Common genetic variants influence human subcortical brain
structures
A full list of authors and affiliations appears at the end of the article.
Abstract
The highly complex structure of the human brain is strongly shaped by genetic influences
1
.
Subcortical brain regions form circuits with cortical areas to coordinate movement
2
, learning,
memory
3
and motivation
4
, and altered circuits can lead to abnormal behaviour and disease
2
. To
investigate how common genetic variants affect the structure of these brain regions, here we
conduct genome-wide association studies of the volumes of seven subcortical regions and the
intracranial volume derived from magnetic resonance images of 30,717 individuals from 50
cohorts. We identify five novel genetic variants influencing the volumes of the putamen and
caudate nucleus. We also find stronger evidence for three loci with previously established
influences on hippocampal volume
5
and intracranial volume
6
. These variants show specific
volumetric effects on brain structures rather than global effects across structures. The strongest
effects were found for the putamen, where a novel intergenic locus with replicable influence on
volume (rs945270; P = 1.08 × 10
−33
; 0.52% variance explained) showed evidence of altering the
expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume
clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport.
Identification of these genetic variants provides insight into the causes of variability inhuman
brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.
At the individual level, genetic variations exert lasting influences on brain structures and
functions associated with behaviour and predisposition to disease. Within the context of the
Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, we
conducted a collaborative large-scale genetic analysis of magnetic resonance imaging (MRI)
scans to identify genetic variants that influence brain structure. Here, we focus on
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Correspondence and requests for materials should be addressed to: P.M.T. (pthomp@usc.edu) or S.E.M.
(Sarah.Medland@qimrberghofer.edu.au).
*
These authors contributed equally to this work.
§
These authors jointly supervised this work.
†
A list of authors and affiliations appears in the Supplementary Information.
Online Content Methods, along with any additional Extended Data display items and Source Data, are available in the online version
of the paper; references unique to these sections appear only in the online paper.
Supplementary Information is available in the online version of the paper.
Author Contributions Individual author contributions are listed in Supplementary Note 4.
Summary statistics from GWAS results are available online using the ENIGMA-Vis web tool: http://enigma.ini.usc.edu/enigma-vis/.
The authors declare no competing financial interests.
Readers are welcome to comment on the online version of the paper.
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Published in final edited form as:
Nature. 2015 April 9; 520(7546): 224–229. doi:10.1038/nature14101.
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volumetric measures derived from a measure of head size (intracranial volume, ICV) and
seven subcortical brain structures corrected for the ICV (nucleus accumbens, caudate,
putamen, pallidum, amygdala, hippocampus and thalamus). To ensure data homogeneity
within the ENIGMA consortium, we designed and implemented standardized protocols for
image analysis, quality assessment, genetic imputation (to 1000 Genomes references,
version 3) and association (Extended Data Fig. 1 and Methods).
After establishing that the volumes extracted using our protocols were substantially heritable
in a large sample of twins (P < 1 × 10
−4
; see Methods and Extended Data Fig. 11a), with
similar distributions to previous studies
1
, we sought to identify common genetic variants
contributing to volume differences by meta-analysing site-level genome-wide association
study (GWAS) data in a discovery sample of 13,171 subjects of European ancestry
(Extended Data Fig. 2). Population stratification was controlled for by including, as
covariates, four population components derived from standardized multidimensional scaling
analyses of genome-wide genotype data conducted at each site (see Methods). Site-level
GWAS results and distributions were visually inspected to check for statistical inflation and
patterns indicating technical artefacts (see Methods).
Meta-analysis of the discovery sample identified six genome-wide significant loci after
correcting for the number of variants and traits analysed (P < 7.1 × 10
−9
; see Methods): one
associated with the ICV, two associated with hippocampal volume, and three with putamen
volume. Another four loci showed suggestive associations (P < 1 × 10
−7
) with putamen
volume (one locus), amygdala volume (two loci), and caudate volume (one locus; Table 1,
Fig. 1 and Supplementary Table 5). Quantile–quantile plots showed no evidence of
population stratification or cryptic relatedness (Extended Data Fig. 4a). We subsequently
attempted to replicate the variants with independent data from 17,546 individuals. All
subcortical genome-wide significant variants identified in the discovery sample were
replicated (Table 1). The variant associated with the ICV did not replicate in a smaller
independent sample, but was genome-wide significant in a previously published independent
study
6
, providing strong evidence for its association with the ICV. Moreover, two
suggestive variants associated with putamen and caudate volumes exceeded genome-wide
significance after meta-analysis across the discovery and replication data sets (Table 1).
Effect sizes were similar across cohorts (P > 0.1, Cochran’s Q test; Extended Data Fig. 4b).
Effect sizes remained consistent after excluding patients diagnosed with anxiety,
Alzheimer’s disease, attention-deficit/hyperactivity disorder, bipolar disorder, epilepsy,
major depressive disorder or schizophrenia (21% of the discovery participants). Correlation
in effect size with and without patients was very high (r > 0.99) for loci with P < 1 × 10
−5
,
indicating that these effects were unlikely to be driven by disease (Extended Data Fig. 5a).
The participants’ age range covered most of the lifespan (9–97 years), but only one of the
eight significant loci showed an effect related to the mean age of each cohort (P = 0.002;
rs6087771 affecting putamen volume; Extended Data Fig. 5b), suggesting that nearly all
effects are stable across the lifespan. In addition, none of these loci showed evidence of sex
effects (Extended Data Fig. 5c).
In our cohorts, significant loci were associated with 0.51–1.40% differences in volume per
risk allele, explaining 0.17–0.52% of the phenotypic variance (Table 1); such effect sizes are
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similar to those of common variants influencing other complex quantitative traits such as
height
7
and bodymass index
8
. The full genome-wide association results explained 7–15% of
phenotypic variance after controlling for the effects of covariates (Extended Data Fig. 11).
Notably, the genome-wide significant variants identified here showed specific effects on
single brain structures rather than pleiotropic effects across multiple structures, despite
similar developmental origins as in the case of caudate and putamen (Extended Data Fig.
6a). Nevertheless, when we subjected the subcortical meta-analysis results to hierarchical
clustering, genetic determinants of the subcortical structures were mostly grouped into larger
circuits according to their developmental and functional subdivisions (Extended Data Fig.
6b). Genetic variants may therefore have coherent effects on functionally associated
subcortical networks. Multivariate cross-structure
9
analyses confirmed the univariate results,
but no additional loci reached genome-wide significance (Extended Data Fig. 6c). The
clustering of results into known brain circuits in the absence of individually significant
genetic variants found in the cross-structure analysis suggests variants of small effect may
have similar influences across structures. Most variants previously reported to be associated
with brain structure and/or function showed little evidence of large-scale volumetric effects
(Supplementary Table 8). We detected an intriguing association with hippocampal volume
at a single nucleotide polymorphism (SNP) with a genome-wide significant association with
schizophrenia
10
(rs2909457; P = 2.12 × 10
−6
; where the A allele is associated with
decreased risk for schizophrenia and decreased hippocampal volume). In general, however,
we detected no genome-wide significant association with brain structure for genome-wide
significant loci that contribute risk for neuropsychiatric illnesses (Supplementary Table 9).
Of the four loci influencing putamen volume, we identified an inter-genic locus 50 kilobases
(kb) downstream of the KTN1 gene (rs945270; 14q22.3; n = 28,275; P = 1.08 × 10
−33
),
which encodes the protein kinectin, a receptor that allows vesicle binding to kinesin and is
involved in organelle transport
11
. Second, we identified an intronic locus within DCC
(rs62097986; 18q21.2; n = 28,036; P = 1.01 × 10
−13
), which encodes a netrin receptor
involved in axon guidance and migration, including in the developing striatum
12
(Extended
Data Fig. 3b). Expression of DCC throughout the brain is highest in the first two trimesters
of prenatal development
13
(Extended Data Fig. 8b), suggesting that this variant may
influence brain volumes early in neurodevelopment. Third, we identified an intronic locus
within BCL2L1 (rs6087771; 20q11.21; n = 25,540; P = 1.28 × 10
−12
), which encodes an
anti-apoptotic factor that inhibits programmed cell death of immature neurons throughout
the brain
14
(Extended Data Fig. 3c). Consistent with this, expression of BCL2L1 in the
striatum strongly decreases at the end of neurogenesis (24–38 post-conception weeks
(PCW); Extended Data Fig. 8c), a period marked by increased apoptosis in the putamen
13,15
.
Fourth, we identified an intronic locus within DLG2 (rs683250; 11q14.1; n = 26,258; P =
3.94 × 10
−11
), which encodes the postsynaptic density 93 (PSD-93) protein (Extended Data
Fig. 3d). PSD-93 is a membrane-associated guanylate kinase involved in organizing
channels in the postsynaptic density
16
. DLG2 expression increases during early mid-fetal
development in the striatum
13
(Extended Data Fig. 8d). Genetic variants in DLG2 affect
learning and cognitive flexibility
17
and are associated with schizophrenia
18
. Notably, SNPs
associated with variation in putamen volume showed enrichment of genes involved in
apoptosis and axon guidance pathways (Extended Data Fig. 7 and Supplementary Table 7).
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Hippocampal volume showed an intergenic association near the HRK gene (rs77956314;
12q24.22; n = 17,190; P = 2.82 × 10
−15
; Extended Data Fig. 3g) and with an intronic locus
in the MSRB3gene (rs61921502; 12q14.3; n = 16,209; P = 6.87× 10
−11
; Extended Data Fig.
3h), supporting our previous analyses
5,19
of smaller samples imputed to HapMap3
references. Caudate volume was associated with an intergenic locus 80 kb from FAT3
(rs1318862; 11q14.3; n = 15,031; P = 6.17 × 10
−9
; Extended Data Fig. 3e). This gene
encodes a cadherin specifically expressed in the nervous system during embryonic
development that influences neuronal morphology through cell–cell interactions
20
. The ICV
was associated with an intronic locus within CRHR1 that tags the chromosome 17q21
inversion
21
, which has been previously found to influence ICV
6
(rs17689882; 17q21.31; n =
12,822; P = 7.72 × 10
−9
; Extended Data Fig. 3f). Another previously identified variant with
association to ICV (rs10784502)
5,19
did not survive genome-wide significance in this
analysis but did show a nominal effect in the same direction (P = 2.05 × 10
−3
; n = 11,373).
None of the genome-wide significant loci in this study were in linkage disequilibrium with
known functional coding variants, splice sites, or 3′/5′ untranslated regions, although several
of the loci had epigenetic markings suggesting a regulatory role (Extended Data Fig. 3).
Given the strong association with putamen volume, we further examined the rs945270 locus.
Epigenetic markers suggest insulator functionality near the locus as this is the lone
chromatin mark in the intergenic region
22
(Extended Data Fig. 3a). Chromatin
immunoprecipitation followed by sequencing (ChIP-seq) indicate that a variant (rs8017172)
in complete linkage disequilibrium with rs945270 (r
2
= 1.0) lies within a binding site of the
CTCF (CCCTC-binding factor) transcription regulator
23
(Extended Data Fig. 9) in
embryonic stem cells. To assess potential functionality in brain tissue, we tested for
association with gene expression 1 megabase (Mb) up/downstream. We identified and
replicated an effect of rs945270 on the expression of the KTN1 gene. The C allele,
associated with larger putamen volume, also increased expression of KTN1 in the frontal
cortex (discovery sample: 304 neuropathologically normal controls
24
(P = 4.1 × 10
−11
);
replication sample: 134 neuropathologically normal controls (P = 0.025)), and putamen
(sample: 134 neuropathologically normal controls
25
(P = 0.049); Fig. 2a, b). In blood,
rs945270 was also strongly associated with KTN1 expression
26
(P = 5.94 × 10
−31
; n =
5,311). After late fetal development, KTN1 is expressed in the human thalamus, striatum and
hippocampus; it is more highly expressed in the striatum than the cortex
13
(Extended Data
Fig. 8a). KTN1 encodes the kinectin receptor facilitating vesicle binding to kinesin, and is
heavily involved in organelle transport
11
. Kinectin is only found in the dendrites and soma
of neurons, not their axons; neurons with more kinectin have larger cell bodies
27
, and
kinectin knockdown strongly influences cell shape
28
. The volumetric effects identified here
may therefore reflect genetic control of neuronal cell size and/or dendritic complexity. Using
three-dimensional surface models of putamen segmentations in MRI scans of 1,541 healthy
adolescent subjects, we further localized the allelic effects of rs945270 to regions along the
superior and lateral putamen bilaterally, independent of chosen segmentation protocol (Fig.
2c and Extended Data Fig. 10). Each copy of the C allele was associated with an increase in
volume along anterior superior regions receiving dense cortical projections from dorsolateral
prefrontal cortex and supplementary motor areas
29,30
.
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In summary, we discovered several common genetic variants underlying variation in
different structures within the human brain. Many seem to exert their effects through known
developmental pathways including apoptosis, axon guidance and vesicle transport. All
structure volumes showed high heritability, but individual genetic variants had diverse
effects. The strongest effects were found for putamen and hippocampal volumes, whereas
other structures delineated with similar reliability such as the thalamus showed no
association with these or other loci (Supplementary Table 4). Discovery of common variants
affecting the human brain is now feasible using collaborative analysis of MRI data, and may
determine genetic mechanisms driving development and disease.
METHODS
Details of the GWAS meta-analysis are outlined in Extended Data Fig. 1. All participants in
all cohorts in this study gave written informed consent and sites involved obtained approval
from local research ethics committees or Institutional Review Boards. The ENIGMA
consortium follows a rolling meta-analysis framework for incorporating sites into the
analysis. The discovery sample comprises studies of European ancestry (Extended Data Fig.
2) that contributed GWAS summary statistics for the purpose of this analysis on or before 1
October 2013. The deadline for discovery samples to upload their data was made before
inspecting the data and was not influenced by the results of the analyses. The meta-analysed
results from discovery cohorts were carried forward for secondary analyses and functional
validation studies. Additional samples of European ancestry were gathered to provide in
silico or single genotype replication of the strongest associations as part of the replication
sample. A generalization sample of sites with non-European ancestry was used to examine
the effects across ethnicities. In all, data were contributed from 50 cohorts, each of which is
detailed in Supplementary Tables 1–3.
The brain measures examined in this study were obtained from structural MRI data collected
at participating sites around the world. Brain scans were processed and examined at each site
locally, following a standardized protocol procedure to harmonize the analysis across sites.
The standardized protocols for image analysis and quality assurance are openly available
online (http://enigma.ini.usc.edu/protocols/imaging-protocols/). The subcortical brain
measures (nucleus accumbens, amyg-dala, caudate nucleus, hippocampus, pallidum,
putamen and thalamus) were delineated in the brain using well-validated, freely available
brain segmentation software packages: FIRST
31
, part of the FMRIB Software Library
(FSL), or FreeSurfer
32
. The agreement between the two software packages has been well
documented in the literature
5,33
and was further detailed here (Supplementary Table 4).
Participating sites used the software package most suitable for their data set (the software
used at each site is given in Supplementary Table 2) without selection based on genotype or
the associations present in this study. In addition to the subcortical structures of the brain,
we examined the genetic effects of a measure of global head size, the ICV. The ICV was
calculated as: 1/(determinant of a rotation-translation matrix obtained after affine
registration to a common study template and multiplied by the template volume (1,948,105
mm
3
)). After image processing, each image was inspected individually to identify poorly
segmented structures. Each site contributed histograms of the distribution of volumes for the
left and right hemisphere structures (and a measure of asymmetry) of each subcortical region
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