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Genetic pleiotropy between multiple sclerosis and schizophrenia but not bipolar disorder: differential involvement of immune-related gene loci

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Converging evidence implicates immune abnormalities in schizophrenia (SCZ), and recent genome-wide association studies (GWAS) have identified immune-related single-nucleotide polymorphisms (SNPs) associated with SCZ. Using the conditional false discovery rate (FDR) approach, we evaluated pleiotropy in SNPs associated with SCZ (n=21 856) and multiple sclerosis (MS) (n=43 879), an inflammatory, demyelinating disease of the central nervous system. Because SCZ and bipolar disorder (BD) show substantial clinical and genetic overlap, we also investigated pleiotropy between BD (n=16 731) and MS. We found significant genetic overlap between SCZ and MS and identified 21 independent loci associated with SCZ, conditioned on association with MS. This enrichment was driven by the major histocompatibility complex (MHC). Importantly, we detected the involvement of the same human leukocyte antigen (HLA) alleles in both SCZ and MS, but with an opposite directionality of effect of associated HLA alleles (that is, MS risk alleles were associated with decreased SCZ risk). In contrast, we found no genetic overlap between BD and MS. Considered together, our findings demonstrate genetic pleiotropy between SCZ and MS and suggest that the MHC signals may differentiate SCZ from BD susceptibility.
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ORIGINAL ARTICLE
Genetic pleiotropy between multiple sclerosis and
schizophrenia but not bipolar disorder: differential
involvement of immune-related gene loci
OA Andreassen
1,2,3
, HF Harbo
4
, Y Wang
1,2,5,6
, WK Thompson
3
, AJ Schork
5,7,8
, M Mattingsdal
1,9
, V Zuber
1,2,10
, F Bettella
1,2
,
S Ripke
11,12
, JR Kelsoe
3
, KS Kendler
13
,MCODonovan
14
, P Sklar
15
, The Psychiatric Genomics Consortium (PGC) Bipolar
Disorder and Schizophrenia Work Groups
16
, The International Multiple Sclerosis Genetics Consortium (IMSGC), LK McEvoy
5,17
,
RS Desikan
5,17
, BA Lie
18
, S Djurovic
1,2,18
and AM Dale
3,5,6,17
Converging evidence implicates immune abnormalities in schizophrenia (SCZ), and recent genome-wide association studies
(GWAS) have identied immune-related single-nucleotide polymorphisms (SNPs) associated with SCZ. Using the conditional false
discovery rate (FDR) approach, we evaluated pleiotropy in SNPs associated with SCZ (n= 21 856) and multiple sclerosis (MS) (n=43
879), an inammatory, demyelinating disease of the central nervous system. Because SCZ and bipolar disorder (BD) show
substantial clinical and genetic overlap, we also investigated pleiotropy between BD (n= 16 731) and MS. We found signicant
genetic overlap between SCZ and MS and identied 21 independent loci associated with SCZ, conditioned on association with MS.
This enrichment was driven by the major histocompatibility complex (MHC). Importantly, we detected the involvement of the same
human leukocyte antigen (HLA) alleles in both SCZ and MS, but with an opposite directionality of effect of associated HLA alleles
(that is, MS risk alleles were associated with decreased SCZ risk). In contrast, we found no genetic overlap between BD and MS.
Considered together, our ndings demonstrate genetic pleiotropy between SCZ and MS and suggest that the MHC signals may
differentiate SCZ from BD susceptibility.
Molecular Psychiatry (2015) 20, 207214; doi:10.1038/mp.2013.195; published online 28 January 2014
Keywords: false discovery rate; HLA region; multiple sclerosis; polygenic pleiotropy; schizophrenia
INTRODUCTION
Schizophrenia (SCZ) and Bipolar Disorder (BD) are severe mental
disorders, which are among the leading causes of disability
globally.
1
These disorders have a substantial impact on the quality
of life for patients and their families, and are among the most
costly societal disorders.
2
Clinical, epidemiological and genetic
ndings suggest shared risk factors between BD and SCZ, and a
Psychosis Continuum Model has been suggested.
3
Despite the
high heritability, most of the genetic architecture underlying
susceptibility to both SCZ and BD remains to be dened, and the
pathobiological mechanisms underlying these disorders are still
largely unknown. Improved understanding of disease pathobiol-
ogy and genetic risk factors may lead to major health benets
through the development of new treatment and prevention
regimens.
4
An interesting hypothesis regarding SCZ pathology is the
involvement of the immune system, which is derived from
epidemiological and clinical evidence, implicating infections
5
and cytokine abnormalities
6
in the development of SCZ. Recent
genome-wide association studies (GWAS) have robustly identied
markers in the major histocompatibility complex (MHC) associated
with SCZ,
79
also showing association of specic human leukocyte
antigen (HLA) alleles.
10
Although this may further support a role of
the immune system in SCZ pathogenesis, it is also possible that
this association reects nonimmunological factors as several loci
within the MHC are involved in neurobiological processes (for
example, NOTCH4,TRIM26).
11
With regard to BD, despite a large
degree of overlapping clinical characteristics, genetic factors as
well as cytokine abnormalities
6,12
between BD and SCZ, there is
little evidence for MHC associations in GWAS of BD.
13
Our previous
1
NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway;
2
Division of Mental Health and Addiction, Oslo University
Hospital, Oslo, Norway;
3
Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA;
4
Department of Neurology, Oslo University Hospital, Ullevål, and Institute
of Clinical Medicine, University of Oslo, Oslo, Norway;
5
Multimodal Imaging Laboratory, University of California at San Diego, La Jolla, CA, USA;
6
Department of Neurosciences,
University of California, San Diego, La Jolla, CA, USA;
7
Cognitive Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA;
8
Center for Human
Development, University of California at San Diego, La Jolla, CA, USA;
9
Sørlandet Hospital, Kristiansand, Norway;
10
Centre for Molecular Medicine Norway, Nordic EMBL
Partnership, University of Oslo and Oslo University Hospital, Oslo, Norway;
11
Analytical and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA;
12
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA;
13
Virginia Institute for Psychiatric and Behavioral Genetics, Department of
Psychiatry, Virginia Commonwealth University, Richmond, VA, USA;
14
MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Heath Park,
Cardiff, UK;
15
The Division of Psychiatric Genetics and Genomics, Mount Sinai School of Medicine, New York, NY, USA;
16
PGC co-authors are listed separately in Supplementary
Information;
17
Department of Radiology, University of California, San Diego, La Jolla, CA, USA and
18
Department of Medical Genetics, Oslo University Hospital and University of
Oslo, Oslo, Norway. Correspondence: Dr OA Andreassen, NORMENT, KG Jebsen Centre for Psychosis Research, Building 49, Oslo University Hospital, Ullevål, Kirkeveien 166, PO
Box 4956 Nydalen, 0424 Oslo, Norway or Dr AM Dale, Department of Radiology, University of California, San Diego, 8950 Villa La Jolla Drive, Suite C101, La Jolla, CA 92037-0841,
USA.
E-mail: o.a.andreassen@medisin.uio.no or amdale@ucsd.edu
Received 22 June 2013; revised 13 November 2013; accepted 25 November 2013; published online 28 January 2014
Molecular Psychiatry (2015) 20, 207214
© 2015 Macmillan Publishers Limited All rights reserved 1359-4184/15
www.nature.com/mp
analysis of the shared genetic basis of these disorders revealed
signicant genetic overlap, but no common loci in the MHC.
14
Together, this suggests differences in MHC-related genes between
the two disorders.
Multiple sclerosis (MS) is a disease of the central nervous system
in which inammatory processes have an important role and
associations with HLA class I and II loci are well established.
15
The
HLA-DRB1*1501 allele shows strong association with MS suscept-
ibility in most populations with an average odds ratio (OR) of 3.
16
A
recent GWAS in MS identied more than 50 nonMHC regions
associated with MS risk, and immunologically relevant genes were
signicantly over-represented in these regions.
16
Further studies
and meta-analyses have increased the number of detected MS risk
variants.
1719
The immunological mechanisms involved in MS leads
to various degrees of demyelination,
20
and several lines of evidence
point to oligodendrocyte and myelin dysfunction also in SCZ.
21
This
suggests shared risk factors between MS and SCZ. Given the
overlapping phenotypes and polygenic architecture between SCZ
and BD, genetic pleiotropy between BD and MS may also exist.
Combining GWAS from two disorders provides increased power
to detect signicant associations, and recent studies have reported
overlapping single-nucleotide proteins (SNPs) between several
human traits
22,23
and disorders.
13,23
Here, we use MS GWAS data
from the International MS Genetics Consortium (IMSGC)
16
together
with SCZ GWAS
7
and BD GWAS
13
data to investigate pleiotropic
relationships between these disorders. To date, methods to assess
genetic pleiotropy have not taken full advantage of the existing
GWAS data, and the majority of these studies have focused on the
subset of SNPs exceeding a Bonferroni-corrected threshold of
signicance for each trait or disorder.
7,23
However, using this
approach, SNPs with nonsignicant effects in each phenotype, but
signicant in the combined analysis (polygenic pleiotropy), cannot
be detected. We recently developed a novel analytic strategy
14,24,25
that uses all available SNPs in two or more independent GWAS to
identify polygenic pleiotropy and improve gene discovery. Using
this approach, we demonstrated extensive polygenic pleiotropy
between SCZ and BD,
14
and between SCZ and cardiovascular
disease risk factors (CVD),
24
and substantially increased the number
of identied SCZ genetic susceptibility loci.
14,24,25
Here, we applied
these methods to independent large MS, SCZ and BD GWAS to
determine whether MS shares susceptibility loci with these
psychiatric disorders.
MATERIALS AND METHODS
Participant samples
We used summary statistics from a large MS GWAS study performed by
IMSGC,
16
n= 27 148, and from two large GWAS studies from the Psychiatric
GWAS Consortium (PGC), PGC Schizophrenia sample,
7
n= 21 856, PGC
Bipolar disorder sample,
13
n= 16 731. P-values and minor allele frequencies
from the discovery samples were included in the analyses. For follow-up
analysis, we also investigated the PGC major depressive disorder,
26
autism
spectrum disorder
27
and attention decit/hyperactivity disorder
28
GWAS
summary statistics (for additional details, see Supplementary Information).
Statistical analyses
Conditional Q-Q plots for pleiotropic enrichment. To visually assess
pleiotropic enrichment, we used Q-Q plots conditioned on pleiotropic
effects
14,24
(Figures 1a and 2a for BD). For a given associated phenotype,
pleiotropic enrichmentexists if the degree of deection from the
expected null line is dependent on associations with the second
phenotype (for further details see Supplementary Information). We
constructed conditional Q-Q plots of empirical quantiles of nominal
log
10
(P) values for all SNPs and for subsets of SNPs determined by the
signicance of their association with MS. Specically, we computed the
empirical cumulative distribution function (ecdf) of nominal P-values for a
given phenotype for all SNPs and for SNPs with signicance levels below
the indicated cutoffs for the other phenotype (log
10
(P)0, log
10
(P)1,
log
10
(P)2, log
10
(P)3 corresponding to P1, P0.1, P0.01,
P0.001, respectively). Nominal P-values (log
10
(P)) are plotted on the
y-axis, and empirical quantiles (log
10
(q), where q = 1 ecdf(P)) are plotted
on the x-axis. To assess polygenic effects below the standard GWAS
signicance threshold, we focused the Q-Q plots on SNPs with nominal
log
10
(P)o7.3 (corresponding to P>5 × 10
8
). The same procedure was
used for BD. The enrichmentseen in the conditional Q-Q plots can be
directly interpreted in terms of true discovery rate (TDR = 1 FDR).
29
This is
illustrated in Figures 1b and 2b for each range of P-values in the pleiotropic
traits (see Supplementary Information for details).
Conditional replication rate. For each of the 17 substudies contributing to
the nal meta-analysis in SCZ, we independently adjusted the z-scores
using intergenic ination control.
30
We randomly sampled 1000 combina-
tions of eight and nine substudy groupings. We then calculated the eight-
or-nine-study combined discovery z-score and eight-or-nine-study com-
bined replication z-score for each SNP as the average z-score across the
substudies multiplied by the square root of the number of studies. For
discovery samples, the z-scores were converted to two-tailed P-values,
whereas replication samples were converted to one-tailed P-values
preserving the direction of effect in the discovery sample. For each of
the 1000 discovery-replication pairs, cumulative rates of replication were
computed over 1000 equally spaced bins spanning the range of log
10
(P-
values) observed in the discovery samples. The cumulative replication rate
for any bin was the proportion of SNPs with a log
10
(discovery P-value)
greater than the lower bound of the bin with a replication P-valueo0.05
and the same sign as the discovery sample. Cumulative replication rates
were calculated independently for each of the four pleiotropic enrichment
categories. For each category, the cumulative replication rate for each bin
was averaged across the 1000 discovery-replication pairs, and the results
are reported in Figure 1c. The vertical intercept in the gure is the overall
replication rate.
Conditional replication effect size. Using the same z-score adjustment
scheme and sampling method used for estimating cumulative replication
rates (see above), we directly evaluated the relationship of replication
effect size of the discovery sample versus replication samples (Figure 1d)
for each SNP. The effect sizes were conditioned on various enrichment
categories. For visualization, we tted a cubic spline relating the bin mid-
point of z-scores of discovery to the corresponding average replication
z-scores (Figure 1d).
Improving discovery of SNPs in SCZ and BD using conditional FDR.To
improve detection of SNPs associated with SCZ and BD, we employed a
genetic epidemiology approach, leveraging the MS phenotype from an
independent GWAS using conditional FDR as outlined in Andreassen
et al.
14,24
Specically, conditional FDR is dened as the posterior probability
that a given SNP is null for the rst phenotype given that the P-values for
both phenotypes are as small as or smaller than their observed P-values.
We assigned a conditional FDR value for each SNP in SCZ given the P-value
in MS (denoted as FDR
SCZ|MS
). All SNPs with conditional FDR o0.05
(log
10
(FDR)>1.3) in SCZ given the association with MS are listed in
Supplementary Table 1. The same procedure was applied to compute
FDR
BD|MS
for each SNP. To display the localization of the genetic markers
associated with SCZ and BD given the MS effect, we used a Conditional
Manhattan plot, plotting all SNPs within an LD block in relation to their
chromosomal location. As illustrated for SCZ in Figure 3, the large points
represent the signicant SNPs ( log
10
(FDR
SCZ|MS
)>1.3 equivalent to
FDR
SCZ|MS
o0.05), whereas the small points represent nonsignicant SNPs.
All SNPs are shown without pruning(that is, without removing all SNPs
with r
2
>0.2 based on 1000 Genome Project (1KGP) linkage disequilibrium
(LD) structure). The strongest signal in each LD block is illustrated with a
black line around the circles. This was identied by ranking all SNPs in
increasing order, based on the FDR
SCZ | MS
value and then removing SNPs
in LD r
2
>0.2 with any higher-ranked SNP. Thus, the selected locus was the
most signicantly associated with SCZ in each LD block.
Annotation of novel loci. On the basis of 1KGP linkage disequilibrium (LD)
structure, signicant SNPs identied by conditional FDR were clustered
into LD blocks at the LD r
2
>0.2 level. These blocks are numbered (locus #)
in Tables 1 and 2. Any block may contain more than one SNP. Genes close
to each SNP were obtained from the NCBI gene database. Only blocks that
did not contain previously reported SNPs or genes related to previously
reported SNPs were deemed as novel loci in the current study (Tables 1
and 2). Loci that contained either SNPs or genes known to be associated
with SCZ were considered as replication ndings.
Pleiotropy brain disorders
OA Andreassen et al
208
Molecular Psychiatry (2015), 207 214 © 2015 Macmillan Publishers Limited
Figure 2. Genetic pleiotropy enrichment of BD conditional on MS. (a) Conditional Q-Q plot of nominal versus empirical log
10
P-values
(corrected for ination) in bipolar disorder (BD) below the standard GWAS threshold of Po5 ×10
8
as a function of signicance of association
with multiple sclerosis (MS) at the level of log
10
(P)0, log
10
(P)1, log
10
(P)2, log
10
(P)3 corresponding to P1, P0.1, P0.01,
P0.001, respectively. Dotted lines indicate the null hypothesis. (b) Conditional true discovery rate (TDR) plots illustrating the increase in TDR
associated with increased pleiotropic enrichment in BD conditioned on MS (BD|MS).
Figure 1. Genetic pleiotropy enrichment of schizophrenia (SCZ) conditional on MS. (a) Conditional Q-Q plot of nominal versus empirical
log
10
P-values (corrected for ination) in SCZ below the standard GWAS threshold of Po5 ×10
8
as a function of signicance of association
with multiple sclerosis (MS) at the level of log
10
(P)0, log
10
(P)1, log
10
(P)2, log
10
(P)3 corresponding to P1, P0.1, P0.01,
P0.001, respectively. Dotted lines indicate the null hypothesis. (b) Conditional true discovery rate (TDR) plots illustrating the increase in TDR
associated with increased pleiotropic enrichment in SCZ conditioned on MS (SCZ|MS). (c) Cumulative replication plot showing the average
rate of replication (Po0.05) within SCZ substudies for a given P-value threshold shows that pleiotropic enriched SNP categories replicate at a
higher rate in independent SCZ samples, for SCZ conditioned on MS (SCZ|MS). The vertical intercept is the overall replication rate per
category. (d) Z-score-z-score plot demonstrates that the empirical replication z-scores closely match the expected a posteriori effect sizes of
SCZ and are strongly dependent upon pleiotropy with MS. Analysis is based on split half method of the 17 PGC SCZ substudies.
Pleiotropy brain disorders
OA Andreassen et al
209
© 2015 Macmillan Publishers Limited Molecular Psychiatry (2015), 207 214
HLA allele analysis. The PGC1 genotype data from the 17 substudies were
used for HLA imputation (a detailed description of the data sets, quality
control procedures, imputation methods, and, principal components
estimation, are given in Schizophrenia Psychiatric Genome-Wide
Association Study (GWAS) Consortium.
7
First, genotypes of SNPs in the
extended MHC (Major Histocompatibility Complex) (chr6: 25652429-
33368333) of each individual in all the samples were extracted. Then, the
program HIBAG
31
was used to impute genotypes of classical HLA alleles for
Figure 3. Conditional FDR Manhattan plot. Conditional FDR Manhattan plot of conditional log
10
(FDR) values for schizophrenia (SCZ) alone
(gray) and SCZ conditioned on multiple sclerosis (MS; SCZ|MS, red). SNPs with conditional log
10
FDR>1.3 (that is, FDR o0.05) are shown with
large points. A black line around the large points indicates the most signicant SNP in each LD block. This SNP is annotated with the closest
gene. Genes are listed in increasing order in terms of SNPs genomic position within each chromosome from left to right and novel ones are
marked by stars (*). The gure shows the localization of the 21 independent loci on a total of 13 chromosomes.
Table 1. Conditional FDR o0.05, non MHC SNPs associated with schizophrenia given multiple sclerosis
Locus # SNP Location Gene SCZ P FDR SCZ FDR SCZ|MS
1 rs1625579 1p21.3 AK094607
a,b
(MIR137HG) 5.52E-06 4.92E-02 3.69E-02
2 rs17180327 2q31.3 CWC22
b,c
6.37E-06 5.19E-02 3.95E-03
3 rs7646226 3p21-p14 PTPRG
b,c
5.51E-06 4.92E-02 2.43E-02
4 rs9462875 6p21.1 CUL9
b,c
1.20E-05 6.59E-02 4.14E-02
5 rs10257990 7p22 MAD1L1
a,b
5.53E-06 4.92E-02 1.63E-02
6 rs10503253 8p23.2 CSMD1
a,b
3.96E-06 4.70E-02 4.04E-02
rs10503256 8p23.2 CSMD1
a,b
2.27E-06 4.32E-02 1.29E-02
7 rs6990941 8q21.3 MMP16
a,b
2.48E-06 4.32E-02 1.48E-02
8 rs396861 9p24 AK3 6.89E-06 5.19E-02 4.53E-02
9 rs4532960 10q24.32 AS3MT
b
2.65E-06 4.32E-02 1.29E-02
10 rs12411886 10q24.32 CNNM2
a,b
1.79E-06 4.10E-02 1.86E-02
11 rs11191732 10q25.1 NEURL
b
2.55E-06 4.32E-02 2.69E-02
12 rs1025641 10q26.2 C10orf90 7.51E-06 5.54E-02 4.87E-02
13 rs2852034 11q22.1 CNTN5 1.12E-05 6.00E-02 2.90E-02
14 rs540723 11q23.3 STT3A
b
1.82E-06 4.10E-02 2.56E-02
15 rs7972947 12p13.3 CACNA1C
a,b
7.12E-06 5.54E-02 4.87E-02
16 rs2007044 12p13.3 CACNA1C
a,b
2.74E-05 9.43E-02 1.75E-02
17 rs12436216 14q13.2 KIAA0391
b
7.40E-06 5.54E-02 4.87E-02
18 rs1869901 15q15 PLCB2
b
3.66E-06 4.70E-02 4.04E-02
19 rs4887348 15q25 NTRK3 4.69E-05 1.39E-01 3.05E-02
20 rs4309482 18 AK093940 9.66E-06 6.00E-02 1.34E-02
Independent complex or single-gene loci (r
2
o0.2) with SNP(s) with a conditional FDR (SCZ|MS)o0.05 in schizophrenia (SCZ) given association in multiple
sclerosis (MS). All signicant SNPs are listed and sorted in each LD block and independent loci are listed consecutively (Locus #). Chromosome location
(Location), closest gene (Gene), P-value of SCZ (SCZ P-value) and false discovery rate of SCZ, FDR (SCZ) are also listed. All data were rst corrected for genomic
ination.
a
Loci identied by GWASs without leveraging genetic pleiotropy structure between phenotypes.
b
Loci identied using conditional FDR method on
SCZ with CVD.
c
Loci identied using conditional FDR method on SCZ with BD.
Pleiotropy brain disorders
OA Andreassen et al
210
Molecular Psychiatry (2015), 207 214 © 2015 Macmillan Publishers Limited
each sample separately, using the parameters trained on the Scottish 1958
birth cohort data (http://www.b58cgene.sgul.ac.uk/). HLA alleles with
posterior probabilities 0.5 and frequency >0.01 were used in subsequent
analysis. The genotypes of the 63 HLA alleles meeting these criteria were
encoded as binary variables for the following conditional analysis.
Samples with imputed HLA genotypes were combined before the
analysis. First, the logistic regression method implemented in PLINK
32
was
employed to test HLA alleles for associations with SCZ, using the rst ve
principal components and sample indicator variable as covariates. After
Bonferroni correction, ve alleles passed the genomic signicance thresh-
old (7.9 ×10
-4
, see Supplementary Table 2).
The dosages of SNPs in the MHC, imputed based on HapMap3 data,
were tested using logistic regression. The analysis was rst performed with
only sample indicator variables and the rst ve principal components as
covariates and then including, in turn, one of the signicant HLA alleles
from the previous step as an additional covariate. In addition to the SCZ-
associated HLA alleles, four other alleles reported to be associated with MS
were also tested in this framework. A large increase in a SNPs association
P-value upon conditioning on HLA alleles is considered to indicate overlap
with that HLA allele (Supplementary Figure 5).
RESULTS
Enrichment of SCZ SNPs due to association with MSconditional
Q-Q plots
Conditional Q-Q plots for SCZ given level of association with MS
(Figure 1a) show variation in enrichment. Earlier (and steeper)
departures from the null line (leftward shift) with higher levels of
association with MS indicate a greater proportion of true
associations (Figure 1b) for a given nominal P-value. The
divergence of the curves for different conditioning subsets thus
suggests that the proportion of non-null effects varies consider-
ably across different degrees of association with MS. For example,
the proportion of SNPs in the log
10
(p
MS
)3 category reaches a
given signicance level (log
10
(p
SCZ
)>6) that is roughly 50100
times greater than for the log
10
(p
MS
)0 category (all SNPs),
indicating considerable enrichment. The enrichment was signi-
cant after pruning, as shown by the Q-Q plots with condence
intervals given in Supplementary Figure 1. The enrichment also
remained signicant after removing the SNPs with genome-wide
signicant P-values (censored Q-Q plots, Supplementary Figure 2).
In contrast, we found no evidence for enrichment in BD
conditional on MS (Figure 2).
Association with MS increases conditional true discovery rate
(TDR) in SCZ
Variation in enrichment in pleiotropic SNPs is associated with
corresponding variation in conditional TDR, equivalent to one
minus the conditional FDR.
29
A conservative estimate of the
conditional TDR for each nominal P-value is equivalent to 1(P/q)
as plotted on the conditional Q-Q plots (see Methods). This
relationship is shown for SCZ conditioned on MS in a conditional
TDR plot (Figure 1b; TDR
SCZ | MS
, and for BD Figure 2b; TDR
BD|MS
).
For a given conditional TDR, the corresponding estimated nominal
P-value threshold varied by a factor of 100 from the most to the
least enriched SNP category for SCZ conditioned by MS. Since the
conditional TDR is strongly related to predicted replication rate,
the replication rate is expected to increase for SNPs in categories
with higher conditional TDR.
Replication rate in SCZ is increased by MS association
To address the possibility that the observed pattern of differential
enrichment results from spurious (that is, nongeneralizable)
associations due to category-specic stratication or statistical
modeling errors, we examined the empirical replication rate across
independent substudies for SCZ. Figure 1c shows the empirical
cumulative replication rate plots as a function of nominal P-value,
for the same categories as for the conditional Q-Q and TDR plots
in Figures 1a and b. Consistent with the conditional TDR pattern,
we found that the nominal P-value corresponding to a wide range
of replication rates was 100 times higher for log
10
(p
MS
)3
relative to the log
10
(p
MS
)0 category (Figure 1c). Similarly,
SNPs from pleiotropic SNP categories showing the greatest
enrichments (log
10
(p
MS
)3) replicated at highest rates, up to
ve times higher than all SNPs (log
10
(p
MS
)0), for a wide range
of P-value thresholds. This strongly suggests that replication of
SNP associations varies as a function of estimated conditional TDR.
Replication effect size depends upon MS association
Consistent with the pattern observed for replication rates in SCZ
substudies (see above), we found that the effect sizes of SNPs in
enriched categories (for example, log
10
(p
MS
)3) replicated
better than effect sizes of SNPs in less-enriched categories (for
example, log
10
(p
MS
)0; Figure 1d). This indicates that the delity
of replication effect sizes is closely related to the conditional TDR.
SCZ gene loci identied with conditional FDR
Conditional FDR methods
14,24
improve our ability to detect SNPs
associated with SCZ due to the additional power generated by use
of the MS GWAS data. Using the conditional FDR for each SNP, we
constructed a conditional FDR Manhattan plotfor SCZ and MS
(Figure 3). The reduced FDR obtained by leveraging association
with MS enabled us to identify loci signicantly (conditional
FDRo0.05) associated with SCZ on a total of 13 chromosomes.
We subsequently pruned the associated SNPs (removed SNP with
LD r
2
>0.2) and identied a total of 21 independent loci, of which
one complex locus was located in the MHC on chromosome 6
(Table 2) and 20 single-gene loci were located in chromosomes
13, 612, 14, 15 and 18 (Table 1). These loci are marked by large
points with black edges in Figure 3. Only ten of the independent
loci have been identied by previous SCZ GWASs using standard
analysis.
7,10
However, several have also been identied in our
previous analyses of genetic pleiotropy between SCZ and
cardiovascular disease risk factors (CVD)
24
and between SCZ and
Table 2. Conditional FDR o0.05, MHC SNPs associated with
schizophrenia given multiple sclerosis
SNP Location Gene SCZ P FDR SCZ FDR SCZ|
MS
rs9379780 6p22.3 SCGN
a,b
3.25E-06 4.51E-02 1.59E-02
rs3857546 6p21.3 HIST1H1E
a
3.87E-08 4.49E-03 1.47E-03
rs13218591 6p22.1 BTN3A2 4.24E-05 1.23E-01 4.85E-02
rs7746199 6p22.1 POM121L2
a
1.18E-08 2.69E-03 1.59E-03
rs853676 6p22.3-p22.1 ZNF323
a
6.71E-08 2.69E-03 1.59E-03
rs213230 6p22.1 ZKSCAN3
a
3.64E-06 4.70E-02 1.15E-03
rs2844776 6p21.3 TRIM26
a,b,c
2.34E-09 7.23E-04 8.15E-05
rs3094127 6p21.3 FLOT1
a
6.66E-05 1.57E-01 3.68E-02
rs3873332 6p21.33 VARS2 8.61E-04 4.37E-01 4.69E-02
rs1265099 6p21.3 PSORS1C1
a
2.30E-05 9.43E-02 3.38E-03
rs9264942 6p21.3 HLA-B
a,c
3.25E-04 3.26E-01 2.36E-02
rs2857595 6p21.3 NCR3 8.96E-05 1.98E-01 9.55E-03
rs805294 6p21.33 LY6G6C
b
2.93E-05 1.08E-01 3.99E-03
rs3134942 6p21.3 NOTCH4
a,c
3.04E-05 1.08E-01 3.99E-03
rs2395174 6p21.3 HLA-DRA
a,b
8.07E-04 4.37E-01 4.69E-02
rs3129890 6p21.3 HLA-DRA
a,b
1.89E-06 4.10E-02 6.98E-04
rs7383287 6p21.3 HLA-DOB
a,b
3.44E-05 1.08E-01 3.99E-03
rs1480380 6p21.3 HLA-DMA
a,b
3.05E-06 4.51E-02 2.11E-03
SNPs located in the MHC region identied with a conditional FDR (SCZ|MS)
o0.05 in schizophrenia (SCZ) given association in multiple sclerosis
(MS). Chromosome location (Location), closest gene (Gene), P-value of SCZ
(SCZ P-value) and false discovery rate of SCZ, FDR (SCZ) are also listed.
All data were rst corrected for genomic ination.
a
Loci identied using
conditional FDR method on SCZ with CVD.
b
Loci identied using
conditional FDR method on SCZ with BD.
c
Loci identied by GWASs
without leveraging genetic pleiotropy structure between phenotypes.
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OA Andreassen et al
211
© 2015 Macmillan Publishers Limited Molecular Psychiatry (2015), 207 214
BD
14
(Tables 1 and 2). All SNPs with conditional FDRo0.05 are
listed in Supplementary Table 1.
Effect of the size of strata on enrichment
The observed enrichment was further conrmed by performing
the same analysis on additional categories (log
10
(p
MS
)4,
log
10
(p
MS
)5 and log
10
(p
MS
)6, Supplementary Figure 3).
Although the general enrichment pattern persisted, the number of
valid SNPs, which exist in both SCZ and MS data set and also have
valid P-values, in these extra categories was smaller. In total, 425
028 SNPs having valid P-values for both SCZ and MS were
analyzed in this study. They contribute 425 028, 47410, 7077, 1781,
808, 525 and 391 to the six categories conditioned by the
signicance level of MS, respectively.
Distribution of allele frequencies in strata
We also investigated the distribution of the minor allele
frequencies (MAF) of the corresponding SNPs of each stratum
from the 1KGP. Supplementary Figure 4 shows the average MAF *
(1 MAF), namely, the genetic variance, in strata after pruning
SNPs in LD (r
2
>0.2). As the signicance level of SNPs with MS
increases, there is a noticeable increase in average genetic
variance, which is expected as MAF confounds multiplicatively
with the true effect size of the variants.
30
However, the effect of
MAF alone cannot explain the observed enrichment (see
Supplementary Figure 4).
HLA imputation and association analysis
Among the loci identied by conditional FDR methods, eight are
located in the MHC (Table 2). It is possible that these signals may
be driven by common HLA alleles affecting both SCZ and MS. To
test this hypothesis, we imputed HLA class I and class II alleles
using the PGC1 genotype data (see Materials and Methods).
We performed association analysis between imputed HLA alleles
and SCZ. We replicated earlier ndings
10
that the alleles HLA-B
*08:01, HLA-C*07:01, HLA-DRB1*03:01, HLA-DQA1*05:01 and HLA-
DQB1*02:01 are negatively associated with SCZ (Po7.8 ´10
4
).
Among these, HLA-DRB1*03:01 and HLA-DQB1*02:01 have been
reported to be positively associated with MS.
16
However, no
association was seen with SCZ for the strong MS predisposing
HLA-DRB1*15:01 and HLA-DRB1*13:03 alleles, nor for the protec-
tive HLA-A*02:01 allele. We further tested whether SNPs in the
MHC with conditional FDRo0.05 were independent of the
association signal with the classical HLA alleles (see Materials
and Methods). SNPs rs9379780, rs3857546, rs7746199, rs853676
and rs2844776 seem to be independent of the HLA allelic signal
(Supplementary Figure 5 and Supplementary Table 2). We further
tested if the associated HLA alleles were independent of each
other by conditional analysis between them (see Materials and
Methods). The results in Supplementary Table 3 suggest that the
observed associations are driven by a single haplotype block (that
is, ancestral haplotype 8.1), consisting of the ve individual HLA
alleles. However, because of the low number of successfully
imputed HLA alleles, it is difcult to exclude independent signals.
The effect of MHC SNPs on enrichment
We also investigated the effect of MHC-related SNPs (SNPs located
within the MHC or SNPs within 1 Mb and in LD (r
2
>0.2) with such
SNPs) on the observed enrichment for SCZ and BD conditional on
MS (see Supplementary Information and Supplementary Figure 6).
After removing the MHC-related SNPs, the enrichment of SCZ
conditioned on MS was substantially attenuated (Supplementary
Figure 6). In contrast, removing the MHC-related SNPs did not
affect the enrichment of BD conditioned on MS (Supplementary
Figure 6). Further, we also investigated the effect of removing the
MHC-related SNPs on the previously reported enrichment of SCZ
conditioned on BD. As illustrated in Supplementary Figure 8, the
enrichment between BD and SCZ was not affected by removing
the MHC-related SNPs.
Enrichment analysis of other psychiatry disorders
Using the analysis approach described above, we evaluated
genetic enrichment in major depressive disorder,
26
autism spec-
trum disorder
27
and attention decit/hyperactivity disorder
28
GWAS summary statistics from the PGC conditioned on MS.
In contrast to SCZ, none of these phenotypes demonstrated
signicant enrichment (Supplementary Figure 7).
DISCUSSION
Our results demonstrate genetic overlap between SCZ and MS
and identify 21 independent loci associated with SCZ, which are
driven by the overlap in the MHC region. In contrast, despite
known genetic overlap between SCZ and BD, we found no overlap
between BD and MS. As most of the overlap between MS and SCZ
was driven by the MHC, and given the previous evidence that the
genetic overlap between BD and SCZ is located outside the MHC,
our ndings suggest that the MHC cluster could differentiate
between BD and SCZ susceptibility.
These results implicate shared molecular pathways between
SCZ and MS. Many of the common association signals between
SCZ and MS are located on chromosome 6, strongly suggesting
the MHC, and possibly HLA alleles, in SCZ development. This is in
line with previous ndings of common variants in the MHC
associated with SCZ,
7,8,10,33
which seems to implicate immune
factors. The current SNP and HLA allelic analyses indicate that
there could be several risk loci in the MHC for SCZ, possibly
including both classical antigen-presenting HLA alleles and other
immune loci. The DRB1*03:01 and DQB1*02:01 alleles that
increase risk of MS were found to decrease the risk for SCZ.
Other MS-associated HLA alleles were not found to be associated
with SCZ in our analysis. Thus, although MS and SCZ are both
associated with several loci in the MHC, their genetic risk prole
appears to be divergent. Because of the low imputation rate in our
study, it is difcult to draw rm conclusions about precisely which
loci and allelic variants in the MHC are involved in SCZ. Several of
the nonMHC loci suggested to be involved in SCZ susceptibility by
the current study are also related to immune pathways, which are
established as key mechanisms in MS pathogenesis.
16
Further,
support for immune involvement in SCZ comes from recent
ndings of abnormalities in cytokine levels and other immune
markers in SCZ
6,34
and from strong evidence suggesting a role for
prenatal infections in SCZ development.
35
However, given the
presence of neurobiologic loci, like NOTCH4 and TRIM26, within
the MHC, and the role of MHC class I in neuronal plasticity,
11
we
cannot rule out the possibility that the genetic associations found
in the MHC may reect shared pathobiology between SCZ and
MS,
11
in addition to immunological factors. Since myelin is
affected in MS, the current ndings are in line with previous
work suggesting a role of abnormal myelination also in SCZ.
36
Considered together, these ndings strongly indicate the need for
additional research to determine the MHC-related mechanisms
underlying SCZ.
The three-way relationship between SCZ-MS-BD observed here
and in our previous study
14
demonstrates that overlap between
SCZ and MS may be due to shared genetic risk loci mainly in the
MHC, while overlap between BD and SCZ is present across almost
the entire genome with the exception of the MHC. Indeed,
removing the MHC-related SNPs did not affect the enrichment
between BD and SCZ. Together with our current ndings of no
overlap between BD and MS, these results indicate that the MHC
involvement could differentiate SCZ from BD susceptibility. The
lack of pleiotropy between MS and BD indicates that the MHC is
Pleiotropy brain disorders
OA Andreassen et al
212
Molecular Psychiatry (2015), 207 214 © 2015 Macmillan Publishers Limited
not associated with psychosis in general, but is more specically
related to SCZ. This is further supported by the lack of MS-
conditional enrichment observed with autism spectrum disorder,
major depressive disorder and ADHD (Supplementary Figure 7).
Several lines of evidence support the notion of shared genes and
phenotypes between SCZ and BD.
3
Given that the SCZ and BD
data sets entailed partly overlapping controls,
7,13
the lack of
ndings in BD argues against unknown population stratication
factors driving results for SCZ. Some yet unidentied population
stratication related to immune genes within European
populations
37
could still have a role, but this would have also
affected the results of the original GWAS.
The current ndings of overlapping genetic variants in SCZ and
MS show the feasibility of using a genetic epidemiology frame-
work that leverages overlap in genetic signal from independent
GWASs
14,24,25
of brain phenotypes to improve statistical power for
gene discovery. In the original SCZ GWAS sample, three loci were
signicant before replication in additional samples.
7
By combining
the original SCZ sample with the independent GWAS of MS, we
identied signicant pleiotropic signals in a total of 21 loci, only
eleven of which had been reported in previous SCZ GWAS (Tables
1 and 2). This illustrates the increased power of our combined
analytical approach. Note that the conditional FDR method
protects against the possibility that the observed pleiotropic
effects are driven by a strong signal in only one phenotype. It is
likely that the new loci uncovered here would have been
discovered if the number of subjects in the SCZ GWAS had been
adequately large.
38
However, we here show how summary
statistics from independent samples provide a large advantage
for gene discovery without the extra cost and resources needed to
obtain new casecontrol samples. This has signicant implications
for GWAS, which have been criticized for not identifying enough
of the genetic component of known heritable traits, and are
increasingly seen as just a rst step in more sophisticated
analyses.
39
Our ndings also suggest that the etiology of complex brain
disorders may involve a high number of pleiotropic variants, each
with a small effect. SCZ appears to show an especially high degree
of pleiotropy, with signicant overlap with a number of different
phenotypes.
24
It is important to note, however, that this pleiotropy
is not general, but is phenotype specic. The genetic pleiotropy
observed between SCZ and MS is weaker than that between
SCZ and BD.
14
SCZ also shows varying degrees of pleiotropy
with different CVD risk factors, with some showing strong effects
(for example, triglyercides), others showing weaker effects (for
example, blood pressure, waist-hip-ratio) and still others showing
no signicant enrichment (for example, type 2 diabetes
24
). With
regard to SCZ pleiotropy with CVD risk, we showed that several
of the SCZ loci detected here based on pleiotropy with MS
were previously identied, leveraging pleiotropy between
SCZ and CVD.
24
This suggests the likely presence of genetic
pleiotropy between MS and CVD risk factors that is worth further
exploration.
To conclude, the conditional FDR approach identied 21 loci
associated with SCZ conditioned on MS, and this enrichment was
driven by the overlap in the MHC region. Imputation of the HLA
alleles showed that the HLA alleles found to increase MS risk were
associated with decreased SCZ risk. We found no overlap in the
MHC between BD and MS, suggesting that MHC could be a key
difference between SCZ and BD genetics. Our ndings also
indicate that leveraging existing GWASs for pleiotropic genes may
represent an important avenue by which currently available
resources can be optimized to identify more of the missing
heritability of complex phenotypes.
CONFLICT OF INTEREST
The authors declare no conict of interest.
ACKNOWLEDGMENTS
We thank all study participants for their contributions, the researchers involved in
Psychiatric Genomics Consortium (PGC) Bipolar disorder and Schizophrenia Work
Groups, the PGC ADHD, Autism, and MDD Groups, and the SURFsara Computer
Cluster (www.surfsara.nl). The International Multiple Sclerosis Genetics Consortium
and the Wellcome Trust Case Control Consortium2 is acknowledged for providing
data from their MS genome-wide study, supported by the Wellcome Trust (project
085475/B/08/Z and 085475/Z/08/Z).
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... Associations with the immune-related major histocompatibility complex (MHC) locus and with immune loci outside of the MHC region are demonstrated in genome-wide association studies of both disorders (Andreassen et al., 2013;Pouget, 2018;Mullins et al., 2021;Trubetskoy et al., 2022). SCZ and BD share genetics with immune-mediated diseases such as cardiovascular disease, multiple sclerosis and inflammatory bowel disease (Andreassen et al., 2013;Andreassen et al., 2015;Kember et al., 2018;Pouget et al., 2019;Rodevand et al., 2021). Brain imaging and markers in cerebrospinal fluid and post-mortem brain tissue indicate low-grade neuroinflammation in SMDs (Bechter et al., 2010;Trepanier et al., 2016;Marques et al., 2019;Benedetti et al., 2020;Giridharan et al., 2020), and low-grade systemic inflammation is evidenced by a range of blood immune marker studies (Goldsmith et al., 2016;Muneer, 2016;Frydecka et al., 2018;Khoury and Nasrallah, 2018;Kroken et al., 2018;Benedetti et al., 2020). ...
... Despite an elusive etiopathology, compelling evidence underscores the substantial impact of inflammation in the pathogenesis of schizophrenia and could be construed as a chronic inflammatory condition affecting the brain (Debnath 2015). Recent investigations highlight immune system dysfunctions, including altered expression of immune-related cytokines within the brains and CSF of schizophrenia patients (Andreassen et al. 2015;Hyde and Bharadwaj 2015;Patterson 2009;Avramopoulos et al. 2015;Na et al. 2014). Nonetheless, the fundamental disease etiopathology and the precise contribution of the immune system to schizophrenia's pathogenesis remain enigmatic (Debnath 2015;Khandaker and Dantzer 2016). ...
Chapter
Pro-inflammatory T lymphocyte populations (Th17, Th1, and Th17/1 cells) trigger and exacerbate the neuroinflammation through cytokine production and cell-cell contacts with neurons, microglial cells, and other CNS-infiltrating immune cells. A cascade of neurological injuries associated with Th17-related immune pathways arises in the CNS of patients with multiple sclerosis (MS), Alzheimer’s disease (AD), Parkinson’s disease (PD), schizophrenia, and many other autoimmune disorders. Th17 cells and their related Th subsets enhance the recruitment of neutrophils and other immune cells into the inflamed CNS which leads to irreversible neuronal damage. These events mediated via the secretion of interleukin-17 (IL-17), IL-21, IL-22, IL-23, IL-6, interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), and other pro-inflammatory cytokines tend to deteri- orate the neuroinflammation. Therefore, it is not surprising that Th17-related responses are implicated in a wide range of neuroinflammatory and neuroimmune disorders. Here in the chapter, we have described the potential role of Th17 cells and other related pro-inflammatory Th subsets (Th1 and Th17/1) in the patho- genesis of neuroinflammatory disorders and mentioned the detailed mechanisms of neuronal injuries and apoptosis induced by Th17-associated immune responses.
... Numerous genetic loci that increase the incidence of autoimmune diseases have been found in genetic studies of SCZ patients [77]. The genetic overlap between MS and SCZ was also shown to be substantial, and MS risk alleles were linked to a lower chance of SCZ [78]. In addition, a GWAS study analyzed a total of 23,180 patients and identified multiple susceptibility loci for IBD associated with SCZ [79]. ...
Article
Full-text available
Background Observational studies have shown a link between autoimmune diseases and schizophrenia, with conflicting conclusions. Due to the existence of confounding factors, the causal link between autoimmune diseases and schizophrenia is still unknown. Method We conducted a comprehensive Mendelian randomization (MR) analysis of schizophrenia and ten common autoimmune diseases in individuals of European descent using genome-wide association studies (GWASs). To evaluate the relationships between autoimmune diseases and schizophrenia, inverse variance weighted, MR-RAPS, Bayesian weighted MR, constrained maximum likelihood, debiased IVW, MR-Egger, and weighted median were utilized. Several sensitivity analyses were performed to ensure the reliability of the study's results. Results Our findings reveal that genetically predicted ankylosing spondylitis is related to an increased risk of schizophrenia, whereas celiac disease, type 1 diabetes, and systemic lupus erythematosus are associated with a lower risk of schizophrenia. In the reverse MR analysis, our study indicated that genetically predicted schizophrenia is linked to higher risks of ankylosing spondylitis, Crohn's disease, ulcerative colitis, inflammatory bowel disease, and psoriasis. Neither multiple sclerosis nor rheumatoid arthritis have been linked to schizophrenia, and vice versa. Conclusion Despite contradicting some other observational reports, this study showed support for a causal link between autoimmune diseases and schizophrenia. To gain a better understanding of the mechanisms underlying the development of immune-mediated schizophrenia, additional research is required to identify potential mechanisms identified in observational studies.
... Despite an elusive etiopathology, compelling evidence underscores the substantial impact of inflammation in the pathogenesis of schizophrenia and could be construed as a chronic inflammatory condition affecting the brain (Debnath 2015). Recent investigations highlight immune system dysfunctions, including altered expression of immune-related cytokines within the brains and CSF of schizophrenia patients (Andreassen et al. 2015;Hyde and Bharadwaj 2015;Patterson 2009;Avramopoulos et al. 2015;Na et al. 2014). Nonetheless, the fundamental disease etiopathology and the precise contribution of the immune system to schizophrenia's pathogenesis remain enigmatic (Debnath 2015;Khandaker and Dantzer 2016). ...
Chapter
Pro-inflammatory T lymphocyte populations (Th17, Th1, and Th17/1 cells) trigger and exacerbate the neuroinflammation through cytokine production and cell-cell contacts with neurons, microglial cells, and other CNS-infiltrating immune cells. A cascade of neurological injuries associated with Th17-related immune pathways arises in the CNS of patients with multiple sclerosis (MS), Alzheimer’s disease (AD), Parkinson’s disease (PD), schizophrenia, and many other autoimmune disorders. Th17 cells and their related Th subsets enhance the recruitment of neutrophils and other immune cells into the inflamed CNS which leads to irreversible neuronal damage. These events mediated via the secretion of interleukin-17 (IL-17), IL-21, IL-22, IL-23, IL-6, interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), and other pro-inflammatory cytokines tend to deteri- orate the neuroinflammation. Therefore, it is not surprising that Th17-related responses are implicated in a wide range of neuroinflammatory and neuroimmune disorders. Here in the chapter, we have described the potential role of Th17 cells and other related pro-inflammatory Th subsets (Th1 and Th17/1) in the patho- genesis of neuroinflammatory disorders and mentioned the detailed mechanisms of neuronal injuries and apoptosis induced by Th17-associated immune responses.
... This included differential expression of 25 miRNAs, although 5 were not statistically significant after correction for multiple testing following treatment with the viral mimic poly I:C alone, the synthetic cannabinoid HU210 alone, or a combination of them [64]. More strikingly, a recent study by Baulina et al. [63] reported up-regulation of 26 miRNAs from this region in the PBMCs of eight treatment-naive male patients with relapsing-remitting multiple sclerosis (RRMS) compared to four healthy controls, but not in a female cohort of the same size [65], which is an interesting observation considering the shared genetic risk between schizophrenia and MS [66] and also the higher rate of psychiatric disorders among MS patients in comparison to the general population [67][68][69]. ...
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... Some studies, including that by Marrie et al., indicate an elevated risk of schizophrenia in multiple sclerosis patients, suggesting possible comorbidity [148]. Both conditions share clinical, epidemiological, genetic, and immunological commonalities, with inflammation and autoimmunity being central to their pathogenesis [149,150]. Benros et al. highlighted the bidirectional risk between schizophrenia and autoimmune diseases, including multiple sclerosis, pointing to shared immune response and inflammatory processes [151]. Alterations in white matter integrity and myelination are common to both, implying at least a degree of shared pathology [152], whilst studies underlying further mechanistic or genetic basis are still required to address this. ...
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... Although currently there is a lack of direct evidence revealing overlapping determinants between inflamm-aging and psychotic disorders in humans, earlier studies have indicated the genetic contribution of the HLA loci, the best-characterized loci attributable to psychosis [28,29,144,145], to inflamm-aging as well [146][147][148]. Also, abnormal TCR repertoires have been identified in schizophrenia patients, at least in subtypes of them [88,149], whereas reduction of the TCR repertoires is one of the hallmarks of T-cell aging [150]. ...
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