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Genetic variation in North Amerindian populations Covariance with climate.

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Genetic Variation in North Amerindian Populations: Covariance
With Climate
Department of Anthropology (0.H. O’R.), University of Utah, Salt Lake City,
Utah 84112 and Departments of Psychiatry (D.H.O R . , B.K.S., J.D. C.) and
Genetics (B.K.S.), Washington University School of Medicine, The Jewish
Hospital ofSt. Louis, St. Louis, Missouri 63110
Amerindian, Genetic variation, Heterozygosity,
Allelic frequencies at seven polymorphic loci in 74 North
Amerindian populations are examined relative to patterns of climatic variation. Canonical correlation analysis reveals strong and significant associations
of heterozygosity a t the ABO, Ss, D f i y , and P loci with climatic variability.
Principal component analysis demonstrates that these loci tend to form correlated ensembles. Moreover, canonical correlation analysis of component scores
provides support for a n association between polymorphism a t these loci and
environmental variability. The results are concordant with two previous investigations which suggested a relationship between polymorphism for the ABO,
Dufly, and Diego systems and climate. It is suggested that the examination of
broad geographic patterns of genetic variation at multiple loci is a valuable,
but underutilized, method of screening for the effects of long-term systematic
The traditional concerns of anthropological
genetics, and human population genetics in
general, have been to either (1) document the
genetic uniqueness of the populatiods) under
study, or to (2) assess the relative similarity
or dissimilarity among a group of historically
related populations through some form of
distance analysis. On a broader scale, this
same approach has been used to assess genetic variation within and between major
“racial” groups (e.g., Lewontin, 1972; Nei and
Roychoudhury, 1972, 1974; Mitton, 1977;
Latter, 1980; Weiss and Maruyama, 1976).
This analytical orientation has led to a concentration on examining the effects of genetic drift and gene flow on the structure of
gene pools in contemporary human populations.
Relatively little is known about the role of
systematic evolutionary forces in the maintenance and patterning of most human genetic polymorphisms (Harpending, 1974).
During the past 30 years anthropological genetic studies have generated a bounty of allelic frequency data for many populations
throughout the world (e.g., see Mourant et
0 1985 ALAN R. LISS, INC.
al., 1976). We suggest that such collective
data are appropriate for examining broad
geographic patterns of genetic variation in
order to assess the potential role of natural
selection operating on marker loci. An obvious characteristic that varies with geography is climate. The effect of climate on
patterns of morphological variability has
long been appreciated (e.g., Roberts, 1978),
but similar investigations with regard to genetic polymorphisms in humans have been
Levins (1965, 1968) proposed that in heterogeneous environments genetic heterozygosity should increase with environmenta!
variability for those loci important to fitness.
Subsequent investigations found this prediction to hold for a variety of nonhuman organisms (e.g., Smith and Koehn, 1971; Johnson,
1973; Koehn and Mitton, 1972; McLeod et
al., 1981). Bryant (1974) found that 70% of
the geographic variation in genetic heterozygosity of several genera could be accounted
Received December 13, 1983: revised March 5, 1985; accepted
March 14, 1985.
for by climatic variation. He concluded that
“. . . patterns of geographic variation in heterozygosities, for statistically correlated ensembles of loci, may represent adaptive shifts
in response to changes in variability of specific components of the environment” (p. 15).
Moreover, Band (1972) has documented genetic changes in natural populations of D r e
sophila melanogaster that are associated with
minor but significant climatic shifts.
Comparable studies on the relation between genetic heterozygosity and environmental variability in human populations are
rare. Wilson and Franklin (1968) examined
the level of polymorphism a t the Diego (Di)
locus in 97 North, Central, and South Amerindian groups and found that Di(a+) frequencies are elevated in warm, wet environments. Piazza et al. (1981) used 39 independent alleles a t ten loci to evaluate the
role of climate on level of polymorphism. Although they reject climate as a major determinant of total genetic variation, they did
find that two-thirds of the loci examined
showed a significant association with climate. In particular, alleles at the Duffy, haptoglobin, ABO, Rh, MNSs, HLA, and Acid
Phosphatase loci exhibited correlations
greater than 0.4 with distance from the equator, which they took as a n indirect measure
of climatic variability. Ananthakrishnan and
Walter (1972) also reported a significant correlation (r = - .71) between the frequency of
the Acid Phosphatase allele P and mean
annual temperature.
As part of a larger study of genetic variation in Amerindian populations (Suarez et
al., 1985a,b),the present paper examines the
relationship between genetic heterozygosity
in 74 North Amerindian populations and climatic variation derived from mean monthly
precipitation, mean monthly maximum temperature, mean monthly minimum temperature, the standard deviation of each of these,
and elevation.
Eleven allelic frequencies at seven polymorphic loci were recorded for 74 North
Amerindian populations. Two principal criteria were used for inclusion of published frequencies in the present study. First, each
society must have been typed for the ABO, Rh
and MN systems. In addition to these three
blood groups, allelic frequencies for the Ss (of
MNSs), P, Haptoglobin, D a y , Kidd, Diego,
Kell, and Transferrin systems were also obtained. The Kell and Transferrin systems
proved to be virtually monomorphic in these
samples and were subsequently dropped from
the analysis, while too few samples reported
frequencies for the Kidd and Haptoglobin loci
to permit their use. For the Rh system haplotype frequencies were used. In some reports
frequencies of d bearing haplotypes could not
be unambiguously identified. We have therefore pooled these haplotypes for the estimate
of dcde). The very low frequency, or absence,
of most of these haplotypes in unadmixed
Amerindians suggests that any error introduced by this procedure is minimal. For the
MN and Ss loci too few reports included haplotype frequencies to be useful. Accordingly,
these two loci are treated separately. Although all populations had reported frequencies for the ABO, Rh, and MN loci, this was not
the case for all the other systems included in
the analysis. All societies had reported values
for the Duffy system, but one or more samples
lacked a frequency for the Ss, P, or Diego systems. For these missing data values, estimates were obtained using a geographic
distance-weighted average allele frequency
based on all observed values for the locus in
North America. This gives greatest weight to
geographically proximal populations in the
estimation of missing data. Moreover, it tends
to have a “smoothing” effect when viewed in
continental perspective since all observed values contribute to the estimate and no estimate can be outside the observed range. The
analyses that follow were performed on data
sets with missing data estimated and with societies with missing values excluded. The
results were remarkably concordant. We
therefore only report analyses which include
estimates of missing data, where possible, to
take advantage of the larger number of societies included. In total, 2.8% of the data was
It is well known that most North Amerindian populations have experienced admixture from non-Indian gene pools. Although
admixture, per se, should not drastically affect results of investigations of environmental constraints on gene frequency variation,
recent admixture that results in disruption
of equilibrium may have a n effect. In a n effort to accommodate such a n effect, societies
judged to be highly admixed (i.e., frequency
of r > .05) were deleted from the sample and
the analyses repeated. This reduced, less-admixed data set is composed of 57 populations.
The proportion of estimated data is slightly
less (2.5%)than in the total data set.
We take the data base to be representative
of all published genetic studies of native
North Americans. However, given the disappearance of many groups in recent history
and the recency of genetic studies of Amerinds, the representativeness of such studies
of all of native North America is unknown.
Nevertheless, we have made every effort to
use samples from populations still residing
in or near their traditional homelands. For
example, many eastern woodland populations were removed to reservations in the
Midwest in recent historical times. Our samples for these groups come from populations
still residing in the East rather than the
western reservation groups. A list of populations, sample size, loci with missing values,
and original references for the data used in
this study may be found in Appendix I of
Suarez et al. (1985a).
Average per locus heterozygosity (h) was
estimated under the assumption of HardyWeinberg equilbrium (Harpending and
Chasko, 1976; Suarez et al., 1985b). Due to
the decidedly non-normal distribution of h,
we ranked the heterozygosities of each system separately and obtained normal scores
by the method of Blom (1958; Suarez et aL,
1985b). An index of mean heterozygosity (H)
is obtained by computing a n average of normal scores weighted by the number of systems observed. This is a measure of each
population’s degree of heterozygosity relative to all others. Since h is computed only
on non-missing data, sample sizes are 50 and
38 for the full and reduced data sets, respectively, when these variables are the unit of
Climatic variation
The second criterion for inclusion was the
availability of meteorological data for the
geographic location of each society. Climatic
data for each locale were obtained from the
Environmental Technical Applications Center (ETAC) technical library a t Scott Air
Force Base, Illinois. Three classes of climatic
variables were obtained from the World Wide
Climatic Summaries: mean January through
December monthly precipitation, mean
monthly maximum temperature, mean
monthly minimum temperature, and elevation. Mean hourly humidity by month was
also recorded but underreporting of the humidity data from the majority of stations pre-
cluded its use in the analysis. Consequently,
the monthly precipitation and temperatures
were used to compute the mean and standard
deviation of these values over a year. The
resulting six variables and elevation constitute the environmental variables used in the
analysis. Latitude, as used by Piazza et al.
(19811, indirectly indexes more climatological variables than used here, but is insensitive to local climatic differences and the
effects of elevation. We have therefore opted
to use actual climatic variables rather than
latitude to assess environmental heterogeneity.
Statistical analysis
Two primary analytical tools are employed
to assess the relationship between heterozygosity and climatic variation: canonical correlation and principal component analysis.
The aim of canonical correlation analysis
is to derive linear functions of two vector
variables such that the covariance between
the linear functions is maximized. Extraction of maximally correlated linear functions
from the two vector variables proceeds sequentially, subject to the restriction that each
pair of canonical variates be orthogonal to
all previously derived linear combinations
(Cooley and Lohnes, 1971). The number of
canonical variates extracted is limited by the
lesser number of entries in either of the two
vector variables. It is desirable to have fewer
environmental variables than genetic variables in the canonical correlation analyses
since we are interested in the environment
as a limiting factor on genetic variation
rather than the converse. For examination of
the relationship between heterozygosity and
environment, the environmental variables
which contributed least to the first canonical
variate were excluded from subsequent
Unfortunately, the maximization of the
covariance of the canonical variates does not
guarantee that the variance within a vector
variable is maximized. As a check on the
consistency and reliability of the analysis,
the allelic frequencies and environmental
variables were subjected separately to principal component analysis. This assures that
each linear function extracted is orthogonal
to all others, and the amount of variance
explained is maximized. This also encompasses somewhat more data since for ABO and
Rh multiple alleles can be included rather
than a single heterozygosity value for these
systems. The components extracted from
each data base are then rotated to simple
structure using varimax rotation (Harman,
1967). For each component with a n eigenvalue greater than 1.0 the factor score coefficients are used to compute new synthetic
variables (component scores). These new variables are treated a s new vector variables representing genetic and environmental variability and subjected to canonical correlation
All canonical correlation analyses were
performed with the CANCORR routine in
SPSS (Nie et al., 1975) on a Harris 500 computer, while the principal component analyses were done using the BMDP package
(Dixon and Brown, 1979) on a n IBM 370.
The mean and standard deviation of the 11
allele frequencies and seven environmental
variables are given in Table 1 for both the
full and reduced samples. Brief examination
of Table 1 reveals that reducing the data
base by exclusion of highly admixed groups
(based on frequency of r haplotype) alters the
mean gene frequencies very little. Indeed,
only for this haplotype does the frequency
differ by as much as 0.02 between the data
sets. The climatic variables are also very
similar between the two data sets. The only
noticeable difference is the approximately 2”
increase in mean temperatures in the reduced sample relative to the full data set.
This is a function of most of the societies
being excluded for extreme admixture coming from areas north of Mexico. Overall, the
pattern of values in Table 1 suggests that
separate analyses based on the “admixed”
and “less-admixed” data sets should be quite
Canonical correlation analysis
When the normalized, ranked heterozygosities, and climatic variables are subjected to
canonical correlation analysis, strong and
significant associations between genetic heterozygosity and climate are revealed. Partial
results are given in Table 2.
For the full data set two highly significant
canonical correlations (R,) are found between
the two vector variables. The eigenvalues
given in Table 2 are (R,? and reflect the
shared variance between the linear functions. For the first pair of canonical variates
for North America (Rc = .86), the proportion
of variance shared approaches 75%, while for
the second orthogonal pair (Rc = .69) the
corresponding value is 48%.
For the reduced data base only a single
significant canonical correlation is obtained.
At R, = .89, however, it is slightly greater
than the correlation between the first canonical variates in the full data set and rep-
TABLE 1. Means and standard deviations of gene frequencies and climatic variables
Full data set
(N = 74)
Reduced data set
,8622 f ,1465
,1192 f ,1346
.0577 f ,0543
.4895 f ,1367
,3892 f ,1347
,0307 f ,0608
.7417 f ,1055
,3084 f ,1107
,4680 ? ,1632
,7260 f ,1296
,0487 f ,0640
,8816 k ,1416
3041 & .1318
,0653 k ,0534
,5054 + ,1452
,3963 ,1415
,0015 ? ,0069
,7354 k ,1079
,3013 k ,1138
,4654 f ,1701
.7384 k ,1284
.0542 k .0714
2,061.2162 f 2313.3945
3.6765 I 3 . 7 8 7 0
2.3453 f 2.6391
64.8922 f 22.5899
12.3578 i 9.5508
44.0946 f 21.3624
4.4226 f 2.4478
2,134,5714 k 2,449.5623
4.1582 f 4.1835
2.7489 f 2.8699
10.8308 f 9.3881
46.3396 ? 22.1847
4.2674 f 2.3695
= 57)
Gene frequencies
D ia
Climatic variables’
‘ELEV, elevation; MPREC, mean precipitation; SDPREC, standard deviation of precipitation; MTMAX, mean maximum
temperature; SDTMAX, standard deviation of maximum temperature; MTMIN, mean minimum temperature; SDTMIN,
standard deviation of minimum temperature.
TABLE 2. Canonical correlation analysis of
heterozygosity for seven loci and seven climatic
Data base Eigenvalue
< ,001
< .001
'Only statistically significant canonical correlations are retained
in the table.
TABLE 3. Sequential pattern of removal of per locus
heterozygosity with highest contribution to first
canonical variate
Step No.
Full data set
Reduced data set
ABO (2)'
Duffy (2)
Diego (2)
P (11
ABO (1)
P (1)
Diego (1)
s (1)
'No. in parentheses reflects No. of significant canonical
correlations found with cumulative removal of highest loading
'The precipitation variables and elevation did not contribute
strongly to the first canonical variate and were deleted from the
analysis in subsequent steps.
resents nearly 80% of shared variance between the vector variables.
Assessment of the relative contribution of
each variable in a set to the canonical variate
is achieved by examination of the magnitude
of the canonical coefficients (the "loading" of
the variable on the variate). By sequentially
removing the variable with the highest canonical coefficient from the analysis it is possible to determine a subset of variables that
contribute to a significant canonical correlation and a subset for which no significant
association results. Table 3 presents the order of sequential and cumulative removal of
those heterozygosities with the highest loadings on the first canonical variate in each
analysis. The ABO locus had the highest
loading on the first canonical variate in both
data configurations. In addition, the Diego
and P loci are seen to be major contributors
t o significant correlations between heterozygosity and climate in both data arrangements. The Ss locus appears prominently in
the reduced data set, while the Duffy locus
contributes strongly in the full data base.
However, in the full data set the variable
with the highest loading after P was the Ss
locus heterozygosity, while in the reduced
data base, Duffy was the strongest contributor after Ss to the linear function of heterozygosity values. Thus, although the order of
importance is slightly different between the
two data sets, the same variables in both
cases are associated with climatic variation.
Of equal interest is the fact that only heterozygosity values for the Rh and MN systems
do not appear to be at all associated with
The mean temperature variables have the
greatest covariance with the heterozygosities
utilized here, although in some data configurations the measures of temperature variation had moderately high canonical coefficients. Since both maximum and minimum
mean temperatures contributed strongly in most analyses, temperature range may
be the important criterion accounting for the
association between genetic heterozygosity
and climatic variation in these data.
The difference in order of loading of genetic
variables between the two data sets may be
the result of two factors. First, it may reflect
differences in heterozygosities due t o the extent of admixture in groups in the full data
set. Alternatively, since the heterozygosity
data are based only on observed frequencies
(i.e., estimated gene frequencies are not used)
the reduction in number of groups to 38 in
the reduced sample may contribute to this
difference. This latter problem may be overcome by using gene frequency data where,
with missing values estimated, the sample
sizes in the two data bases increase to 74 and
57 societies. Moreover, since allele frequencies and heterozygosity estimates at a locus
are not linearly or monotonically related, examination of both genetic variable sets may
reveal patterns of variation or associations
in one set not apparent in the other. Utilization of allelic frequencies also increases the
number of genetic variables from 7 to 11,
eliminating the need to base the analysis on
a reduced number of climatic variables. The
results of this analysis are given in Table 4.
TABLE 4. Canonical correlation analysis of
heterozygosity for 11 allele frequencies and seven
climatic variables'
Data base Eigenvalue
< ,001
< ,001
'Oiily statistically significant canonical correlations are retained
in the Table.
Once again, for the full data base two pairs
of canonical variates are extracted from the
vector variables. The correlation between the
first pair of canonical variates approaches
.85, representing 72% of shared variance. The
second significant canonical correlation (Rc
= .70) represents a shared variance of nearly
50%. For the reduced data set only a single
canonical correlation is found (Rc = .89).
These results are very similar to those obtained in the analysis of heterozygosities.
Moreover, sequential removal of gene frequencies from the analysis to identify the
subset most associated with climatic variation shows additional similarities to the
previous analysis. In addition to alleles A, S,
FF, and P', the R' and R2 haplotypes of Rh
were also found to be associated with climate
in both data sets. The only difference in order
of importance of these frequencies with respect to climate in the two data sets is that
Duffy is more strongly associated with climate than P1 in the full data set while the
opposite is true in the reduced set. Heterozygosity at the Diego locus was strongly associated with climate in the previous analysis
but is only moderately associated when gene
frequencies are used. Although the major difference in the two data sets is in the fre-
quency of r, in neither data set did this
chromosomal segment show any association
with climate. This suggests that these results faithfully reflect a true correspondence
between patterns of genetic variation at specific loci and geographic distribution of climate; not a n artifact of European admixture
in Amerindian populations.
Examination of bivariate correlations (not
shown) of allele frequencies with climatic
variables reveal that only the MN locus was
not consistently associated with one or more
of the climatic variables. If, as has been suggested, genes evolve as correlated units (Lewontin, 1965; Franklin and Lewontin, 1970;
Allard and Kahler, 1972), "it is important to
delineate these polymorphic units, rather
than evaluate possible selection on individual genes" (Bryant, 1974:2).
Principal component analysis
In order to maximize the proportion of variation accounted for within each data set, and
to identify those loci that form correlated
units, principal component analysis (PCA)
with varimax rotation to simple structure
was undertaken for each data configuration.
Table 5 summarizes the PCA for the full
complement of North American data. For the
TABLE 5. Rotated factor loadings for eleven allele frequencies and seven climatic
Variables for full data set'
D in
% Variance
Climatic variables
Q Variance
- ,946
- ,495
- ,407
- ,309
'Component scores less than .25 have been omitted. See text for discussion.
- .556
TABLE 6. Bivariate correlations between allelic frequency and climatic principal
components for both data configurations'
Full data set
cc 1
Reduced data set
- .0547
- .2329
- ,1447
'W, genetic component; CC, climatic component
*P < .05.
TABLE 7. Canonical correlation analysis of genetic and
climatic component scores for both data configurations
Data base Eigenvalue
< ,001
< ,001
allelic frequency data, five components with
eigenvalues greater than 1.0 were found to
account for 78.87% of the variation in the
original data. Component 1 is characterized
by high loadings for the ABO locus and moderate loadings for Ss, Diego, and r of Rh;
component 2 by Duffy, P, and Ss with a moderate contribution from R2 and r; component
3 by the R', R', and r haplotypes of R h
component 4 by loci MN,Diego, and a moderate loading by Duffy; component 5 is dominated by RO and r of Rh. All climatic
variables load on the first principal component. However, component 2 is characterized
by elevation and moderate loadings for mean
precipitation and the standard deviation of
Bivariate correlations between allelic frequency component scores and component
scores for the climatic variables are seen in
Table 6. Fully four of the five genetic components have significant correlations with climate (CC 1) in the full data base. Only
genetic component 5, characterized by Ro and
r, is not strongly correlated with the climate
factors. This is interesting since examination
of the bivariate correlations between the
original variables has already revealed that
the MN locus has little if any association
with these climatic variables. That component 4, which is dominated by this locus, is
significantly correlated with the first cli-
matic component suggests that it is the other
variables that load on this component which
result in the correlation; namely, Duffy and
Diego. This is consistent with the earlier
When the component scores are subjected
to canonical correlation analysis, a significant Rc of .79 is obtained (Table 7). Th'is accounts for a shared variance of 62%between
linear functions of the component scores for
the genetic and climatic variables. When the
first component of the genetic data (characterized by loci ABO, Ss, and to some degree
by Diego and r) is removed, the Rc (= 5 7 )
remains significant (P < .001). Indeed, the
first two components may be removed from
the analysis and a n R, of .44 still remains
significant at the .05 level.
The PCA for the reduced data set differs
only slightly. Here (Table 81, four components from the genetic data are found with
eigenvalues greater than 1.0, accounting for
72.47% of the variation in the original data.
Characterization of the components may be
seen as component 1,ABO and S, with moderate loadings from Diego, Duffy and PI;
component 2, R 1 , R2, and contributions from
S , Duffy, P1, and r; component 3, Ro, r, PI,
and D a y ; component 4, M, Diego, r, and
moderate contributions from Duffy. The PCA
for the climatic variables of the reduced data
base is virtually identical to that seen for the
full data set.
Canonical correlation analysis of the genetic and climatic component scores for the
reduced data base (Table 7) resulted in a significant correlation of .80. Removal of the
components 1and 2 from the analysis results
in a nonsignificant correlation between the
two vector variables. Here the ABO, Ss, and
D a y loci and R1 and R2 of Rh, which dominate the first two components, may be con-
TABLE 8. Roto.ted factor loadings for 11 allele frequencies and seven climatic variables
for the reduced data set'
- ,952
- ,918
- ,388
- ,559
- ,404
- .474
% Variance
Climatic variables
% Variance
- ,978
- ,971
- ,800
'Loadings less than 2 5 have been omitted. See text for discussion.
sidered strongly associated with climate.
Moreover, although P has its highest loading
on component 3, which is not significantly
associated with climate (see Table 6) and does
not contribute to the significant canonical
correlations, it also has high loadings on the
first two components and was found to be
strongly associated with patterns of climatic
variation in the earlier analysis. Polymorphism at this locus, then, may also be influenced by constraints imposed by the
The explication of polymorphism for individual loci has had a long and controversial
history, with adherents to both major schools
of thought: selection (e.g., Burns and Johnson, 1971; Ayala, 1972; Stebbins and Lewontin, 1972) and neutral mutation (e.g.,
Kimura, 1968; King and Jukes, 1969; Kimura and Maruyama, 1971). While selection
through a variety of vectors has been proffered as an explanation of polymorphism for
a few loci in humans (e.g., ABO with maternal-fetal incompatibility and disease, Morton
et al., 1966; Brues, 1954, 1963; Levine, 1943;
G6PD deficiency and malaria, Siniscalco et
al., 1961; the effects of maternal fetal incompatibility on the Rh system, Levine, 1943;
and perhaps haptoglobin and disease, Eaton
et al., 1982), only for the hemoglobins has a
direct selective effect of the environment
been satisfactorily demonstrated (e.g., Allison, 1954; Livingstone, 1967).
In the present work, several polymorphic
loci have been examined relative to climatic
variation to determine whether or not individual Amerindian populations occupy the
same relative positions in two different measurement spaces: one genetic and the other
climatic. Both the ABO and Duffy loci are
consistently associated with climate in all
analyses. These loci were also found by Piazza et al. (1981) to be strongly correlated
with distance from the equator. Polymorphism a t the Ss and P loci also appear strongly
associated with climatic variation.
The relationship of heterozygosity a t the
Diego locus to climate is somewhat more
problematic. Although it appeared to be
strongly associated with climatic variables
in the analysis of heterozygosity, it was only
marginally associated with climate in the
analysis of gene frequencies. In addition, the
Diego locus usually does not load highly on
any one single component in PCA. Rather, it
generally loads moderately on two or three
components; and always in association with
ABO. Moreover, Wilson and Franklin (1968)
reported a strong association with climate for
Diego frequencies in Amerindians.
The present demonstration of significant
association between genetic polymorphism
and climatic variation does not necessarily
imply a causal relationship. A confounding
factor here is the collinearity of the north-tosouth migration of the original colonists and
climatic gradients (i.e., there is a pronounced
north-south latitudinal component to climatic variation). Indeed, latitude may be
substituted for the climatic variables in these
analyses with little change in the overall
results. The major difference observed when
substituting latitude for the climatic variables, is a stronger contribution of the Rh
phenotypes, particularly r in the full data
set. This would appear to reflect the presence
of greater admixture and suggest that, since
this is not the case when climatic variables
are used, the demonstrated association between genetic polymorphism and climatic
variation is real and not illusory. Further,
when true climatic variables are used in the
analysis, only few and minor differences are
found between the full data base and the
less-admixed sample. It should be noted that
this is the case whether only observed values
are used or whether missing data have been
An additional factor is a n earlier suggestion that degree of heterozygosity is influenced by level of sociocultural integration
(Beak and Kelso, 1974). We have demonstrated (Suarez et al., 198513)that an association between cultural level and genetic
heterozygosity was lacking in these data
when latitude was controlled for.
Finally, the collinearity of the north-tosouth migration of early Amerindians and
climate may confound these results. However, generation of synthetic gene frequency
maps (Suarez et al., 1985a)from North Amerindian gene frequency data provided little
evidence for patterns of gene frequency variation concordant with migration patterns of
much antiquity. The autocorrelation of migration, climate, and latitude may confound
these analyses with only a slight augmentation of the correlations between genetic variation and climate. Thus, the results of the
present analyses are that much more striking. These results indicate that the effects of
systematic pressures acting on human gene
pools may be detected by adopting a broad
continental perspective of gene frequency
variation. Whether this systematic pressure
is the result of ancient, long-range migrations or selection through environmental
constraints remains to be completely determined.
1. Seven polymorphic loci in 74 North
Amerindian populations are examined for
covariation with seven climatic variables.
2. Significant association between four genetic loci (-0, Ss, D&y, and P) and the
constellation of climatic variables is
3. The Diego locus also appeared correlated
with climatic variation in some analyses but
not in others. More data and further work
are required to clarify this association.
4. The climatic variables used here are
crude and incomplete indices of either climatic variation or environmental heterogeneity. That strong and significant associations between the genetic and climatic domains were nonetheless found suggests that
the associations are real, and that the systematic pressure on these loci may be
We gratefully acknowledge the assistance
of Mr. Wayne McCullom and Staff Sgt.
George Elder of the ETAC Technical Library,
Scott Air Force Base, Illinois. We have benefitted from discussions with Dr. E.J.E. Szathmary, and Dr. F. Auger graciously provided
unpublished gene frequencies for the James
Bay Cree. We wish to express our gratitude
to Dr. T.R. Przybeck for his generous programming help. This work was supported in
part by MH 31302 and MH 14677 from the
United States Public Health Service and by
a Faculty Development Grant from the Research Committee of the University of Utah.
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