OPEN
Citation: Transl Psychiatry (2014) 4, e341; doi:10.1038/tp.2013.114
© 2014 Macmillan Publishers Limited All rights reserved 2158-3188/14
www.nature.com/tp
ORIGINAL ARTICLE
Human cognitive ability is influenced by genetic variation in
components of postsynaptic signalling complexes assembled by
NMDA receptors and MAGUK proteins
WD Hill1, G Davies1,2, LN van de Lagemaat3, A Christoforou4,5, RE Marioni1,2,6, CPD Fernandes4,5, DC Liewald1, MDR Croning3, A Payton7,
LCA Craig8, LJ Whalley9, M Horan10, W Ollier7, NK Hansell11, MJ Wright11, NG Martin11, GW Montgomery11, VM Steen4,5, S Le Hellard4,5,
T Espeseth12,13, AJ Lundervold14,15, I Reinvang12, JM Starr1, N Pendleton10, SGN Grant3, TC Bates1 and IJ Deary1
Differences in general cognitive ability (intelligence) account for approximately half of the variation in any large battery of cognitive
tests and are predictive of important life events including health. Genome-wide analyses of common single-nucleotide
polymorphisms indicate that they jointly tag between a quarter and a half of the variance in intelligence. However, no single
polymorphism has been reliably associated with variation in intelligence. It remains possible that these many small effects might be
aggregated in networks of functionally linked genes. Here, we tested a network of 1461 genes in the postsynaptic density and
associated complexes for an enriched association with intelligence. These were ascertained in 3511 individuals (the Cognitive
Ageing Genetics in England and Scotland (CAGES) consortium) phenotyped for general cognitive ability, fluid cognitive ability,
crystallised cognitive ability, memory and speed of processing. By analysing the results of a genome wide association study
(GWAS) using Gene Set Enrichment Analysis, a significant enrichment was found for fluid cognitive ability for the proteins found in
the complexes of N-methyl-D-aspartate receptor complex; P = 0.002. Replication was sought in two additional cohorts (N = 670 and
2062). A meta-analytic P-value of 0.003 was found when these were combined with the CAGES consortium. The results suggest that
genetic variation in the macromolecular machines formed by membrane-associated guanylate kinase (MAGUK) scaffold proteins
and their interaction partners contributes to variation in intelligence.
Translational Psychiatry (2014) 4, e341; doi:10.1038/tp.2013.114; published online 7 January 2014
Keywords: GWAS; intelligence; NMDA-RC; pathway analysis; synapse
INTRODUCTION
Performances on diverse cognitive tasks are universally positively
correlated and a latent trait of general cognitive ability
(intelligence) can be quantified, typically accounting for just
under half of the variation in any large battery of cognitive tests.1
This trait is stable and predictive of health, longevity and a range
of socioeconomic outcomes.2 Genome-wide analyses of common
single-nucleotide polymorphisms (SNPs) indicate that, over the life
course, these SNPs or variants in linkage disequilibrium (LD) with
these SNPs jointly explain between 26 and 51% of the variance in
intelligence differences.3,4 Despite this, no single polymorphism
has been reliably associated with general intelligence.5 Functional
networks of genes that jointly regulate a complex function6 may
allow aggregation of information present in current SNP chips to
elucidate the molecular pathways underlying cognitive
1
differences.7 Here, we combine gene-based statistics (Versatile
Gene-based Association Study, VEGAS)8 with a competitive test of
enrichment (Gene Set Enrichment Analysis, GSEA)9,10 to test
whether genetic variation in the postsynaptic protein complexes
of the excitatory synapses in the human brain show a greater
association with intelligence than genes from outside these
networks. Testing for associations between cognitive abilities and
gene networks might yield a substantial increase in power
compared with single-gene methods.11
Candidate phenotypes implicated in cognitive differences
centre on the central nervous system including variation in white
matter integrity12,13 and brain volume.14,15 However, to explore
the genetic foundations of intelligence further, a more specific
target is preferable. The synapse is a particularly rich target system
both because of the large number of genes expressed16 and
because of direct evidence for the effects of mutations in this
Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, Edinburgh, UK; 2Medical Genetics Section, The University of
Edinburgh Molecular Medicine Centre, Institute of Genetics and Molecular Medicine, Western General Hospital Edinburgh, Edinburgh, UK; 3Genes to Cognition Programme,
Centre for Clinical Brain Sciences and Centre for Neuroregeneration The University of Edinburgh, Edinburgh, UK; 4Center for Medical Genetics and Molecular Medicine, Haukeland
University Hospital, Bergen, Norway; 5Dr E. Martens Research Group for Biological Psychiatry, Department of Clinical Science, University of Bergen, Bergen, Norway; 6Queensland
Brain Institute, The University of Queensland, Brisbane, QLD, Australia; 7Centre for Integrated Genomic Medical Research, University of Manchester, Manchester, UK; 8Public
Health Nutrition Research Group Section of Population Health, University of Aberdeen, Aberdeen, UK; 9Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK;
10
Centre for Clinical and Cognitive Neurosciences, Institute Brain, Behaviour and Mental Health, University of Manchester, Manchester, UK; 11Queensland Institute of Medical
Research, Brisbane, QLD, Australia; 12Department of Psychology, University of Oslo, Oslo, Norway; 13KG Jebsen Centre for Psychosis Research, Oslo University Hospital, Oslo,
Norway; 14Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway and 15Kavli Research Centre for Aging and Dementia, Haraldplass Hospital,
Bergen, Norway. Correspondence: Dr IJ Deary, Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square,
Edinburgh EH8 9JZ, UK.
E-mail: ian.deary@ed.ac.uk
Received 1 May 2013; revised 12 September 2013; accepted 21 October 2013
Enriched association of MAGUK genes with intelligence
WD Hill et al
2
hPSD
NMDA/MAGUK-RC
51
1231
107
1 2
4
17
24
AMPA-RC
3
5
mGlu5-RC
Figure 1. Venn diagram showing the overlap of three gene
complexes and their relative genetic overlap within the proteins
of the full human postsynaptic density (hPSD). Numbers of genes in
each gene set and overlap of these are also shown. Note: The full
hPSD consists of all genes associated with proteins in the hPSD.18
The genetic constituents of the AMPA-RC (α-amino-3-hydroxy-5methyl-4-isoxazoiepropionic acid receptor complex), mGlu5-RC
(metabotropic glutamate 5 receptor complex) and NMDA-RC (Nmethyl-D-aspartate receptor complex) are taken from mouse-based
proteomic experiments.19
system on cognition.17 Here we investigate a specific component
within the synapse, the postsynaptic density (PSD).
Mutations in genes expressed in the PSD have been linked to
many dozens of neurological and cognitive disorders.18–20 The
PSD can update its own responsiveness to subsequent input on
very short and long time scales.21 At the genetic level, evidence
suggests that the elaboration of complex learning involved
duplication and subsequent divergence of genes in the PSD.22
This was followed by strong conservation of function in the
vertebrate line,23 indicative of a finely tuned system. The PSD,
therefore, is a promising candidate for seeking genes in which
variation is associated with intelligence.
The PSD and associated complexes
Among the proteins comprising the mammalian PSD, three
complexes are of particular importance in mediating neural
transmission: The NMDA-RC (N-methyl-D-aspartate receptor complex), mGlu5-RC (the metabotropic glutamate 5 receptor complex) and the AMPA-RC (α-amino-3-hydroxy-5-methyl-4isoxazolepropionic acid receptor complex)19 (see Figures 1 and 2).
The AMPA-RC is the primary basis of rapid excitatory
neurotransmission in the mammalian brain24,25; in addition, the
induction of long-term potentiation (LTP) is induced, in part, by
the summation of AMPA-mediated excitatory postsynaptic
potentials.26 Using in vivo rat models, it has been possible to
show that an increase in the amplitude and duration of the
excitatory postsynaptic potentials, produced by AMPA-RC activation, is associated with an increase in LTP and performance in
memory tasks.27
Synaptic plasticity is dependent on both the NMDA-RC28 and
mGlu5-RC.29 The mGlu5-RC, consisting of some 52 proteins
forming the metabotropic Gαq-linked G-protein-coupled glutamate receptor,19 is closely associated with longer-term modulation and maintenance of LTP.30–34 NMDA/MAGUK-RC is involved in
rapid processing of information and updating of AMPA-RC
responsiveness.28 The NMDA-RC consists of neurotransmitter
receptors, ion channels and signalling proteins scaffolded at the
postsynaptic membrane where they function to convert information in patterns of action potentials into biochemical signals
underlying memory and other aspects of cognition.35 Mutations in
NMDA-RC have been implicated in the aetiology of over 100 brain
Translational Psychiatry (2014), 1 – 8
Figure 2. Schematic of a central nervous system excitatory synapse
showing the proteins in the postsynaptic terminal organised into
multi protein complexes assembled with glutamate receptors
(AMPA-RC (α-amino-3-hydroxy-5-methyl-4-isoxazoiepropionic acid
receptor complex), NMDA (N-methyl-D-aspartate and mGluR (metabotropic glutamate 5 receptor complex) receptors shown).
disorders, including those with cognitive deficits such as schizophrenia, autism and intellectual disability;18,20,35–37 this supports
the linkage of the NMDA-RC to both cognitive and psychiatric
disorders. During the review process, an additional synaptic
component, the activity-regulated cytoskeleton-associated (ARC)
protein, was included. ARC has been reliably associated with both
LTP38 and LTD39 with ARC mRNA being transported to active
synaptic regions via the dendritic spine where it is then translated
and serves to modulate AMPA trafficking.40 De novo mutations in
the ARC protein have been implicated in schizophrenia,20 a
disease in part predicted by a low premorbid cognitive ability,41
which may be due to a shared genetic component between the
two traits.42
Here we tested for an association between genetic variation in
these four gene networks and non-pathological cognitive variation. This was done using experimentally determined gene sets
based on proteins detected in the PSD of human and mouse
brains.18,19 The cognitive phenotypes studied were general
cognitive ability, fluid cognitive ability, crystallised cognitive
ability, memory and processing speed. The GSEA9,10 program
was used to test whether gene sets corresponding to these
components showed significant enrichment for the five cognitive
phenotypes. The discovery samples were those of the Cognitive
Ageing Genetics in England and Scotland (CAGES) consortium.3
Replication of significant findings was sought in two independent
samples from Norway and Australia.
MATERIALS AND METHODS
Participants
The CAGES consortium, consisting of 3511 relatively healthy middle-aged
and older individuals, includes the Lothian Birth Cohorts of 1921 and of
1936 (LBC1921 and LBC1936),43 the Aberdeen Cohort of 1936 (ABC1936)44
and the Manchester and Newcastle Longitudinal Studies of Cognitive
Ageing Cohorts.45
© 2014 Macmillan Publishers Limited
Enriched association of MAGUK genes with intelligence
WD Hill et al
3
The LBC1921 cohort consists of 550 (316 females) individuals, most of
whom took part in the Scottish Mental Survey 1932.46–48 Most resided in
Edinburgh city and the surrounding Lothian region at about age 79 when
they were first recruited to the LBC1921 study between 1999 and 2001.
Their mean age was 79.1 years (s.d. = 0.6). Subjects were identified by
examining the records of those registered with a general practitioner in
the area and by media advertisements. They were healthy, older
individuals all of whom lived independently in the community.47 Venous
whole blood was collected for DNA extraction following informed consent.
Ethical approval was granted by The Lothian Research Ethics Committee.
LBC1936 was recruited in a similar manner to LBC1921. It consists of
1091 (543 females) individuals most of whom took part in the Scottish
Mental Survey 1947. Most were living in and around Edinburgh when they
were recruited to the LBC1936 between 2004 and 2007. Their mean age
was 69.5 years (s.d. = 0.8).49 They were healthy, older individuals all of
which lived in the community. Venous whole blood was collected for DNA
extraction following informed consent. Ethical approval was granted by
Scotland’s Multicentre Research Ethics Committee and the Lothian
Research Ethics Committee.
ABC1936 consists of 498 (255 females) individuals who were drawn from
the original members of Scottish Mental Survey 1947 and were living in the
Aberdeen area when recruited between 1999 and 2003. Their mean age
was 64.6 (s.d. = 0.9) years. They were healthy, older individuals all of whom
lived independently in the community.44 Each had venous whole blood
extracted in order to collect DNA samples following informed consent. The
Grampian Research Ethics Committee granted ethical approval.
The Manchester and Newcastle Longitudinal study of Cognitive Ageing
Cohorts were assembled in order to measure individual differences in the
effects of ageing on mental ability.45 Participants were collected and tested
over a 20-year period beginning in 1983/1984 that resulted in an initial
sample size of 6063 (4238 females) with a median age of 65 years ranging
from 44 to 93 years. Participants were healthy and lived independently in
the community.45 Venous whole blood was taken for DNA extraction from
805 of the Manchester cohort (572 females) and 758 of the Newcastle
cohort (536 females) following informed consent. Ethical approval was
granted by the University of Manchester.
The first replication cohort was formed from healthy twins and their
non-twin siblings recruited as part of the Brisbane Adolescent Twin Study
(BATS)50 and those who subsequently had cognitive phenotypes collected
through participation in cognition and imaging studies (n = 2062).51,52
Together they were drawn from 928 families that included 339
monozygotic pairs and one set of monozygotic triplets. Participants were
female (1093) and male (969), with ages ranging from 15.4 to 29.6 years
(mean = 16.6, s.d. = 1.5). The studies were approved by the Human
Research Ethics Committee at the Queensland Institute of Medical
Research, as well as the institutional ethics boards at the University of
Queensland and the Wesley Hospital.
The second replication cohort was the Norwegian Cognitive NeuroGenetics (NCNG) cohort53 that consists of 670 healthy individuals (457
females) with an age range of 18–79 years (mean = 47.6, s.d. = 18.3).
Participants were drawn from and tested in Bergen (n = 171) and Oslo
(n = 499). Permission to take and store blood samples for genotyping along
with cognitive and magnetic resonance imaging data in a bio-bank and to
establish a registry for relevant information was granted by the Norwegian
Department of Health. Ethical approval was granted by the REK Sørøst
(Norwegian Ethical Committee), NCNG: project ID S-03116.
Cognitive phenotypes
Four cognitive phenotypes were tested for association in this study. These
were general fluid cognitive ability (gf), crystallised cognitive ability,
memory and processing speed. Fluid ability describes an individual’s ability
to deal with novel information,54 often involving abstract reasoning tasks
with little or no verbal component. Whereas different tests were used in
the construction of each general factor, correlations between g factors
formed from different batteries are typically high.55
The gf score for the three Scottish cohorts was derived by using the raw
scores from each test and subjecting them to a principal component
analysis where the first unrotated component was extracted using
regression. Following this, the effects of age and sex were controlled
using a linear regression model with the factor score being the dependent
variable. The standardised residuals were extracted from this model and
were used in subsequent analyses.
For the LBC1921 cohort, gf was derived from the Moray House Test,46
Raven’s Standard Progressive Matrices,56 phonemic verbal fluency57 and
© 2014 Macmillan Publishers Limited
Wechsler Logical Memory scores58. The general factor for LBC1936 was
formed from six non-verbal tests from the Wechsler Adult Intelligence
Scale IIIUK (WAIS-IIIUK): Digit Symbol Coding, Block Design, Matrix Reasoning, Digit Span Backwards, Symbol Search, and Letter-number
Sequencing59. The general fluid ability factor for ABC1936 was formed
from the Rey Auditory Verbal Learning Test,57 the Uses of Common
Objects,60 Raven’s Standard Progressive Matrices56 and Digit Symbol from
the WAIS Revised (WAIS-R).61
The factor for general fluid ability in the Manchester and Newcastle
ageing cohort was derived using the two parts of the Alice Heim test 462
and the four sub-tests of the Culture Fair Test.63 Age at test and sex were
controlled using residualisation, and these standardised residuals for each
of the tests were then subjected to a maximum likelihood factor analysis. A
general factor was extracted using regression, and missing data points
were accounted for by sampling the posterior distribution of factor scores
for each subject using Mplus.64
Crystallised ability describes the level of knowledge an individual has
acquired over the life course.54 It is typically assessed by means of
language-based tests including reading ability or measurements of
vocabulary. For LBC1921, LBC1936 and ABC1936 this was represented by
the score from the National Adult Reading Test.65 For the Manchester and
Newcastle cohorts, sections A and B from the Mill Hill vocabulary test66
were used. These sections were administered without a time limit and
were summed to give a single score. The raw scores from each of the tests
representing crystallised ability were subjected to a linear regression with
age and sex as predictors and the test score as the dependent variable. The
standardised residuals from these models were used for all subsequent
analyses.
Verbal declarative memory (memory) and information processing speed
(speed) were each measured by a single test in each cohort. In the
LBC1921 cohort, the total score from both the immediate and delayed
recall sections of the Logical Memory test from the Wechsler memory scale
revised58 was used. In LBC1936, it was the total from the immediate and
delayed recall sections from the logical memory test from WAIS-IIIUK.59 In
ABC1936, a modified version of the Rey Auditory and Verbal Learning
Test57 was used where a set of 15 words was read to the subject who then
repeated aloud as many as they could. Following this, the same list was
read out again and the subject was again asked to recall as many words as
they could. The word list was presented a total of five times and the
participants final score was the total number of words summed across the
five presentations. In the Manchester and Newcastle cohorts, a cumulative
verbal recall task45,55 was used in which four presentations of a list of 15,
six letter nouns was read aloud to the participant. A recall phase was
administered between each presentation where the participants were
instructed to write down as many of the words as they could recall. The
final score was the total recalled across all four presentations. The raw
scores from each of the tests representing memory were subjected to a
linear regression with age and sex as predictors, and the test score as the
dependent variable. The standardised residuals from these models were
used for all subsequent analyses.
Information processing speed (speed) was measured in each cohort
using a single test. The digit symbol subtest of the WAIS-IIIUK 59 was carried
out by LBC1921 and LBC1936, whereas in ABC1936 the WAIS-R version61
was used. The Savage Alphabet Coding Task67 was used in the Manchester
and Newcastle cohorts. The raw scores from each of the single tests
representing speed were subjected to a linear regression with age and sex
as predictors, and the test score as the dependent variable. The
standardised residuals from these models were used for all subsequent
analyses. In response to a reviewer’s request, a fifth cognitive phenotype, a
general factor of cognitive ability (g), was created and tested. This g factor
was constructed using the tests measuring fluid and crystallised abilities in
each of the cohorts in the CAGES consortium. A separate g factor was
derived within each cohort. In ABC1936, LBC1921 and LBC1936 the total
number of correct responses on the National Adult Reading Test was
included along with the tests used in the respective gf phenotypes in
principal component analysis. The participants’ scores on the first
unrotated component were extracted using regression. Following this,
the effects of age and sex were regressed out.
In the Manchester and Newcastle cohorts, the effects of age and sex
were regressed out from both the gf factor and the score from the Mill Hill
vocabulary test. Following this the standardised residuals from the Mill Hill
and the gf factor were summed and the mean derived. This mean score
was used to represent the g factor.
Translational Psychiatry (2014), 1 – 8
Enriched association of MAGUK genes with intelligence
WD Hill et al
4
Replication cohorts
In the Australian Sample, performance IQ was used as an indicator of gf.
This was derived from scores on the Spatial and Object Assembly tests
according to the manual for the Multidimensional Aptitude Battery.68 Each
test was administered with a 7-min time limit. A general factor of cognitive
ability (g) was represented by the full-scale IQ score derived using the
Multidimensional Aptitude Battery.68 In the Norwegian sample, the Matrix
Reasoning subtest from the Wechsler Abbreviated Scale of Intelligence69
was used as an indicator of gf. Each participant’s raw score from this test
was subjected to a linear regression using their age and sex as predictor
variables. The standardised residuals from this model were used in
subsequent analyses.
Genotyping and quality control
Genotyping and quality control procedures implemented here have been
described previously;3 however, this study makes use of imputed data as
detailed below. The 3782 participants in the discovery cohorts had DNA
extracted and were genotyped for 599 011 common SNPs using an
Illumina610 QuadV1 chip (Illumina, San Diego, CA, USA). After quality
control, 549 692 SNPs were retained in 3511 participants (2115 females).
Individuals were removed due to unresolved gender discrepancies,
relatedness or call rateo0.95, as well as evidence of non-Caucasian
descent. SNPs included in the analysis had a call rate of >0.98, minor allele
frequency of >0.01 and a Hardy–Weinberg equilibrium test of P>0.001.
Multidimensional scaling (MDS) analysis was performed to test for
population stratification and any outliers were excluded. The first four
MDS components, based on the remaining individuals, were then included
as covariates in subsequent analyses.3 Imputation was performed in each
cohort using the MACH70 software (v1.0.16) to the HapMap phase II CEU
(NCBI build 36 release 22) reference panel. Imputed SNPs were retained for
analysis with an imputation quality score of greater than 0.3 and a minor
allele frequency of >0.005.
The genotyping and quality control for BATS have been described
previously.71 In this Australian sample, 2104 participants had DNA
extracted from blood and were genotyped on the Illumina Human 610Quad chip (Illumina). Following quality control, 529 379 SNPs were retained
in 2062 (1093 female and 969 male) participants. Individuals were removed
due to unresolved gender discrepancies or evidence of non-Caucasian
descent. SNPs were included if they met the criteria of call rate >0.95,
minor allele frequency >0.01 and a Hardy–Weinberg equilibrium test of
~2 500 000
SNPs
~2 500 000
SNPs
~2 500 000
SNPs
P>0.00001.71 Multidimensional scaling analysis of SNP data showed three
components. To control for population stratification, all three components
were entered as covariates along with age and sex in the analyses.
The genotyping and quality control for the NCNG have been described
previously.3,53 For this Norwegian sample, DNA was extracted from blood
using the Qiagen Gentra Autopure LS system (Qiagen, Valencia, CA, USA).
Genotyping took place on the Illumina Human 610-Quad Beadchip
(Illumina). Quality control was carried out using the ‘check.marker’ function
from the R package GenABEL.72 Identity-by-state was used to assess cryptic
relatedness, with cases where Identity-by-state threshold exceeded 0.85
being removed. Population structure was assessed using multidimensional
scaling analysis where individuals who were suspected of possible recent
non-Norwegian ancestry were removed. Individuals were also removed if
the heterozygosity value was >2 s.d. from the sample mean or where sex
could not be determined. SNPs were excluded if the call rate was o0.95, a
minor allele frequency of o0.01 and a Hardy-Weinberg Equilibrium (exact
test) P-valueo0.001. The final sample consisted of 554 225 SNPs in 670
individuals.
PSD gene sets
The genes responsible for the expression of the PSD and its subcomponents are available at the G2C database (http://www.genes2cognition.org/
db/GeneList). The size of the gene sets used along with the degree of
overlap between the gene sets is shown in Figure 1.
The human-derived PSD (hPSD) was ascertained based on experimentally identified proteins, where hPSDs were isolated from neocortical
samples of nine adults (mean age = 47.0 years, s.d. = 15.74, three females)
who had undergone a variety of medically necessary neurosurgical
procedures.18 The protein preparations were pooled into three samples
from three individuals, each sample containing normal non-diseased tissue
from at least two of three cortical regions (frontal, parietal and temporal
lobes). These three samples were then subjected to proteomic profiling
using liquid chromatography tandem mass spectrometry.18 The full set
consisting of 1461 genes, details genes whose proteins were found in at
least two pooled samples, whereas the consensus set features the 748
genes found in all three samples. Only autosomal genes were included in
the present analyses leaving 1386 genes in the full hPSD and 714 in the
concensus hPSD (94.8% of the full hPSD and 95.4% of the consensus list).18
The NMDA-RC gene set was based on previous studies.19 NMDA-R
complexes were isolated using affinity to a peptide derived from the
carboxy terminus of the NR2B protein and analysed by liquid
~2 500 000
SNPs
~2 500 000
SNPs
Meta-analysis of the five CAGES cohorts
SNPs assigned to genes and gene
based statistic derived
Five gene sets
from proteomic
experiments
UCSC
Genome
browser
hg 18
Gene Set Enrichment Analysis of candidate gene sets
Enrichment examined using 1000 permuted
gene sets of equal length
BATS (n=2104)
Replication of enrichment sought in two
independent cohorts
NCNG (n=670)
Figure 3. Data processing stages from top to bottom. The five cohorts from the Cognitive Ageing Genetics in England and Scotland (CAGES)
consortium underwent single-marker analysis70 separately before the results were meta-analysed.75 Single-nucleotide polymorphisms (SNPs)
were then assigned to genes based on their position as indicated in the UCSC Genome browser hg18 assembly and a gene-based statistic was
derived using Versatile Gene-Based Association Studies (VEGAS).8 A priori-selected gene sets detailing the molecular composition of the PSD
were brought in18,19 and enrichment of these sets in cognition was sought using Gene Set Enrichment Analysis (GSEA).9,10 Gene sets which
were enriched were then compared with 1000 randomly selected gene sets of the same length to examine the strength of the enrichment
found. Gene sets which survived this procedure were then taken forward for replication in two independent cohorts.
Translational Psychiatry (2014), 1 – 8
© 2014 Macmillan Publishers Limited
Enriched association of MAGUK genes with intelligence
WD Hill et al
5
chromatography tandem mass spectrometry. The identified list of proteins
overlapped substantially with an NMDA receptor complex (NRSC)
identified earlier.73 The earlier complex was an amalgamation of lists
derived by immunoprecipitation from mouse forebrain with an NMDA
receptor NR1 subunit antibody and the same NR2B carboxy terminal
peptide. The combined NMDA-RC list consists of 186 genes of which
181 are autosomal and were included in this study. Genes coding for the
mGlu5-RC were those identified using an antibody against mGluR5 protein
in rat brain lysates.74 Of 52 mouse orthologues of these genes that have
been identified,19 all 50 autosomal genes were included in the present
analyses. The AMPA-RC comprised a set of nine proteins and corresponding genes isolated by immunoprecipitation using an antibody against the
GluR2 protein (Gria2).19 The seven autosomal genes from this set were
included in the present analyses. The ARC protein gene set was taken from
Kirov et al20 and included the same 25 of 28 (89.3%) genes used in their
analyses.
Statistical analysis
Data were processed through the following six steps (see Figure 3). First,
association analyses were performed in each cohort using Mach2QTL.70
Second, these results were then combined using an inverse varianceweighted meta-analysis in METAL.75 The third step was to use VEGAS8 to
conduct gene-based analyses of association for each of the five cognitive
phenotypes on the results of the meta-analysis. SNPs were assigned to
genes according to their position on the UCSC Genome browser hg18
assembly with a gene boundary of ±50 kb of 5′ and 3′ untranslated
regions. The gene-based statistic was then derived using each SNP within
the specified boundary, with VEGAS controlling for the number of SNPs in
each gene and the LD between those SNPs. Gene-based P-values were
then –log(10) transformed and rank ordered for each phenotype. In the
fourth step, the specific gene set enrichment hypotheses were tested
using a competitive test of enrichment, GSEA.9,10 GSEA uses a candidate
list of gene identifiers and a genome-wide set of genes that are ranked by
the strength of their association with a phenotype. GSEA tests whether
gene identifiers in the candidate set fall higher in the genome-wide
ranking than would be expected by chance. A running–sum Kolmogorov–Smirnov statistic weighted by the P-value from the genome-wide gene
ranking set is derived. This process is repeated and the final enrichment Pvalue corresponds to the proportion of runs in which the test gene set
ranked higher than the permuted set. Here 15 000 permutations were
used. Gene sets meeting the pre-determined discovery criteria of an
uncorrected enrichment P-value ≤0.05, and/or false discovery rate (FDR)corrected q-value of o0.25 were empirically validated as follows (step
five). Each significant gene set (NMDA-RC and mGlu5-RC) was compared
against P- and FDR values derived from 1000 randomly sampled gene sets
of the same length.76 Empirical significance was set for P- and FDR values
of the observed gene set as being smaller than 95% of those obtained in
the random gene lists. Gene sets passing this criterion were taken forward
to step six: replication in the BATS and NCNG cohorts.
Replication
In the Australian BATS sample, initial analysis of the genotyped data was
conducted using Multipoint engine for rapid likelihood inference
(MERLIN),77 allowing control for relatedness between participants in this
family-based sample. In the Norwegian NCNG cohort, single-marker
analysis was carried out using PLINK.78 In both samples, an additive
inheritance model was used and the same data processing steps were
used as in the discovery cohort. As only the NMDA-RC gene set met the
criteria to be deemed significant against any cognitive variable, it was the
only set in which a replication was sought. Following replication, the
enrichment P-values from each of the three cohorts (CAGES, NCNG and
BATS) were combined using Stouffer’s weighted Z-transform method.79,80
The discovery cohort P-value was corrected for multiple comparisons using
a Bonferroni correction for the five gene sets tested × four phenotypes, that
is, a correction for 20 tests (0.002 × 20 = 0.04) before being combined with
NCNG and BATS.
RESULTS
Genome-wide association (GWA) analyses of the association
between each of the five cognitive phenotypes was undertaken
for the full set of imputed SNPs in each of the five CAGES cohorts.
Analyses have already been reported for fluid and crystallised
© 2014 Macmillan Publishers Limited
ability on non-imputed data;3 however, here we use imputed data.
Moreover, we report for the first time the GWAS analyses for
memory and processing speed phenotypes in these cohorts. A
meta-analysis was then performed on results from the five cohorts
using METAL.75 No SNP reached genome-wide significance for any
of the five cognitive phenotypes.
Gene-based association
Gene-based analysis of the meta-analytic SNP association data
combining information from the five cohorts found no single gene
significantly associated with any of the five phenotypes. The most
significant gene-based P-values for general cognitive ability, fluid
cognitive ability, crystallised ability, memory and processing
speed, respectively, were for FNBP1L (P = 3 × 10−5), BCAR3
(P = 4.0 × 10− 6), RFFL (P = 7.0 × 10 −5), OR4P4 (P = 4.0 × 10−5) and
EIF5A2, (P = 4.9 × 10−5). The gene with most evidence for association in the earlier GWA in this cohort (FNBP1L for gf)3 ranked
second in these analyses (P = 1.9 × 10 − 5). This slight difference is
likely to be because of the use of imputed SNPs in the present
analyses, and that phenotype construction differed for the
Manchester and Newcastle cohorts between this and the previous
analysis.
Enrichment analysis of PSD gene sets
Next we test our principal hypothesis that variation in genes that
code for the proteins in the PSD is involved in the normal range of
variation of cognitive abilities. GSEA analyses were performed on
each the six gene sets for each of the cognitive phenotypes. Of the
six gene sets, the NMDA-RC was significant (P = 0.002) for gf
(Table 1). mGlu5-RC had an FDR also under 0.25, but had a P-value
of 0.133. The NMDA-RC was also found to have an enriched
association with general cognitive ability (P = 0.0084). No significant support for enrichment was found for any of the other
three phenotypes for any other gene set. By comparison with
1000 randomly ascertained sets of 181 genes, both the P-value
and FDR obtained for the NMDA-RC was lower than that of 99.7%
of the random gene sets in the gf phenotype. In the case of the
association of mGlu5-RC with gf, comparison with 1000 randomly sampled lists did not provide significant support for
enriched association (observed P-value o83.0%; FDR o84.1% of
random gene sets). Upon examination, no significant enrichment
was found between the ARC gene set and gf, crystallised ability,
memory and mental speed with P-values of 0.87, 0.09, 0.61 and
0.68 being found, respectively.
Table 1. Shows the results of enrichment analysis on six candidate
gene lists from the PSD in gf in the CAGES cohorts
Complex name
hPSD full
hPSD consensus
NMDA-RC
mGlu5-RC
AMPA-RC
ARC
Replication samples
NMDA-RC (BATS)
NMDA-RC (NCNG)
Number of genes
Empirical P-value
FDR
1386
714
181
50
7
25
0.628
0.242
0.002
0.133
0.595
0.870
0.705
0.542
0.221
0.203
0.804
0.870
180
180
0.012
0.371
0.012
0.371
Abbreviations: AMPA-RC, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor complex; BATS, Brisbane Adolescent Twin Study; FDR,
false discovery rate; hPSD,human postsynaptic density; mGlu5-RC, the
metabotropic glutamate receptor complex 5; NMDA-RC, N-methyl-Daspartate receptor signalling complex/membrane-associated guanylate
kinase associated signalling complex; NCNG, Norwegian Cognitive
NeuroGenetics. The replication of the NRSC gene set in both BATS and
NCNG cohorts is included.
Translational Psychiatry (2014), 1 – 8
Enriched association of MAGUK genes with intelligence
WD Hill et al
6
To ensure that the enriched association was not driven by a
single gene, the most significant gene from the NMDA-RC set and
the mGlu5-RC set were removed. Once DNM2 was removed from
the mGlu5-RC list, no significant evidence of enrichment with gf
remained. However, removing the top gene from the NMDA-RC
gene set – PRDX2 –attenuated the enrichment with gf but it
remained significant (P = 0.006). This was repeated with g, where
once the most significant gene was removed from the NMDA-RC
(PLCG1), significance remained (P = 0.024). These results support
the hypothesis that genetic variation in NMDA-RC is associated
with general intelligence differences and more specifically with
fluid ability but not with the PSD, more broadly, nor the AMPA or
mGlu5 receptor complexes.
Replication
The enrichment of the NMDA-RC gene set in fluid cognitive ability
was tested for replication in the Norwegian and Australian cohorts
using identical methods to those used above in the discovery
sample, that is, gene-based analysis using VEGAS, followed by a
GSEA unit-weighted analysis with 15 000 permutations. Enrichment testing in the BATS and the smaller NCNG cohorts yielded, Pvalues of 0.012 and 0.371, respectively. The association remained
significant in BATS after removing the top gene (RAB3A) from the
set (P = 0.024), indicating that multiple genes were contributing to
the enrichment signal in both CAGES and BATS. A meta-analysis of
these results for the NMDA-RC across the discovery cohort, and
two replication samples was determined using Stouffer’sweighted Z-transform method.79,80 The probability of obtaining
these results across the three independent cohorts, corrected for
multiple testing in the discovery cohort and tested against the null
hypothesis of no association was P = 0.003. By omitting the
discovery cohort, the enrichment of the NMDA-RC across BATS
and NCNG remained significant (P = 0.018), supporting the
enriched association of the NMDA-RC with fluid ability. The
NMDA-RC also demonstrated an enriched association with a
general factor of cognitive ability in the BATS cohort P = 0.043.
DISCUSSION
The present study used a hypothesis-driven approach to test the
joint effect of multiple variants clustered in the same biological
network on human intelligence differences. In drawing upon the
synapse proteomic data sets, the results suggested that SNP
variation in the genes encoding the NMDA/MAGUK receptor
complex is enriched for association with both general cognitive
ability and general fluid cognitive ability in humans. This finding
linking NMDA-RC to fluid ability provides evidence that genetic
variation in the macromolecular machines formed by MAGUK
scaffold proteins and their interaction partners contributes to
variation in intelligence.
By contrast with the NMDA-RC, other components of the PSD
were not found to be significantly enriched for variation in
cognitive abilities in this study. These results raise the question of
why the NMDA-RC should be preferentially involved in fluid-type
intelligence. The present results suggest that association of the
NMDA-RC with gf does not simply follow from its being a part of
the synapse or having a role in the excitatory transmission system,
as three other systems found in the synapse did not show
enrichment, and all are activated once the receptors bind with
glutamate or are found only at glutamatergic synapses. However,
the lack of an enriched association for the AMPA-RC or the mGlu5RC could be due to the small numbers of genes involved in their
expression, meaning that even greater sample sizes would be
required to detect an enrichment of these complexes. The lack of
an enriched association of ARC with gf may also reflect simply a
lack of power. Alternatively, it may be that lack of enrichment with
Translational Psychiatry (2014), 1 – 8
the ARC protein for cognition implies that this system is specific
for schizophrenia rather than for general cognitive ability.
The NMDA-RC is enriched for both learning and synaptic
plasticity phenotypes in mice,35 and the same proteins have been
shown to be involved in human learning disabilities.35 These
findings validate the utility of rodent models for human cognitive
function. In addition, they suggest that combinations of SNPs in
LD with common SNPs found within the genes of the NMDA-RC
may result in variation in synaptic plasticity, which in turn is
responsible for some of the observed differences in human
intelligence.
Variation in the NMDA-RC has been implicated in
schizophrenia20,81 and intellectual disability17,37,82,83 with mutations in individual scaffolding molecules SAP102/Dlg2 and PSD93/
Dlg3 linked to these disorders, respectively. The present finding of
a link between intellectual function and variation in the NMDA-RC
therefore supports a genetic link between schizophrenia and
intelligence, in keeping with behaviour genetic42 research, and
also with recent polygenic risk studies of a sub-set of the present
cohorts that indicated an overlap of polygenic risk factors for
schizophrenia and for cognitive ageing.84 The genetic link
between schizophrenia and cognitive abilities appears to be
region rather than variant specific. Where de novo copy number
variation at the NMDA-RC is associated with schizophrenia,20 it is
common SNP variation, in the same region, which shows an
enriched association with the normal range of cognitive abilities.
However, neither the common SNPs nor copy number variations
associated with schizophrenia have been shown to be associated
with intelligence differences in a non-elderly cohort.85
Enrichment was found for fluid ability and not for crystallised
ability, memory or processing speed. If gene effects directly
impact on specific functions (rather than on general ability per se),
then analyses targeting these specific functions (such as speed or
memory) are known to be significantly more powerful than are
analyses of a composite or latent factor such as fluid ability.86
Here, the enriched association of the NMDA-RC was found for the
fluid ability composite rather than specific functions. The finding
that genetic association for the fluid ability phenotype proved the
stronger indicator, then, is compatible with generalist genetic
action as opposed to functional specificity.87 This is further
supported with the finding that the NMDA-RC is enriched for
general cognitive ability. This enrichment was, however, attenuated compared with gf, indicating that genetic variation of the
NMDA-RC is preferentially linked to non-verbal cognitive tasks and
solving problems that incorporate novel information.
Whereas the mGlu5-RC gene set showed weak evidence of
enrichment in the initial GSEA analysis, this did not survive
permutation testing. It was shown to be due to a single gene,
DNM2, rather than an over representation of mGlu5-RC genes in
the upper portion of the total gene list. This is in contrast with the
NMDA-RC gene set where multiple genes were involved in the
enrichment signal in both CAGES and in BATS, consistent with the
notion that it is variation in the network and not in a single gene,
which contributes to normal variation in fluid ability.
In summary, large-scale molecular studies indicate that
intelligence is polygenic3,4 that is compatible with a range of
genetic models, the most extreme of which would be that all
genes matter with roughly equal effect. Here, using GSEA, we
tested the hypothesis that that some genes matter more than
others. Specifically, we found that genes in pathways related to
postsynaptic functioning are enriched. The results suggested that
a major component of the postsynaptic region, the NMDA-RC, is
preferentially associated with normal variation in intelligence. The
NMDA-RC pathway appears to be specifically enriched for
association with fluid ability, providing a lead towards understanding a source of some of the variation in human intelligence
differences.
© 2014 Macmillan Publishers Limited
Enriched association of MAGUK genes with intelligence
WD Hill et al
7
CONFLICT OF INTEREST
The authors declare no conflict of interest.
ACKNOWLEDGMENTS
We thank the cohort participants who contributed to these studies and the research
staff who collected phenotypic data. Genotyping of the CAGES cohorts and the
analyses conducted here were supported by the UK’s Biotechnology and Biological
Sciences Research Council (BBSRC). Phenotype collection in the Lothian Birth Cohort
1921 was supported by the BBSRC, The Royal Society and The Chief Scientist Office of
the Scottish Government. Phenotype collection in the Lothian Birth Cohort 1936 was
supported by Research Into Ageing (continues as part of Age UK’s The Disconnected
Mind project). Phenotype collection in the Aberdeen Birth Cohort 1936 was
supported by BBSRC, the Wellcome Trust and Alzheimer’s Research UK. Phenotype
collection in the Manchester and Newcastle Longitudinal Studies of Cognitive Ageing
cohorts was supported by Social Science Research Council, Medical Research Council,
Economic and Social Research Council, Research Into Ageing, Wellcome Trust and
Unilever plc. The work was undertaken in The University of Edinburgh Centre for
Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong
Health and Wellbeing Initiative (G0700704/84698). Funding from the BBSRC, EPSRC,
ESRC and MRC is gratefully acknowledged. Australian BATS sample: we especially
thank the twins and their families for participation. The research was supported by
the Australian Research Council (A7960034, A79906588, A79801419, DP0212016
and DP0343921), with genotyping funded by the National Health and Medical
Research Council (Medical Bioinformatics Genomics Proteomics Program, 389891).
The Norwegian study was supported by the Bergen Research Foundation (BFS), the
University of Bergen, the Research Council of Norway (including FUGE grant nos.
151904 and 183327, Psykisk Helse grant no. 175345, 154313/V50 to IR and 177458/
V50 to TE), Helse Vest RHF (Grant 911397 and 911687 to AJL) and Dr Einar Martens
Fund. We thank the Centre for Advanced Study (CAS) at the Norwegian Academy of
Science and Letters in Oslo for hosting collaborative projects and workshops
between Norway and Scotland in 2011–2012. SGNG and LV van de L were supported
by the MRC, Wellcome Trust and European Union Seventh Framework Programme
under grant agreements number 241498 ‘EUROSPIN’ project, 242167 ‘SynSys project’
and 241995 ‘GENCODYS’ project.
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