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Identification of common variants associated with human
hippocampal and intracranial volumes
Identifying genetic variants influencing human brain structures
may reveal new biological mechanisms underlying cognition
and neuropsychiatric illness. The volume of the hippocampus is
a biomarker of incipient Alzheimer’s disease1,2 and is reduced
in schizophrenia3, major depression4 and mesial temporal lobe
epilepsy5. Whereas many brain imaging phenotypes are highly
heritable6,7, identifying and replicating genetic influences has
been difficult, as small effects and the high costs of magnetic
resonance imaging (MRI) have led to underpowered studies.
Here we report genome-wide association meta-analyses and
replication for mean bilateral hippocampal, total brain and
intracranial volumes from a large multinational consortium.
The intergenic variant rs7294919 was associated with
hippocampal volume (12q24.22; N = 21,151; P = 6.70 × 10−16)
and the expression levels of the positional candidate gene
TESC in brain tissue. Additionally, rs10784502, located within
HMGA2, was associated with intracranial volume (12q14.3;
N = 15,782; P = 1.12 × 10−12). We also identified a suggestive
association with total brain volume at rs10494373 within
DDR2 (1q23.3; N = 6,500; P = 5.81 × 10−7).
The hippocampal formation is a key brain structure for learning,
memory8,9 and stress regulation10 and is implicated in many neuro
psychiatric disorders. Further, overall brain and head sizes are altered
in many disorders and are significantly correlated with general cogni
tive ability11–13. Hippocampal, total brain and intracranial volumes
are highly heritable in non-human primates14,15 and in humans6,7.
Finding loci that influence these measures may lead to the identifica
tion of genes underlying susceptibility for neuropsychiatric diseases.
Here we sought to identify common genetic polymorphisms influ
encing hippocampal, total brain and intracranial volumes in a large
multinational consortium.
Our discovery sample comprised 17 cohorts of European ancestry
from whom genome-wide SNPs and structural MRI data were collected
(Supplementary Tables 1–3). Unselected population samples and
case-control studies were included, with cases ascertained for neuro
psychiatric disorders including depression, anxiety, Alzheimer’s dis
ease and schizophrenia. To distinguish whether putative effects at these
loci varied with disease status, analyses were run in the full sample
(N = 7,795) and in a healthy subsample (N = 5,775). To help disentangle
overall brain size effects from those specific to hippocampal volume,
associations were assessed with and without controlling for total brain
and intracranial volumes (Online Methods). As the initial goal of the
study was to explore associations with hippocampal volume, total brain
and intracranial volumes were analyzed in healthy subjects only.
Phenotypes were computed from three-dimensional anatomical
T1-weighted magnetic resonance images, using validated auto
mated segmentation programs16–18 (Supplementary Fig. 1 and
Supplementary Tables 4 and 5). Extensive quality control analysis of
segmentation was performed on sample outliers; subjects with poorly
delineated brain volume phenotypes were removed (Supplementary
Figs. 2–6). The mean bilateral hippocampal volume across the dis
covery cohorts was 3,917.4 mm3 (s.d. = 441.0 mm3).
Heritability of structural brain phenotypes was estimated in a
sample of Australian monozygotic and dizygotic twins and their
siblings (Queensland Twin Imaging (QTIM) study; N = 646, including
ungenotyped participants; age range = 20–30 years) for hippocampal
volume (h2 = 0.62), total brain volume (h2 = 0.89) and intracranial
volume (h2 = 0.78). Hippocampal volume was also highly heritable in
an extended pedigree cohort of Mexican-Americans from the United
States (Genetics of Brain Structure and Function (GOBS); N = 605;
age range = 18–85; h2 = 0.74), as were total brain volume (h2 = 0.77)
and intracranial volume (h2 = 0.84). All heritability estimates were
highly significant (P < 0.001).
To enable consortium-wide comparison of ancestry and to adjust
appropriately for population stratification, each site conducted
multidimensional scaling (MDS) analyses comparing their data
to the HapMap 3 reference populations (Supplementary Fig. 7).
All subsequent analyses included the following covariates: sex,
linear and quadratic effects of age, interactions of sex with age covariates,
MDS components and dummy covariates for different magnetic
resonance acquisitions. Analyses were filtered for genotyping and
imputation quality (Supplementary Fig. 8 and Supplementary
Table 6); distributions of test statistics were examined at the cohort
level through Manhattan and quantile-quantile plots (Supplementary
Figs. 9–24). We conducted fixed-effects meta-analysis with METAL,
applying genomic control19 (Supplementary Figs. 25–32). For com
pleteness and to account for heterogeneity across sites, a randomeffects meta-analysis was also performed 20 (Supplementary
Figs. 33–40). We attempted in silico replication of the top five loci for
each trait within the combined CHARGE Consortium discovery set
and 3C replication sample21 (N = 10,779), as well as in two cohorts
of European ancestry (imputed to the Utah residents of Northern
and Western European ancestry (CEU) and/or Toscani in Italy (TSI)
HapMap cohorts; N = 449) and in two additional cohorts (imputed
to combined CEU and Yoruba in Ibadan, Nigeria (YRI), and to
A full list of authors and affiliations appears at the end of the paper.
Received 6 September 2011; accepted 19 March 2012; published online 15 April 2012; doi:10.1038/ng.2250
552
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© 2012 Nature America, Inc. All rights reserved.
–log10 (P value)
8
r
2
100
rs7294919
0.8
0.6
0.4
0.2
80
6
60
4
40
2
20
0
NCRNA00173
MAP1LC3B2
115.6
c
10
8
0
C12orf49
RNFT2
HRK FBXW8
TESC
r
2
100
rs10784502
0.8
0.6
0.4
0.2
80
6
60
4
40
2
20
0
0
RPSAP52
HMGA2
FBXO21
NOS1
115.8
116.0
Position on chr. 12 (Mb)
rs7294919
Hippocampal volume
Intracranial volume
Plotted
SNPs
–log10 (P value)
10
b
Hippocampal volume
Plotted
SNPs
116.2
64.4
d
Recombination rate (cM/Mb)
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a
Recombination rate (cM/Mb)
Figure 1 Association results and meta-analysis
of effects in individual and combined analyses.
(a) The strongest association with hippocampal
volume was found for rs7294919. Fixedeffects meta-analysis P values are shown41
after controlling for intracranial volume using
all subjects in the discovery sample. (b) The
strongest association with intracranial volume
was found for rs10784502. Fixed-effects metaanalysis P values are shown in healthy subjects
only. (c,d) The effect within each sample
contributing to the meta-analysis is shown in
forest plots for hippocampal volume (c) and
intracranial volume (d). Association data using
intracranial volume as a phenotype were not
available for the EPIGEN sample. Head size was
not controlled for in the CHARGE Consortium
association analyses.
LLPH IRAK3
HELB
TMBIM4
GRIP1
64.6
64.8
Position on chr. 12 (Mb)
65.0
rs10784502
Intracranial volume
Mexican ancestry in Los Angeles, California
ADNI
ADNI
(MEX); N = 842). We also undertook custom
BFS
BFS
genotyping of the two most promising
BIG
BIG
fBIRN
fBIRN
SNPs in two additional samples of European
IMAGEN
IMAGEN
ImaGene
ImaGene
ancestry (BIG replication and Trinity College
LBC1936
LBC1936
MooDs
Dublin/National University of Ireland,
MooDs
MPIP
MPIP
Galway (TCD/NUIG); N = 1,286).
NCNG
NCNG
QTIM
QTIM
In general, previously identified poly
SHIP
SHIP
SHIP–TREND
morphisms associated with hippocampal
SHIP–TREND
SuperStruct
SuperStruct
volume showed little association in our metaSYS
SYS
TOP
analysis (BDNF, TOMM40, CLU, PICALM,
TOP
UMCU
UMCU
ENIGMA discovery
ZNF804A, COMT, DISC1, NRG1, DTNBP1;
ENIGMA discovery
CHARGE
CHARGE
BIG
replication
Supplementary Table 7), nor did SNPs pre
BIG
replication
EPIGEN
NESDA
viously associated with schizophrenia22 and
NESDA
TCD/NUIG
TCD/NUIG
bipolar disorder23 (Supplementary Table 8).
GOBS
GOBS
NIMH–IRP
NIMH–IRP
The most significant SNPs in each analysis
Combined
Combined
−5
from the discovery sample (P ≤ 5 × 10 )
are listed (Supplementary Tables 9–16). No
–200 –100 0 100 200 300 400 500
–100,000 –50,000
0
50,000 100,000
markers reached genome-wide significance
(P < 1.25 × 10−8; Online Methods) in the dis
Effect in mm3 per allele (standard error)
Effect in mm3 per allele (standard error)
Effect allele: C
Effect allele: C
covery sample alone. However, the strongest
associations for hippocampal and intracranial
volumes were replicated, yielding results at genome-wide significance composed of elderly subjects. Meta-analysis of the Enhancing Neuro
(Fig. 1 and Table 1; see Supplementary Tables 17–25 for additional Imaging Genetics through Meta-Analysis (ENIGMA) discovery
results and gene-based tests24).
and replication samples with those from the CHARGE Consortium
In our discovery sample, two SNPs in the same linkage disequilib yielded a highly significant association for rs7294919 (P = 6.70 ×
rium (LD) block showed strong associations with hippocampal volume 10−16; N = 21,151).
after controlling for intracranial volume (rs7294919 and rs7315280;
rs7294919 lies between HRK and FBXW8 (12q24.22; Fig. 1)
r2 = 0.81, CEU 1000 Genomes Pilot 1). A random-effects analysis of and is not in LD with any SNPs within coding sequences, UTRs or
the discovery sample, conducted to examine heterogeneity between splice sites within 500 kb (r2 > 0.4) in the CEU sample from the
cohorts, reduced significance only slightly for rs7294919 (P = 4.43 × 1000 Genomes Project Phase 1. To determine whether the observed
10−7) compared to the primary fixed-effects analysis (P = 2.42 × association is related to a regulatory mechanism, we examined poten
10−7). The association was consistent, although stronger, in the full tial cis effects of this variant on expression levels of genes within a
sample compared to the healthy subset (Fig. 2). Notably, the association 1-Mb region. In temporal lobe tissue resected from 71 individuals
was robust to the effects of head and brain size (Fig. 2), and the locus with mesial temporal lobe epilepsy and hippocampal sclerosis in the
was not significantly associated with intracranial volume (P = 0.54) University College London (UCL) epilepsy cohort, we examined
or total brain volume (P = 0.41). This suggests an effect at the level of association between rs4767492 (a proxy for rs7294919, which was
the hippocampus rather than on brain size in general. The direction of not directly genotyped; r2 = 0.636 in 1000 Genomes Project Phase 1)
the effect was consistent across samples and ages (Fig. 1). Haplotype and expression levels. This analysis suggested an association (P =
analysis of directly genotyped variants near rs7294919 in two samples 0.006, controlling for age) with expression of the TESC gene, which
confirmed that the association was present across the haplotype and lies 3′ to FBXW8 (149 kb; Fig. 3). To corroborate this finding, we
that the causal variant was well marked by rs7294919 (Supplementary used the publicly available SNPExpress database (see URLs), which
Note). rs7294919 was also significantly associated with hippocampal includes data on gene expression in post-mortem frontal cortex from
volume in the cohorts from the CHARGE Consortium, which are 93 subjects. In this independent sample, expression levels of TESC
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Table 1 Results from the genome-wide association meta-analyses of mean hippocampal, intracranial and total brain volumes
Sample
Mean bilateral hippocampal volumea
rs7294919b
Discovery Fixed-effects model
Random-effects model
ENIGMA CEU and TSI replication
ENIGMA CEU and YRI or MEX replication
Discovery and replication
CHARGE in silico replication
ENIGMA and CHARGE
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Intracranial volumec
rs10784502d
Discovery Fixed-effects model
Random-effects model
ENIGMA CEU and TSI replicatione
ENIGMA CEU and YRI or MEX replication
Discovery and replication
CHARGE in silico replication
ENIGMA and CHARGE
Total brain volumef
rs10494373g
Discovery Fixed-effects model
Random-effects model
ENIGMA CEU and TSI replication
ENIGMA MEX replication
Discovery and replication
N
Freq. of the
effect allele
7,795
0.104
1,735
842
10,372
10,779
21,151
0.101
0.125
0.106
0.093
0.099
5,778
0.488
1,130
699
7,607
β (mm3)
S.E. (mm3)
P value
2.42 ×
4.43 ×
0.246
0.285
1.99 ×
3.40 ×
6.70 ×
10−7
10−7
Heterogeneity
P value
Variance
explained (%)h
0.913
0.910
0.924
0.127
0.347
0.442
0.419
0.242
0.241
0.042
0.095
0.177
0.458
0.265
50.27
50.12
22.05
27.77
42.74
52.70
47.58
9.71
9.65
19.00
25.96
8.22
8.45
5.89
0.525
0.348
0.479
11860.73
11841.80
15758.59
1928.43
11395.74
2319.00
2270.07
5244.69
6215.31
2007.27
3.14 × 10−7
3.93 × 10−7
0.003
0.756
1.37 × 10−8
0.783
0.771
0.065
0.710
0.217
0.281
0.280
0.468
0.008
0.261
8,175
15,782
0.501
0.491
7429.56
9006.71
1630.92
1265.78
5.23 × 10−6
1.12 × 10−12
NA
0.145
0.110
0.166
5,778
0.082
117
605
6,500
0.107
0.097
0.085
13693.29
13562.00
8435.89
26883.36
14778.23
3187.51
3114.17
20256.09
8608.20
2957.14
1.74 × 10−5
2.69 × 10−5
0.678
0.001
5.81 × 10−7
0.688
0.728
NA
NA
0.182
0.198
0.194
0.001
0.964
0.240
10−7
10−10
10−16
Freq., frequency. CEU, TSI YRI and MEX refer to the HapMap 3 reference panels most representative of the sample and used for imputation; NA, not applicable.
aMean
bilateral hippocampal volume association results were corrected for intracranial volume, sex, age, age2, sex × age, sex × age2 and four MDS components, and individuals with disease were
included in the analysis. brs7294919 is located at 12q24.22: position 115,811,975. Effect allele, C; non-effect allele, T. Genomic positions are based on the NCBI36/hg18 (March 2006)
genome assembly. cAssociation results for intracranial volume were corrected for sex, age, age2, sex × age, sex × age2 and four MDS components, and individuals with disease were excluded from
this analysis. drs10784502 is at 12q14.3: position 64,630,077. Effect allele, C; non-effect allele, T. eIntracranial volume and total brain volume were available for two participants in MPIP and
one participant in the BIG cohort who did not have hippocampal volume measures. The proxy SNP rs8756 was genotyped in the TDC/NUIG cohort. fAnalysis for total brain volume was corrected
for sex, age, age2, sex × age, sex × age2 and four MDS components, and individuals with disease were excluded. Total brain volume was not available for the ENIGMA replication cohorts. Within
the CHARGE Consortium, a normalized version of total brain volume was analyzed and defined as total brain volume intracranial volume, and, because of this, the results are not comparable between consortia. grs10494373 is at 1q23.3: position 160,885,986. Effect allele, C; non-effect allele, A.hCalculated as 2pq × β2 / (s.d.)2, where p and q are the minor and major allele frequencies, β is the unstandardized regression coefficient and s.d. is from the phenotype in the absence of covariate corrections. Intracranial volume phenotypic variance from the ENIGMA discovery
sample was used to calculate percent variance explained in the CHARGE in silico replications, as this information was not available from the CHARGE consortium.
again significantly differed by genotype (rs4767492; P = 0.0021).
Additional replication came from the UK Brain Expression Database,
where TESC expression in post-mortem brain tissues from 134 indivi
duals free from neurological disorders showed a strong difference by
genotype in temporal cortex (rs7294919; P = 9.7 × 10−4 for gene and
4.8 × 10−5 for exon 8). Given the small sample sizes and low minor
allele frequency of this SNP (MAF = 0.099), no homozygotes for
the minor allele were observed in any brain tissue sample, limiting
the inferences we can draw regarding mode of action. Expression of
HRK showed little evidence of association with the proxy genotype
rs7294919
–5
Hippocampal I ICV; healthy only (P = 9.2 × 10 )
–7
Hippocampal I ICV; all subjects (P = 2.4 × 10 )
Hippocampal I TBV; healthy only (P = 0.00022)
–7
Hippocampal I TBV; all subjects (P = 5.8 × 10 )
in the UCL epilepsy cohort (P = 0.11) or SNPExpress (P = 0.16) but
was associated with rs7294919 in temporal cortex within the UK
Brain Expression Database (P = 0.0051). Additional associations
were observed in peripheral blood mononuclear cells (PBMCs;
Supplementary Note).
The expression results in brain tissue suggest that TESC is a primary
positional candidate for our quantitative trait locus (QTL). Studies of
mouse and chicken embryos show that TESC is expressed throughout
the brain during development, with the strongest expression in the devel
oping telencephalon and mesencephalon and near the developing ven
tricles25. TESC also has moderate expression in the human hippocampus
during adulthood (Allen Institute Brain Atlas, see URLs; Fig. 3). Its pro
tein product, tescalcin, interacts with the Na+/H+ exchanger (NHE1)26,
which is involved in the regulation of intracellular pH21, cell volume
and cytoskeletal organization27. TESC expression is strongly regulated
during cell differentiation in a cell lineage–specific fashion28,29. Our data
suggest that this role in cell proliferation and differentiation is relevant
for hippocampal volume and brain development.
Hippocampal I Other; healthy only (P = 0.00012)
–7
Hippocampal I Other; all subjects (P = 2.2 × 10 )
–40
–20
3
0
20
40
Effect in mm per allele (standard error)
Effect allele: C
554
60
80
100
Figure 2 Association of rs7294919 with hippocampal volume stratified
by disease and covariates. Effects are consistent in the discovery sample
regardless of whether individuals with disease (N = 7,795) or only healthy
subjects (N = 5,775) were included. The effect is also consistent whether
accounting for intracranial volume (ICV), total brain volume (TBV) or
without a measure of head size (Other).
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© 2012 Nature America, Inc. All rights reserved.
295.56
117.00
229.37
71.45
163.17
25.89
96.98
GA
GG
P = 0.0021
Frontal cortex
rs7294919
10
8
AA
GA
GG
P = 0.4532
Peripheral blood
Fetal human TESC expression
TESC expression values
162.56
AA
d
rs4767492
361.75
GA
P = 0.0057
Temporal cortex
6
4
VLPFC
THAL
TAU
TAC
STRIAT
PAS
OFC
MS
MPFC
HIPP
2
DLPFC
6.10
5.83
5.56
5.29
5.02
4.75
4.48
4.21
3.94
3.67
3.40
c
rs4767492
208.12
AA
CBLM
e
b
rs4767492
OCC
a
7.40
7.24
7.08
6.92
6.76
6.60
6.44
6.28
6.12
5.96
5.80
CT TT CT TT CT TT CT TT CT TT CT TT CT TT CT TT CT TT CT TT CT TT CT TT
AvgALL AvgCTX CRBL FCTX HIPP MEDU OCTX PUTM SNIG TCTX THAL WHMT
P value
f
0.014 2.9 ×
10
–3
Brain (Br)
H0351.2001
Gray matter (GM)
Telencephalon (Tel)
Cerebral cortex (Cx)
Limbic lobe (LL)
Hippocampal formation (HiF)
0.78 0.031 0.43 0.49 0.47 0.50 0.88
4.8 ×
–5
10
0.93 0.68
Adult human TESC expression
H0351.2002
Figure 3 Regulatory effects of hippocampal-associated variant and
expression of TESC within the hippocampus. (a–d) The locus most
associated with hippocampal volume was also associated with mRNA
expression of the TESC gene in brain in three independent samples,
the UCL epilepsy cohort (a), the SNPExpress database (b), where a
proxy SNP was used, and the UK Brain Expression Database (d), where
differences in TESC expression of the directly genotyped hippocampal
variant (rs7294919) were strongest in the temporal cortex (TCTX)
(red box) but also found in the average expression of all cortex (AvgCTX)
and average expression of all brain structures tested (AvgALL). Symbol
color represents genotype in a and d. These regional gradients in
expression support the hypothesis that the SNP may associate with
hippocampal but not total brain volume. No effects were detected in
PBMCs from the SNPExpress database (c). CRBL, cerebellar cortex;
FCTX, frontal cortex; HIPP, hippocampus; MEDU, medulla (specifically
FL
HiF OL PL TL
Str
DT MES CbCx PTg MY
FL
HiF OL PL
TL
Str
DT MES PTg MY
the inferior olivary nucleus); OCTX, occipital cortex; PUTM, putamen;
TESC - A_23_P76538
TESC - A_23_P76538
SNIG, substantia nigra; THAL, thalamus; WHMT, intralobular white
–2.5
2.5
matter. (e) TESC is differentially expressed within the fetal human brain
−12
42
(P = 1.33 × 10 ), with the highest expression in striatum (STRIAT) and hippocampus (HIPP) . Box plots represent median and 25th to 75th
percentiles. Upper and lower lines show minimum and maximum values, respectively. CBLM, cerebellum; DLPFC, dorsolateral prefrontal neocortex;
MPFC, medial prefrontal neocortex; MS, motor-somatosensory neocortex; OCC, occipital visual neocortex; OFC, orbital prefrontal neocortex; PAS,
parietal association neocortex; TAC, temporal association neocortex; TAU, temporal auditory neocortex; THAL, mediodorsal thalamus; VLPFC,
ventrolateral prefrontal neocortex. (f) TESC has moderate to high gene expression throughout the adult human hippocampus (shown in green), as
visualized in the Allen Institute Human Brain Atlas using Brain Explorer 2 software. An inferior view of the brain is shown in two subjects; the anterior
portion of the brain is at the top. The colors of spheres within the hippocampus indicate the Z-scores of TESC expression normalized within each subject
across brain structures. Heat maps show that expression of TESC is higher in the hippocampus (HiF) and striatum (Str) than in other brain structures.
The strongest association with intracranial volume was observed at
rs10784502 (Table 1), an intronic SNP near the 3′ UTR of the HMGA2
gene (12q14.3; Fig. 1). This locus was associated with intracranial
volume across lifespan, as shown by the strong replication in samples
from healthy elderly individuals in the CHARGE Consortium. The
combined analysis resulted in the identification of a highly significant
association (P = 1.12 × 10−12). Of note, rs10784502 has been reliably
associated with increased adult height (P = 3.636 × 10−32; effect allele:
C)30. The genetic correlation between height and intracranial volume
within the QTIM sample was significant (rg = 0.31; P = 1.34 × 10−7), as
was that observed in the GOBS sample (rg = 0.20; P = 0.026), suggest
ing modest overlap of shared genetic determinants. rs10784502 also
had an effect on total brain volume in the discovery sample (P = 9.49 ×
10−5). When considering the results from the intracranial volume metaanalysis in SNPs previously associated with height 31–33 (NSNPs = 175;
Nature Genetics
VOLUME 44 | NUMBER 5 | MAY 2012
Supplementary Fig. 41), a clear inflation of the test statistic was
observed (λ = 1.44), indicating that SNPs associated with height
are also associated with intracranial volume. This enrichment,
which was not observed for hippocampal volume (Supplementary
Figs. 42 and 43), was due to a systematically higher degree of association
throughout the candidate SNP set rather than a small number of
large effects. Structural equation modeling showed that the effect of
rs10784502 on intracranial volume could not completely be accounted
for by the indirect effects of this SNP on height or by the correlation
between height and intracranial volume (Supplementary Fig. 44).
Examining correlations between rs10784502 and expression levels
of genes within a 1-Mb region, we identified a significant effect
on the expression of HMGA2 (P = 0.0077) as the single significant
result in the GOBS transcriptional profile data. Additionally, HMGA2
expression levels in PBMCs were significantly negatively genetically
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letters
c orrelated with intracranial volume (rg = −0.49; P = 0.016) in this
cohort. These results support HMGA2 as a positional candidate gene
underlying our observed QTL. HMGA2 encodes the high-mobility
group AT-hook 2 protein, which is a chromatin-associated protein
that regulates stem cell renewal during development34. It is implicated
in human growth through genetic association studies and the pres
ence of rare mutations35 and also has known roles in neural precursor
cells36. Whether both functions are due to the same underlying mech
anisms warrants further study.
To test for pleiotropic effects of rs7294919 and rs10784502, we
examined the influence of these variants on cognition in the Brisbane
Adolescent Twin Study37 (N = 1642). The C allele of rs10784502, which
was associated with increased intracranial volume, was also associated
with increased full-scale IQ, as measured via the Multidimensional
Aptitude Battery38 (effect size (β) = 1.29, standard error (S.E.) = 0.47;
P = 0.0073; phenotypic correlations are shown in Supplementary
Table 26). This effect was driven by performance (PIQ; β = 1.74,
S.E. = 0.61; P = 0.0044) rather than by verbal subtests (VIQ;
P = 0.103). rs7294919 was not associated with full-scale IQ (P = 0.139)
or PIQ (P = 0.489) but showed nominal association with VIQ (effect
allele: C; β = 0.126, S.E. = 0.062; P = 0.043).
No associations at genome-wide significance were detected for
total brain volume. Following inclusion of the replication samples, the
strongest evidence for association was detected at rs10494373 within
DDR2 (1q23.3; P = 5.81 × 10−7) (Table 1), which encodes a receptor
tyrosine kinase involved in cell growth and differentiation39.
The current study identified and replicated two quantitative trait
loci for hippocampal and intracranial volumes across lifespan in a
large sample including both healthy subjects and those with neuro
psychiatric diagnoses. The rs7294919 variant was associated with
decreased hippocampal volume of 47.6 mm3 or 1.2% of the average
hippocampal volume per risk allele. Although further work is neces
sary to confirm the causal variant(s) and functional mechanisms, this
QTL influencing hippocampal volume differences may act by regulat
ing expression of TESC specifically within the brain. In addition, the
C allele of rs10784502 is associated, on average, with 9,006.7 mm3
larger intracranial volume, or 0.58% of intracranial volume per risk
allele and is weakly associated with increased general intelligence by
approximately 1.29 IQ points per allele.
It has previously been hypothesized that brain imaging endo
phenotypes would have large effect sizes; however, this has proven not
to be the case for the specific volumetric traits measured here, which had
comparable effect sizes to those observed in other genome-wide asso
ciation studies of complex traits40. Notably, the discovery sample had
99.92% power to detect variants with effect sizes of 1% of the variance
for MAF ≥ 0.05. It remains to be determined whether specific genetic
variations linked to volumetric brain differences are also associated with
other neuropsychiatric disorders, brain function and other cognitive
traits. If this is the case, neuroimaging genetics may also discover new
treatment targets related to the neurobiology of these disorders, in addi
tion to improving phenomenologically based diagnostic criteria.
URLs. Allen Institute Brain Atlas, http://human.brain-map.org/;
SNPExpress database, http://compute1.lsrc.duke.edu/softwares/
SNPExpress/index.php; ADNI database, http://adni.loni.ucla.edu/;
ADNI acknowledgements, http://adni.loni.ucla.edu/wp-content/
uploads/how_to_apply/ADNI_Acknowledgement_List.pdf; the
Foundation for the NIH, http://www.fnih.org/; ADNI informa
tion, http://www.adni-info.org/; Brain Research Imaging Centre
Edinburgh, http://www.bric.ed.ac.uk/; SINAPSE Collaboration,
http://www.sinapse.ac.uk/; fBIRN, http://www.birncommunity.org/;
556
SYS, http://www.saguenay-youth-study.org/; SHIP, http://ship.
community-medicine.de/; ENIGMA Consortium protocols, http://
enigma.loni.ucla.edu/protocols/; Mx, http://www.vcu.edu/mx/;
SOLAR, http://solar.txbiomedgenetics.org/; Genetic Power Calculator,
http://pngu.mgh.harvard.edu/~purcell/gpc/; HapMap, http://hapmap.
ncbi.nlm.nih.gov/; Data upload site for participating studies, http://
enigma.loni.ucla.edu/; METAL, http://www.sph.umich.edu/csg/
abecasis/Metal/; METASOFT, http://genetics.cs.ucla.edu/meta/;
matSpD, http://gump.qimr.edu.au/general/daleN/matSpD/.
Methods
Methods and any associated references are available in the online
version of the paper at http://www.nature.com/naturegenetics/.
Note: Supplementary information is available on the Nature Genetics website.
Acknowledgments
Some authors received commercial funding unrelated to the topic of this paper.
N.J.v.d.W. received speaking fees from Eli Lilly & Company and Wyeth and served
on advisory panels of Eli Lilly & Company, Pfizer, Wyeth and Servier. A.A. received
an investigator-initiated unrestricted research grant from Bristol-Myers Squibb
and speaker’s fees from AstraZeneca, Bristol-Myers Squibb and GlaxoSmithKline.
H.J.G. received external research support from the German Research Foundation,
the Federal Ministry of Education and Research Germany, speaker’s
honoraria from Bristol-Myers Squibb, Eli Lilly & Company, Novartis, Eisai,
Boehringer Ingelheim and Servier and travel funds from Janssen-Cilag,
Eli Lilly & Company, Novartis, AstraZeneca, Lundbeck and the SALUS–Institute
for Trend-Research and Therapy Evaluation in Mental Health. M.N. received
research grants from the Federal Ministry of Education and Research, Germany,
the German Research Foundation, BioRad Laboratories, Siemens AG, Zeitschrift
für Laboratoriumsmedizin, Bruker Daltronics, Abbott, Jurilab Kuopio, Roche
Diagnostics, Instand and Becton Dickinson. H.V. received external research
support via research grants from Hofmann La Roche, the Humboldt Foundation,
the Federal Ministry of Education and Research (Germany) and the German
Research Foundation. M.W. is on the following scientific advisory boards: Lilly,
Araclon and Institut Catala de Neurociencies Aplicades, the Gulf War Veterans
Illnesses Advisory Committee, VACO, Biogen Idec and Pfizer. M.W. received
funding for consulting from Astra Zeneca, Araclon, Medivation/Pfizer, Ipsen,
TauRx Therapeutics, Bayer Healthcare, Biogen Idec, Exonhit Therapeutics, SA,
Servier, Synarc, Pfizer and Janssen; for travel from NeuroVigil, CHRU–Hopital
Roger Salengro, Siemens, AstraZeneca, Geneva University Hospitals, Lilly, the
University of California, San Diego–ADNI, Paris University, Institut Catala de
Neurociencies Aplicades, the University of New Mexico School of Medicine,
Ipsen, Clinical Trials on Alzheimer’s Disease (CTAD), Pfizer, AD PD Meeting,
Paul Sabatier University, Novartis and Tohoku University; and research support
from: Merck, Avid, DoD, VA. M.W. received honoraria from PMDA/ the Japanese
Ministry of Health, Labour, and Welfare, Tohoku University, Neuro Vigil, Insitut
Catala de Neurociencies Aplicades. M.W. owns stock options for Synarc, Elan.
Organizations contributing to the Foundation for the US NIH and thus to the
National Institute on Aging (NIA)-funded Alzheimer’s Disease Neuroimaging
Initiative included Abbott, the Alzheimer’s Association, the Alzheimer’s Drug
Discovery Foundation, Anonymous Foundation, AstraZeneca, Bayer Healthcare,
BioClinica (ADNI 2), Bristol-Myers Squibb, the Cure Alzheimer’s Fund, Eisai,
Elan, Gene Network Sciences, Genentech, GE Healthcare, GlaxoSmithKline,
Innogenetics, Johnson & Johnson, Eli Lilly & Company, Medpace, Merck,
Novartis, Pfizer, Roche, Schering Plough, Synarc and Wyeth.
ADNI: The ADNI study was supported by the US NIH (U01 AG024904) and the
Foundation for the NIH for genotype and phenotype data collection, the NIH
(RC2 AG036535-01) for data analysis, the NIA (R01 AG019771-09) for additional
data analysis and NCRAD (U24AG021886) for DNA used in part for the GWAS.
Data used in preparation of this article were obtained from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database (see URLs). As such, the investigators
within ADNI contributed to the design and implementation of ADNI and/or
provided data but did not participate in analysis or writing of this report. The
ADNI sample wishes to acknowledge the investigators who contributed to the
design and implementation of ADNI (see URLs). Data collection and sharing for
this project were funded by ADNI (NIH grant U01 AG024904). ADNI is funded
by the NIA, the National Institute of Biomedical Imaging and Bioengineering
(NIBIB) and through generous contributions from Abbott, AstraZeneca AB,
Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical
Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline,
VOLUME 44 | NUMBER 5 | MAY 2012
Nature Genetics
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© 2012 Nature America, Inc. All rights reserved.
letters
Innogenetics, Johnson & Johnson, Eli Lilly & Company, Medpace, Merck and
Cocpany, Novartis AG, Pfizer, F. Hoffman–La Roche, Schering-Plough and
Synarc, as well as from nonprofit partners at the Alzheimer’s Association and
the Alzheimer’s Drug Discovery Foundation, with participation from the US
Food and Drug Administration (FDA). Private sector contributions to ADNI are
facilitated by the Foundation for the NIH (see URLs). The grantee organization
is the Northern California Institute for Research and Education, and the study is
coordinated by the Alzheimer’s Disease Cooperative Study at the University of
California, San Diego. ADNI data are disseminated by the Laboratory of Neuro
Imaging at the University of California, Los Angeles. This research was also
supported by NIH grants (P30 AG010129 and K01 AG030514) and by the Dana
Foundation. ADNI was launched in 2003 by the NIA, the NIBIB, the FDA, private
pharmaceutical companies and nonprofit organizations as a 5-year public- private
partnership. The primary goal of ADNI has been to test whether serial MRI),
positron emission tomography (PET), other biological markers and clinical and
neuropsychological assessments can be combined to measure the progression of
mild cognitive impairment (MCI) and early Alzheimer’s disease. Determination
of sensitive and specific markers of very early Alzheimer’s disease progression
is intended to aid researchers and clinicians in developing new treatments and
monitoring their effectiveness, as well as lessening the time and cost of clinical
trials. The Principal Investigator of this initiative is M.W. Weiner. ADNI is
the result of efforts of many coinvestigators from a broad range of academic
institutions and private corporations, and subjects have been recruited from over
50 sites across the United States and Canada. The initial goal of ADNI was to
recruit 800 adults ages 55 to 90 to participate in the research—approximately 200
cognitively normal older individuals to be followed for 3 years, 400 people with
MCI to be followed for 3 years and 200 people with early Alzheimer’s disease to be
followed for 2 years. For up-to-date information, please visit the ADNI website
(see URLs).
BIG: The BIG study wishes to acknowledge S. Kooijman for coordination of
sample collection and A. Heister, M. Naber, R. Makkinje, M. Hakobjan and
M. Steehouwer for genotyping. The BIG study was supported by a Biobanking
and Biomolecular Resources Research Infrastructure Netherlands (BBMRI-NL)
complementation grant for brain segmentation and the Netherlands Organisation
for Scientific Research (NWO) Horizon Breakthrough grant (grant number
93511010 (to A.A.V.).
Bipolar Family Study: The Bipolar Family Study wishes to thank the Scottish
Mental Health Research Network for research assistant support, the Brain Research
Imaging Centre Edinburgh (see URLs), a center in the Scottish Funding Council
Scottish Imaging Network–A Platform for Scientific Excellence (SINAPSE)
Collaboration (see URLs), for image acquisition and the Wellcome Trust Clinical
Research Facility for genotyping. Genotyping was supported by the National
Alliance for Research on Schizophrenia and Depression (NARSAD) Independent
Investigator Award (to A.M.M.), and data collection was supported by the Health
Foundation Clinician Scientist Fellowship.
fBIRN: fBIRN wishes to acknowledge D.B. Keator for leading fBIRN
neuroinformatics development, B.A. Mueller for image calibration and quality
assurance and A. Belger, V.D. Calhoun, G.G. Brown, J.M. Ford, G.H. Glover,
R. Kikinis, K. Lim, J. Laurriello, J. Bustillo, G. McCarthy, D.S. O’Leary, B. Rosen,
A.W.T. and J.T. Voyvodic for their leadership contributions to fBIRN scanner and
sequence calibration, tool development and data collection efforts. The fBIRN
study was supported by the US NIH (U24 RR21992) for phenotypic data collection.
Genotyping was performed with the support of the grant RBIN04SWHR to F.M.
from the Italian Ministry of University and Research.
GOBS: The GOBS study was supported by the US NIH (MH0708143 and MH083824
to D.C.G., MH078111 and MH59490 to J.B., C06 RR13556 and C06 RR017515).
P.K. was also supported by an NIH grant (EB006395).
IMAGEN: IMAGEN is funded by the European Commission Framework
Programme 6 (FP-6) Integrated Project IMAGEN (PL037286), the European
Commission Framework Programme 7 (FP-7) Project Alzheimer’s Disease,
Alcoholism, Memory, Schizophrenia (ADAMS), the FP-7 Innovative Medicine
Initiative Project European Autism Interventions (AIMS), the UK Department of
Health National Institute of Health Research (NIHR)–Biomedical Research Centre
Mental Health program and the MRC programme grant Developmental Pathways
into Adolescent Substance Abuse (93558).
ImaGene: ImaGene wishes to acknowledge J. Lee and J. Lane for processing
the blood samples, The Easton Consortium for Alzheimer’s Disease Drug
Discovery and Biomarker Development and the Alzheimer’s Disease Research
Center (ADRC) funded by the NIA at the University of California, Los Angeles
(AG16570).
Nature Genetics
VOLUME 44 | NUMBER 5 | MAY 2012
LBC1936: We thank the participants in LBC1936. We thank C. Murray, A.J.
Gow, S.E. Harris, M. Luciano, P. Redmond, E. Sandeman, I. Gerrish, J. BoydEllison, N. Leslie, A. Howden and C. Scott for data collection and preparation.
This project is funded by the Age UK’s Disconnected Mind programme and also
by Research Into Ageing (251 and 285). The entire genome association part of
the study was funded by the Biotechnology and Biological Sciences Research
Council (BBSRC) (BB/F019394/1). Analysis of brain images was funded by UK
MRC grants (G1001401 and 8200). The work was undertaken by The University
of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of
the cross council Lifelong Health and Wellbeing Initiative (G0700704/84698).
Imaging was performed at the Brain Research Imaging Centre, Edinburgh, a
center in the SINAPSE Collaboration. Funding from BBSRC, the Engineering
and Physical Sciences Research Council (EPSRC), the Economic and Social
Research Council (ESRC) and the MRC and Scottish Funding Council through the
SINAPSE Collaboration is gratefully acknowledged. L.M.L. is the beneficiary of a
postdoctoral grant from the AXA Research Fund.
MooDS: This work was funded by the German Federal Ministry of Education
and Research (BMBF) in the National Genome Research Network (NGFN-plus)
through the MooDs grant Molecular Causes of Major Mood Disorders and
Schizophrenia (coordinator M.M.N.). Additional funding for genotyping was
provided by a NARSAD Distinguished Investigator award to A.M.-L.
MPIP: The MPIP Munich Morphometry Sample comprises images acquired as
part of the Munich Antidepressant Response Signature Study and the Recurrent
Unipolar Depression (RUD) Case-Control Study performed at the MPIP and
control subjects acquired at the Department of Psychiatry at the LudwigMaximilians-University. We wish to acknowledge A. Olynyik and radiographers
R. Schirmer, E. Schreiter and R. Borschke for image acquisition and data
preparation. We thank D.P. Auer for local study management in the initial phase
of the RUD study. We are grateful to GlaxoSmithKline for providing the genotypes
of the RUD Case-Control Sample. We thank the staff of the Center of Applied
Genotyping (CAGT) for generating the genotypes of the MARS cohort. The study
is supported by a grant from the Exzellenz-Stiftung of the Max Planck Society. This
work has also been funded by the BMBF in the framework of the National Genome
Research Network (NGFN) (FKZ 01GS0481).
NCNG: We would like to thank the personnel involved in recruitment and data
collection and, in particular, P. Due-Tønnessen for clinical assessment of the MRI
images. The NCNG study was supported by Research Council of Norway grants
(154313/V50 and 177458/V50). The NCNG GWAS was financed by grants from
the Bergen Research Foundation, the University of Bergen, the Research Council of
Norway (FUGE; Psykisk Helse), Helse Vest Regionalt Helseforetak (RHF) and the
Dr Einar Martens Fund.
NESDA-NTR: Funding was obtained from the NWO (MagW/ZonMW 904-61-090;
985-10-002; 904-61-193; 480-04-004; 400-05-717, Addiction-31160008;
911-09-032; SPI 56-464-14192 and Geestkracht Program, 10-000-1002),
the Center for Medical Systems Biology (CMSB; NWO Genomics), NBIC/
BioAssist/RK/2008.024, BBMRI-NL, Biobanking and Biomolecular Resources
Research Infrastructure, the VU University, the EMGO Institute for Health and
Care Research and Neuroscience Campus Amsterdam, the European Science
Foundation (EU/QLRT-2001-01254), the European Community’s FP7 (HEALTHF4-2007-201413), the European Science Council (ERC) Genetics of Mental Illness
(230374), Rutgers University Cell and DNA Repository (cooperative agreement
NIMH U24 MH068457-06), the US NIH (R01D0042157-01A) and the Genetic
Association Information Network (a public-private partnership between the NIH
and Pfizer, Affymetrix and Abbott Laboratories).
NIMH-IRP: This study was supported by funding from the Intramural Research
Program of the National Institute of Mental Health (NIMH) from the NIH
and the US Department of Health and Human Services (K99 MH085098 to
G.L., 1ZIA MH002810 to F.J.M. and 1ZIA MH002790 to W.C.D.). The content of
this publication does not necessarily reflect the views or policies of the Department
of Health and Human Services, nor does mention of trade names, commercial
products or organizations imply endorsement by the US government.
QTIM: We are extremely grateful to the twins for their participation,
the radiographers at the Centre for Advanced Imaging at the University of
Queensland for image acquisition and the many research assistants and support
staff at the Queensland Institute of Medical Research for twin recruitment and
daily management, and we especially thank K. Johnson for MRI scanning and
processing, A. Henders for DNA processing and preparation and S. Gordon for
quality control and management of the genotypes. Phenotyping was funded by the
US National Institute of Child Health and Human Development (R01 HD050735)
and the Australian National Health and Medical Research Council (NHMRC)
557
letters
(project grant 496682). Genotyping was funded by the NHMRC (Medical
Bioinformatics Genomics Proteomics Program, 389891). G.M. was supported by
an NHMRC Fellowship (613667), and G.Z. was supported by Australian Research
Council (ARC) Future Fellowship (FT0991634). S.E.M. is funded by an ARC Future
Fellowship (FT110100548). J.L.S. was supported by the Achievement Rewards
for College Scientists foundation and the US NIMH (F31 MH087061). D.P.H. is
partially supported by a National Science Foundation (NSF) Graduate Research
Fellowhip Program (GRFP) grant (DGE-0707424). P.T. was also supported by the
NIH (grants U01 AG024904, AG040060, EB008432, P41 RR013642, HD050735,
AG036535, AG020098 and EB008281).
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© 2012 Nature America, Inc. All rights reserved.
SYS: The Saguenay Youth Study Group wishes to thank the following individuals
for their contribution in acquiring and analyzing the data: N. Arbour,
M.-È. Bouchard, A. Houde, A. Gauthier and H. Simard for the recruitment and
assessment of participating families, M. Bérubé, S. Masson, S. Castonguay and
M.-J. Morin for MRI acquisition and E. Ding and N. Qiu for MR data management.
We thank J. Mathieu for the medical follow up of participants in whom we detected
any medically relevant abnormalities. We are grateful to all families for participating
in the study. The Saguenay Youth Study Group is supported by the Canadian
Institutes of Health Research, the Heart and Stroke Foundation of Quebec and
the Canadian Foundation for Innovation. For more information, please see the
study website (see URLs).
SHIP: The Study of Health in Pomerania (SHIP) is supported by the German
Federal Ministry of Education and Research (grants 01ZZ9603, 01ZZ0103 and
01ZZ0403) and the German Research Foundation (DFG; GR 1912/5-1). Genomewide data and MRI scans were supported by the Federal Ministry of Education and
Research (grant 03ZIK012) and a joint grant from Siemens Healthcare, Erlangen,
Germany, and the Federal State of Mecklenburg–West Pomerania. The University
of Greifswald is a member of the Center of Knowledge Interchange program of the
Siemens AG. We thank all staff members and participants of the SHIP study, as well
as all of the genotyping staff for generating the SHIP SNP data set. The genetic data
analysis workflow was created using the Software InforSense. Genetic data were
stored using the database Caché (InterSystems).
SHIP-TREND: The authors from SHIP are grateful to M. Stanke for the
opportunity to use his Server Cluster for SNP Imputation. This cohort is part of
the Community Medicine Research net (CMR) of the University of Greifswald,
which is funded by the German Federal Ministry of Education and Research
and the German Ministry of Cultural Affairs, as well as by the Social Ministry of
the Federal State of Mecklenburg–West Pomerania. CMR encompasses several
research projects that share data from the population-based Study of Health in
Pomerania (SHIP; see URLs). The work is also supported by the German Research
Foundation (DFG; GR 1912/5-1) and the Greifswald Approach to Individualized
Medicine (GANI_MED) network funded by the Federal Ministry of Education and
Research (grant 03IS2061A). Genome-wide data and MRI scans were supported
by the Federal Ministry of Education and Research (grant 03ZIK012) and a joint
grant from Siemens Healthcare, Erlangen, Germany, and the Federal State of
Mecklenburg–West Pomerania. The University of Greifswald is a member of the
Center of Knowledge Interchange program of the Siemens AG.
Superstruct: We thank the investigators and participants who contributed to the
brain genomics data collection for Superstruct at Massachusetts General Hospital
and Harvard University, with funding from the Simons Foundation, the Howard
Hughes Medical Institute and the US NIH (grant MH079799).
TOP: We thank the study participants of TOP and the personnel involved in data
collection and logistics, especially T.D. Bjella. This work was supported by the Oslo
University Hospital–Ullevål, the Eastern Norway Health Authority (2004-123),
the Research Council of Norway (167153/V50, 163070/V50 and 183782/V50),
and by Eli Lilly & Company (who covered part of the genotyping costs).
TCD: We wish to express our sincere thanks to all participants and to clinical staff
who facilitated patients’ involvement. In particular, we acknowledge colleagues
from the Trinity College Institute of Neuroscience A. Bodke, J. McGrath, F. Newell,
H. Garavan, and J. O’Doherty for their support in sample collection. Collection and
analysis of these samples were funded by the Wellcome Trust (072894/z/03/z-Gill)
and the Science Foundation Ireland (08/IN.1/B1916_Corvin).
EPIGEN: Work from the London Cohort was supported by research grants
from the Wellcome Trust (grant 084730 to S.M.S.), University College London
(UCL)/University College London Hospitals (UCLH) Comprehensive Biomedical
Research Centre/Specialist Biomedical Research Centres (CBRC/SBRC) (grant
114 to S.M.S.), the European Union Marie Curie Reintegration (to M. Matarin and
S.M.S.), the UK NIHR (08-08-SCC), the Comprehensive Local Research Network
(CLRN) Flexibility and Sustainability Funding (FSF) (grant CEL1300 to S.M.S.),
The Big Lottery Fund, the Wolfson Trust and the Epilepsy Society. This work was
558
undertaken at UCLH/UCL, which received a proportion of funding from the UK
Department of Health’s NIHR Biomedical Research Centres funding scheme.
Work from the Royal College of Surgeons in Ireland was supported by research
grants from the Science Foundation Ireland (Research Frontiers Programme
award 08/RFP/GEN1538) and Brainwave–the Irish Epilepsy Association. The
collection of Belgian subjects was supported by the Fonds National de la Recherche
Scientifique (grant FC 63574 / 3.4.620.06 F) and the Fonds Erasme pour la
Recherche Médicale at the Université Libre de Bruxelles.
UCL Institute of Neurology Control Brain Tissue Collection: Funding was
provided by the UK MRC (grant G0901254), the MRC Sudden Death Brain and
Tissue Bank and the Sun Health Research Institute Brain Bank.
UMCU: The UMCU study was supported by the Netherlands Organization for
Health Research and Development ZonMw (917.46.370 to H.E.H.) and the US
NIMH (MH078075 to R.A.O.).
AUTHOR CONTRIBUTIONS
The ENIGMA support group designed the project, established the consortium,
determined the analysis and quality control procedures, offered analytical support
and performed and coordinated cross-site and replication analyses. This group
included J.L.S., S.E.M., A.A.V., D.P.H., M.J.W., B.F., N.G.M. and P.M.T. The imaging
protocols group determined and refined protocols for computing brain measures
from the MRI scans and helped sites implement them as needed. This group
included J.L.S., R.T., A.M.W., T.E.N., M.J. and M. Rijpkema. The genetics
protocols group created analysis methods for imputation, quality control and
association testing of genome-wide data and helped to ensure that protocols
were implemented consistently across all sites. This group included S.E.M.,
J.L.S., A.A.V. and D.P.H. The meta-analysis was carried out by the meta-analysis
group, consisting of S.E.M., R.E.S., J.L.S., D.P.H., A.A.V., M.J.W., N.G.M., B.F. and
P.M.T. The first draft was written by J.L.S., S.E.M., A.A.V., D.P.H., M.J.W., B.F.,
N.G.M. and P.M.T. Local image processing, involving statistical analysis and analysis
of the data, was performed by J.L.S., A.M.W., D.P.H., R.B., Ø.B., M.M.C., O.G.,
M. Hollinshead, A.J.H., S.M.M., A.C.N., M. Rijpkema, N.A.R., M.C.V.H.,
T.G.M.v.E., S.W., D.G.B., S.L.R., J.L.R., M.-J.v.T., S.E., P.T.F., P.K., J.L.L., R.M.,
G.B.P., J. Savitz, H.G.S., K.S., A.W.T., M.V.d.H., N.J.v.d.W., N.E.M.V.H., H.W.,
A.M.D., C.R.J., D.J.V., E.J.C.d.G., G.I.d.Z., T.E., G.F., P.H., H.E.H.P., K.L.M., A.J.S.,
L.S., J.B., D.C.G., K.N., E.L., A.M.-L., P.G.S., L.G.A., K.S.H., T.P., M.D., R.P., N.H.,
K.W., I.A., Ø.B., A.M.D., D.H., M.C., S.A., N.D., C. Depondt, M. Pandolfo, E.J.R.,
D.M.C., J.C.R., J.R., J.T., R.T., C.L., S.M., A.H., C.D.W., N.J., D.J.H., L.T.W. and
M. Hoogman. Local genetics processing, involving statistical analysis and analysis
of the data, was performed by J.L.S., S.E.M., A.A.V., A.M.W., D.P.H., M.B., A.A.B.,
A. Christoforou, G. Davies, J.-J.H., L.M.L., G.L., P.H.L., D.C.L., X.L., M. Mattingsdal,
K.N., E. Strengman, K.v.E., T.G.M.v.E., S.W., S.K., L.A., R.M.C., M.A.C., J.E.C., R.D.,
T.D.D., N.B.F., H.H.H.G., M.P.J., J.W.K., M. Mattheisen, E.K.M., T.W.M., M.M.N.,
M. Rietschel, V.M.S., A.W.T., J.A.V., S.C., S.D., T.M.F., P.H., S.L.H., G.W.M., O.A.A.,
H.G.B., R.A.O., B.W.P., A.J.S., L.S., J.B., D.C.G., M.J.W., N.G.M., A.L., E.B.B., C.W.,
B.P., B.M.-M., G.C., Z.P., G.H., M.N., A.T., D.K., M. Matarin, S.M.S., G.L.C., N.K.H.,
M.E.R., D.W.M., C.O., A. Corvin, M.G., J.F., J.C.R., A.R., M. Ryten, D.T., N.S., C.S.,
R.W., J. Hardy, M.E.W. and M.A.A.d.A. Local study oversight and management,
involving joint supervision of research, contribution of reagents, materials and/or
analysis tools, was carried out by R.L.B., R.D., P.T.F., R.S.K., I.M., R.L.O., I.R., I.A.,
W.C.D., P.H., F.M., A.M.-L., D.J.P., S.G.P., J.M.S., M.W.W., O.A.A., M.E.B., H.G.B.,
E.J.C.d.G., I.J.D., G.I.d.Z., T.E., G.F., H.E.H.P., F.J.M., K.L.M., R.A.O., T.P., Z.P.,
B.W.P., A.J.S., L.S., J.W.S., J.M.W., J.B., D.C.G., M.J.W., B.F., P.M.T., A.M.M.,
J. Hall, M. Papmeyer, E. Sprooten, J. Sussmann, S.M.L., J.B.P., L.G.A., G.C., D.R.,
E.M., G.S., K.S.H., P.G.S., E.B.B., D.I.B., H.J.G., H.V., K.A., C.M., G. Donohoe,
F.H., A.V.S., V.G., C.T., M.W.V., L.J.L., C. DeCarli, S.S., J.C.B., M.A.I., A.A. and J. Hardy.
COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
Published online at http://www.nature.com/naturegenetics/.
Reprints and permissions information is available online at http://www.nature.com/
reprints/index.html.
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Manon Bernard17, Andrew A Brown16,18, Dara M Cannon19, M Mallar Chakravarty21, Andrea Christoforou22,23,
Martin Domin24, Oliver Grimm25, Marisa Hollinshead26,27, Avram J Holmes26, Georg Homuth28,
Jouke-Jan Hottenga29, Camilla Langan20, Lorna M Lopez30,31, Narelle K Hansell2, Kristy S Hwang1,32,
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Morten Mattingsdal16,40, Sebastian Mohnke41, Susana Muñoz Maniega30,42,43, Kwangsik Nho33,44,
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Mina Ryten54, Li Shen33,34, Emma Sprooten48, Eric Strengman55,56, Alexander Teumer28, Daniah Trabzuni54,57,
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Michael Gill46,47, Harald H H Göring64, Donald J Hagler70, David Hoehn49, Florian Holsboer49,
Martine Hoogman5,7,71,72, Norbert Hosten24, Neda Jahanshad1, Matthew P Johnson64, Dalia Kasperaviciute73,
Jack W Kent Jr64, Peter Kochunov69,74, Jack L Lancaster69, Stephen M Lawrie48, David C Liewald30,
René Mandl15, Mar Matarin73, Manuel Mattheisen75–77, Eva Meisenzahl78, Ingrid Melle16,79, Eric K Moses64,
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G Bruce Pike83, Ralf Puls24, Ivar Reinvang84,85, Miguel E Rentería2,86, Marcella Rietschel25, Joshua L Roffman37,
Natalie A Royle30,42,43, Dan Rujescu78, Jonathan Savitz45,87, Hugo G Schnack15, Knut Schnell88,89,
Nina Seiferth41, Colin Smith90, Vidar M Steen22,23, Maria C Valdés Hernández30,42,43, Martijn Van den Heuvel15,
Nic J van der Wee59,60, Neeltje E M Van Haren15, Joris A Veltman5, Henry Völzke91, Robert Walker90,
Lars T Westlye84, Christopher D Whelan63, Ingrid Agartz16,92, Dorret I Boomsma29, Gianpiero L Cavalleri63,
Anders M Dale53,70, Srdjan Djurovic16,93, Wayne C Drevets45,87, Peter Hagoort7,52,72, Jeremy Hall48,
Andreas Heinz41, Clifford R Jack Jr94, Tatiana M Foroud34,95, Stephanie Le Hellard22,23, Fabio Macciardi51,
Grant W Montgomery2, Jean Baptiste Poline96, David J Porteous30,97, Sanjay M Sisodiya73, John M Starr30,98,
Jessika Sussmann48, Arthur W Toga1, Dick J Veltman62, Henrik Walter41,89, Michael W Weiner99–102,
the Alzheimer’s Disease Neuroimaging Initiative (ADNI)103, EPIGEN Consortium103, IMAGEN Consortium103,
Saguenay Youth Study Group (SYS)103, Joshua C Bis104, M Arfan Ikram105–107, Albert V Smith108,109,
Vilmundur Gudnason108,109, Christophe Tzourio110,111, Meike W Vernooij105–107, Lenore J Launer112,
Charles DeCarli113,114, Sudha Seshadri115,116, Cohorts for Heart and Aging Research in Genomic Epidemiology
(CHARGE) Consortium103, Ole A Andreassen16,79, Liana G Apostolova1,32, Mark E Bastin30,42,43,117,
John Blangero64, Han G Brunner5, Randy L Buckner26,27,37,68, Sven Cichon75,76,118, Giovanni Coppola32,119,
Greig I de Zubicaray86, Ian J Deary30,31, Gary Donohoe46,47, Eco J C de Geus29, Thomas Espeseth84,85,120,
Guillén Fernández7,52,71, David C Glahn8,9, Hans J Grabe13,121, John Hardy54, Hilleke E Hulshoff Pol15,
Mark Jenkinson122, René S Kahn15, Colm McDonald20, Andrew M McIntosh48, Francis J McMahon35,
Katie L McMahon123, Andreas Meyer-Lindenberg25, Derek W Morris46,47, Bertram Müller-Myhsok49,
Thomas E Nichols122,124, Roel A Ophoff15,65, Tomas Paus21, Zdenka Pausova17, Brenda W Penninx59,60,62,125,
Steven G Potkin51, Philipp G Sämann49, Andrew J Saykin33,34,95, Gunter Schumann39, Jordan W Smoller36,37,
Joanna M Wardlaw30,42,43, Michael E Weale50, Nicholas G Martin2,128, Barbara Franke5–7,128, Margaret J Wright2,128 &
Paul M Thompson1,128 for the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium126
1Laboratory
of Neuro Imaging, David Geffen School of Medicine, University of California, Los Angeles, California, USA. 2Genetic Epidemiology Laboratory, Queensland
Institute of Medical Research, Brisbane, Queensland, Australia. 3Quantitative Genetics Laboratory, Queensland Institute of Medical Research, Brisbane, Queensland,
Australia. 4Broad Institute of Harvard University and MIT, Cambridge, Massachusetts, USA. 5Department of Human Genetics, Radboud University Nijmegen Medical
Centre, Nijmegen, The Netherlands. 6Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. 7Donders Institute for
Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands. 8Olin Neuropsychiatry Research Center, Institute of Living, Hartford
Hospital, Hartford, Connecticut, USA. 9Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA. 10Laboratory of Human Genetics
and Cognitive Functions, Institut Pasteur, Paris, France. 11Centre Nationale de Recherche Scientifique (CNRS) Unité de Recherche Associée (URA) 2182 Genes,
Synapses and Cognition, Institut Pasteur, Paris, France. 12Department of Neuroscience, Université Paris Diderot, Sorbonne Paris Cité, Paris, France. 13Department of
Psychiatry and Psychotherapy, University of Greifswald, Greifswald, Germany. 14Institute of Psychology, Department of Clinical Psychology and Psychotherapy,
University of Heidelberg, Heidelberg, Germany. 15Department of Psychiatry, Rudolf Magnus Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
16Institute of Clinical Medicine, University of Oslo, Oslo, Norway. 17The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada. 18Institute of Basic
Medical Sciences, Department of Biostatistics, University of Oslo, Oslo, Norway. 19Clinical Neuroimaging Laboratory, Department of Anatomy, National University of
Ireland Galway, Galway, Ireland. 20Clinical Neuroimaging Laboratory, Department of Psychiatry, National University of Ireland Galway, Galway, Ireland. 21Rotman
Research Institute, University of Toronto, Toronto, Ontario, Canada. 22Dr Einar Martens Research Group for Biological Psychiatry, Department of Clinical Medicine,
University of Bergen, Bergen, Norway. 23Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway. 24Department of
Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany. 25Central Institute of Mental Health, University of Heidelberg–Medical
Faculty Mannheim, Mannheim, Germany. 26Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA. 27Howard
Hughes Medical Institute, Cambridge, Massachusetts, USA. 28Interfaculty Institute for Genetics and Functional Genomics, University of Greifswald, Greifswald,
Germany. 29Department of Biological Psychology, Neuroscience Campus Amsterdam, VU University, Amsterdam, The Netherlands. 30Centre for Cognitive Ageing and
Cognitive Epidemiology, The University of Edinburgh, Edinburgh, UK. 31Department of Psychology, The University of Edinburgh, Edinburgh, UK. 32Department of
Neurology, David Geffen School of Medicine, University of California, Los Angeles, California, USA. 33Department of Radiology and Imaging Sciences, Center for
Neuroimaging, Indiana University School of Medicine, Indianapolis, Indiana, USA. 34Center for Computational Biology and Bioinformatics, Indiana University
School of Medicine, Indianapolis, Indiana, USA. 35Mood and Anxiety Disorders Section, Human Genetics Branch, Intramural Research Program, National Institute
of Mental Health (NIMH), US National Institutes of Health (NIH), US Department of Health and Human Services, Bethesda, Maryland, USA. 36Psychiatric and
Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, USA. 37Department of Psychiatry,
Massachusetts General Hospital, Boston, Massachusetts, USA. 38Taub Institute for Research on Alzheimer Disease and the Aging Brain, Columbia University Medical
Center, New York, New York, USA. 39Medical Research Council (MRC)–Social, Genetic and Developmental Psychiatry (SGDP) Centre, Institute of Psychiatry, King’s
College London, London, UK. 40Research Unit, Sørlandet Hospital, Kristiansand, Norway. 41Department of Psychiatry and Psychotherapy, Charité-Universitaetsmedizin
Berlin, Campus Mitte, Berlin, Germany. 42Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, UK. 43Brain Research
Imaging Centre, The University of Edinburgh, Edinburgh, UK. 44Division of Medical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA. 45Section on
Neuroimaging in Mood and Anxiety Disorders, Intramural Research Program, NIMH, NIH, US Department of Health and Human Services, Bethesda, Maryland, USA.
46Neuropsychiatric Genetics Research Group, Department of Psychiatry, Institute for Molecular Medicine, Trinity College, Dublin, Ireland. 47Trinity College Institute of
Neuroscience, Trinity College, Dublin, Ireland. 48Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK. 49Max Planck Institute of
Psychiatry, Munich, Germany. 50Department of Medical & Molecular Genetics, King’s College London, London, UK. 51Department of Psychiatry and Human
Behavior, University of California, Irvine, California, USA. 52Donders Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, The Netherlands.
53Department of Neurosciences, University of California, San Diego, La Jolla, California, USA. 54Department of Molecular Neuroscience, University College London,
London, UK. 55Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands. 56Rudolf Magnus Institute, University Medical Center
Utrecht, Utrecht, The Netherlands. 57Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia. 58Mind Research Network,
Albuquerque, New Mexico, USA. 59Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands. 60Leiden Institute for Brain and Cognition,
Leiden University, Leiden, The Netherlands. 61Behavioural and Cognitive Neuroscience Neuroimaging Center, University Medical Center Groningen, Groningen,
560
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The Netherlands. 62Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands. 63Department of Molecular and Cellular Therapeutics,
Royal College of Surgeons in Ireland, Dublin, Ireland. 64Department of Genetics, Texas Biomedical Research Institute, San Antonio, Texas, USA. 65Center for
Neurobehavioral Genetics, University of California, Los Angeles, USA. 66Division of Neurology, Beaumont Hospital, Dublin, Ireland. 67Department of Neurology,
Hopital Erasme, Universite Libre de Bruxelles, Brussels, Belgium. 68Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital,
Boston, Massachusetts, USA. 69Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA. 70Department of
Radiology, University of California, San Diego, La Jolla, California, USA. 71Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre,
The Netherlands. 72Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands. 73Department of Clinical and Experimental Epilepsy, University College
London, Institute of Neurology, London, UK. 74Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore,
Maryland, USA. 75Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany. 76Institute of Human Genetics, University of Bonn, Bonn,
Germany. 77Institute for Genomic Mathematics, University of Bonn, Bonn, Germany. 78Department of Psychiatry, Ludwig-Maximilians-University (LMU), Munich,
Germany. 79Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway. 80Institute of Clinical Chemistry and Laboratory Medicine, University of
Greifswald, Greifswald, Germany. 81German Center for Neurodegenerative Disorders (DZNE), Bonn, Germany. 82Department of Psychiatry, University of Texas Health
Science Center at San Antonio, San Antonio, Texas, USA. 83Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada. 84Center for the Study of
Human Cognition, Department of Psychology, University of Oslo, Oslo, Norway. 85Centre for Advanced Study, Oslo, Norway. 86School of Psychology, University of
Queensland, Brisbane, Queensland, Australia. 87Laureate Institute for Brain Research, Tulsa, Oklahoma, USA. 88Department of General Psychiatry, Heidelberg
University Hospital, University of Heidelberg, Heidelberg, Germany. 89Department of Psychiatry, Division of Medical Psychology, Bonn, Germany. 90The MRC Sudden
Death Tissue Bank in Edinburgh, Department of Pathology, University of Edinburgh, Edinburgh, UK. 91Institute for Community Medicine, University of Greifswald,
Greifswald, Germany. 92Department of Research and Development, Diakonhjemmet Hospital, Oslo, Norway. 93Department of Medical Genetics, Oslo University
Hospital, Oslo, Norway. 94Aging and Dementia Imaging Research Laboratory, Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota, USA.
95Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA. 96Neurospin, Institut d’Imagerie Biomédicale
(I2BM), Commissariat à l’Energie Atomique, Gif-sur-Yvette, France. 97Medical Genetics Section, Molecular Medicine Centre, Institute of Genetics and Molecular
Medicine, The University of Edinburgh, Western General Hospital, Edinburgh, UK. 98Geriatric Medicine Unit, The University of Edinburgh, Royal Victoria Hospital,
Edinburgh, UK. 99Departments of Radiology, University of California, San Francisco, California, USA. 100Department of Medicine, University of California, San
Francisco, California, USA. 101Department of Psychiatry, University of California, San Francisco, California, USA. 102Veterans Affairs Medical Center, San Francisco,
California, USA. 103A full list of members is provided in the Supplementary Note. 104Cardiovascular Health Research Unit, Department of Medicine, University of
Washington, Seattle, Washington, USA. 105Department of Epidemiology, Erasmus Medical Center University Medical Center, Rotterdam, The Netherlands.
106Department of Radiology, Erasmus Medical Center University Medical Center, Rotterdam, The Netherlands. 107Netherlands Consortium for Healthy Aging, Leiden,
The Netherlands. 108Icelandic Heart Association, Kopavogur, Iceland. 109Faculty of Medicine, University of Iceland, Reykjavik, Iceland. 110University of Bordeaux,
U708, Bordeaux, France. 111Institut National de la Santé et la Recherche Médicale (INSERM), Neuroepidemiology, U708, Bordeaux, France. 112Laboratory of
Epidemiology, Demography, and Biometry, NIH, Bethesda, Maryland, USA. 113Department of Neurology, University of California, Davis, Sacramento, California, USA.
114Center of Neuroscience, University of California, Davis, Sacramento, California, USA. 115Department of Neurology, Boston University School of Medicine, Boston,
Massachusetts, USA. 116National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA. 117Division of Health Sciences
(Medical Physics), The University of Edinburgh, Edinburgh, UK. 118Institute for Neuroscience and Medicine (INM-1), Research Center Juelich, Juelich, Germany.
119Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, California, USA. 120Department
of Biological and Medical Psychology, Faculty of Psychology, University of Bergen, Bergen, Norway. 121German Center for Neurodegenerative Diseases (DZNE),
Rostock/Greifswald, Greifswald, Germany. 122Functional Magnetic Resonance Imaging of the Brain (FMRIB) Centre, Oxford University, Oxford, UK. 123Centre for
Advanced Imaging, University of Queensland, Brisbane, Queensland, Australia. 124Department of Statistics, University of Warwick, Coventry, UK. 125Department of
Psychiatry, University Medical Center Groningen, Groningen, The Netherlands. 126Information on the consortium is provided in the Supplementary Note. 127These
authors contributed equally to this work. 128These authors jointly directed this work. Correspondence should be addressed to P.M.T. (thompson@loni.ucla.edu).
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ONLINE METHODS
All participants provided written informed consent, and studies were approved
by the respective Local Research Ethics committees or Institutional Review
Boards. MRI scans came from previously collected data. Suggested protocols
for imaging analysis are publicly available on the ENIGMA Consortium web
site (see URLs); however, any validated segmentation software was permitted.
Accuracy of segmentation programs is influenced by scanner and head-coil
type and scanner sequences and by participant characteristics, such as age. Each
site was permitted to use any validated automated segmentation algorithm that
worked most accurately in their data set. The two most commonly used hippo
campal segmentation packages were the FMRIB’s Integrated Registration and
Segmentation Tool (FIRST)16 from the FMRIB Software Library (FSL) package
of tools43 and FreeSurfer17. Brain volume, the sum of gray and white matter
excluding ventricles and cerebrospinal fluid (CSF), was calculated using the
FSL FMRIB’s Automated Segmentation Tool (FAST)44 package or FreeSurfer.
Estimated total intracranial volume was calculated through registration of each
MRI scan to a standard brain image template18, using either FSL FLIRT45 or
FreeSurfer (exceptions referenced in Supplementary Table 2). To calculate
intracranial volume, the inverse of the determinant of the transformation
matrix was multiplied by the template volume (1,948,105 mm3). Extensive
quality control analysis on phenotype segmentations included manual exami
nation of phenotype volume histograms (Supplementary Figs. 2–6) and box
plots of all volumetric measures. Outliers were manually evaluated by over
laying the automated segmentations on the original MRI scan. Subjects were
excluded from the analysis if structures were poorly segmented.
As assessed previously, the correlation in volumes between automatic and
manually segmented hippocampi was high; the accuracy was reported to be
higher with FreeSurfer than with FIRST in one study (FreeSurfer r = 0.82;
FIRST r = 0.66)46 and similar between the two in another (FreeSurfer
r = 0.73; FIRST r = 0.71)47. Scan-rescan reliability was also high for both
methods (FreeSurfer intraclass correlation (ICC) = 0.98; FIRST ICC = 0.93)48.
We undertook a large-scale assessment to determine the correspondence
between segmentations from both FSL and FreeSurfer in the same subjects.
Correspondence was found to be reasonably high for average bilateral hippo
campal segmentation (r = 0.75; N = 6,093; Supplementary Table 4). This is
close to the agreement between different human raters, as quantified by interrater reliability (ICC = 0.73–0.85)49,50, which may be a reasonable upper bound
on the accuracy of automated segmentation. Brain volume and intracranial
volume were delineated with high correspondence between the two methods
(r = 0.95, r = 0.90, respectively; N = 4,321).
Heritability estimates for trait measures were calculated in two family-based
samples, QTIM and GOBS. Estimates for the QTIM sample used a twin and
sibling analysis within Mx. An extended family analysis in Sequential Oligogenic
Linkage Analysis Routines (SOLAR)51 was used for the GOBS sample.
Given sample size and the heritability of hippocampal volume, power calcu
lations were performed using the Genetic Power Calculator52. We had 99.92%
power to detect variants with effect sizes of 1% of the variance and 71.16% power
to detect variants with effect sizes of 0.5% of the variance for MAF ≥ 0.05.
All cohorts were genotyped using commercially available arrays. Genetics
protocols were developed to standardize the filtering, imputation and asso
ciation of genome-wide genotype data (see ENIGMA protocols in URLs).
SNPs were filtered out of samples on the basis of standard quality control
criteria, including low MAF (<0.01), poor genotype calling (call rate of <95%)
and deviations from Hardy-Weinberg equilibrium indicating possible errors
in genotyping (P < 1 × 10−6). Genotyping methods and exceptions to these
thresholds are summarized in Supplementary Table 3.
Genetic homogeneity within each sample was assessed through MDS plots
(Supplementary Fig. 7). Ancestry outliers were excluded through visual
inspection. A standardized population template from HapMap 3 represent
ing those sampled was selected for imputation. Performance of software for
imputation is generally similar between the most used methods53,54 for com
mon variants (MACH55, IMPUTE56 and BEAGLE57); the protocols provided
included use of the MACH tool. As raw genotype data were not directly trans
ferred to the meta-analysis site, a histogram of allele frequency differences
between each contributing group and the HapMap 3 CEU population was gen
erated for each group (Supplementary Fig. 8) to further examine genotyping
and imputation quality. A simulation to determine the effect of varying quality
Nature Genetics
control thresholds on imputation quality (Supplementary Table 6) showed
that the minor variation in quality control thresholds and imputation reference
panels between sites was unlikely to have influenced imputation accuracy.
Genome-wide association analyses were performed that included and
excluded individuals with disease. Including individuals with disease (all
subjects) offers advantages of greater sample size and wider phenotype dis
tribution, which may provide greater power to detect genetic effects58–60. We
reanalyzed phenotypes after we excluded individuals with disease to confirm
that the observed associations were not due to confounds relating to disease,
medication or the possibly altered environments and experiences of these
persons. To aid in the interpretation of results, we reanalyzed hippocampal
volume after controlling for intracranial volume and total brain volume in two
separate analyses. This helped to determine whether the observed associations
were caused by direct effects on hippocampal volume or were attributable
to more global associations with head size. In addition, genome-wide asso
ciation analyses of intracranial volume and brain volume were conducted in
the healthy controls to clarify whether observed associations were specific to
hippocampal volume or influenced brain size in general. Participating sites
were asked to conduct five genome-wide association analyses (three analyses
of hippocampal volume, intracranial volume and brain volume). In addi
tion, cohorts with groups of individuals with disease were asked to perform
hippocampal analyses including data from these individuals.
Evidence for association was assessed using the allelic dosage of each SNP
(accounting for familial relationships in the GOBS, QTIM and SYS samples).
SNP-derived covariates were tested as fixed effects, while explicitly modeling
the genetic relationships between family members in these pedigree-based
studies51,61,62. Analyses used multiple linear regression with the phenotype
of interest as a dependent variable and the additive dosage of each SNP as an
independent variable of interest, controlling for covariates of population strati
fication (four MDS components), age, age2, sex and the interactions between
age and sex and age2 and sex. Dummy covariates were used to control for dif
ferent scanner sequences or equipment within a site when needed. We refer to
these covariates as ‘other covariates’, and these were included in all analyses.
The extensive regression model was used to statistically control for factors
known to affect hippocampal volume that are not specific genetic influences.
Recommended protocols for association were provided to the studies based
on those used in mach2qtl software (see ENIGMA protocols).
To combine information from multiple studies, we generated a secure webaccessible upload site for participants to upload their association results. An
automated system parsed the uploaded results files (see URLs). This parser
was designed to read raw results files from a variety of association software
packages (mach2qtl, PLINK, SOLAR, SNPTEST, QUICKTEST, Merlin-offline
and ProbABEL), perform a series of tests on the incoming data to ensure
quality, correctly assign the effect allele (dependent on both the imputation
and association programs used) and correctly scale the β values and standard
errors from association into the same units. Quality control was performed
on imputed SNPs to filter out SNPs with low frequency (MAF of <0.01) or
poor imputation quality (estimated R2 of <0.3). Result files and summary
statistics from each group were pooled for meta-analysis. Meta-analysis was
undertaken for each SNP across all groups based on a fixed-effects model using
an inverse standard error–weighted meta-analysis protocol implemented in
METAL19. Genomic control was applied at the level of each study and at the
meta-analysis level to adjust for population stratification or cryptic relatedness
not accounted for by MDS components63. To account for heterogeneity across
samples, a random-effects meta-analysis20 was also conducted via the pro
gram METASOFT without using genomic control. Using KGG64 we performed
gene-based tests on the double genome–controlled meta-analysis results, using
the extended Simes test24 to obtain an overall P value for association of the
entire gene with a 50-kb boundary on either side. Results from genes with
P ≤ 1 × 10−4 are presented (Supplementary Tables 18–25).
Meta-analysis was performed separately on the discovery sample, the
CEU and TSI replication sample and the CEU and YRI or MEX replication
sample. These results were then pooled to form the combined meta-analysis
statistics for discovery and replication. The in silico replication results from
the CHARGE Consortium were added to this, and a final meta-analysis was
conducted. The location of Manhattan and quantile-quantile plots is specified
in Supplementary Table 27.
doi:10.1038/ng.2250
© 2012 Nature America, Inc. All rights reserved.
npg
To appropriately account for the multiple comparisons conducted, we first
sought to determine the effective number of independent phenotypes among
the eight highly correlated genome-wide association analyses. This was cal
culated by creating an 8 × 8 matrix derived from cross-correlations of metaanalytic t statistics of association for each SNP across phenotypes. The result
ing correlation matrix provided an estimate of the similarity between pheno
types after adjusting for covariates of interest and appropriately controlling for
family structure. The effective number of tests was then calculated by summing
eigenvalues of the correlation matrix, weighted by a formula that appropriately
controls false positive rates in simulation65. The effective number of tests was
determined to be 4 and an overall genome-wide significance threshold of
5 × 10−8/4 = 1.25 × 10−8 was used throughout the manuscript.
Regulatory potential of SNPs identified in the genome-wide association
analysis was examined in three samples. In the UCL epilepsy cohort, tissue
was obtained from resection material from affected individuals who had
undergone surgery for drug-resistant mesial temporal lobe epilepsy with
hippocampal sclerosis, according to established clinical protocols. Total RNA
from the middle temporal cortex (Brodmann areas 20 and 21) from 86 sub
jects was isolated and randomly hybridized to Affymetrix Human Exon 1.0
ST arrays, and quality control analysis was performed using standard meth
ods. The effects of several methodological (day of expression hybridization,
RNA integrity number (RIN)) and biological covariates (sex, age and medica
tion) on exon–gene expression relationships were tested for significance. Of
these individuals, 71 had participated in a published epilepsy genome-wide
association study, and, therefore, genotyping data were available. Details of
sample collection and genotyping quality control steps have been published
previously66. These samples were assayed with Illumina HumanHap550v3
(N = 44) and Illumina Human610-Quadv1 (N = 27) arrays.
In the UK Brain Expression database, post-mortem brain tissues from 134
individuals free from neurological disorders were obtained from the MRC
Sudden Death Brain Bank in Edinburgh and Sun Health Research Institute67.
Genotype information was obtained using Illumina HumanOmni 1M arrays
and standard quality control methods. Expression profiling was conducted in
up to ten separate brain regions for each individual brain using the Affymetrix
GeneChip Human Exon 1.0 ST array. Expression levels were normalized using
the Robust Multi-array Analysis (RMA) algorithm restricting to probe sets
containing more than three probes, unique hybridization target (probes that
map to a single position within the genome) and supported by evidence from
EntrezGene. The average signals for all neocortex (AvgCTX) and all brain
regions (AvgAll) were tested, as were individual cortical and subcortical
regions. Any significant association where the probe set contained the SNP
or a SNP in high LD (r2 > 0.50) was removed from further analysis.
SNPExpress, a publically available database, was also used for replication of
the findings. The SNPExpress database68 used autopsy-collected frontal cortex
brain tissue in 93 samples from human subjects with no neuropsychiatric con
ditions and PBMCs in 80 samples. In this database, transcript expression levels
were measured on Affymetrix Human ST 1.0 exon arrays, and genome-wide
genotyping was performed using Illumina HumanHap550K arrays.
Raw gene expression data from human fetal brain were gathered from a
published study42. Post-mortem specimens from four late mid-fetal human
brains (18, 19, 21 and 23 weeks of gestation) were collected from the Human
Fetal Tissue Repository at the Albert Einstein College of Medicine. Details
of specimens, tissue processing, microdissection and neuropathological
assessment have been described elsewhere42.
doi:10.1038/ng.2250
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