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Craniometric variation among modern human populations.

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AMERICAN JOURNAL, OF PHYSICAL ANTHROPOLOGY 95:53-62 (1994)
Craniometric Variation Among Modern Human Populations
JOHN H. RELETHFORD
Department of Anthropology, State Uniuersity of New York College at
Oneontu, Oneontu, New York 13820
KEY WORDS
Craniometrics, Genetic differentiation, Human
variation, Modern human origins
ABSTRACT
Previous studies of genetic markers and mitochondrial DNA
have found that the amount of variation among major geographic groupings of
Homo sapiens is relatively low, accounting for roughly 10% of total variation.
This conclusion has had implications for the study of human variation and
consideration of alternative models for the origin of modern humans. By
contrast, it has often been assumed that the level of among-group variation
for morphological traits is much higher. This study examines the level of
among-group variation based on craniometric data from a large sample of
modern humans originally collected by W.W. Howells. A multivariate method
based on quantitative genetics theory was used to provide an estimate of
FST - a measure of among-group variation that can be compared with results
from studies of genetic markers. Data for 57 craniometric variables on 1,734
crania were analyzed. These data represent six core areas: Europe, SubSaharan Africa, Australasia, Polynesia, the Americas, and the Far East. An
additional set of analyses was performed using a three-region subset (Europe,
Sub-Saharan Africa, and the Far East) to provide comparability with several
genetic studies.
The minimum FST (assuming complete heritability) for the three-region
analysis is 0.065, and the minimum F S T for the six-region analysis is 0.085.
Both of these are less than the average FST from genetic studies (average
estimates of 0.10-0.11). The smaller value of the minimum F S T estimates is
expected since it provides an estimate of FsTexpected under complete heritability. Using an estimate of average craniometric heritability from the literature provides an estimate of FST of 0.112 for the three-region analysis and
0.144 for the six-region analysis. These results show that genetic and craniometric data are in agreement, qualitatively and quantitatively, and that there
is limited variation in modern humans among major geographic regions.
0 1994 Wiley-Liss, Inc.
In recent years increasing attention has
been paid to the implications of patterns of
modern human variation for understanding
the origin and evolution of modern humans
(Stringer and Andrews, 1988). One important finding has been the relatively low level
of variation among geographic regions compared to high levels of variation within regions. A variety of data (genetic markers,
nuclear DNA, and mitochondrial DNA) all
suggest low levels of population differentiation in modern humans, often less than
0 1994 WILEY-LISS, INC.
found among subspecies of other organisms
(e.g., Lewontin, 1972; Latter, 1980; Nei and
Roychoudhury, 1982; Ryman et al., 1983;
Livshits and Nei, 1990; Vigilant et al., 1991;
Stoneking, 1993; Takahata, 1993). These
findings have had several implications for
the study of human variation and human
evolution. First, such low levels of population differentiation imply that the vast maReceived October 24,1993; accepted March 7,1994
54
J.H. RELETHFORD
jority of genetic variation is among individuals within groups, and not due to variation
among groups. As such, these findings show
little support for the concept of race or racial
classifications (Lewontin, 1972, but see Nei
and Roychoudhury, 1982). Second, some authors have suggested the low level of population differentiation supports a relatively recent divergence of modern humans into
distinct geographic regions-the “Garden of
Eden” or replacement model (e.g., Cann et
al., 1987; Stringer and Andrews, 1988; Rouhani, 1989).An alternative hypothesis, multiregional evolution, posits an earlier divergence of Homo erectus approximately 1
million years ago (e.g., Wolpoff, 1989). Here,
the low level of differentiation is seen as due
to relatively high rates of gene flow among
geographic regions.
To date, all studies of population differentiation on a worldwide scale have used data
from genetic markers (blood groups, serum
proteins and enzymes, and so forth) and mitochondrial DNA (mtDNA). Differentiation
of complex traits, such as cranial and facial
measurements, has seldom been addressed
in this context. Many discussions assume
greater morphological differentiation relative to genetic differentiation. Nei and Roychoudhury, for example, state that morphological variation among major races is
“conspicuous”(1982: 40). Stringer and Andrews state that our species “shows great
morphological variation. However, in contrast to this, genetic variation between human populations is low overall.” (1988:
1264). These and other statements presume
extensive worldwide morphological variation. One notable exception is Howell’s
(1989) study of worldwide craniometric variation. On the basis of comparisons of modern crania with several archaic forms, he
concluded that craniometric variation
among modern Homo sapiens is fairly limited. However, he performed no formal comparison of differentiation based on craniometrics and genetic markers.
The study of quantitative traits has for
many decades followed what Howells
(1973b)termed a model-free approach. Here,
traditional statistical methods are applied
and interpreted in light of general predictions from population genetics theory. This
approach contrasts with the model-bound
approach often used in analysis of genetic
markers, where specific parameters of models are estimated. The past decade or so has
seen a resurgence in the study of quantitative traits in anthropology, focusing more
and more on model-bound approaches and
parameter estimation (e.g., Relethford and
Lees, 1982; Rogers and Harpending, 1983;
Williams-Blangero et al., 1990). Recent developments in methodology allow estimation of parameters of microevolutionary
change from quantitative traits, which can
then be compared with similar estimates
from genetic markers and mtDNA.
The purpose of this paper is to present
estimates of the degree of population differentiation among world regions based on
craniometric data. These estimates provide
an index of the relative degree of variation
among and within regions. These estimates
are then compared to typical values found
from studies of genetic markers and
mtDNA. The results indicate that the degree of differentiation is essentially the
same in both genetic markers and craniometric traits. These results lend further
support to a relatively recent divergence of
modern humans, and also suggest that multivariate measures of average worldwide
craniometric variation may primarily reflect a balance between gene flow and genetic drift.
MATERIALS AND METHODS
The data used in this study were graciously provided by Dr. W.W. Howells and
are from his long-term study of craniometric
variation in modern humans (Howells,
1973a, 1989).Portions o r all of Howells’data
have been used in a variety of studies focusing on within-group and among-group craniometric variation of modern humans (e.g.,
Guglielmino et al., 1977; Lynch, 1989; Franciscus and Long, 1991; Konigsberg and
Blangero, 1993; Kramer, 1993; Relethford
and Harpending, 1994). The present study
uses data on 57 cranial and facial measurements taken on 1,734 modern human crania
(907 males and 827 females) from six world
regions: Europe, Sub-Saharan Africa, Australasia, Polynesia, Americas, and the Far
East. Howells chose three local populations
CRANIOMETRIC VARIATION AMONG MODERN HUMANS
TABLE 1 . Samule sizes
Region
Europe
Sub-Saharan Africa
Australasia
Polynesia
Americas
Far East
Total
55
TABLE 2. List of craniometric variables
Males
Females
Total
164
135
153
157
148
150
907
153
148
145
137
133
111
827
317
283
298
294
281
261
1734
from within each of these six regions for
maximum representation of average patterns of variability within each region. This
sampling strategy avoids potential problems
that might come from reliance on a single
local population to represent a geographic
region. Even given different locations and
time periods for local populations, Howells’
(1989) analyses show that the local samples
cluster together relative to differences
among regions. Dates for samples range
from the early 20th century to 2,000 years
ago. Even “recent” samples were chosen to
provide the best representation of geographic regions as might have existed prior
to 500 years ago; thus, the samples are not
influenced to any great extent by the dramatic changes in migration and population
size in Homo sapiens (Howells, 1989). Sample sizes are reported in Table 1.A complete
list of variables used is provided in Table 2.
The degree of population differentiation is
most often expressed as the ratio of amonggroup variation to total variation expected
under panmictic conditions. For genetic
markers, the proportion of total diversity attributed to variation among regions is often
computed as
Glabello-occipital length
Nasio-occipital length
Basion-nasion length
Basion-bregma height
Maximum cranial breadth
Maximum frontal breadth
Bistephanic breadth
Bizygomatic breadth
Biauricular breadth
Minimum cranial breadth
Biasterionic breadth
Basion-prosthion length
Nasion-prosthion height
Nasal height
Orbit height, left
Orbit breadth. left
Bigugal breadth
Nasal breadth
Palate breadth, external
Mastoid height
Mastoid breadth
Bimaxillary breadth
Zygomaxillary subtense
Bifrontal breadth
Nasio-frontal subtense
Biorbital breadth
Dacryon subtense
Interorbital breadth
Naso-dacrval subtense
Simotic chord
Simotic subtense
Malar length, inferior
Malar length, maximum
Malar subtense
Cheek height
Supraorbital projection
Glabella projection
Foramen magnum length
Nasion-bregma chord
Nasion-bregma subtense
Nasion-subtense fraction
Bregma-lambda chord
Bregma-lambda subtense
Bregma-subtense fraction
Lambda-opisthion chord
Lambda-opisthion subtense
Lambda-subtense fraction
Vertex radius
Nasion radius
Subspinale radius
Prosthion radius
Dacryon radius
Zygoorbitale radius
Frontomalare radius
Ectoconchion radius
Zygomaxillare radius
M1 alveolus radius
See Howells (1973a, 1989) for additional description
number of studies have since applied this (or
a similar) method to genetic data from human populations. These studies, based on a
larger number of loci, suggest a value of
roughly 10% for among-group variation
(Latter, 1980; Nei and Roychoudhury, 1982;
Ryman et al., 1983; Livshits and Nei, 1990).
Similar results have been found for mitochondrial DNA after adjustment for a maternal mode of inheritance-two studies reported by Takahata (1993) give an average
value of 0.097.
The partitioning of variances using equation (1)cannot be applied directly t o quantiHs - Hr
(1)
tative traits because total genotypic or pheHs ’
notypic variation (all groups pooled) is not
where Hs is the heterozygosity expected if the same as that expected under panmixia.
the entire species were a single panmictic The total genotypic variance of a total sampopulation and Hr is the average heterozy- ple (such as a species) reflects a combination
gosity within regions. Lewontin (1972) was of within-group and among-group variation
one of the first to use this method on genetic (Relethford and Blangero, 1990). An altermarkers from major human regions native method based on another method of
(“races”)and found an average value across genetic analysis is required. Equation (1)
loci of 0.063; that is, roughly 6% of species- and similar derivations provide an estimate
wide variation was due to among-group of Wright’s (1951) familiar FST measurevariation, while almost 94% of total varia- the ratio of among-group variation to total
tion was due to within-group variation. A variation expected under panmixia (note
56
J.H.RELETHFORD
that the quantity computed in equation (1)
also goes by a variety of other names, such
as f GsnR , , r,, and others). An estimate of
FST can be computed from the v a r i a n c s o variance matrix of population relationships,
better known as an R matrix. The diagonal
elements of this matrix, rLi,represent the
genetic distance of population i to a regional
centroid, defined by the mean allele frequencies over all populations being studied.
The diagonal elements are computed as
where p i is the allele frequency for population i and p is the mean allele frequency
averaged over all populations. The rii value
is then averaged over all alleles, and FST is
estimated as the average rii value over all
populations, weighted by population size
(Harpending and Jenkins, 1973; Workman
et al., 1973). The higher the value ofFST, the
greater the variation around the contemporary allele frequencies, indicating greater
differentiation.
F S T can be estimated from quantitative
traits using an equal and additive effects
model of polygenic inheritance (WilliamsBlangero and Blangero, 1989; Relethford
and Blangero, 1990). Following standard
quantitative genetics theory for loci with
two alleles (e.g., Falconer, 19811, let a, 0,
and -a represent the genotypic values of
the three genotypes a t any given locus. This
model also assumes that these genotypic
values are the same across all loci (Falconer,
1981; Rogers and Harpending, 1983). The
phenotypic means can then be written as
xi= 2api
for population i, and
i = 2aJ
However, this value cannot be estimated directly from data (Relethford and Blangero,
1990) and must be estimated instead from
the relationship
(61
whereg, is the pooled within-group additive
genetic variance (Rogers and Harpending,
1983). Substituting equations (3) and (4) for
the numerator in equation (2), and substituting equations (5) and (6) for the denominator in equation (2), gives
where
The total mean (x) and the pooled withingroup additive genetic variance &), are
both obtained as weighted estimates over all
samples, and the weighting is by population
size. (Note: The definition of cii given here
differs from that originally given by Relethford and Blangero [19901).
The multivariate extension of this
method, based on g groups and t traits, presented by Relethford and Blangero (1990), is
used here. First, all data are converted into
standardized scores by variable. Second, a g
by t matrix is computed consisting of deviations of group means from the total means
pooled over all populations (A). Third, the
pooled within-group additive genetic vari(3) ance-covariance matrix is computed (G).
Fourth, a codivergence matrix ( C ) is computed as
(4)
for the total sample. The total additive genetic variance (under the assumption of
panmixia) can also be written in terms of the
allele frequencies, as
c = AG-~A',
(9)
where G is the pooled within-group additive
genetic variance<ovariance matrix, and the
prime ('1 indicates matrix transposition.
This matrix is then divided by t to give an
average value over all traits (C/t).Since FST
is defined as
CRANIOMETRIC VARIATION AMONG MODERN HUMANS
57
FST The lowest possible value of FsToccurs
substitution of the diagonal elements of Clt
for ciiin equation (7)and combining with
equation (10) gives
Solving for F S T gives
The weighting factor, w i ,is usually defined in terms of relative census size, but in
the case of the present analysis these values
are unknown during recent human evolution. Thus, this value is set equal to llg
(equal weighting of all populations). While
this might not be an accurate reflection of
past population sizes, it does offer comparability with estimates obtained from genetic
markers. The weighting factor is also used
in computing the variance-covariance matrix and the matrix of pooled means (Relethford and Blangero, 1990). Note that FSTas
used here (and in studies of genetic markers) refers to variation around a contemporary array of allele frequencies, and not a
hypothetical array of ancestral allele frequencies, which are never known.
Computation of the pooled within-group
additive genetic variance-covariance matrix
(G) requires information on heritabilities. If
these are not available, the pooled withingroup phenotypic variance-covariance matrix (V) can be substituted to provide an estimate of the minimum FST value (WilliamsBlangero and Blangero, 1989; Relethford
and Blangero, 1990). This method assumes
that all heritabilities are equal to 1(G = V),
and that the additive genetic covariance matrix is proportional to the phenotypic covariance matrix. That this value is a minimum
value is clear from consideration of equations (9) and (12). For any given trait, G =
h v where h2 is the heritability of the trait.
As h2 increases C decreases, and so does
when h2 = 1(G = V).
Minimum FST is a conservative statistic;
it implies that actual genetic differentiation
is at least as great as that estimated under
the assumption that G = V. As such, the
minimum F S T should be less than an estimate ofFsT computed from genetic markers.
The amount less reflects the average heritability over all traits. A rough approximation
of the actual F S T value can be made using
estimates of average heritability of craniometric traits as reported in the literature
(e.g., Devor, 1987). This rough approximation assumes a high correspondence between genetic and phenotypic correlation
matrices, which is supported in a review of
quantitative genetics analyses (Cheverud,
1988).
The relationship between the number of
traits and the number of loci is affected by
the intercorrelation of variables. Rogers and
Harpending (1983) show that one completely heritable quantitative trait is expected t o provide the same information as a
single locus trait with two alleles. If traits
are highly intercorrelated the effective number of loci decreases. While methods exist for
estimating the effective number of loci
(Williams-Blangero and Blangero, 1992),
they require detailed pedigree data, which
are not available for this study.
The methods described here were applied
to Howells’ data to determine the minimum
FST values for major geographic regions.
Comparative data from genetic markers for
major geographic groups usually focus on
six groups (roughly similar to the ones used
here) or the three “major races” of humans“Caucasoid,” “Negroid,” and “Mongoloid.”
For comparability, I have computed the
minimum FST for all six of Howells’ core areas as well as for the three geographic regions usually associated with the “major
races”-Europe, Sub-Saharan Africa, and
the Far East. The match between populations across studies is not perfect, but it is
certainly useful enough for the purpose of
rough comparison of craniometrics and genetic markers.
Preliminary analyses were performed
separately by sex. Analyses were also performed pooling the two sexes, which pro-
J.H. RELETHFORD
58
TABLE 3. Minimum FST values for craniometr‘ic traits
Number of
regions
Regions included
Male
Female
Europe
Sub-Saharan Africa
Far East
Europe
Sub-SaharanAfrica
Australasia
Polynesia
Americas
Far East
0.076 (0.002)
0.063 (0.002)
0.065 (0.001)
0.093 (0.002)
0.085 (0.002)
0.085 (0.001)
Pooled
These are minimum possible FST values obtained using complete heritability. For comparison with other FST values, such as from genetic
markers, these values must be adjusted for average heritability as described in the text.
Standard errors are in parentheses.
DISCUSSION
How do these estimates compare to genetic studies? For maximum comparison, I
selected studies sampling many loci and
closely equivalent in terms of geographic regions, both in number and composition. Several genetic studies have looked at variation
among the three geographic regions of Europe, Sub-Saharan Africa, and East Asia.
Nei and Roychoudhury (1982) examined diversity among the three “major races.”
These groupings correspond fairly closely
with geographically defined units, except for
some exceptions such as the inclusion of
samples from India in the “Caucasoid (=
European) group. They found a value of
RESULTS
0.088 based on 62 protein loci and a value of
The minimum FSTvalues and their stan- 0.109 based on 23 blood groups. The average
dard errors are reported in Table 3, sepa- of these two data sets is 0.099. Livshits and
rately by sex and pooled, for both three-re- Nei (1990) performed a similar analysis degion and six-region analyses. The standard fined more in terms of geographic regions
errors are all very small relative to the FST than traditional racial groupings. On the bavalues, indicating highly significant among- sis of 86 protein and enzyme loci, and 33
group variation. Depending on sex and the blood group loci, they found an FSTvalue of
number of regions in the analysis, the mini- 0.114.Averaging the similar results of these
mum FST values range from 0.063 to 0.085. two studies gives a mean estimate of 0.107.
Contrary to some opinion, craniometric vari- The minimum FST value from craniometrics
ation across regions within our species is is less than this, as expected if heritabilities
fairly limited. These estimates confirm are less than 1.
Howells’ (1989) impression of limited variHow much less? Given estimates of miniability in modern humans. The minimum mum FsdMin FsT)from metric traits and
FST values are somewhat higher for males.
an estimate of average heritability, the estiIn any case, the pooled minimum FST values mate of genetic FsT is equal to
do not differ much from either sex, particularly the females. All discussions for the reMin FST
mainder of this paper will focus on the
(13)
FST =
Min FsT + h2(1 - Min FST)
pooled minimum FsT values.
vides better correspondence with genetic
markers that are most frequently collected
for both sexes. To avoid problems in sexrelated size variation, all data were first
standardized within each sex prior t o pooling, a common approach in studies of quantitative differentiation (Williams-Blangero
and Blangero, 1989, 1990). The FST values
were corrected for sample size bias as described by Relethford (1991). Standard errors for minimum FST were derived according to analytic formulae developed recently
by John Blangero (personal communication).
CRANIOMETRIC VARIATION AMONG MODERN HUMANS
[derived from formulae presented by Relethford and Blangero (199011. Devor (1987) reports average heritabilities for craniometric
traits from four populations based on path
analysis. The average heritability ranges
from 0.48 to 0.61 across these populations,
with an overall average of 0.55. This estimate is conservative since a number of craniometric traits measured on living subjects
involve soft tissue that is apt to be affected
more by environmental influences than
hard tissue (e.g., nose breadth), so that heritabilities for skeletal measures are likely to
be higher (Devor, 1987). Using an estimate
of h2 = 0.55 for the three-region analysis
(Min FST = 0.065), the estimated value of
F S T is 0.112, which is virtually identical
with the estimate from genetic markers using three geographic regions (0.107).
For comparison with the six-region study,
I selected two genetic studies that focused
on six roughly comparable samples of populations. Ryman et al. (1983) looked at genetic diversity for 25 loci (including proteins, blood groups, and HLA markers).
Their groupings are roughly similar to those
used here, except for the use of a “Caucasoid group for Europe and a more general
sample from Oceania rather than one exclusively from Polynesia. Their estimate of FST
is 0.099. Another six-region analysis was
performed by Latter (1980),who pooled Australasia and Polynesia into an Oceanic
grouping, and also added a separate group
representing the Middle East and India that
is not represented in the craniometric data
set. Latter used 18 loci (10 blood groups, 3
proteins, and 5 enzymes) giving an estimate
of 0.104. The average of the two studies is
0.102, virtually identical to the average of
the three-region genetic analyses. This
value is greater than the minimum FST
value obtained from craniometrics (0.085).
Using an average heritability of 0.55 gives
an estimate of FsT = 0.144, which is higher
than that found for genetic markers, but
still indicates a low level of among-group
variation.
Of course, the estimated FST values depend on specific choice of average heritability. For the three-region analysis, for example, the estimate of FST ranges from 0.148
59
for an average heritability of 0.4 to 0.090 for
an average heritability of 0.7. Using different heritabilities for different traits would
change matters as well, although the overall
estimate of F S T would still fall within this
range for reasonable values of craniometric
heritabilities. While we cannot provide an
exact estimate Of F S T without greater information on heritability, it is important to
note that regardless of the specific average
value chosen, all of the above F S T estimates
are in rough agreement with the range from
genetic studies, and all support the idea of a
relatively low degree of among-group variation.
The estimates presented here (both minimum FST and estimated FST) are based on a
large data set with many variables. However, we must remember that the 57 variables used here are not independent and do
not represent 57 independent loci. Howells
(1973a) applied factor analysis to a portion
of these data and found 18 separate morphometric factors. While I am not claiming any
kind of direct correspondence between factors and loci, his analyses do suggest that
the large number of traits actually provide
information on a fair number of independent
traits.
In spite of potential problems in sample
composition and other methodological concerns, the overall results are quite striking.
In both the three-region and six-region analyses the craniometric data show minimum
FSTvalues less than found in studies of genetic markers, as expected under the hypothesis that both types of data reflect approximately the same microevolutionary
forces. Rough estimates of heritability show
the fit of the three-region analysis in particular to be excellent.
The results are also in agreement with
studies using mitochondria1DNA. Takahata
(1993) reports estimates of F S T of 0.31 and
0.46. Since mtDNA is haploid and maternally inherited, these estimates must be divided by 4 for comparison with genetic
markers and craniometrics. This division
gives F S T = 0.078 and 0.115 (average =
0.097), remarkably similar to the approximate estimate of 10% from both genetic
markers and craniometrics, and particu-
60
J.H. RELETHFORD
larly interesting since mtDNA has a higher
mutation rate than nuclear DNA and is apparently unaffected by selection (Stoneking,
1993).
There are several important implications
from these results for both contemporary
human variation and patterns of human evolution. As noted by Lewontin (1972) and
subsequent studies, the low degree of
among-group genetic variation relative to
total species variation argues strongly
against traditional typological racial classifications. While among-group variation is
statistically significant in all cases, the overall degree of among-group variation is too
low to produce any substantial accuracy in
racial classifications. This finding has been
suggested by some to be different from the
degree of among-group variation in morphological traits (Nei and Roychoudhury, 1982;
Stringer and Andrews, 1988), although no
formal analysis has accompanied these suggestions. The results presented here indicate that there is limited variation among
major geographic or “racial” clusters in modern humans for both genetic and craniometric measures.
It might be expected that craniometric
variation would also be affected by natural
selection andor developmental acclimitization, since a number of studies have shown
significant correlations of craniometric
traits with various climatic indices (e.g.,
Beals, 1972; Guglielmino et al., 1977; Beals
et al., 1984).While suggestive, these studies
are not proof of selection. Guglielmino et
al.’s study used a subset of Howells’ data,
and noted that a distinction between Africa
and Australia, on one hand, and Europe and
the Far East, on the other, suggesting size
differences corresponding to latitude. Recent analyses, however, suggest that these
relationships may reflect differences in population size and genetic drift across geographic regions (Relethford and Harpending, 1994).
In any case, the real issue is the meaning
of the close correspondence in FSTestimates
from genetic markers, mitochondrial DNA,
and craniometrics. Does this correspondence reflect similar patterns of population
history and structure (gene flow and genetic
drift), and/or coincidental correspondence
due to natural selection? Gene flow and ge-
netic drift are expected to have the same
impact on all loci, whereas the effects of selection may vary across loci. In the absence
of loci that show particularly large effects of
selection, FSTis generally taken as an average index of population relationships
brought about by a balance between gene
flow and genetic drift. The low level of average among-group variation in modern humans might reflect selection, although it is
difficult to construct scenarios by which selection will produce the same degree of variation in so many diverse traits, including
mitochondrial DNA, which is considered essentially neutral (Harpending et al., 1993;
Stoneking, 1993).
Not all traits should be expected to necessarily show the same levels of among-group
variation. In particular, individual traits affected strongly by selection might show divergent estimates of FST. For example, I
have looked at mean male skin reflectance
at 685 nanometers from published reports
on 56 populations in Europe, Sub-Saharan
Africa, and the Far East. Using these data,
the minimum FST is 0.647, which is considerably higher than the level of among-group
variation exhibited by craniometrics. Preliminary analyses using averages over additional wavelengths show similar results.
The most obvious, and hardly surprising,
conclusion is that skin color has been
strongly affected by natural selection in
such a way that variation among geographic
regions is large.
The close correspondence in FST estimates
across diverse data sets is not necessarily
ammunition for the continued selectionistneutralist debate. The issue is not whether
genetic markers or craniometrics are affected by selection, but rather what can be
inferred from the average level of amonggroup variation. The level of variation
among major geographic regions relates to
the ongoing controversy regarding the origin of anatomically modern Homo sapiens.
Which of the current models, if any, are
most compatible with low levels of amonggroup variation? While some authors see
low FST values as indicative of a recent divergence from an initial African population,
others see those same values as reflecting
continued gene flow between geographic regions following a much earlier (1,000,000
CRANIOMETRIC VARIATION AMONG MODERN HUMANS
years BP) African origin. At present, there
are no definitive conclusions regarding
which model best explains the low FST values, although some preliminary analyses
have argued against the multiregional
model on the grounds that interregional
gene flow would have had to be extremely
high, andor that effective population size
would have to have been much larger than
estimated from genetic surveys (e.g., Rouhani, 1989; Harpending et al., 1993; Takahata, 1993). Another criticism is that the
high levels of gene flow required by the multiregional model could make regional continuity less likely (Stoneking, 1993). This debate will continue.
The results of the present study do not
provide a clear choice between the competing models of human origins. They do, however, provide further support for the continued finding of low levels of among-group
variation. Any future discussion of the origin of modern humans must deal with this
similarity. Contrary to some earlier views,
the level of morphological variation in modern humans is low.
ACKNOWLEDGMENTS
I am most grateful to W.W. Howells for
the use of his data. I thank Henry Harpending, W.W. Howells, and Mark Stoneking for
their valuable insights. I also thank John
Blangero for providing analytic formula for
the computation of the standard errors of
minimum FST This research was supported
in part by a SUNY Graduate Research Initiative Grant awarded to JHR.
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