the myriad of spellings for some “tribal” names. On the other hand we en- countered a number of reports of groups who were designated by the same name but who nonetheless resided a considerable distance apart from one another. Each such case was considered on an individual basis, and occa- sionally both groups were retained in the data file. We are aware that a certain amount of arbitrariness may have entered into these decisions but reasoned that if the groups did not differ appreciably in their allele frequencies, then the inclusion of both samples should not seriously alter the maps. How-

GENE FREQUENCY MAPS 2 19

ever, if they did differ substantially, then both ought to be included lest the maps present a false picture of homogeneity.

Because the voluminous and scattered nature of the Amerindian gene frequency literature occasioned discretionary decisions, we include two appendices. Appendix I gives the names, sample size, and references for those populations that were used to generate the maps. Appendix I1 lists the population samples that were deleted from the data file and the reason(s) for their deletion. Although we attempted a thorough literature review, it is clear that our sample is not exhaustive. In- deed, after all of the analyses were com- pleted, we discovered that two samples (the Rama and Sumo [Matson and Swanson, 1963)) had been inadvertently excluded from the survey. No doubt others have also been missed.

In order to perform the principal component analyses it was necessary that no population sample contain missing data for any of the genetic markers retained in the data base. As can be seen in Appendix I, 18 samples were missing gene frequency estimates for the Ss system, 4 samples had no estimates for the P system, and 7 samples lacked data for the Diego blood groups. Only a single sample, the Chiapaneca (Matson and Swanson, 19591, was missing data for two systems. Missing gene frequencies were estimated as follows: The geographic location of each population was determined on an X-Y grid of the Mercator projection of North America (Fig. 1). For each marker system separately, the Euclidian distances between a population with a missing gene frequency and all other measured populations were cal- culated, and a distance-weighted average was computed. This procedure gives the greatest weight to geographically close populations while remote populations make a negligible, albeit "smoothing," contribution to the estimate. This estimation procedure was chosen because it is sensible and could be applied in an impartial fashion. However, the method is not without its problems, especially for geographical regions where cultural discontinuities are found. For Amerindians this problem is perhaps most critical where the northern ranges of the Athapaskan-speaking samples border the southern fringes of the Eskimo samples, and, incidentally, influ- enced our decision to retain both the Kutchin sample from Old Crow and the Kutchin sam-

Fig. 1. Geographic location of the North Amerindian populations. Open circles denote samples judged to be highly admixed.

ple from Fort Yukon. Fortunately, neither of these two groups required any estimation, and neither is close to the Ungava Eskimo sample (which required estimation of DI).

Admixture It is likely that most if not all of the 82

population samples listed in Appendix I contain some European admixture. Initially we had hoped to derive maps of synthetic gene frequencies that reflect, as accurately as the data permitted, genetic variation prior to contact. Accordingly, we attempted to form a subsample of populations that were mostly free of European admixture. It is generally agreed that among the more commonly tested systems, the alleles K , Lua, A C , P", A2, and r (cde) were absent from pre-Columbian populations and that their presence in modern samples bespeaks admixture. Unfortunately most of the entries in Appendix I have not been typed for Kell, Lutheran, adenylate kinase, or acid phosphatase and too few have been subtyped for A2 to make these systems useful for assessing admixture on a uniform basis for the whole sample. Consequently we abandoned the notion of selecting an un-

220 B.K. SUAREZ, J.D. CROUSE, AND D.H. OROURKE

mixed subset and instead settled for a subsample of “less” admixed populations; de- fined by the presence of the RH haplotype dcde) at a frequency of less than 5%. As with other judgments we were forced to make, the choice of 5% is arbitrary and represents an expedient compromise between the desire to minimize the possible effects of European admixture without eliminating most of the data base. By the above criterion, 19 population samples were judged to be significantly admixed, leaving 63 groups in the “less” admixed subsample. The deleted samples are noted in Appendix I.

Map construction Principal-component analysis (with vari-

max rotation) was used sequentially to ex- tract orthogonal factors, first for the full data set and then for the reduced (less admixed) subset. To generate the maps, an X-Y coordi- nate grid was superimposed on a map of North America, and each population was lo- cated at the x-Y node closest to the location from which the sample came. Then for each component separately, the factor score of an “unpopulated” node was estimated by com- puting a distance weighted average from all “populated” nodes as was done earlier in es- timating missing allele frequencies. This procedure guaranteed that the final surface would pass above each populated node at pre- cisely the correct height. For each rotated principal component the data were stored as an X-Y matrix containing both the real factors scores (populated nodes) and the inter- polated factor scores (unpopulated nodes).

These matrices were then rescaled such that all nodes lying in the “ocean” were given a value of zero, while the “land” points took on integer values in the range 1-255. The data were transferred to magnetic tape and interfaced with a VAX-750 computer with interactive image storage and display pe- ripherals (see Arvidson et al., 1982). The byte- encoded data were then assigned equally- spaced gray levels and displayed on a high- resolution television monitor and recorded on 35-mm film using a Matrix Instruments Model 3000 Graphic Film Recorder. Since we were interested in evaluating how the number of plotted levels affects interpretation, and since the resolving capacity of the graphics system far exceeds that of the human eye, we decided to plot each map at 5, 10, and 20 levels of gray, respectively. For the maps presented here, the darker the gray level, the

lower the population’s score on that principal component.

RESULTS

Principal-component analysis of 11 alleles from seven genetic systems resulted in the extraction of five factors whose eigenvalues exceeded 1.0 in the full sample, while in the reduced, less admixed, sample, four factors had eigenvalues greater than 1.0. However, only the first three components were retained for map construction. In the full data set, the first three factors explained 27.5%, 19.0%, and 11.4% of the variance, and in the less admixed sample they explained 31.4%, 16.9%, and 12.2% of the variance, respectively. Table 1 reports the rotated factor loadings for the full sample. As can be seen, component 1 is characterized by a very high loading from ABO and moderately high loadings from s, DI, and those RH haplotypes that contained the d allele; component 2 by S, P, and FY and two RH haplotypes (R2 and those bearing 6) and component 3 by two RH haplotypes (R1 and R2). Table 2 presents the rotated factor loadings for the less admixed sample. The loading pattern for component 1 is seen to be similar to that found in the full sample except for the more pronounced loadings of P and FY. Component 2, however, is quite different, with both R1 and R2 dominat- ing the factor. In the full sample these two haplotypes were the only variables to load high on component 3. This shift to component 2 in the reduced sample reflects the removal of populations with appreciable d frequencies as this allele in the full sample had loaded moderately on the second factor. Component 3 of the reduced sample is characterized by high loading of the R” haplotype

TABLE 1. Results ofprincipal-component analysis of 11 gene frequencies for the full data set of 82 North

Amerindian wowulation samwhs

Rotated factor loadings Comuonent 1 ComDonent 2 Comuonent 3 Allele

0 A R’(CDe)

Ro(cDe) rY+r’+r”+r M S P+ FYa D P

RWE)

0.954

0.088 0.054 0.005

-0.942

-0.401 -0.018

0.359 0.055

0.314 -0.039

0.125

0.086

0.243 0.442

0.682 0.688

-0.140

-0.428

-0.023

-0.727 -0.052

0.000 -0.019

0.975 -0.840 -0.147 -0.233

0.099 0.149 0.109

-0.237 0.139

GENE FREQUENCY MAPS 221

TABLE 2. Results ofprincipal-component analysis of 11 gene frequencies for the reduced, less admixed, North Amerindian sample (N = 63)

Rotated factor loadings Allele Component 1 Component 2 Component 3

0 A R'(CDe) R%DE) Ro(cDe) IJ + r' + r" + r M S P+ FYa DF

0.947 0.026

0.056 0.970 -0.946 -0.047

-0.114 -0.906 0.015 -0.253 0.137 -0.260

-0.051 0.201 0.555 0.351 0.287 0.291

0.360 0.089 -0.394 -0.387

TABLE 3. Population samples with a significant Mahalanobis distance (p < 0.05) to the centroid of the

reduced factor space - Full data set (N = 82) Less admixed data set (N = 63)

Ahousat Ahousat Cat a w b a Chilcotin Dogrib Choco Eskimo (Aivilik) Eskimo (Aivilik) Eskimo (Copper) Eskimo (Inupik) Eskimo (Inupik) Eskimo (Wales) Eskimo (Wales) Kutchin Penobscot

Chilcotin

and moderate loadings from d-bearing haplotypes, s, P, and FY.

Prior to discussing the maps, it is instruc- tive to examine the factor scores of individual populations so that multivariate outliers can be identified. Since outliers are likely to have extremely high or low factor scores on one or more of the components, they will serve as "geographic anchors" for the gray tones. Accordingly, the Mahalanobis distance of each sample to the centroid of the reduced factor space was computed. For the full sample this analysis was based on the five components with eigenvalues greater than 1.0, while in the reduced sample it was based on the four components with eigenvalues greater than 1.0. Populations significantly distant from the centroid (P < 0.05) are reported in Table 3. With the exception of the Catawba of South Carolina and the Choco of Panama, all other outliers reside north of the 50th parallel. Thus the principal component maps can be expected to display steeper contrast gradients in Canada and Alaska than in the remainder of the continent.

0.070 -0.038 -0.124 -0.196

-0.520 0.840

0.024 0.398 0.513

-0.446 -0.022

DISCUSSION

Figure 2 (A-C) shows the patterning of variation in the first three principal components for the full data base displayed at ten levels. In all maps of Figure 2 there is a general north-south gradient of increasing intensity. The details of this trend, however, differ per- ceptibly among the three maps. The persist- ence of a north-south gradient in each map is surprising since the factors on which they are based are orthogonal to one another. For the first component (Fig. 2A), a number of distinct foci of low intensity (dark areas) can be seen more-or-less ringing the northern reaches of the continent. This component is dominated by variation at ABO with populations having a high frequency of the A allele receiving the lowest factor scores. This is most apparent in the broad region inhabited by the Blood, Blackfoot, and Chilcotin groups. High A allele frequency in these populations has generally been attributed to drift, although European admixture is clearly present (Szathmary, 1983; Szathmary and Auger, 1983). Likewise Eskimo populations are notable for having low frequency of the 0 and high frequency of the A allele, relative to other Amerindian groups, and this is clearly reflected in the maps. Two further features of Figure 2A deserve comment. The first point is the very light intensity area on the north coast of western Canada. This is the geographical region inhabited by the Athapaskan-speaking Kutchin. The Kutchin have a very low frequency of the A allele. This alone would distinguish them from their Eskimo neighbors. However, it is two other systems, neither of which load high on the first principal component, that conspire to give the Kutchin a higher score on this factor


Fig. 2. Synthetic gene frequency maps based on a principalcomponent analysis of seven loci in 82 North Amerindian populations. A, B, and C are, respectively, the first, second, and third principal components, each displayed at ten gray levels.

than any other North American sample. In particular, the Kutchin have an unusually high frequency of the RH haplotype R2(eDE), and, in the sample a t our disposal, appear monomorphic for the Duffy system. This il- lustrates how even low loading systems can affect the patterning seen in these maps.

The second striking feature of Figure 2A is the projection of a medium intensity area from southcentral and southwestern Canada to the U.S. southwest. The southern termi- nus of this projection is in the region inhabited by the Navajo and Apache and reflects the genetic affinity between the northern and


southern Athapaskan speakers. Indeed, the projection appears to describe an arc through the central plains which is the putative migration route taken by the Southern Atha- paskans after leaving the Yukon.

Figure 2B portrays variation in the second principal component which is dominated by a negative loading for Duffy, high positive loadings for P and S, and moderate loadings for two RH “alleles” (positive for d-bearing haplotypes and negative for R2). Unlike Fig- ure 2A, the pattern observed here does not lend itself to ready interpretation with respect to known population movements. Rather, the general north-south intensity gradient is seen to be punctuated by small pockets of very high intensity especially a t various coastal sites. These light-intensity areas correspond to groups with appreciable frequencies of the d allele andor low frequencies of FY”. This combination of frequencies is prominent in Amerindian groups with considerable European admixture. Thus, with the exception of the Miskito Indi- ans of Nicaragua whose high score on the second component is due in large part to an unusually low FYa frequency (perhaps owing to random drift), all other high-intensity areas correspond to groups with substantial admixture (The Koniag Eskimos of Alaska, the Penobscot of the northeast coast, the Ca- tawba of the southeast coast, and the Jicaque of Honduras).

Variation in the third principal component is displayed in Figure 2C. This component is dominated by two RH haplotypes. R1 loads strongly positive, while R2 loads strongly negative. It is noteworthy that despite the similarity in high frequencies of cDE in both Eskimos and Indians, particularly in western Canada, the map retains the distinction between these two ethnic groups. Somewhat surprising is the similarity in intensity areas between the Athapaskans and other western groups and the Cree, Naskapi, and Montag- nais of Eastern Canada.

As noted earlier, we attempted to assess the effects of European admixture by deleting 19 highly admixed populations from the data base, recomputing the principal components, and regenerating the maps. Figure 3 (A-C) shows the patterning of variation in the first three components for the reduced data set again displayed with ten shades of gray. As was the case for the full sample, the first component continues to be dominated by variation at the ABO locus. Because of

the exclusion criterion, however, the d allele of the RH system no longer loads significantly on this component. The overall topography of Figure 3A is remarkably similar to that of Figure 2A, preserving as it does the Eskimo-Indian dichotomy, the relationship of the Athapaskan-speaking groups and the overall north-south gradient of increasing intensity. It should be noted, however, that while the synthetic gene frequency maps tend to support the notion that the Eskimo populations in this sample are relatively distinct from the Na-Dene speaking Indians, they shed little light on possible Eskimo origins since we chose not to include any Siberian samples (see Szathmary and Ossen- berg [1978] for a discussion of competing hypotheses regarding Eskimo origins).

Comparison of the maps for the second and third components of the reduced data set with those of the full set more clearly highlight the impact of removing the significantly admixed groups. Whereas variation in the frequency of the R1 and R2 haplotypes dominated the third component in the full data base, they now dominate the second component. Accordingly, Figure 3B bears a greater resemblance to Figure 2C than to Figure 2B, while preserving the Eskimo-Indian dichotomy. Figure 3C is rather distinctive and difficult to interpret, in part because if reflects residual variation in the RH-negative allele in those few populations that have a frequency greater than zero but less than 5%. As with the other maps generated from the less admixed data set, Figure 3C gives the impression of greater topographic homogeneity relative to the maps of Figure 2. Euro- pean admixture has generally been viewed as introducing homogeneity into the gene frequency distribution of Amerindians by reducing the effects of microdifferentiation owing to drift or historical accident. This does not appear to be the case for the maps of Figure 3. However, the homogeneity of these maps may be more apparent than real since so many samples north of the Rio Grande were excluded from the less admixed sample (open circles of Fig. 1). Thus our attempt to assess the extent to which European admixture affects patterning is severely compro- mised because of the exclusion of so many data points. Nonetheless, comparison of the maps generated from the first principal components of the full data set (Fig. 2A) and the reduced set (Fig. 3A) suggests considerable similarity and tends to support Spuhler’s


Fig. 3. Synthetic gene frequency maps based on a principalcomponent analysis of seven loci in 63 less admixed Amerindian populations. A, B, and C are, respectively, the first, second and third principal components, each displayed at ten gray levels.

(1979) observation that Indian-Indian mixture is a more important determiner of cur- rent affinities than gene flow from European sources.

It is obvious that the criterion we used to diagnose the presence of significant admix-

ture is inadequate since it is based on only a single system. Indeed, there is even contro- versy regarding whether the cde haplotype can be considered an infallible indicator of admixture. Post et al. (1968), for instance, make a case for its presence in aboriginal


Fig. 4. The first principal component of the full data set (Fig. 2A) plotted at 5 and 20 levels (plates A and C) and the first principal component of the less admixed data set (Fig. 3A) also plotted at 5 and 20 levels (plates B and D).

Americans, albeit at a very low frequency. It is expected that admixture estimates will vary depending on which systems are used. For instance, Szathmary and Reed (1972) obtained an admixture estimate for an Ojibwa community (Wikwemikong) of 20.4% based

on red cell acid phosphatase. However, when based on the adenylate kinase locus, admixture was estimated at 84.8%. Neither is it obvious if estimates based solely on the presence of the cde haplotype are likely to over- or under-estimate the amount of admixture

226 B.K. SUAREZ, J.D. CROUSE, AND D.H. O'ROURKE

present. For instance, Szathmary and Reed (1972) estimated the proportion of European ancestry for the two Penobscot samples and Montagnais sample at 54.9-69.9% and 0%, respectively, utilizing only the presence of cde. In a latter analysis using an expanded battery of markers, Szathmary and Auger (1983) obtained estimates of 45.9% for the Penobscot and 6.8% for the Montagnais. Thus, in one case the estimate of admixture decreased with the addition of more information, while in the other it increased.

The number of levels used in map construction is arbitrary. In order to gain insight into how this variable might influence interpretation, we decided to plot the first principal component of the full data set and the less admixed data set at 5 and 20 levels (Fig. 4 A-D, respectively). It is clear that the major features of the earlier maps are still pre- served. However, reducing the number of levels to five heightens the contrast and gives the illusion that large irregular regions are truly genetically distinctive. On the other hand, the 20-level maps belie the presumed intraregional homogeneity seen at 5 and 10 levels; particularly for the reduced sample (Fig. 4D). Here, while the extremes of the range are still conspicuous, the greater number of gradations between them produces a smoother transition. Barring the still-apparent Athapaskan migration, the north-south increasing gradient appears to be more pronounced in the 20-level maps, although it is in the 5-level maps that the distinctiveness of the reduced sample is most accentuated.

In conclusion, synthetic gene frequency maps in the future will likely serve a useful descriptive role because of their ability to summarize information at many different loci and because of their ability to highlight population affinities as well as discontinuities that may not be obvious from tables of nu- merical data. Moreover, as pointed out by Piazza et al. (1981b), since sequentially ex- tracted principal components are orthogonal, each may well indicate the action of separate forces (e.g., migration, drift or environmental constraints) on the gene pools of widely scattered populations. Whether synthetic gene frequency maps are truly capable of showing more than relatively recent migration (like that of the Athapaskans) is presently un- known and perhaps can be best answered by computer simulation studies. It is tempting to interpret a clinal topography as a remnant of past migration much as Menozzi et al. (1978) have interpreted their EuropeanNear

Eastern maps as evidencing the demic diffusion of Neolithic farmers. Indeed, the 20-level maps of Figure 4, because of their more-or- less smooth intensity gradations, could eas- ily be interpreted as reflecting ancient migrations or, for that matter, the spread of agriculture out of the Mayan region. We leave such fanciful interpretation to others. For those so inclined we would argue that the limiting factor is not simply the quality and quantity of the data, nor the sophistica- tion of the image-processing technology, but also a modicum of imagination.

ACKNOWLEDGMENTS

We are indebted to Mr. Craig Leff, staff member of the Regional Planetary Image Fa- cility, McDonnell Center for the Space Sci- ences, Washington University, St. Louis, Missouri, for his kind help in producing the maps. We thank Dr. E.J.E. Szathmary, De- partment of Anthropology, McMaster Univer- sity, Hamilton, Ontario, for her construc- tive criticism and for bringing to our atten- tion a number of pertinent studies. Dr. F. Auger kindly made available the unpub- lished gene frequencies of the Quebec Cree. Finally we wish to thank anonymous review- ers for their extensive comments of an earlier version of this paper. This work was sup- ported, in part, by USPHS grants MH31302 and MH14677.

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960

Mat

son

and

Swan

son,

19

59

RH

freq

uenc

ies

pool

ed o

ver

seve

ral d

iffe

rent

gr

oups

RH

freq

uenc

ies

Mat

son

and

Swan

son,

Tej

ada

et a

l., 1

961

1959

4 F

Bro

wn

et a

l., 1

958

Cha

isso

n, 1

963

Cor

dova

et a

l., 1

967

Bro

wn

et a

l., 1

958;

B

oyd

and

Boy

d, 1

949

Hul

se, 1

957

Schr

eide

r, 1

955;

Sa

laza

r-M

ellB

n an

d A

rtea

ga,

1951

; A

rtea

ga e

t al.,

195

2 M

atso

n et

al.

, 19

68

Bro

wn

et a

l., 1

958

Tej

ada

et a

l., 1

961

pool

ed o

ver

seve

ral d

iffe

rent

gr

oups

A

ppen

dix

II. C

ontin

ued.

N

w

N

AP

PE

ND

IX I

I. Po

pula

tion

sam

ples

tha

t w

ere

dele

ted

(con

tinu

ed)

Prin

cipa

l rea

son

for e

xclu

sion

R

eplic

ate

Mis

sing

Po

pula

tion

sam

ple

N <

20

data

O

ther

C

omm

ents

R

efer

ence

Qui

che

X

X

RH

freq

uenc

ies

Tej

ada

et a

l., 1

961

pool

ed o

ver

seve

ral d

iffe

rent

D

OU

PS

Hul

se a

nd F

ires

tone

, Q

uina

lt

X

Sem

inol

e

Seri

Swin

omis

h T

oton

ac

Tus

caro

ra

Tze

ltal M

aya

Ute

Y

akim

a Y

uma

Zun

i

X X X

X

X

X x X X X X X

X

Gro

up is

X

Rep

orte

d as

poo

led

tran

spla

nted

wit

h Y

aqui

X A

utho

rs re

port

ty

ping

err

ors f

or

MN

Ss a

nd R

H;

not u

sefu

l for

1961

Po

llitz

er e

t al.,

19

7 1

Nov

elo

et a

l., 1

964

Hul

se, 1

957

Cor

dova

et a

l., 1

967

Art

eaga

et a

l., 1

952

Moh

n et

al.,

196

3 E

riks

on e

t al.

, 197

0

com

pari

son

Mat

son

and

Pipe

r, 1

947

Hul

se, 1

957

Bro

wn

et a

l., 1

958

X

Supe

rsed

ed b

y ne

w

Mou

rant

et a

l., 1

976

m 7:

F3

U

d a 0

h

$5

genetic variation in north amerindian populations: association with sociocultural complexity

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