Meta-analysis of genome-wide SNP- and pathway-based associations for facets of neuroticism
Journal of Human Genetics
Meta-analysis of genome-wide SNP- and pathway- based associations for facets of neuroticism
Song E Kim 0
Han-Na Kim 0
Yeo-Jun Yun 0
Seong Gu Heo 0
Juhee Cho 1 2 3
Min-Jung Kwon 1 4
Yoosoo Chang 1 5
Seungho Ryu 1 5
Hocheol Shin 6
Chol Shin 7
Nam H Cho 8
Yeon Ah Sung 9
Hyung-Lae Kim 0
0 Department of Biochemistry, School of Medicine, Ewha Womans University , Seoul , Korea
1 Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University , Seoul , Korea
2 Biostatistics and Clinical Epidemiology Center, Research Institute for Future Medicine, Samsung Medical Center, School of Medicine, Sungkyunkwan University , Seoul , Korea
3 Department of Health, Behavior and Society and Epidemiology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD , USA
4 Department of Laboratory Medicine, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University , Seoul , Korea
5 Department of Occupational Medicine, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University , Seoul , Korea
6 Department of Family Medicine and Health Screening Center, Kangbuk Samsung Hospital, School of Medicine, Sungkyunkwan University , Seoul , Korea
7 Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Korea University Ansan Hospital , Ansan , Korea
8 Department of Preventive Medicine, School of Medicine, Ajou University , Suwon , Korea
9 Department of Internal Medicine, School of Medicine, Ewha Womans University , Seoul, Korea Anyangcheon-ro, Yangcheon-Gu, Seoul 158-710 , Korea
Neuroticism is a heritable personality trait that is comprised of distinct sub-factors, or facets. Sub-factors of neuroticism are linked to different emotional states or psychiatric symptoms and studying the genetic variants associated with these facets may help reveal the biological mechanisms underlying psychiatric disorders. In the present study, a meta-analysis of genome-wide association studies for six facets of neuroticism was performed in 5584 participants from three cohorts. Additionally, a Gene Set Enrichment Analysis was conducted to find biological pathways associated with each facet. Six neuroticism facets (N1: anxiety, N2: angry hostility, N3: depression, N4: self-consciousness, N5: impulsivity and N6: vulnerability) were assessed using the Korean version of the Revised NEO Personality Inventory. In the single-nucleotide polymorphism-based analysis, results showed genome-wide significance for N2 within the MIR548H3 gene (rs1360001, P = 4.14 ? 10 ? 9). Notable genes with suggestive associations (Po1.0 ? 10 ? 6) were ITPR1 for N1, WNT7A for N2, FGF10 and FHIT for N3, DDR1 for N4, VGLL4 for N5 and PTPRD for N6. In the pathway-based analysis, the axon guidance pathway was identified to be associated with multiple facets of neuroticism (N2, N4 and N6). The focal adhesion and extracellular matrix receptor interaction pathways were significantly associated with N2 and N3. Our findings revealed genetic influences and biological pathways that are associated with facets of neuroticism. Journal of Human Genetics (2017) 62, 903-909; doi:10.1038/jhg.2017.61; published online 15 June 2017
Certain personality traits have been associated with risk factors of
psychological illnesses. One example is neuroticism, which is a
personality trait characterized by a tendency toward various negative
emotions such as sadness, fear and anger.1 A number of studies
examining the association between personality and psychiatric
illness have shown that neuroticism is strongly linked with anxiety,
mood and substance use disorders.2 Additionally, neuroticism is a
well-known predictive factor of the onset and prognosis of depressive
symptoms.3 The five factor model of personality (FFM), which is the
most widely used measure of personality, defines neuroticism as a trait
that encompasses a broad spectrum of heterogeneous traits,
hierarchically organized into lower-level traits called facets. The six facets of
neuroticism are anxiety, angry hostility, depression, self-consciousness,
impulsivity and vulnerability.4 Each facet of neuroticism may be
specifically associated with emotional and behavioral aspects of
psychiatric symptoms or mental illness. For example, neuroticism
that is characterized by high anxiety and depression is highly
correlated with symptoms of anxiety and depressive disorders.
Most studies on the heritability of personality have confirmed
that genetic factors substantially influence neuroticism and its facets.
Neuroticism shares the genetic and biological components of
psychiatric symptoms that are related to mood disorders.2 Genetic association
studies have identified suggestive genes linked to neuroticism such as
PDE4D,5 SNAP25,6 NKAIN2,7 OR1A2,8 and more specifically the
depression facet of neuroticism such as RORA.9 Recently, large GWAS
have found genetic loci associated with neuroticism in genome-wide
significant levels,10?12 and genome-wide SNPs explained about 15% of
the variance in neuroticism.10,12 However, there have been few
consistent findings of genetic loci linked to neuroticism across the
genome-wide association studies, and further studies may be useful to
confirm the candidate genes reported so far.
The facet-level traits share common components summarized by a
broad dimension of personality as well as reflect specific and narrow
phenotypes than the broad factor to which the facet is related.13
Neuroticism score is produced by a summation of the multiple facets,
which are variable across individuals, and the broad dimension seems
not to reflect specific characteristics of each facet. Genetic association
studies of facet-level personality traits may have the advantage of
improving statistical power by reducing noise due to the phenotypic
variability.9 It was suggested that the heritability of personality traits
reflect the heritability of common variance among facets as well
as specific heritable components underlying each facet.13 Thus,
studying the genetic variants of facets may aid in understanding the
biological mechanisms underlying the associations between specific
and common components of neuroticism and phenotypically related
Genome-wide associations (GWA) typically evaluate the effect of
single-nucleotide polymorphisms (SNP), but such studies have only
limited power to detect loci with small to moderate effects on complex
traits.14 Personality traits are polygenic phenotypes. Therefore, a large
number of genetic variants may be involved, and each may make small
contributions and interact with each other. The associations between
phenotypes and variants within functionally related gene groups can
be examined using pathway-based approaches, which integrate the
results of GWA studies with genes or gene sets of certain biological
pathways.15 Moreover, such pathway analysis investigates biological
pathways that are functionally important for cellular and molecular
processes and may provide information on the underlying biological
mechanisms of complex traits. This method has the advantages of
increased effect size and statistical power and can be used to identify
the biological pathways associated with personality16 and
neuropsychiatric phenotypes including schizophrenia.17
In the present study, we performed a meta-analysis of GWA results
to identify genes that influence the facet-level traits of neuroticism in a
total of 5584 participants from three cohorts. The aim of this
study was to identify genetic variants associated with each facet of
neuroticism, and to explore the respective affected biological pathways.
MATERIALS AND METHODS
The total sample size for the GWA study was 5584 subjects from three cohorts.
The Ansan Ansung cohort was a rural and urban community-based cohort,
which included 1167 men and 1257 women, ranging in age from 29 to
79 years.18 The young women cohort in Korea included 1120 Korean women,
aged 18?40 years.19 The Kangbuk Samsung cohort included 1143 men and
897 women ranging in age from 19 to 69 years, and these individuals were
recruited from the Health Screening and Examination cohort of the Kangbuk
Samsung Hospital in Korea.20 All cohorts were reviewed and approved by local
institutional review boards, and written informed consent was obtained.
All applicable institutional and governmental regulations concerning the ethical
use of human volunteers were followed during our research.
Neuroticism was assessed using the Korean version of the Revised NEO
personality inventory (NEO PI-R; PSI consulting Corp., Seoul, Korea). The
NEO PI-R consists of 30 facets, six for each dimension of the five personality
traits. The facets of neuroticism are anxiety (N1), angry hostility (N2),
depression (N3), self-consciousness (N4), impulsivity (N5) and vulnerability
(N6). Raw scores were standardized into T-scores using Korean combined-sex
norms (N = 7418; mean = 50; s.d. = 10) to confirm similarities with the Korean
normative sample data (PSI Consulting Corp.). The broad dimensions of
neuroticism were calculated by summation of raw scores of six facets and
converted to T-scores (mean = 50, s.d. = 1). The Cronbach?s alpha coefficients
for neuroticism and six facets were 0.908, 0.657, 0.544, 0.742, 0.494, 0.604 and
0.577, respectively. The Korean version of the NEO PI-R has been
demonstrated to provide good reliability and validity.21
SNP genotyping was conducted using Illumina or Affymetrix microarray chips,
as displayed in Table 1. Quality control (QC) procedures were conducted to
remove SNPs with missing genotyping rates 45%, minor allele frequency
(MAF) o0.01, or Hardy?Weinberg equilibrium o1 ? 10 ? 6. Sample QC
procedures were performed using PLINK version 1.90 to eliminate ineligible
subjects with minor allele frequency o0.05, Hardy?Weinberg Equilibrium
P-value o10 ? 6, genotyping rate o0.95. The subjects with sex inconsistency
and relatedness were also removed. After QC, autosomal SNPs (344 547 for the
Ansan Ansung cohort, 636 744 for the Young women cohort, and 226, 706 for
the Kangbuk Samsung cohort) were used for imputation. Genotype imputation
increases statistical power and the possibility of detecting a causal SNP.22
Imputation was conducted using IMPUTE2 with the 1000 Genomes Phase 3
reference panel containing all populations.
SNPs with an imputation quality score (R2) greater than 0.8 and an
MAF of 4 0.01 were used for the association analysis. The final number
of SNPs after imputation was 4 594 951 for the Ansan Ansung cohort,
6 427 507 for the Young women cohort, and 5 927 272 for the Kangbuk
Samsung cohort. The number of overlapping SNPs among the three cohorts
was 4 352 680.
The GWA was performed with best guess imputed genotypes using additive
linear regression model in PLINK ver. 1.90. T-scores of neuroticism and six
facets were used as quantitative variables to examine the association with SNPs.
Age and sex were included in the analysis as covariates. A weighted inverse
variance method in METAL was conducted to perform a meta-analysis of the
GWA results. The threshold value of Po5 ? 10 ? 8 was used to determine
statistical significance.23 A less stringent criterion of Po1 ? 10 ? 6 was used to
denote a suggestive association. Genomic inflation factors (?) ranged from
1.000 to 1.025 for all analyses. Multidimensional scaling (MDS) and principal
component analysis (PCA) confirmed that population stratification was not
observed in the overall dataset (Supplementary Figure S1).
To identify biological pathways, a Gene Set Enrichment Analysis with
Meta-Analysis Gene Set Enrichment of Variant (MAGENTA) algorithms was
conducted. MAGENTA tests the enrichment of associations with a disease or
trait within gene sets.15 The detailed processes of MAGENTA were described by
Segre et al. Briefly, the P-values of the SNPs determined by GWAs were used as
input, and gene scores were obtained from the most significant P-value among
the SNPs located within each gene. After gene scores were corrected for
confounders including the gene size, number of SNPs and linkage
disequilibrium-related properties by using step-wise multiple linear regression
analysis, gene-set enrichment P-values were obtained for highly ranked gene
scores. Gene set enrichment analysis P-values, which were assigned a cut-off
threshold set to the 75th percentile, may be suitable to detect weaker signals of
Abbreviations: Chr, chromosome; HetChiSq, heterogeneity chi-square statistic; HetPval, heterogeneity P-value; MAF, minor allele frequency; N1, anxiety; N2, angry hostility; N3, depression;
N4, self-consciousness; N5, impulsivity; N6, vulnerability; SNP, single-nucleotide polymorphism.
aDirection is described the SNP beta-coefficient from Ansan Ansung, Kangbuk Samsung and Young Women cohort in sequence.
bNearest genes are defined as the closest genes to the SNP within signal boundary or the closest genes within a 100 kb window.
associations to polygenic traits or for complex than the 95th threshold.15
Pathway information was obtained from the KEGG (186 pathways) and
REACTOME (674 pathways) molecular signature databases (MsigDB) v5.1
(downloaded in April 2014). For meta-analysis of pathway-based association,
MAGENTA were applied to all three cohort individually, and the meta P-value
were calculated by combining nominal P-values for each pathway from three
cohorts using Fisher?s method of ?metap? in R. Meta P-values were corrected for
multiple testing by a Bonferroni method. Multiple comparison corrections were
applied separately in each pathway database (KEGG: 0.05/186 = 2.69 ? 10 ? 4;
REACTOME: 0.05/674 = 7.42 ? 10 ? 5) because gene sets between two databases
considerably overlap.15 Pathways showing meta P-values with significance levels
were considered to be associated with phenotypes.
The bivariate correlation analysis was performed to examine the associations
between facet-level traits of neuroticism by using Pearson correlation analysis.
Genetic correlations between facets were calculated with by using a LD score
regression method (LDSC v1.0.0, https://github.com/bulik/ldsc).
Meta-analysis of SNP-based associations
The Manhattan and quantile-quantile plots for SNP-wide
metaanalysis results for the six facets of neuroticism are displayed in
Supplementary Figure S2 and 3. Bivariate correlations and genetic
correlations between the facets are shown in Supplementary Table S1
and 2. The top-ranked SNPs obtained from the meta-analysis of
GWAs for facets are presented in Table 2. One SNP (rs1360001) was
identified at the level of genome-wide significance (Po5 ? 10 ? 8), and
41 SNPs were identified to have suggestive associations (Po1 ? 10 ? 6)
in the meta-analysis for facets. Top-ranked results from SNP-based
meta-analysis of a broad dimension of neuroticism are shown in
Supplementary Table S3.
For anxiety (N1), the strongest association was found to be
with rs78773901 (Z = 4.665, P = 3.09 ? 10 ? 6), which is located
in inter-genic regions. Another top-ranked SNP was rs4685767
(Z = ? 4.506, P = 6.59 ? 10 ? 6), which is located within the inositol
1,4,5-trisphosphate receptor, type 1 (ITPR1) gene.
For hostility (N2), the most significant association at the
genomewide level (Z = 5.878, P = 4.14 ? 10 ? 9) was with rs1360001, which is
located within the microRNA 548h-3 gene (MIR548H3). Additionally,
the SNP rs74627388 had a suggestive association (Z = 4.841,
P = 1.29 ? 10 ? 6) and is located within the Wnt family member 7A
For depression (N3), the top-ranking SNP was rs2929855
(Z = ? 4.591, P = 4.41 ? 10 ? 6), which is located near the fibroblast
growth factor 10 gene (FGF10). A suggestive association was found
between N3 and rs17257269 (Z = ? 4.485, P = 7.29 ? 10 ? 6), which is
located within the fragile histidine triad gene (FHIT).
The SNP found to have the highest association with self-consciousness
(N4) was rs308254 (Z = 4.822, P = 1.43 ? 10 ? 6), which is located in
inter-genic regions. Among the top-ranked SNPs was rs79425212
Platelet activation signaling and aggregation
Signaling by rho GTPases
NCAM signaling for neurite outgrowth
ECM receptor interaction
Integrin cell surface interactions
ECM receptor interaction
CA dependent events
DAG and IP3 signaling
Renal cell carcinoma
SLC-mediated transmembrane transport
CRMPS in SEMA3A signaling
Abbreviations: AS, Ansan Ansung cohort; KS, Kangbuk Samsung cohort; N1, anxiety, N2, angry hostility, N3, depression, N4, self-consciousness, N5, impulsivity, N6, vulnerability;
YW, Young women cohort.
aCombined nominal P-value of three cohorts were calculated by Fisher?s method.
aSignificance thresholds for pathway-based meta-analysis were determined separately for each database (KEGG: 0.05/186 = 2.69 ? 10?4; REACTOME: 0.05/674 = 7.42 ? 10?5).
(Z = 4.811, P = 1.51 ? 10 ? 6), which is located close to the discoidin
domain receptor 1 (DDR1) gene.
The strongest association with impulsivity (N5) was identified for
rs114649649, which is located within Plakophilin 1 (PKP1) gene
(Z = ? 4.896, P = 9.79 ? 10 ? 7). The rs12494147 SNP, located in the
vestigial like family member 4 gene (VGLL4), was identified to have
the suggestive association with N5 (Z = 4.663, P = 3.11 ? 10 ? 6).
The top-ranking SNP associated with vulnerability to stress (N6)
was identified as rs2784611 (Z = 4.521, P = 6.17 ? 10 ? 6), which is
located within the protein tyrosine phosphatase receptor type D
Meta-analysis of pathway-based associations
To explore the biological pathways underlying the facet-level traits of
neuroticism, we performed a MAGENTA analysis on the results of
three GWAs. A combined P-value for each pathway, across the three
cohorts, was estimated. The significant pathways associated with the
facets of neuroticism are described in Table 3. Top-ranked results
from pathway-based meta-analysis of a broad dimension of
neuroticism are shown in Supplementary Table S4.
Following the pathway analysis for N1, three pathways from the
REACTOME dataset and 2 pathways from KEGG were identified as
significant. The strongest association was found to be with platelet
activation signaling and aggregation from REACTOME
(P = 4.50 ? 10 ? 6). Focal adhesion was a top-ranked pathway from
KEGG for N1 (P = 9.77 ? 10 ? 5).
Among 4 pathways identified in the REACTOME dataset and two
pathways identified from the KEGG database to be significantly
associated with N2, focal adhesion was the top-ranked pathway
(P = 1.45 ? 10 ? 7). Axon guidance (P = 2.10 ? 10 ? 6), neural cell
adhesion molecule (NCAM) signaling of neurite outgrowth
(P = 7.03 ? 10 ? 6) and the extracellular matrix (ECM) receptor
interaction (P = 5.91 ? 10 ? 5) were also significantly associated with N2.
Focal adhesion gene sets from KEGG comprised the top-ranked
pathway associated with N3 (P = 7.21 ? 10 ? 7). The ECM receptor
interaction pathway was observed for N3 (P = 5.88 ? 10 ? 6).
For N4, Axon guidance which was identified from the REACTOME
datasets (P = 1.71 ? 10 ? 5), was the most significant among the
top-ranked pathways. The calcium-dependent event pathway
(P = 5.84 ? 10 ? 5) and diacylglycerol (DAG) and inositol triphosphate
(IP3) signaling (P = 6.72 ? 10 ? 5) pathways were also observed in
association with N4.
One significant pathway associated with N5 was renal cell
carcinoma from KEGG (P = 2.04 ? 10 ? 4). For N6, axon guidance
was the top-ranked pathway (P = 4.31 ? 10 ? 5). Significant pathways
associated with N6 included solute carrier (SLC)-mediated
transmembrane transport (P = 5.01 ? 10 ? 5) and collapsin response mediator
proteins (CRMPs) in the semaphorin 3A (SEMA3A) signaling pathway
(P = 6.50 ? 10 ? 5).
Our study reports the results from a meta-analysis of both SNP- and
pathway-based data and reveals genetic influences on the six facets of
neuroticism. Identifying these genetic factors may provide information
critical for understanding the biological basis of psychiatric disorders
that share the behavioral and emotional aspects of specific facets.9 We
identified several interesting candidate genes, including ITPR1 (which
affects the N1 facet), MIR548H3 and WNT7A (which affects N2),
FGF10 and FHIT (which affect N3), DDR1 (which affects N4), VGLL4
(which affects N5) and PTPRD (which affects N6), and these genes
have previously been associated with other behavioral or psychiatric
phenotypes. While these candidate genes did not reach a significant
GWA with each facet, we identified significant biological pathways
through a meta-analysis of three studies using MAGENTA. Moreover,
we found that certain biological pathways were associated with a
specific facet, such as calcium-dependent events (N4) and CRMPs in
the SEMA3A signaling (N6), while other pathways, such as axon
guidance (N2, N4 and N6), focal adhesion and ECM receptor
interaction (N2 and N3) were associated with multiple facets.
With respect to anxiety, ITPR1 was the most biologically plausible
gene among the top-ranking SNPs. ITPR1 encodes the inositol
1,4,5-trisphosphate receptor, which modulates the concentration
and release of Ca2+ by regulating intracellular Ca2+ channels.24
Fluctuations in Ca2+ caused by ITPR1 receptors has been implicated
in neural activity and synaptic plasticity.25 Inositol 1,4,5-trisphosphate
receptors are enriched in the amygdala, and expression of the receptor
subtypes has crucial effects on amygdala functions, such as regulating
fear and anxiety.26 Animal studies have shown that the activity of
inositol 1,4,5-trisphosphate receptors is dependent on dopaminergic
neural transmission and is suggested to underlie one of the
mechanisms of drug dependency.27 Anxiety and substance use disorders are
highly comorbid, and anxiety disorders are correlated with the
occurrence and the severity of alcohol use disorders.28 It is possible
that ITPR1 plays a biological role in the clinical and behavioral links
between anxiety and drug abuse.
The most significant association at the genome-wide level was
found between hostility and rs1360001 located within microRNA
548h-3 (MIR548H3). Emerging evidence suggests that microRNA may
play an important role in development of central nervous system and
neuropsychiatric diseases.29,30 Although the function of microRNA in
personality has been unknown, our finding was interesting given the
fact that neuroticism is strongly associated with symptoms of
psychiatric disorders.2 Hostility facet with suggestive association was
found in WNT7A gene. This gene encodes a Wnt protein family
member that plays a role in cell signaling.31 The WNT7A gene was also
identified in a previous GWA study for bipolar disorder.32 The Wnt
signaling pathway is important for neural growth and plasticity, which
have been suggested as possible mechanisms of depressive disorder.33
With respect to the depression facet, FGF10 and FHIT genes were
found to have a suggestive association. FGF10 gene is a component of
the fibroblast growth factor system, which has previously been
reported as the one of the mechanisms of major depressive
disorder.34 Several GWA studies have reported an association between
FHIT and depressive disorder.35,36 This gene is also associated with
symptoms of anxiety and psychological distress.37 Our finding suggests
that the FHIT gene is involved in the depression facet of neuroticism
and is a good candidate for involvement in psychiatric disorders that
are phenotypically related to this trait.
Self-consciousness trait showed a possible association with the
variant located close to the discoidin domain receptor 1 (DDR1)
gene. Previous studies have found that expression of DDR1 increases
in parallel with neural myelination during mice brain development.38
This gene has also been suggested to be a susceptibility gene for
The suggestive associations with impulsivity were identified in PKP1
and VGLL4 genes. PKP1 gene was identified in a GWA study of panic
disorder.40 The function of VGLL4 in personality is unknown;
however, this gene is associated with comorbid depression and alcohol
Trait vulnerability showed suggestive associations with the PTPRD
gene. This gene encodes one of the members of the protein tyrosine
phosphatase family, which regulates various cellular processes,
including cell growth and differentiation.41 PTPRD was previously
associated with other personality traits such as persistence from
Cloninger?s Temperament Scales42 and psychiatric symptoms such
as anxiety and depression.37,43 PTPRD is also one of the candidate
genes for obsessive compulsive disorder,44 autism,45 and
attentiondeficit hyperactivity disorder.46 These neuropsychiatric disorders
originate from the interplay between environmental and genetic
factors. It is possible that PTPRD influences individual differences in
the vulnerability to environmental stress, which plays a critical role in
the risk for neuropsychiatric disorders. Neural dysfunction, caused
by changes in such neurodevelopmental processes as axonal guidance
and synapse formation, may explain the associations between PTPRD
and neuropsychiatric conditions.47
Meta-analysis using pathway-based approaches revealed several
biological pathways that overlapped among the facets and pathways
that were specific to each facet. Axon guidance was significantly
associated with N2, N4 and N6, which were also correlated with each
other. Axon guidance has also previously reported to be linked to a
broad dimension of neuroticism.16 Axon guidance is associated with
neural developmental processes and hierarchically belongs to a
developmental biology pathway in the REACTOME database. Our
findings suggest that neural development processes pathway may be a
shared genetic component of lower-level traits of neuroticism.
Another notable finding is that focal adhesion and ECM receptor
interaction are related to hostility and depression. Focal adhesions are
large protein complexes that contain integrin and connect the
cytoskeleton of cells to the ECM.48 It has previously been reported
that focal adhesion plays an essential role in cell migration during
brain development and is involved in the abnormal neurodevelopment
underlying schizophrenia.49 The hostility trait is linked to aggressive
and violent behaviors and is also closely related to depressive
symptoms.50 Our findings suggest that the focal adhesion and ECM
receptor pathways are good candidates for future studies that will
focus on the biological mechanisms linked to hostility and depression.
Interestingly, we identified an association between
calciumdependent events and self-consciousness. Calcium ions are important
for neural functions, such as excitability, synaptic plasticity and
transmitter release.51 Calcium signaling in neurons regulates the release
of neurotransmitters, such as serotonin and norepinephrine,52
which are functionally important for behaviors and emotions.
Self-consciousness is known to be closely linked to shame, social
anxiety, and the fear and anxiety emotions.53 It is possible that
calcium-related pathways are related to the neural functions involved
in self-regulating negative emotions and psychological distress as well
as the susceptibility to anxiety and mood disorders.
Several limitations in the present study should be noted. Facet-level
traits of neuroticism scored by FFM showed lower reliability of
measurements. The present finding may be promising for further
genetic studies for neuroticism related phenotypes using higher
reliability of measurements. In the SNP-based meta-analysis, we
identified one SNP that reached significance at genome-wide levels,
but we did not find any SNPs previously associated with neuroticism
in other GWAs5?7,11,54 or our previous studies.8 One possible reason
stems from differences in the population used in this study compared
to that used in previous research, which was mainly performed in
Caucasian subjects. Another possible explanation entails differences in
the reference panel for imputation, since we used the 1000 Genomes
in this study and HapMap in our previous studies.8 Nevertheless, the
facet-level pathway analysis herein replicated the
neuroticismassociated pathways identified in our previous pathway-based
association study on the NEO personality inventory.16 These findings
suggest that detection of the genetic influences of complex traits at the
single SNP level is difficult and that the interactions and cooperative
effects of the genes comprising biological pathways may play more
important roles in complex traits, especially in behavioral phenotypes
such as personality traits.
In conclusion, the candidate genes identified in this study to
influence the facets of neuroticism have been previously implicated
in other psychiatric disorders and warrant further study. The
pathwaybased meta-analysis implicated meaningful biological pathways in the
facets of neuroticism; moreover, these pathways, such as axon
guidance, have previously been associated with a broad dimension of
neuroticism,16 as well as with psychiatric disorders, such as focal
adhesion with schizophrenia.49 Additionally, the pathway analysis
suggests that pathways underlying neurodevelopmental processes are
associated with multiple facets of neuroticism. Taken together, these
results contribute to the understanding of the genetic influences and
biological mechanisms underlying facet-level traits of neuroticism and
phenotypically related disorders.
CONFLICT OF INTEREST
The authors declare no conflict of interest.
This research was supported by the National Research Foundation of Korea,
funded by the Ministry of Education
Ministry of Science, ICT & Future Planning (NRF-2014R1A2A2A04006291) and a
grant of Korea Health Technology R&D Project through the Korea Health
Industry Development Institute (KHIDI), funded by the Ministry of Health &
Welfare, Republic of Korea (HI14C0072). The genotype data of Ansan Ansung
cohort were gratefully made available by the Center for Genome Science, Korea
National Institute of Health, Korea Centers for Disease Control and Prevention. It
was also supported with the computing resources by Global Science experimental
Data hub Center (GSDC) Project and Korea Research Environment Open
NETwork (KREONET) in Korea Institute of Science and Technology Information
(KISTI). SNP genotyping data is publicly available upon proper requesting process
at the Clinical Omics Data Archive (CODA, http://coda.nih.go.kr).
Supplementary Information accompanies the paper on Journal of Human Genetics website (http://www.nature.com/jhg)
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54 Smith, D. J. , Escott-Price , V. , Davies , G. , Bailey , M. E. , Colodro-Conde , L. , Ward , J. et al. Genome-wide analysis of over 106 000 individuals identifies 9 neuroticismassociated loci . Mol. Psychiatry 21 , 749 - 757 ( 2016 ). This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License . The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material . To view a copy of this license , visit http://creativecommons.org/licenses/by-nc -sa/4 .0/ r The Author(s) 2017