The ongoing adaptive evolution of ASPM and Microcephalin is not explained by increased intelligence
Human Molecular Genetics
The ongoing adaptive evolution of ASPM and Microcephalin is not explained by increased intelligence
Nitzan Mekel-Bobrov 2 4
Danielle Posthuma 0 8
Sandra L. Gilbert 4
Penelope Lind 6
M. Florencia Gosso 0 8
Michelle Luciano 6
Sarah E. Harris 5
Timothy C. Bates 5
Tinca J.C. Polderman 0 3
Lawrence J. Whalley 9
Helen Fox 9
John M. Starr 10
Patrick D. Evans 2 4
Grant W. Montgomery 6
Croydon Fernandes 4
Peter Heutink 0 7 8
Nicholas G. Martin 6
Dorret I. Boomsma 0 7 8
Ian J. Deary 5
Margaret J. Wright 6
Eco J.C. de Geus 0 8
Bruce T. Lahn 4
0 Department of Biological Psychology
1 The Author 2007. Published by Oxford University Press. All rights reserved
2 Committee on Genetics, Howard Hughes Medical Institute, University of Chicago , Chicago, IL 60637 , USA
3 Department of Child and Adolescent Psychiatry Erasmus MC , Sophia, Rotterdam , The Netherlands
4 Department of Human Genetics
5 Department of Psychology
6 Queensland Institute of Medical Research , Brisbane , Australia
7 Department of Clinical Genetics and Anthropogenetics, Section of Medical Genomics, Vrije Universiteit Medical Center , Amsterdam , The Netherlands
8 Center for Neurogenomics and Cognitive Research , CNCR
9 Department of Mental Health, University of Aberdeen, Clinical Research Centre, Royal Cornhill Hospital , Aberdeen , UK
10 Department of Geriatric Medicine, University of Edinburgh, Royal Victoria Hospital , Edinburgh , UK
Recent studies have made great strides towards identifying putative genetic events underlying the evolution of the human brain and its emergent cognitive capacities. One of the most intriguing findings is the recurrent identification of adaptive evolution in genes associated with primary microcephaly, a developmental disorder characterized by severe reduction in brain size and intelligence, reminiscent of the early hominid condition. This has led to the hypothesis that the adaptive evolution of these genes has contributed to the emergence of modern human cognition. As with other candidate loci, however, this hypothesis remains speculative due to the current lack of methodologies for characterizing the evolutionary function of these genes in humans. Two primary microcephaly genes, ASPM and Microcephalin, have been implicated not only in the adaptive evolution of the lineage leading to humans, but in ongoing selective sweeps in modern humans as well. The presence of both the putatively adaptive and neutral alleles at these loci provides a unique opportunity for using normal trait variation within humans to test the hypothesis that the recent selective sweeps are driven by an advantage in cognitive abilities. Here, we report a large-scale association study between the adaptive alleles of these genes and normal variation in several measures of IQ. Five independent samples were used, totaling 2393 subjects, including both family-based and population-based datasets. Our overall findings do not support a detectable association between the recent adaptive evolution of either ASPM or Microcephalin and changes in IQ. As we enter the post-genomic era, with the number of candidate loci underlying human evolution growing rapidly, our findings highlight the importance of direct experimental validation in elucidating their evolutionary role in shaping the human phenotype.
The most striking trend in human evolution is the rapid
increase in brain size over the past 3 – 4 million years, and
the associated increase in cognitive complexity (
advances in comparative genomics and population genetics
have facilitated rapid progress in studying the genetic basis
of human brain evolution. By searching for signatures of
adaptive evolution in genes known to function in the mammalian
brain, a large number of candidate loci have been identified
). One of the most intriguing findings thus far has been
the prominent role played by genes associated with primary
), a developmental defect in fetal brain
). Given the atavistic reduction in brain size
associated with their pathological state (6), the finding of
strong signatures of positive selection in three of the four
primary microcephaly genes identified to date (
7 – 12
led to many speculations regarding their role in human brain
evolution, particularly in terms of cognition (3). As with
other candidate loci identified to date, however, the functional
role of their adaptive evolution remains speculative, given the
technical and ethical limitations inherent to studying human
). Thus, the next major challenge is to characterize
the functional significance of adaptive substitutions at
identified candidate loci.
The two primary microcephaly genes, ASPM (abnormal
spindle-like microcephaly associated ) and Microcephalin
(MCPH1), not only show signatures of adaptive evolution in
the lineage leading to humans (
7,8,10 – 12
), but have also
continued to evolve adaptively since the emergence of
anatomically modern humans, with the adaptive alleles rising from
one copy, as recently as 6000 and 40 000 years ago, to a
worldwide frequency of 30% and 70%, respectively
). The presence of both the adaptive and neutral alleles
of these genes at moderate frequencies provides an ideal
opportunity for using natural trait variation within humans for
testing hypotheses regarding the phenotypic substrate of their
selection. A recent study reported no association between the
alleles of ASPM and Microcephalin and normal variation in
brain size (16). These results, however, were based on a very
small number of individuals, comprising highly divergent
ethnic backgrounds, and whole-brain volume measurements,
which may obscure inter-individual variation in specific
regions of the brain that are of particular importance to
cognitive complexity (
). Ultimately, the putative substrate of
selection is intelligence, not brain size. Consequently, we
chose to focus directly on heritable measures of intelligence,
rather than brain size as an indirect proxy. Variation in
intelligence, as measured by standard IQ tests, is one of the most
heritable behavioral traits identified to date in humans. With
heritability estimates ranging from 25 – 40% in early childhood
(19) to 80% in adulthood (
), IQ scores are the best available
measures of intelligence for testing genetic associations.
To test the hypothesis that the recent selective sweep at
ASPM and Microcephalin is due to increased intelligence,
we genotyped the diagnostic sites that distinguish the adaptive
derived allele (D-allele) from the ancestral allele (A-allele) of
each gene. We used a large-scale dataset comprising three
independent family-based samples (a sample of Dutch
children, a Dutch adult sample and an Australian adolescent
N, sample size. The Dutch and Australian samples are family-based
samples. In the case of MZ twins, only one individual is included in
this table for the purpose of interpretation of allele frequencies—the
totals therefore are lower than the actual totals available for association
tests, and therefore differ from those in Tables 2 and 3; for the association
analyses both individuals of MZ twins are included, by taking zygosity
status into account.
sample), as well as two independent population-based
samples (the LBC1921 sample from the Lothian region of
Scotland, and the ABC1936 sample from the Scottish city of
Aberdeen). The five replicate samples totaled 2393 individuals
for whom several measures of intelligence were available
from previous assessments. Our overall findings suggest that
intelligence, as measured by these IQ tests, was not detectably
associated with the D-allele of either ASPM or Microcephalin.
We genotyped subjects from the five samples for previously
identified single nucleotide polymorphisms (SNPs), diagnostic
of the ASPM and Microcephalin D-alleles (
). Table 1
lists the occurrence and frequency of each genotype class of
ASPM and Microcephalin for the five replicate samples.
Allele and genotype frequencies of ASPM and Microcephalin
were comparable across all samples. Both genes were found to
be in Hardy – Weinberg equilibrium (HWE) within each
sample, by exact test of random mating with a Markov
chain method for unbiased estimation of significance
). Similarly, combining all samples together by
Fisher’s method (
) showed no violation of HWE. The
implication of this finding for the model of adaptive evolution on
these genes cannot be determined, given that a lack of
deviation from HWE does not mean that its underlying
assumptions are not violated (
Figure 1 shows the mean IQ scores of the three ASPM and
Microcephalin genotypes for each replicate sample. Scores are
corrected for age and sex, and z-transformed for comparison
across samples. Untransformed values are provided in
Table 2. Scores are listed for three different measures of
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intelligence. Although the specific tests underlying these
measures vary among the replicate samples, depending on
their geographic origin, they generally correspond to an
overall intelligence quotient [full-scale IQ (FIQ) in the
familybased samples, and Moray House Test (MHT) in the
population-based samples], as well as specific measures of
verbal reasoning (VIQ) and nonverbal reasoning (PIQ). The
overall intelligence quotient (MHT) for the LBC1921
individuals was measured at age 11 and repeated at age 79, and is
consequently given for both ages.
To test for association between the D-alleles of ASPM and
Microcephalin and IQ, multiple non-independent tests were
carried out for each replicate sample, modeling both additive
and dominant effects. For the family-based samples, we used
a quantitative transmission disequilibrium test (qTDT) that is
applicable to nuclear families of any size and does not
necessitate parental data (
). Based on the orthogonal model
developed by Fulker et al. (
), genotypic effects are decomposed
into between-family and within-family components, such
that in the absence of spurious associations due to admixture,
both components can be used to implement the more powerful
population-based test, but if admixture is detected, a
familybased test can still be carried out using the within-family
component on its own. Results for the three family-based samples
are given in Table 3; where admixture was detected results of
the population-based association tests, are omitted, as they
cannot be interpreted. Analogous to the family-based
samples in the absence of admixture, the two Scottish
samples were tested for population-based association by a
simple regression model. Results for the two Scottish
population-based samples are given in Table 3.
For ASPM, the D-allele showed statistically significant
association with increased performance in both FIQ and VIQ
in the Dutch Adults sample, using the family-based tests of
additive effects (P ¼ 0.04 for FIQ), and of additive and
dominant effects (P ¼ 0.01 for FIQ and VIQ). Statistically
significant association was also found with increased PIQ, in
the population-based test of additive and dominant effects
in the LBC1921 sample (P ¼ 0.05). These results, however,
were not replicated in other samples. Moreover, in the
Australian sample, increased FIQ was associated with the A-allele of
ASPM, rather than the D-allele, using the population-based
test of additive and dominant effects (P ¼ 0.05); in the
second Scottish sample, ABC1936, this same test showed a
statistically significant association between the A-allele and
increased PIQ. Thus, two of the three measures found to be
associated with the D-allele of ASPM showed opposite
association in other samples.
For Microcephalin, the D-allele showed significant
association with increased FIQ, VIQ and PIQ in the Dutch Children
sample, using the family-based tests of additive effects
(P ¼ 0.02 for FIQ and VIQ) and of additive and dominant
effects (P ¼ 0.03 for FIQ and P ¼ 0.02 for PIQ), as well as
the population-based test of additive and dominant effects
(P ¼ 0.03 for FIQ and P ¼ 0.00 for PIQ). Similar to ASPM,
however, these results were not replicated in other samples,
and in the Dutch Adults sample, VIQ was significantly
associated with the A-allele, rather than the D-allele, of
Microcephalin in the family-based test of additive and dominant effects
(P ¼ 0.03).
Our results do not show a consistent association between the
adaptive alleles of ASPM and Microcephalin and any of the
several measures of intelligence we tested. For ASPM,
significant associations were found between the D-allele and
increased FIQ, VIQ, and PIQ in the Dutch Adults and the
LBC1921 samples. Each association, however, was observed
in only one sample and was not replicated in any of the
other datasets. Furthermore, in two of the other datasets,
increased FIQ and PIQ were associated with the A-allele
instead. For Microcephalin, a significant association was
seen between the D-allele and all three measures of
intelligence in the Dutch Children sample. Similar to ASPM,
however, these results were not reproducible in any of the
other samples, and an opposite pattern of association was
seen in the Dutch Adults sample, with increased VIQ
showing significant association with the A-allele.
Furthermore, for Microcephalin, the D-allele is present at a very
low frequency (Table 1). Consequently, the observed effect
in the Dutch Children sample is driven by a relatively small
group of homozygotes.
There are several issues to consider in interpreting these
results. On its own, the irreproducibility across different
datasets could be attributed to either lack of power in testing for
what might be a relatively small effect, or a false positive
result. Given, however, that of the three measures of
intelligence found to be associated with the ASPM and Microcephalin
D-alleles, all three showed significant association with the
A-alleles in other samples, a false positive result appears to be
the most parsimonious interpretation. Furthermore, the large
size of this dataset suggests that differences in age, environment,
genetic background or phenotyping methods, are unlikely to
account for the variation in effect, especially since each of
these variables is replicated across more than one sample.
Thus, our overall findings do not support a detectable
association between the D-allele of either ASPM or
Microcephalin and increased IQ. Furthermore, examination of the
allelic effects of both genes together shows no evidence of a
heterozygous or epistatic effect. This result is consistent
with the recent report (
) of a null relationship between
ASPM and Microcephalin variation and brain size—a
biological correlate of normal variation in intelligence (
Nevertheless, we cannot rule out the possibility that our
analysis fails to detect a correlation due to the small effect of the
correlation. Furthermore, our study is in no way an exhaustive
test of brain-related functions. Indeed, given the abundant
expression of ASPM and Microcephalin in the developing
brain and the restricted disease phenotype, we would argue
that these genes remain very strong candidates for
understanding human brain evolution and the D-alleles might confer
some advantage in brain function not readily measured by
conventional IQ tests.
Primary microcephaly is a particularly interesting disorder
from an evolutionary perspective. Based on the phenotype of
the human disease, the expression pattern of the underlying
genes, their intracellular function in cell cycle progression,
and most recently gene knockdown experiments, primary
microcephaly is best characterized as a defect in the regulation
of neural progenitor cell transition from proliferative to
M F (
D D D
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neurogenic division (
). Over a decade ago, Rakic
proposed that genes regulating the timing of this transition
may explain evolutionary modifications in brain size (29).
Indeed, the phenotype of primary microcephaly is atavistic:
characterized by severe reduction in brain volume, a simplified
gyral pattern of the cortex, and a hypoplastic skull vault, yet
no gross abnormality in cortical architecture, it bears a
unique resemblance to the early hominid condition (
Moreover, the decrease in brain volume is strongly correlated with
decreased cognitive capacity (
). Thus, the identification of
signatures of adaptive evolution at three of the four primary
microcephaly genes identified to date, and the extremely
rapid selective sweeps at ASPM and Microcephalin in the
recent history of our species, makes them prime candidates
for studying the genetic basis of human brain evolution.
Yet, how much can we learn from loss-of-function mutations
about a gene’s specific role in a species’ evolutionary history? In
fact, we can ask more broadly, how informative is the general
function of a gene for understanding the phenotypic
consequences of specific nucleotide substitutions? A number of
recent reviews on the genetic basis of human evolution have
called for the use of data from human disease, model organisms
and in vitro systems to relate human-specific substitutions to the
unique aspects of our anatomy and physiology (
Because of the technical and ethical limitations of studying
human subjects, the need to rely on this type of inductive
reasoning is much greater than in evolutionary studies of model
organisms. Nonetheless, as our findings suggest, the relationship
between a gene’s known function and its role in shaping a
species’ evolutionary history may be far more complex. Thus,
although general functional classification is a necessary and
powerful first step for identifying candidate genes and forming
hypotheses regarding their evolutionary significance, ultimately,
phenotypic characterization of evolutionary signatures will
require developing new deductive approaches based on direct
Functional characterization of adaptive evolution in model
organisms has made tremendous progress in the past two
decades by capitalizing on intraspecies variability and the
increasing number of molecular markers to identify adaptive
33 – 35
). In studies of other species, for which
experimental crosses are not possible, analysis of variation in natural
populations has shown great promise (
). Whereas older
selective sweeps in humans have often gone to completion,
and consequently are no longer polymorphic in reference to
the selective event, the adaptive alleles of genes with a recent
history of positive selection are more likely to have
not reached fixation. These loci provide a unique opportunity
to use natural trait variation within humans for phenotypic
characterization of signatures of positive selection. Thus, we
propose the use of candidate gene-based association studies as
a method for identifying the phenotypic substrate underlying
signatures of ongoing adaptive evolution.
MATERIALS AND METHODS
The Dutch children cohort consisted of 384 subjects (181
males) from 170 families. There were 91 MZ pairs, 76 DZ
pairs and 47 of their non-twin siblings. Mean age at the time
of testing was 12.4 (SD ¼ 0.9). The Dutch children were
part of an ongoing study on the genetics of attention (
Participation in this study included a voluntary agreement to
provide buccal swabs for DNA isolation and genotyping.
The Dutch adult cohort consisted of 361 subjects (168
males) from 174 families. There were 42 MZ pairs, 61 DZ
pairs, 1 DZ triplet and 152 siblings. Mean age at the time of
testing was 36.4 (SD ¼ 12.4). The Dutch adults were part of
an ongoing study on the genetics of brain function (
Participation in this study included a voluntary agreement to
donate a blood sample for DNA isolation and genotyping.
The Australian adolescent cohort consisted of 985 subjects
(482 males) from 460 families. There were 94 MZ pairs,
298 DZ pairs and 201 siblings. Mean age at the time of
testing was 16.4 (SD ¼ 0.7). The Australian adolescents
were part of an ongoing study on the genetics of cognition
). Participation in this study included a voluntary
agreement to donate a blood sample for DNA isolation and
genotyping. Parental genotypes but no parental IQ scores
were available. Recruitment of LBC1921 has been described
). Briefly, the LBC1921 consisted of 526
subjects (219 males) who took part in the Scottish Mental
Survey of 1932 at the age of 11 years, and were retested
recently at age 79. All participants lived independently in
the community. For this study, inclusion criteria were no
history of dementia and a Mini-Mental State Examination
score of 24 or greater. Recruitment of ABC1936 has been
described previously (
), and consisted of 205 subjects
(109 males) who took part in the Scottish Mental Survey of
1947 at the age of 11 years, and were retested recently at
age 64. All participants lived independently in the community.
Inclusion criteria were as for LBC1921.
For the Dutch Children sample, cognitive ability was assessed
with the Dutch adaptation of the Wechsler Intelligence Scale
for Children-Revised (WISC-R) (
). For the Dutch Adult
sample, cognitive ability was assessed with the Dutch
adaptation of the Wechsler Adult Intelligence Scale III-Revised
). VIQ, PIQ and FIQ were calculated
following the WAIS-III guidelines and were normally distributed.
For the Australian sample, the Multi-dimensional Aptitude
Battery (MAB) (
) was used. Scaled scores for VIQ, PIQ
and FIQ were calculated following the manual and were
normally distributed. Although the scaled scores were based upon
Canadian normative data, this does not affect the validity of
the scale for testing genetic effects within samples. All
LBC1921 and ABC1936 subjects took a version of the
MHT, No. 2 (
), a general mental ability test at age 11
years, in the Scottish Mental Surveys of 1932 and 1947,
respectively. LBC1921 repeated the same test at about age
79 years. The test was previously described in detail (40).
The ABC1936 were re-tested at about age 64 years.
Nonverbal reasoning was examined in LBC1921 and ABC1936
using Raven’s Standard Progressive Matrices (Raven) (
and is referred to as Performance IQ (PIQ) in the Scottish
samples. The National Adult Reading Test (NART) assessed
premorbid or prior cognitive ability (
47 – 49
), and is taken as
an index of Verbal IQ (VIQ) in the Scottish samples. Although
the MHT scores, VIQ, PIQ and FIQ are standardized measures
with respect to age and sex, we still found small significant
effects of age and sex. We therefore conducted all analyses
on the residuals (i.e. corrected for age and sex). Corrections
were carried out separately within each sample.
DNA collection and genotyping
Zygosity was assessed using 11 polymorphic microsatellite
markers (Het . 0.80) in the Dutch samples and nine in the
Australian sample with P (DZjconc) ,1024. Genotyping
was performed blind to familial status and phenotypic
data. Genotyping of ASPM for all samples except for
ABC1936 was performed by automated sequencing of
A44871G, as described previously (
), using the primer
pair TCAGACAATGGCATTCTGCT and
CTGCCTGAACACAAGTCTCT. In the ABC1936 sample, genotyping was
performed using the C45126A SNP instead. Each PCR product
was sequenced on both strands. Genotyping of Microcephalin
was performed by automated sequencing of the diagnostic
G37995C SNP, as described previously (
), using the
primer pair AGAAATTTCTGAGTAATCTTTCAAAGG and
ACTGAGGAACTCCTGGGTCT. Each PCR product was
sequenced on both strands. In the Dutch Children sample,
ASPM genotyping failed for 16 subjects and Microcephalin
for five subjects. In the Dutch Adults sample, ASPM failed
for 53 subjects and Microcephalin for 12 subjects. In the
Australian sample, ASPM failed for one subject. In the LBC1921
sample, ASPM failed for 13 subjects and Microcephalin for
eight subjects. In the ABC1936 sample, ASPM failed for 19
subjects and Microcephalin for 17 subjects. Genotyping was
performed at the Howard Hughes Medical Institute
(LBC1921: ASPM, Microcephalin), Vrije Universiteit
Medical Center, Amsterdam (Dutch Children and Adults
samples: ASPM, Microcephalin), Queensland Institute of
Medical research (Australian sample: ASPM, Microcephalin)
and by KBiosciences (Herts, UK, http://www.kbioscience.co.
uk) using KASPar chemistry (ABC1936: ASPM,
For the family-based samples, qTDTs were conducted using
the program QTDT, which implements the orthogonal model
developed by Abecasis et al. (
) as an extension of the
of Fulker et al. (
). This model allows one to decompose
the genotypic effect into orthogonal between- (bb) and
within- (bw) family components, and also models the residual
sib-correlation as a function of polygenic or environmental
factors. MZ twins can be included and are modeled as such,
by adding zygosity status to the data file. They are not
informative to the within family component (unless they are
paired with non-twin siblings), but are informative for the
between-family component. The between-family association
component is sensitive to population admixture, whereas the
within-family component is unaffected by spurious
associations due to population structure. Thus, if population
structure creates a false association, the test for association
using the within-family component is still valid, though
usually less powerful. The full model including additive
effects as well as dominance for each sample is represented
as: EðyijÞ = m + babiab + bdbidb + bawijaw + bdwijdw, where
E( yij) represents the expected phenotypic value of sib j from
the ith family, m denotes the overall trait mean (equal for all
individuals), babi is the coefficient for the between families
additive genetic effect for the ith family, bdbi is the coefficient
for the between families dominant genetic effect for the ith
family, bawij denotes the coefficient as derived for the within
families additive genetic effects for sib j from the ith family,
bdwij denotes the coefficient as derived for the within families
dominant genetic effects for sib j from the ith family, ab and
aw are the estimated additive between and within parameters,
db and dw are the estimated dominance between and within
family parameters. The test for spurious association tests
whether ab ¼ aw and db ¼ dw. If this test is significant,
association can be tested by Ho: aw ¼ dw ¼ 0. If there is no
population stratification, the expectation simplifies to
population-based association represented by a simple
regression model EðyijÞ = m + baia + bdid, where a denotes
the overall or total additive genetic effect and d denotes the
total dominance effect. The test for ‘total’ association is then
Ho: a ¼ d ¼ 0. As the Scottish samples are population
based, the within-family test cannot be conducted, and in
these samples only the latter simple regression test for
association was performed.
We thank Nancy J. Cox, Anna Di Rienzo, Carole Ober,
Jonathan K. Pritchard, Eric J. Vallender, Martine van Belzen
and David Duffy for technical support and/or input on the
manuscript. Supported by the Universitair Stimulerings
Fonds (grant 96/22), the Human Frontiers of Science
Program (grant rg0154/1998-B), the Netherlands Organization
for Scientific Research (NWO) (grant NWO/SPI 56 –
464-14192). Also, supported by the Centre for Medical
Systems Biology (CMSB), a centre of excellence approved
by the Netherlands Genomics Initiative/NWO. DP is
supported by GenomEUtwin grant (EU/QLRT-2001 – 01254)
and by NWO/MaGW Vernieuwingsimpuls 452 – 05 – 318.
The Lothian Birth Cohort 1921 is supported by the UK’s
Biotechnology and Biological Sciences research Council. The
Aberdeen Birth Cohort 1936 is supported by the Wellcome
Trust. IJD is the recipient of a Royal Society-Wolfson
Research Merit Award. Australian data collection was
funded by ARC grants (A79600334, A79906588,
A79801419, DP0212016, DP0343921); ML is supported by
ARC Fellowship DP0449598. We thank the participants and
their families for their cooperation.
Conflict of Interest statement. None declared.
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