Rare copy number deletions predict individual variation in intelligence.
ABSTRACT Phenotypic variation in human intellectual functioning shows substantial heritability, as demonstrated by a long history of behavior genetic studies. Many recent molecular genetic studies have attempted to uncover specific genetic variations responsible for this heritability, but identified effects capture little variance and have proven difficult to replicate. The present study, motivated an interest in "mutation load" emerging from evolutionary perspectives, examined the importance of the number of rare (or infrequent) copy number variations (CNVs), and the total number of base pairs included in such deletions, for psychometric intelligence. Genetic data was collected using the Illumina 1MDuoBeadChip Array from a sample of 202 adult individuals with alcohol dependence, and a subset of these (N = 77) had been administered the Wechsler Abbreviated Scale of Intelligence (WASI). After removing CNV outliers, the impact of rare genetic deletions on psychometric intelligence was investigated in 74 individuals. The total length of the rare deletions significantly and negatively predicted intelligence (r = -.30, p = .01). As prior studies have indicated greater heritability in individuals with relatively higher parental socioeconomic status (SES), we also examined the impact of ethnicity (Anglo/White vs. Other), as a proxy measure of SES; these groups did not differ on any genetic variable. This categorical variable significantly moderated the effect of length of deletions on intelligence, with larger effects being noted in the Anglo/White group. Overall, these results suggest that rare deletions (between 5% and 1% population frequency or less) adversely affect intellectual functioning, and that pleotropic effects might partly account for the association of intelligence with health and mental health status. Significant limitations of this research, including issues of generalizability and CNV measurement, are discussed.
Article: Genetics of intelligence.[show abstract] [hide abstract]
ABSTRACT: This article provides an overview of the biometric and molecular genetic studies of human psychometric intelligence. In the biometric research, special attention is given to the environmental and genetic contributions to specific and general cognitive ability differences, and how these differ from early childhood to old age. Special mention is also made of multivariate studies that examine the genetic correlation between intelligence test scores and their correlates such as processing speed, birth weight and brain size. After an overview of candidate gene associations with intelligence test scores, there is a discussion of whole-genome linkage and association studies, the first of which have only recently appeared.European Journal of HumanGenetics 07/2006; 14(6):690-700. · 4.40 Impact Factor
Article: Genetics, genes, genomics and g.Molecular Psychiatry 02/2003; 8(1):1-5. · 13.67 Impact Factor
Article: Does a fitness factor contribute to the association between intelligence and health outcomes? Evidence from medical abnormality counts among 3654 US Veterans[show abstract] [hide abstract]
ABSTRACT: We suggest that an over-arching ‘fitness factor’ (an index of general genetic quality that predicts survival and reproductive success) partially explains the observed associations between health outcomes and intelligence. As a proof of concept, we tested this idea in a sample of 3654 US Vietnam veterans aged 31–49 who completed five cognitive tests (from which we extracted a g factor), a detailed medical examination, and self-reports concerning lifestyle health risks (such as smoking and drinking). As indices of physical health, we aggregated ‘abnormality counts’ of physician-assessed neurological, morphological, and physiological abnormalities in eight categories: cranial nerves, motor nerves, peripheral sensory nerves, reflexes, head, body, skin condition, and urine tests. Since each abnormality was rare, the abnormality counts showed highly skewed, Poisson-like distributions. The correlation matrix amongst these eight abnormality counts formed only a weak positive manifold and thus yielded only a weak common factor. However, Poisson regressions showed that intelligence was a significant positive predictor of six of the eight abnormality counts, even controlling for diverse lifestyle covariates (age, obesity, combat and toxin exposure owing to service in Vietnam, and use of tobacco, alcohol, marijuana, and hard drugs). These results give preliminary support for the notion of a superordinate fitness factor above intelligence and physical health, which could be further investigated with direct genetic assessments of mutation load across individuals.Intelligence.
Rare Copy Number Deletions Predict Individual Variation
Ronald A. Yeo1*, Steven W. Gangestad1, Jingyu Liu2,3, Vince D. Calhoun2,3, Kent E. Hutchison1,2,4
1Department of Psychology, University of New Mexico, Albuquerque, New Mexico, United States of America, 2The Mind Research Network, Albuquerque, New Mexico,
United States of America, 3Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, United States of America,
4Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado, United States of America
Phenotypic variation in human intellectual functioning shows substantial heritability, as demonstrated by a long history of
behavior genetic studies. Many recent molecular genetic studies have attempted to uncover specific genetic variations
responsible for this heritability, but identified effects capture little variance and have proven difficult to replicate. The
present study, motivated an interest in ‘‘mutation load’’ emerging from evolutionary perspectives, examined the
importance of the number of rare (or infrequent) copy number variations (CNVs), and the total number of base pairs
included in such deletions, for psychometric intelligence. Genetic data was collected using the Illumina 1MDuoBeadChip
Array from a sample of 202 adult individuals with alcohol dependence, and a subset of these (N=77) had been
administered the Wechsler Abbreviated Scale of Intelligence (WASI). After removing CNV outliers, the impact of rare genetic
deletions on psychometric intelligence was investigated in 74 individuals. The total length of the rare deletions significantly
and negatively predicted intelligence (r=2.30, p=.01). As prior studies have indicated greater heritability in individuals
with relatively higher parental socioeconomic status (SES), we also examined the impact of ethnicity (Anglo/White vs.
Other), as a proxy measure of SES; these groups did not differ on any genetic variable. This categorical variable significantly
moderated the effect of length of deletions on intelligence, with larger effects being noted in the Anglo/White group.
Overall, these results suggest that rare deletions (between 5% and 1% population frequency or less) adversely affect
intellectual functioning, and that pleotropic effects might partly account for the association of intelligence with health and
mental health status. Significant limitations of this research, including issues of generalizability and CNV measurement, are
Citation: Yeo RA, Gangestad SW, Liu J, Calhoun VD, Hutchison KE (2011) Rare Copy Number Deletions Predict Individual Variation in Intelligence. PLoS ONE 6(1):
Editor: Henry Harpending, University of Utah, United States of America
Received October 6, 2010; Accepted December 13, 2010; Published January 26, 2011
Copyright: ? 2011 Yeo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research was supported by grants from the National Institute on Alcoholism and Alcohol Abuse (AA012238 and AA013930; KH), the National
Institute of Biomedical Imaging and Bioengineering (R01EB005846; VDC) and a grant from the Mind Research Network. The funders had no role in study design,
data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Behavior genetic studies over the past several decades leave little
doubt that psychometric intelligence (IQ or g) is partially heritable,
with estimates varying from 40% to 80% and increasing with age
[1,2]. But what genetic factors play a role? This question,
traditionally important, has become of even greater interest in
light of documented associations of intelligence with health [3,4],
mortality [5,6], psychopathology [7,8], and diverse social out-
comes , in conjunction with evidence that these relationships
are partially or largely due to heritable components of g [10–13].
The significance of variations in intelligence has also been
examined among individuals with alcohol dependence, as lower
intelligence as assessed in childhood or in early adulthood predicts
greater comorbidity , a greater propensity for hangovers ,
greater mortality from alcohol-related health problems , and
poor treatment outcomes .
Beginning nearly two decades ago, behavior geneticists
increasingly searched for individual allelic variations associated
with g. Despite a good number of candidate gene and SNP (single
nucleotide polymorphism) association studies, very little progress
has been made—so little, in fact, that a recent review led the
authors to conclude, ‘‘it is not possible confidently yet to name one
genetic locus unequivocally associated with the quantitative trait of
intelligence’’ (, p. 219). It seems clear that no one locus
accounts for more than a very small amount of the genetic
variation in g.
If no one locus accounts for much variation in g, a likely
possibility is that g is massively polymorphic. Recent studies of
stature and personality variations (e.g., neuroticism) show that
many loci contribute effects, with no one locus accounting for
more than 1% of the variation [19,20]. A reasonable conjecture is
that much of this variation arises due to mutation, with frequencies
of secondary functional alleles being low. As mutations at many
loci may affect expression of high-level phenotypic features (whose
development is affected by many individual pathways), such
features (e.g., stature) may be affected by many genetic variations.
Researchers have proposed that g is just such a phenotypic
feature [21–25]. In part, this conjecture emerged from findings
that g is associated with developmental instability, as assessed by
composite measures of random variations in bodily symmetry.
Meta-analyses show human developmental instability is associated
PLoS ONE | www.plosone.org1January 2011 | Volume 6 | Issue 1 | e16339
with reduced psychometric intelligence and neurodevelopmental
disorders [26,27]. More generally, developmental instability is
associated with worse health outcomes, illness, and fetal stresses
, which could be due to deleterious effects of widespread
mutation and explain links of intelligence with mortality. In fact,
however, mutations could affect g independent of developmental
instability too; a great many genes are expressed in the brain ,
and genome-wide pleiotropy is substantial [29–31].
One problem with testing these ideas by examining the
association between the genome-wide mutation load and g (or
any other phenotypic trait) is that, technologically, it is not possible
to measure genome-wide mutations at this time. But recent
advances in genomic studies have led to the discovery of one
potentially important class of rare variations that can be assessed
with current technology, copy number variations (CNVs). It was
once thought that the ‘‘normal’’ human genome could be defined
by a shared reference genomic structure, one specifying all single
nucleotide sites. In its extreme form, this view implies that all
genetic variation between any two (‘‘normal’’) individuals consist
merely of the aggregate of base differences across all 3 billion or so
single nucleotide sites. Geneticists have long recognized the
existence of exceptions—insertions, deletions, or inversion of long
chromosomal segments in individual genomes. Recent discoveries,
however, show that ‘‘exceptions’’ are anything but unusual
[32,33]. A substantial portion of the genome is subject to ‘‘copy
number variation’’—differences across individuals in number of
copies of a chromosomal segment at least 1000 bases long (i.e.,
rather than possessing 2 copies of the segment, possessing 0, 1, 3 or
more). Thus far, several thousand such sequences have been found
in the human genome, comprising about 30% of it . Variation
across individuals, then, consists not only of differences at single
nucleotide sites, but also in number of copies of particular DNA
Ultimately, CNVs originate as mutational events . Like
most point mutations, the majority are probably mildly deleteri-
ous. That is particularly true of deletions, and especially ones
maintained at low frequencies (,5%, ); on average, insertions
appear to be less deleterious, and some CNVs may exist at high
relative frequencies due to relaxed selection. Also similar to point
mutations, CNVs may persist in a lineage for many generations
despite selection against them. Compared to point mutations,
however, CNVs have a much higher de novo mutation rate, 10–
1000 times as great [34,36], which means that, for a given strength
of selection against them, their equilibrium frequency in the
population will be greater. Moreover, whereas point mutations
affect a single nucleotide base, CNVs affect many, with multiple
genes sometimes affected by a single CNV. As a result, CNVs may
account for more total inter-individual genetic variation than
single nucleotide variants combined . Unlike point mutations,
genome-wide CNVs can be measured in population studies using a
number of methods, including SNP microarrays. A recent
population study found that CNVs larger than 500 Kb occur in
5–10% of the population, with 1–2% possessing one or more CNV
1 Mb or larger. On average, individuals possessed 3–7 CNVs, very
conservatively estimated in this study .
Recent studies have revealed an elevated incidence of rare
deletions in schizophrenia, autism, and mental retardation .
Some studies have linked CNVs in specific locations to phenotypic
variation. For example, Bi et al.  have found submicroscopic
duplications in 17p13.3 involving LIS1 are linked with structural
brain abnormalities and developmental delay. Other studies have
noted the presence of widespread, rare abnormalities in associa-
tion with such diagnoses as autism , schizophrenia , and
bipolar disorder . Some of these disorders covary with
intelligence, with, e.g., 92% of the phenotypic covariation between
schizophrenia and intelligence reflecting genetic covariation (e.g.,
). In addition, neurodevelopmental disorders such as schizo-
phrenia are characterized by increased developmental instability
Not all diseases are associated with specific CNVs . There
may be systematic reasons why CNVs affect psychological traits in
particular. CNVs are not randomly distributed throughout the
chromosome, as they tend to be over-represented in ‘‘hot-spots,’’
regions where high rates of segmental duplication (in effect,
insertions that have been driven to near-fixation) lead to more
frequent non-allelic homologous recombination (e.g., ). These
regions of segmental duplications, in turn, tend to have evolved
relatively recently. Though comprising only about 5% of the
human genome, for instance, segmental duplications account for
more divergent evolution between chimpanzees and humans than
all single base-pair changes combined . These regions are
likely to play critical roles in the development and expression of
many traits derived in the human lineage, phenotypically
distinguishing us from close relatives. Not surprisingly, then,
widespread segmental duplications contain genes involved in
neuronal development or expressed in neural tissues, perhaps
central to human-specific cognitive features (e.g., [48–50]). As a
result, CNVs (particularly large, rare deletions) may more strongly
affect these same features, thereby influencing, e.g., g.
In the current study, we sought to test the prediction that rare
CNVs covary with g across its normal range. The definition of
‘‘rare’’ is somewhat arbitrary and different researchers have used
different criteria. In studies of schizophrenia, 1% frequency
(percentage of a sample with the variant) has often been used as
a cutoff for rare . Studies have used a 5% cutoff to separate
‘‘common’’ from ’’non-common’’ variants . In some parlanc-
es, variants with 5% of less representation include those that are
‘‘rare’’ as well as ones that are of ‘‘low frequency.’’ More common
variants are expected to have less severe phenotypic effects ,
which could include an effect on g, prompting our decision to use
the 5% figure. Hence, variants we aggregated include those that
qualify as both rare and infrequent (though, we note, we also
examined associations using more strict cutoffs of 3% and 1%; see
Results). We measured CNV deletions in two ways: total number
of CNV deletions and total length (in bases) of CNV deletions
(where the latter measure weights each CNV by its length). Given
recent findings that heritability of intelligence may be greater in
children  and adolescents  from social backgrounds with
relatively greater parental socioeconomic status, we also undertook
preliminary analysis of this issue. Though we made no prediction
regarding rare insertions, for completeness we also examined
associations between g and these variants.
Materials and Methods
Subjects were recruited from the general community for a study
designed to investigate genetic correlates of alcohol dependence
and related phenotypes such as intelligence (14). Inclusion criteria
were (1) age between 21 and 55; (2) within 21 days of their last
drink; (3) drinking levels of more than 14 drinks per week (females)
or 21 drinks per week (males) during four consecutive weeks within
three months of beginning the study; (4) negative drug screen for
opiates, cocaine, or amphetamine; (5) must meet DSM IV criteria
for alcohol dependence; and (6) must have a Clinical Institute
Withdrawal Assessment (CIWA)  score of less than 8,
indicating no need for medical detoxification. Alcohol dependence
was also measured continuously with the alcohol dependence scale
CNVs and Intelligence
PLoS ONE | www.plosone.org2 January 2011 | Volume 6 | Issue 1 | e16339
(ADS), a 25-item test that has four subscales tapping loss of
behavioral control, obsessive-compulsive drinking style, and
psychophysical and pyschoperceptual withdrawal symptoms .
All subjects provided written consent. The study was approved
by the University of New Mexico Human Research and Review
Committee according to principles expressed in the Declaration of
Participants provided at least 5 ml of saliva into a sterile 50 ml
conical centrifuge tube. DNA was then extracted from the sample,
purified, and hybridized. Detection of 1,199,187 SNP and CNV
markers across the entire genome was performed using the
Illumina Human 1 M Duo BeadChip Array according to the
manufacturer’s directions. We did not analyze DNA from the X
and Y chromosomes, reducing the number of SNPs to 1,147,842.
The data were further scanned and 10,272 loci with missing
measurements were removed. The median distance between
adjacent markers was approximately 2.5 kbp.
Details of the series of procedures used to quantify CNVs are
described in Chen, Liu, and Calhoun . Briefly, principal
component analysis (PCA) was performed to minimize noise
effects and remove extraneous sources of variance, including batch
effects, as well as variances related to GC percentage. Next,
samples were eliminated if they appeared to be outliers as
determined by the standard deviation of the Log R Ratio larger
than 0.28 (see ). The preprocessed data were segmented
independently using two methods, the circular binary segmenta-
tion (CBS) algorithm implemented in MATLAB and a hidden
Markov model (HMM) algorithm implemented in PennCNV .
To be counted as a CNV, a segment needed to be identified by
both approaches. This is a conservative approach to CNV
identification designed to increase reliability of detection. We
classified rare CNVs as those occurring in 5% or less of the
sample. For both rare deletions and insertions we calculated the
total number of CNVs, as well as the total number of base pairs
included in each type of abnormality.
All participants were administered the vocabulary and matrix
reasoning subtests of the Wechsler abbreviated scale of intelligence
(WASI; ), from which a full scale intelligence quotient (FSIQ)
was calculated. The vocabulary test taps verbal/crystallized
functioning and the matrix reasoning test taps nonverbal/fluid
reasoning. A FSIQ score was derived from these two tests using
age-appropriate norms. The average reliability of the FSIQ is 0.93
. The subtests have a mean of 50 and a standard deviation of
10, while FSIQ has a mean of 100 and a standard deviation of 15.
Genetic data was available for 202 participants, while WASI
data were available for only 77. Initial evaluation of CNV
numbers was performed on the larger sample, so as to maximize
accurate evaluation of the shape of the frequency distributions. Six
samples were discarded using Need’s criteria, leaving a sample of
196 participants. At total of 13,557 CNVs were detected, 7249
deletions, 6308 insertions (minimum=10, median=51, maxi-
mum=560). The observed frequency distribution of CNV
number was markedly skewed, as a few participants had extremely
high numbers of CNVs. As oversensitivity of CNV detection for
these individuals likely led to unrealistically high values , we
eliminated extreme outliers, ones exceeding Tukey’s criterion of
the third quartile value plus three times the inter-quartile range
. For the participants with WASI data, use of this criteria
resulted in discarding 3 cases (4% of the sample), resulting in a
final data set of 74 participants (51 male, 23 female). Self-reported
ethnicity of the sample was White/Anglo (N=31, 42%), Latino
(N=26, 35%), Native American (N=4, 6%), African-American
(N=2, 3%), Asian (N=1, 1%), and Mixed (N=9, 12%). One
person chose not to report ethnicity. Table 1 provides descriptive
statistics on demographic variables, genetic variables, and test
Pearson correlation coefficients between rare CNV variables
and intelligence test performance are presented in Table 2. As
predicted, the total base pair length of rare deletions negatively
and significantly (p=.01) covaried with FSIQ. A trend was noted
for the number of rare deletions (p=.08). Figure 1 presents a
scatter plot of the relationship between total rare deletion length
and FSIQ. Length of deletions was also negatively correlated with
the Matrix Reasoning subtest (p=.013), but not the Vocabulary
Adding total ADS score as a covariate did not alter these
findings, and total ADS did not correlate with any CNV variable
(all p values .0.17). Hence, these findings are not driven by an
association between FSIQ and severity of alcohol dependence.
One might also wonder, however, whether they are driven by
duration of alcohol abuse. If this were the case, one would
anticipate correlations between age, as a proxy measure for
duration of alcohol abuse, and the three cognitive measures. None
of these correlations was significant (all p values greater than 0.58).
For completeness, we report that rare insertion variables were
not related to intellectual ability (Table 2).
We performed regression analyses to examine possible moder-
ating effects of sex and ethnicity, where ethnicity was simply coded
as ‘‘Anglo/White’’ and ‘‘Other Ethnicity’’. We were not interested
in ethnicity per se, but rather were interested in possible
moderating effects of parental socioeconomic status (SES), for
which ethnicity might serve as a proxy variable. Prior research has
found that parental SES affects heritability estimates for
intelligence. Data on parental SES were not available for this
sample. In New Mexico, where the sample was collected,
individuals of other ethnicity (e.g., Latino, Native American), on
average, come from lower SES backgrounds than those of Anglo/
Table 1. Descriptive statistics on demographic, genetic, and
test data (N=74).
Education (years)13.42 2.13 8–20
Alcohol Dependence Scale16.91 7.234–43
Number rare deletions 10.95 5.481–25
Length rare deletions (bp)210,618 14,3868083 - 626,241
Number rare insertions6.479.82 0–63
Length rare insertions 356,23853,327 0–2,278,718
WASI Full Scale IQ 98.20 13.5971–135
WASI Matrix Reasoning50.139.4829–70
CNVs and Intelligence
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In these regression analyses (run on PASW [formerly SPSS]
17.0 GLM univariate), FSIQ served as the criterion variable.
Predictors entered were (1) sex, (2) ethnicity, (3) rare deletion
length, (4) rare insertion length, and (5) interactions of each of the
CNV variables with sex and ethnicity. Results revealed a main
effect of deletions, partial eta =0.38, F(1,63) =11.00, p=0.002.
This effect was significantly moderated by ethnicity, partial eta
=0.39, F(1,63) =11.48, p=0.001. Ethnicity also had a main effect
independent of deletions and insertions, b=0.53 F(1,63)=24.36,
p,.001. No other effect was statistically robust, all p..28.
To evaluate the robustness of the effect across varying
definitions of infrequent or rare deletion, we repeated this
regression analysis twice, each time substituting a measure of
CNV length based on a different, more stringent definition of rare
CNVs,. Specifically, we calculated length using criteria for rare
CNVs of ‘‘#3%’’ and ‘‘#1%’’. In each analysis, the effect of
deletion length was statistically significant, with the #3%
definition yielding slightly stronger effects than our original
,5% definition, and the #1% definition yielding slightly weaker
effects: partial eta =.41, p,.001, and. 31, p=.013, respectively.
Both analyses also yielded the significant interaction between
deletion length and ethnicity. In sum, then, the effect of infrequent
deletions in this sample is not peculiar to a criterion of a relative
frequency of 5% or less.
To examine the nature of the deletion x ethnicity interactions,
we separately computed correlations for the Anglo/White and
Other groups. As can be seen in Table 3, much stronger
relationships between deletions and intelligence test performance
were observed in the Anglo/White ethnic category. Figure 2 shows
a scatterplot of the relationship between length of rare deletions
and Full Scale IQ in the Anglo/White group.
We computed the residual variance in FSIQ with variation
associated with deletion length in each group was removed. There
was no difference in residual variance across groups, p=0.41.
Hence, the Anglo/White group had significantly more variance in
FSIQ associated with deletions, but there was no significant
difference in the amount of variance in IQ not associated with
No significant differences related to ethnicity category were
found in independent samples t-tests (all p values .0.3) for any of
the four genetic variables (number and length of rare insertions
and deletions). We emphasize, then, that, while rare CNV deletion
lengths covary differently for the two ethnic categories (possibly
due to differences in parental SES), main effects of CNVs do not
contribute to differences in cognitive performance of the two
The greater the size of rare and infrequent deletions, as
represented by the number of base pairs lost, the lower an
individual’s psychometric intelligence. In contrast to SNP effects
investigated in prior studies, the current effect size is substantial,
Table 2. Pearson correlation coefficients (and significance
levels) between Wechsler Abbreviated Scale of Intelligence
variables and the number and total size (in base pairs) of rare
deletions and insertions (N=74).
Full Scale IQVocabulary
Length rare deletions
Number rare deletions .21 (p=.08)
Length rare insertions
2.03 (ns) .02 (ns)
Number rare insertions
Figure 1. Scatterplot of the relationship between length of total rare deletions (base pairs) and Full Scale Intelligence Quotient
(r=2 2.30, p=.01).
CNVs and Intelligence
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accounting for 9% of the phenotypic variance in the full sample
and 45% in the Anglo/White sub-sample.
There are several important implications of this finding. Before
discussing them, however, we consider the generalizability of the
current results. Our sample consisted of individuals with alcohol
dependence. Do associations between intelligence and rare
deletions exist in other populations? Only replication in other
samples of healthy controls and clinical groups will provide a clear
answer to this critical question. At present, however, no specific
observations suggest the relationship is unique to the current
sample. First, overall WASI performance is solidly in the average
range, only very slightly below the mean level of the general
population. Second, controlling for a measure of alcohol
dependence (the ADS) did not diminish the association. Third,
the greater genetic effect in the Anglo/White sample is consistent
with prior studies in healthy children and adolescents showing
greater heritability in higher SES groups [52,53,61]. In light of
these findings, we have no reason to believe that the associations
we observed will not generalize to healthy populations.
The measure of CNV deletions we used aggregates many
different deletions. The genetic effect demonstrated here is
different in kind than that revealed by studies of individual SNPs
or large aggregations of SNPs (e.g., ). The rare or infrequent
deletions we tabulated were scattered across the genome, and by
definition occur at a given locus in less than 5% of our sample (that
is, 9 of 196 individuals). But in fact, most had fewer. In that
sample, we detected 3363 distinct rare or infrequent CNVs (that is,
ones at different sites). Of those, nearly 80% were detected in
fewer than ,1% (1 or 2). A mere 3% were detected in more than
3% of individuals (7–9). The mean, median, and mode of the
percentage detected were ,1%, 5%, and 5%, respectively. And
these values include insertions; deletions tended to represented in
rarer CNVs than insertions (see also ).
These facts have two important implications. First, different
people have different deletions. Indeed, given the distribution of
infrequent deletions, it stands to reason that the large majority of
random pairs of individuals share none of these deletions. And for
any two individuals possessing even a handful of them, the
probably that they share precisely the same ones is vanishingly
Second and relatedly, it must then be the case that many
different individual CNVs have common effects on intellectual
functioning, with no one CNV deletion possessing any more than
a very small effect. (Indeed, though total CNV deletions predict
Table 3. Pearson correlation coefficients (and significance levels) between Wechsler Abbreviated Scale of Intelligence variables
and the number and total size (in base pairs) of rare deletions in Anglo/White (N=31) vs. Other (N=42) ethnic group categories.
Group Full Scale IQVocabularyMatrix Reasoning
Anglo/WhiteLength rare deletions
Number rare deletions
Other Length rare deletions
2.11 (ns).04 (ns)
Number rare deletions
Figure 2. Scatterplot of the relationship between length of total rare deletions (base pairs) and Full Scale Intelligence Quotient in
the Anglo/White group (r=2 2.68, p, ,.001).
CNVs and Intelligence
PLoS ONE | www.plosone.org5 January 2011 | Volume 6 | Issue 1 | e16339
FSIQ much better than any SNP does, any particular deletion may
do no better than any individual SNP). Our sample is of
insufficient size to examine the effects of many individual CNV
deletions. But with 3364 individual rare variations possible, it
seems unlikely that only a small subset carries the effect we
observe. The reason is simple: Each CNV deletion can possibly
account for only a tiny amount of total variation in the aggregate
measure of infrequent deletions. Were it the case that only a small
subset (say ,10%—still, well over 100) had reliable associations,
their variation accounted for would be overwhelmed by the
variation in the remaining non-relating deletions (say .90%),
leaving a very small amount of variation accounted for. Hence, the
only plausible view is that a sizeable number of infrequent
deletions covary with IQ.
Though the total number of possible deletions is too many for us
to examine all, we did examine a small subset in analyses not
detailed in this report. Itsara et al.  listed CNV deletions in 13
different chromosomal regions that previous work has shown are
associated with psychological disorders (schizophrenia, autism,
mental retardation). Of our sample of 74, a total of 14 individuals
had a deletion in one of these regions. (Five regions accounted for
all 14 cases.) We asked whether, after controlling for ethnicity and
sex, the IQ scores of these 14 significantly differed from those of
the remainder of the sample. They did not, F(1, 68) ,1, p=.50.
From this analysis, we cannot conclude that deletions in these
regions do not have effects; they may well have small effects.
Rather, the point is that, not surprisingly, this small subset
previously found to predict psychological outcomes do not drive
the association of our rare deletion composite and g. (Details of
results available from the authors by request.)
We did not screen CNVs to have any particular function or be
located in any particular place in the genome. Why, then, would
deletions in many regions of the genome affect intellectual
functioning? We discuss two important considerations.
First, there may exist massive pleiotropy . A given gene
likely contributes to many different metabolic and developmental
pathways, so loss of genetic material at a particular locus may have
widespread effects. Hence, even genes with primary functions
pertaining to outcomes other (or broader) than intellectual
functioning may affect intellectual functioning. Relatedly, a great
many genes contribute to brain function; in the mouse, as many as
80% of all genes are expressed in the brain . As a result,
randomly placed deletions may be more apt to have a deleterious
effect on brain function than not.
The association of rare deletions with intelligence may, in this
context, shed light on why lower intelligence predicts more
comorbid health and mental health problems: holes in the genome
may have widespread consequences, especially for brain function.
For example, two disorders commonly comorbid with alcohol
problems are the ‘‘externalizing’’ disorders of ADHD and
antisocial behavior. Each of these disorders is associated with
reduced intelligence  and its phenotypic correlation with each
disorder is completely accounted for by genetic covariance
[12,63]. The current results lead to the prediction that these
other disorders would also be associated with greater rare
deletions. For another example outside the substance abuse field,
consider the greater risk for Alzheimer’s disease conferred by
lower premorbid intelligence [64,65]. Though greater intelligence
might be protective in innumerable ways related to lifestyle
choices, greater mutation load might also lead to greater metabolic
stress and reduced capacity for maintenance of brain integrity
Second, it is possible that genetic material prone to deletion is
more associated with intellectual functioning than randomly
selected genetic material in the human genome. One process
generating both deletions and insertions, nonallelic homologous
recombination (NAHR), tends to produce CNVs near ‘‘hot-spots’’
. These hotspots themselves tend to be rich in segmental
duplication, repeated segments that have evolved through positive
selection for particular duplications. Segmental duplications are
much more common in primates than other mammals . As
recent human evolution may have involved duplication more than
alteration at single nucleosides, in addition to the fact that brain
size and function evolved in hominines, one might well expect that
relatively more material duplicated in the genome is expressed in
the human brain; some research supports this expectation .
Another process leading to CNVs, non-homologous end joining
(NHEJ) may especially produce them in sub-telomeric regions
, and may have led to relatively more recent changes . We
did not have sufficient statistical power to assess relative
contributions of deletions in different chromosomal locations.
Future studies, however, might benefit from doing so.
Naturally, we do not suggest that every rare CNV deletion affects
intellectual functioning. Almost certainly, many do not. But again,
were it the case that only very small proportion of deletions did so,
there would exist only a very weak association between FSIQ and
a composite of deletions. As this is not what we observed, it seems
very likely that a meaningful, substantial proportion of CNVs are
associated with intellectual functioning.
As emphasized in the introduction, a primary impetus for
examining associations between rare CNV deletions and intellec-
tual functioning is the theoretical perspective that argues that
genetic variation in psychometric intelligence is largely due to the
existence of individually rare but, at a genome-wide level,
ubiquitous deleterious variants—mutations that are selected
against—rather than the existence of recently arisen, positively
selected variants . Our results are consistent with this proposal.
Although CNV deletions may constitute a major form of
deleterious variants, this theoretical perspective also expects that
some mutations at the single nucleoside level affect intelligence as
well. Our findings are also consistent with Miller’s theory  that
mate selection based on intelligence may provide a mechanism to
optimize ‘‘good genes’’ in offspring (but see also ).
We found that rare deletion length predicted intelligence in our
Anglo/White sample, but not in our Other Ethnicity sample.
These results are consistent with previous findings that the
heritability of g is greater in children and adolescents coming
from high SES backgrounds [52,53] (though see  for a recent
contrary report based on a large and diverse adult sample). The
relatively enriched environments of high SES families may
potentiate the expression of genetic make-ups promoting high
intellectual performance. As we found no difference across
ethnicities in residual variance in intelligence once deletion length
was controlled, it remains possible that total absolute variance in
FSIQ associated with non-genetic factors does not differ across
groups. It should also be noted that ethnicity is an imperfect
indicator of parental SES, which was not available to us, and that
the sociocultural aspects of minority status may impact neurode-
There are two sets of important limitations of the current study.
The first relates to our sample. Though our results have clear
implications for the origins of comorbidity among externalizing
disorders, and the potential to help account for the genetic
vulnerability associated with alcohol abuse, these issues can be best
pursued in future studies that provide diagnoses of all possible
comorbid disorders and include a healthy control group. The lack
CNVs and Intelligence
PLoS ONE | www.plosone.org6 January 2011 | Volume 6 | Issue 1 | e16339
of a significant association between alcohol dependence and
intelligence and the aggregate CNV measures may be due to the
fact that the sample was limited to only individuals with alcohol
dependence. In light of the numerous failures of replication in
history of molecular genetic studies of intelligence , efforts at
replication and extension would also benefit from a larger sample
The second set of important limitations concerns our assessment
of CNVs. Though we believe we have achieved valid and reliable
estimates, it is undoubtedly the case that we did not capture all
rare CNVs in our participants’ genomes. Some smaller CNVs and
those in regions not as well mapped by the reference genome may
have gone undetected. Whether inclusion of such CNVs would
strengthen or weaken our findings is unknown. We have made two
major assumptions in the quantification of CNVs, which should be
systematically evaluated in larger studies. First, we arbitrarily
defined rare CNVs as those occurring in 5% or fewer of our
participants. However, use of 1% and 3% cutoffs led to similar
effect sizes. An important question for future research is how
different phenotypes relate to CNVs occurring at different
population frequencies. Possibly, variations in intelligence within
the normal range are subject to less selection pressure than
phenotypic variation associated with debilitating disorders such as
schizophrenia or autism, and hence, associated with relatively
more common variants.
As more common CNVs might be less deleterious , a more
stringent cutoff may have produced stronger results. Studies
linking total rare CNVs to schizophrenia have used a 1%
frequency criterion . Second, we eliminated approximately
8% of our sample due to extreme CNV total values, though we
used standard procedures to do so. Outliers can obviously have a
strong effect on effect size estimates, and continued advances in
analytic quality control will be important.
Conceived and designed the experiments: RAY SWG JL VDC KEH.
Performed the experiments: KEH JL. Analyzed the data: RAY SG.
Contributed reagents/materials/analysis tools: JL VDC. Wrote the paper:
RAY SWG JL VDC KEH.
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