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Background: Concurrence of substance use disorders (SUDs) is high in individuals with psychiatric illnesses; more importantly, individuals with both disorders (dual diagnosis) have more severe symptoms. Psychiatric disorders have been proposed to share a genetic susceptibility with SUDs. To explore this shared genetic susceptibility, we analyzed whether any of the polygenic risk scores (PRSs) for psychiatric disorders could be associated to dual diagnosis in patients with schizophrenia (SCZ) or bipolar disorder (BD). Methods: We included 192 individuals of Mexican ancestry: 72 with SCZ, 53 with BD, and 67 unrelated controls without psychiatric disorders. We derived calculations of PRS for autism spectrum disorders, attention-deficit/hyperactive disorder, BD, major depression, and SCZ using summary genome-wide association statistics previously published. Results: We found that dual diagnosis had a shared genetic susceptibility with major depressive disorder (MDD) and SCZ; furthermore, in individuals with BD, dual diagnosis could be predicted by PRS for MDD. Conclusions: Our results reinforce the notion that individuals with dual diagnosis have a higher genetic susceptibility to develop both disorders. However, analyses of larger sample sizes are required to further clarify how to predict risks through PRS within different populations.
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ORIGINAL ARTICLERev Invest Clin. 2019;71:321-9
Exploratory Analysis of Polygenic
Risk Scores for Psychiatric Disorders:
Applied to Dual Diagnosis
J J. M-M1,2, T B. G-C1,3, A D. G-M2,4, C A.
T-Z5, I E. J-R1, E S-U6, O R-M7,
N L8, M E9, L M-K10  H N2,8*
1Academic Division of Health Sciences, Universidad Juárez Autónoma de Tabasco, Villahermosa, Tabasco, Mexico;
2Genomics Laboratory of Psychiatric and Neurodegenerative Diseases, Instituto Nacional de Medicina Genómica,
Mexico City, Mexico; 3Multidisciplinary Academic Division of Jalpa de Méndez, Universidad Juárez Autónoma
de Tabasco, Jalpa de Méndez, Mexico; 4Hospital Psiquiátrico Infantil “Juan N. Navarro”, Servicios de Atención
Psiquiátrica, Mexico City, Mexico; 5Multidisciplinary Academic Division of Comalcalco, Universidad Juárez Autónoma
de Tabasco, Comalcalco, Mexico; 6Neurosciences Center, Universidad Autónoma de Nuevo León, Monterrey, Mexico;
7Palliative Care Unit, Instituto Nacional de Cancerología, Mexico City, Mexico; 8Grupo de Estudios Médicos y Familiares
Carracci, Mexico City, Mexico; 9Department of Psychiatry and Center of Excellence in Neurosciences, Texas Tech
University Health Sciences Center, El Paso, Texas, USA; 10Unit of Population Genomics Applied to Health, Faculty
of Chemistry, Universidad Nacional Autónoma de México - Instituto Nacional de Medicina Genómica, Mexico City,
Received for publication: 28-02-2019
Approved for publication: 24-04-2019
DOI: 10.24875/RIC.19003013
Background: Concurrence of substance use disorders (SUDs) is high in individuals with psychiatric illnesses; more importantly,
individuals with both disorders (dual diagnosis) have more severe symptoms. Psychiatric disorders have been proposed to share
a genetic susceptibility with SUDs. To explore this shared genetic susceptibility, we analyzed whether any of the polygenic risk
scores (PRSs) for psychiatric disorders could be associated to dual diagnosis in patients with schizophrenia (SCZ) or bipolar
disorder (BD). Methods: We included 192 individuals of Mexican ancestry: 72 with SCZ, 53 with BD, and 67 unrelated controls
without psychiatric disorders. We derived calculations of PRS for autism spectrum disorders, attention-deficit/hyperactive dis-
order, BD, major depression, and SCZ using summary genome-wide association statistics previously published. Results: We found
that dual diagnosis had a shared genetic susceptibility with major depressive disorder (MDD) and SCZ; furthermore, in indi-
viduals with BD, dual diagnosis could be predicted by PRS for MDD. Conclusions: Our results reinforce the notion that individu-
als with dual diagnosis have a higher genetic susceptibility to develop both disorders. However, analyses of larger sample sizes
are required to further clarify how to predict risks through PRS within different populations. REV INVEST CLIN. 2019;71:3219
Key words: Dual diagnosis. Polygenic risk scores. Schizophrenia. Bipolar disorder.
Corresponding author:
*Humberto Nicolini
Genomics Laboratory of Psychiatric
and Neurodegenerative Diseases
Instituto Nacional de Medicina Genómica
Grupo de Estudios Médicos y Familiares Carracci
Mexico City, Mexico
No part of this publication may be reproduced or photocopying without the prior written permission of the publisher. © Permanyer 2019
REV INVEST CLIN. 2019;71:321-9
Psychiatric disorders are complex diseases caused by
multiple risk factors1,2. During the past decade, the
discovery of genetic factors involved in the suscepti-
bility to these disorders has increased rapidly, due to
novel techniques such as genome-wide association
studies (GWAS)3-6. GWAS have identified multiple loci
among common genomic variants that have been as-
sociated to one or more psychiatric disorders. Meth-
ods to determine polygenic risk scores (PRSs) have
been developed to summarize and use what is known
about these multiple disease-associated loci as risk
prediction tools7,8. PRS can be described as the sum-
mation of disorder-associated alleles across many
loci, weighted by their effect sizes estimated from
GWAS in one individual to predict one person’s likeli-
hood of developing a disease with a genetic compo-
nent. Therefore, a high value of PRS could be trans-
lated into an increased risk of a particular disorder for
having more individual risk variants, each one known
to be associated with the same disorder. The calcula-
tion of how much an individual variant increases a
disease risk, or how groups of variants increase the
risk, derives from former GWAS9. Recent studies of
PRS as risk prediction tools for a disease have exam-
ined how different complex diseases might share
polygenetic backgrounds. They have also evaluated
whether PRS can be used to predict particular traits
or subtypes within groups of individuals who have the
same diagnosis10-12.
Many epidemiological analyses have shown that psy-
chiatric disorders such as bipolar disorder (BD),
schizophrenia (SCZ), and major depressive disorder
(MDD) have high comorbidity with substance use dis-
orders (SUDs)13,14. The comorbidity between SUD
and other psychiatric disorders is so high that the
term “dual diagnosis” was created to specify comor-
bidity of these disorders15-17. Dual diagnosis, when it
occurs, has been associated with multiple negative
physical and psychosocial outcomes such as poorer
quality of life, higher rates of relapse of substance
use, and increased suicide risk18-20. Individuals with a
dual diagnosis show increased severity of symptoms,
which place them at high risk21. Although etiologic
mechanisms underlying dual diagnosis have not been
clearly established, some have suggested that indi-
viduals with dual diagnosis could have a greater ge-
netic susceptibility22,23. Recently, PRSs for psychiatric
disorders have been proposed to be useful for explor-
ing the shared genetic susceptibility between psychi-
atric disorders and SUD, to understand the genetic
basis of dual diagnosis24-26. In addition, PRSs of psy-
chiatric disorders have been studied to find risk pre-
dictors for dual diagnosis27-30. However, one of the
main limitations to this approach is that PRSs of psy-
chiatric disorders were derived from GWAS in Euro-
pean populations, which have a highly homogeneous
genetic background, and they have not been conduct-
ed in populations with a high degree of genetic ad-
mixture. There are nations around the world with a
high degree of admixture, including the Mexican pop-
ulation31, which comes from the combination of sev-
eral indigenous groups and a few European popula-
tions. Therefore, some researchers are concerned
with the generalization of the use of PRS in non-Eu-
ropean populations, and how the use of PRS could be
translated to populations with heterogeneous genet-
ic background32-34. In this sense, our aim was to ex-
plore the performance of PRS calculated from previ-
ous GWAS for psychiatric disorders when applied to
Mexican individuals with a high degree of admixture
who have been diagnosed with SCZ or BD, many of
whom had a dual diagnosis.
Target sample
All participants were recruited at the Carracci Medical
Group and evaluated using the Diagnostic Interview
for Genetic Studies (DIGS)35. Diagnoses were as-
signed using the DSM-V criteria for BD, SCZ, and SUD.
A total of 192 individuals of Mexican ancestry were
included in the analysis. Inclusion criteria: The par-
ticipant’s parents and grandparents had to be of
Mexican ancestry, meaning that mother, father, and
four grandparents were born in Mexico; participants
had to be 18 years of age or older. The group of cases
consisted of 125 unrelated outpatients; 72 (49 males
and 23 females) had a lifetime diagnosis of SCZ and
53 (25 males and 28 females), a lifetime diagnosis of
BD. All patients were under psychiatric treatment for
at least 3 weeks after this study began. For the con-
trol group, we used the same inclusion criteria and
excluded individuals with BD, SCZ, MDD, SUD,
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anxiety disorders, or history of suicidal behavior. The
control group consisted of 67 subjects with no life-
time history of any psychiatric disorder and who had
no relatives with a known history of psychiatric disor-
ders. We defined dual diagnosis as diagnosis of SCZ
or BD with one or more lifetime SUDs (tobacco, alco-
hol, and illegal drugs)36. The prevalence of lifetime
SUD (dual diagnosis) in the group of patients with
SCZ was 40.28% (n = 29) and 57.72% (n = 29) in
patients with BD. An overview of sociodemographic
characteristics of the sample is reported in Table 1.
This protocol was approved by the ethics and investi-
gation committee of the National Institute of Ge-
nomic Medicine under the approval number
23/2015/I. All participants were informed of the
aims of the study and gave their written consent be-
fore the study began. All protocols were performed
the following guidelines of the Helsinki Declaration.
Discovery samples
As discovery samples, we used the publicly available
summary statistics from GWAS to obtain the single-
nucleotide polymorphisms (SNPs) and associated
effect sizes, minor allele frequencies, and effect al-
leles, to be included in the PRSs calculation for each
disorder. Data from these discovery samples came
from the Psychiatric Genomics Consortium (PGC)37
and could be accessed from
edu/pgc/results-and-downloads. Data in the PGC
portal are summary statistics derived from GWAS
previously performed in the following disorders: au-
tism spectrum disorders (ASD)38, attention-deficit/
hyperactivity disorder (ADHD)39, BD3, MDD40, and
Genotyping and imputation
of target sample
DNA was extracted from peripheral leukocytes using
a salting-out commercial protocol, following the
specifications established by the provider (Qiagen,
USA). Genotyping was performed using the whole-
genome genotyping platform PsychArray BeadChip
(Illumina, USA) following the protocol and conditions
established by the provider. PsychArray includes
Table 1. Summary of sociodemographic data
Bipolar disorder
(n = 53)
(n = 72)
(n = 67)
Age (years, ± SD) 37.58 (14.03) 33.53 (9.98) 34.37 (12.51)
Years of education
(years, ± SD)
12.49 (4.03) 10.53 (3.54) 13.16 (5.22)
Male (n, %) 25 (47.17) 49 (68.06) 26 (38.81)
Female (n, %) 28 (52.83) 23 (31.94) 41 (61.19)
No SUD 24 (45.28) 43 (59.72) 67 (100.00)
Dual diagnosis 29 (57.72) 29 (40.28) 0 (0.00)
Alcohol 23 (43.40) 21 (29.17) 0 (0.00)
Tobacco 29 (54.72) 29 (40.28) 0 (0.00)
Cocaine 8 (15.09) 1 (1.39) 0 (0.00)
Cannabis 5 (9.43) 9 (12.50) 0 (0.00)
Inhalants 3 (5.66) 1 (1.39) 0 (0.00)
Stimulants 3 (5.66) 1 (1.39) 0 (0.00)
Dual diagnosis was defined as having a psychiatric disease diagnosis (BD or SCZ) and at least one SUD. SUDs included abuse or dependence of
alcohol, cocaine, cannabis, inhalants, and/or stimulants. There were no other SUDs in this sample. SD: Standard deviation, No SUD: Patients
without dual diagnosis. BD: Bipolar disorder, SUD: Substance use disorder, SCZ: Schizophrenia.
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REV INVEST CLIN. 2019;71:321-9
approximately 560,000 polymorphisms distributed
across the whole genome, as well as some variants
previously associated with diverse mental psychiatric
disorders including BD and SCZ. As quality control
(QC), we removed all the SNPs with a minor allele
frequency (MAF) of 5%, a Hardy–Weinberg equilib-
rium (HWE) p < 0.00001 for a Chi-square test; ad-
ditionally, polymorphisms with a genotyping rate
lower than 95% were removed. The genotyped data-
base is available as supplementary information 1.
After the genotyping process, we performed an impu-
tation using Beagle software; the 1000 Genomes da-
tabase was utilized as reference42-44. For the following
analyses, we included only SNP with a Chi-square test
p-value for an HWE lower than 0.00001, a MAF high-
er than 0.05, and an allelic R2 higher than 0.445. After
imputation and QC filter, we obtained a total of
4,835,917 SNPs.
Polygenic risk score calculation
PRSs are measures developed to reduce the calcula-
tion of risk profiles due to polygenicity in complex
diseases to a simpler and more manageable, single
score for everyone. The PRSs for one individual are
the summation of GWAS associated alleles to a dis-
order/trait, weighted by their effect sizes46. To calcu-
late the individual PRS, we used summary statistics
based on published GWAS from the Psychiatric Ge-
nomic Consortium (data free to download from:
loads) utilizing the algorithm implemented in PRSice9.
PRSice calculated PRS using two sets of data, one
discovery and one target sample. The discovery sam-
ple is the summary statistics from a GWAS for a
specific trait or disease, with enough power to detect
an association at genome-wide significance (i.e., data
downloaded from GWAS and meta-analysis reported
by the PGC). The target sample is the sample where
PRSs are going to be calculated (our target sample
was the genotype data obtained after genotyping,
genotyping QC, imputation, and imputation QC).
Once we had the discovery and the target samples,
PRSice performed two steps: first, the clumping pro-
cess, where polymorphisms in linkage disequilibrium
(LD) between the associated loci in the target sample
and the discovery sample were unified47. In this analy-
sis, we used the following clumping criteria: 250 kb
and pairwise LD R2 < 0.1. Second, PRSice calculated
the individuals’ PRS using different p-values thresholds
for the associated variants in the discovery sample
(with a lower bound of p = 0.0001 and an upper bound
of p = 0.5; increments of 0.00005, which generated
9999 different thresholds). After the different thresh-
olds were performed, the best-fitted model estimates
were reported. This high-resolution approach allowed
us to calculate the best-fitted PRS for the target sam-
ple. In this study, we report the PRS for the best esti-
mates of models. All the models were adjusted by age,
gender, and the first five principal components of
global ancestry; for p-value multiple testing correc-
tions, we performed 1000 permutations tests. The
global ancestry estimation was performed with princi-
pal components analysis implemented in the GCTA
software48, using a panel of 200 ancestry informative
markers previously reported to reach at ancestry es-
timations in American populations49. For ancestry cal-
culation, the following populations were used as refer-
ences: Utah’s residents with northern and western
European ancestry (CEU), Yoruba residents in Ibadan
from a Nigeria population (YRI) reported in the 1000
Genomes Project44, and 25 individuals of Mexican
Amerindian (MA) ancestry genotyped with the multi-
ethnic genotyping array (Illumina, San Diego, CA,
USA). Once we calculated the best-fitted PRS, we
compared the mean of these PRSs for psychiatric dis-
orders between cases (subjects with BD and SCZ) and
controls using a Welch t-test and considered a sig-
nificant association when p < 0.05.
Calculation of SUD correlation
with psychiatric diseases PRSs
One common application of PRS is to test for common
genetic variation shared by two different traits or
disorders46. To determine whether any of the psychi-
atric PRS (out of those for ADHD, ASD, MDD, SCZ,
and BD) calculated with the previous algorithm, had
a shared genetic etiology with a dual diagnosis phe-
notype within our sample, we performed a Nagelker-
ke test implemented in PRSice50,51. PRSice reports the
variance explained by PRS in the analyzed phenotype,
calculated as the difference in the Nagelkerke’s pseu-
do-R2 from a model including the score and covariates
versus a model including only the covariates. In these
correlation tests, we recorded the phenotype of dual
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diagnosis as cases (29 individuals with both SUD and
BD and 29 individuals with both SUD and SCZ) and
defined a non-SUD phenotype consisting of all indi-
viduals without SUD diagnoses (67 individuals with
either BD or SCZ who did not have a comorbid SUD
and the 67 controls). Furthermore, for these analy-
ses, we used the best-fitted models as described
above. After finding which PRS explained a higher
variance with SUD, we performed a Welch t-test
comparison of this PRS between patients diagnosed
with one psychiatric disease only (BD or SCZ without
SUD) and patients with dual diagnosis (BD or SCZ
with SUD) and considered a significant association
when p < 0.05.
Effect of global ancestry estimation
on the best-fitted PRS
To establish whether ancestry influences the PRS that
shared a genetic effect with dual diagnosis, we per-
formed a Spearman correlation test implemented in
R52. We compared the global ancestry principal com-
ponent 1 (PC1) and principal component 2 (PC2) with
the PRS (best-fitted PRS and covariants adjusted). We
only included PC1 and PC2 because these two global
ancestry principal components divide individuals into
two main populations31. We considered a correlation
with PRS and global ancestry component if p < 0.05.
In the analysis of PRS for psychiatric disorders, we
found that the only PRS that showed statistical dif-
ferences between psychiatric cases (72 subjects
diagnosed with SCZ and 53 subjects diagnosed with
BD) and controls (67 subjects) was the PRS for MDD.
A summary of the means of each PRS for ADHD, ASD,
BD, MDD, and SCZ is reported in Table 2.
SCZ and major depression shared
genetic etiology with dual diagnosis
PRSs for ADHD, ASD, and BD did not share a genetic
etiology at a significant level with dual diagnosis in
our population: ADHD p = 0.1570, ASD p = 0.0538,
and BD p = 0.1585. In contrast, SCZ and MDD:PRS
each showed a significant shared genetic etiology
with the dual diagnosis phenotype: SCZ (Nagelkerke
Pseudo-R2 = 0.0283, corrected p = 0.0423, n = 8058
SNPs) and MDD (Nagelkerke Pseudo-R2 = 0.0451,
corrected p = 0.0118, n = 334 SNPs). As can be ob-
served, the MDD:PRS explained a higher amount of
variance (4.51%) predicting placement in the dual
diagnosis group than did the SCZ:PRS (2.83%).
Once we identified that MDD and SCZ PRS had a
shared genetic etiology with a dual diagnosis in our
sample, we performed a pair-wise comparison of the
individual MDD and SCZ PRS in patients with a dual
diagnosis and patients without a dual diagnosis, to
determine whether these PRSs (for MDD or SCZ)
could be used to detect a subgroup of patients who
had SUD within each diagnostic category (BD or SCZ
patients). In this analysis, patients with a dual diag-
nosis in the BD group had a higher MDD:PRS when
compared to patients with BD without a dual diagno-
sis, and this difference reached statistical significance
(p < 0.05) (Fig. 1). In contrast, when the MDD:PRS
was applied only to patients diagnosed with SCZ, it
Table 2. Comparisons of psychiatric disorders polygenic risk scores between patients diagnosed with psychiatric disorders and
Polygenic risk score Cases
(n = 125)
(n = 67)
ADHD:PRS −0.0013 (0.0002) −0.0013 (0.0002) 0.1817
ASD:PRS −0.0035 (0.0024) −0.0032 (0.0021) 0.3738
BD:PRS 0.0213 (0.0196) 0.0211 (0.0189) 0.9404
SCZ:PRS 0.0011 (0.0004) 0.0011 (0.0005) 0.7602
MDD:PRS −0.0041 (0.0018) −0.0033 (0.0025) 0.04754
ADHD:PRS: Attention-deficit hyperactivity disorder:polygenic risk score, ASD:PRS: Autism spectrum disorders:polygenic risk score,
BD:PRS: Bipolar disorder:polygenic risk score, SCZ:PRS: Schizophrenia:polygenic risk score, MDD-PRS: Major depression disorder-polygenic
risk score. The reported p-value is result of a Welch t-test.
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REV INVEST CLIN. 2019;71:321-9
did not distinguish between SCZ patients with and
without a dual diagnosis of SUD. The SCZ:PRS did not
show statistically significant differences in detecting
a dual diagnosis when it was applied only to patients
with SCZ (SCZ patients with and without SUD) or to
patients with BD (BD patients with and without SUD).
The pair-wise comparisons are shown in Table 3.
Possible global ancestry deviation
of MDD and SCZ-PRS
To explore whether genetic admixture could influ-
ence PRS, we analyzed how global ancestry within
the sample could affect the best-fitted PRS (also,
adjusted after covariants). As PRSs of MDD and SCZ
were the only ones that shared a genetic etiology
with a dual diagnosis, we performed correlation tests
with all the participants (SCZ, BD, and healthy con-
trols) of each individual PRS and the global ancestry
principal components (PC1 and PC2), which are the
two components that separated main populations31, to
search whether PRS calculations could have an ances-
try-dependent deviation. In this analysis, both MDD
(PC1: rho = −0.20, p = 0.01 and PC2: rho = −0.19,
p = 0.01) and SCZ (PC1: rho = −0.61, p = 2.2e-16
and PC2: rho = −0.61, p = 2.2e-16) PRSs were cor-
related with PC1 and PC2. Of the two, SCZ:PRS had
a stronger correlation with the two global ancestry
components (Fig. 2).
Figure 1. Comparisons of schizophrenia (SCZ) and major depressive disorder (MDD) polygenic risk score (PRS) in patients with
and without dual diagnosis (substance use disorder). (A) Comparison of MDD:PRS in patients with bipolar disorder (BD) (dual
diagnosis patients had significantly higher scores than BD only subjects, p = 0.0005) and patients with SCZ (non-significant
difference). (B) Comparison of SCZ-PRS in BD and SCZ patients. SCZ:PRS differences between dual diagnosis patients and pa-
tients with only the primary disorder were not significantly different in either BD or SCZ subjects.
Table 3. Comparisons of MDD and SCZ polygenic risk scores
Bipolar disorder Schizophrenia
Polygenic risk
No substance
p-value No substance
SCZ:PRS 0.0010
0.6392 0.0011
MDD:PRS −0.0048
0.0007 −0.0041
Dual diagnosis was considered if a patient had a psychiatric disease diagnosis (BD or SCZ) and substance use. SCZ:PRS: Schizophrenia:polygenic
risk score, MDD:PRS: Major depression disorder:polygenic risk score. The reported p-value resulted from Welch t-test. BD: Bipolar disorder,
SUD: Substance use disorder.
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PRSs are of potential value for determining subphe-
notypes within a larger phenotype or main diagnosis
in psychiatric disorders53,54. In the present study, we
evaluated whether the current PRS (available for phe-
notypes of ADHD, ASD, BD, SCZ, and MDD) could also
correlate with a lifetime history of dual diagnosis, in
individuals diagnosed with SCZ or BD. Our results
showed that both MDD and SCZ-PRSs had an impact
on a dual diagnosis in the total sample. Nevertheless,
when applied only to patients with one diagnosis, only
MDD:PRS was found to differentiate patients diag-
nosed with BD and dual diagnosis from patients diag-
nosed with BD without a dual diagnosis.
Our study suggests that both the MDD:PRS and the
SCZ:PRS might be of use in detecting risk for a dual
diagnosis; however, when PRSs are applied only to a
specific diagnosis, we suggest that MDD:PRS, used in
patients with BD, is the only specific PRS which cor-
relates with a dual diagnosis within the specific diag-
noses of SCZ or BD, respectively. The shared genetic
susceptibility between MDD and alcohol dependence
(AD) might be what drove this result within our BD
patients, especially when noting that the main sub-
stance of SUD in our samples was (apart from nico-
tine) alcohol abuse/dependence. These findings are
consistent with the study of Andersen et al.55, where
they suggested that shared genetic susceptibility con-
tributed to MDD and AD comorbidity.
Although other studies have applied PRS to explore
the shared genetic background between psychiatric
disorders and SUDs24,25, all the approximations have
been made in populations of European ancestry and
not in admixed populations. Our study is one of the
first approximations on how to apply psychiatric PRS
in admixed populations. Our results suggest that PRS
must be applied with caution in admixed populations
such as the Mexican population, which has individuals
with varying levels of admixture31,49,56. In relation to
this, we found that SCZ:PRS showed a correlation with
global ancestry components. The difference in PRS
based on demographic history has been previously
explored32,57. Martin et al. evaluated eight complex
traits PRS in the 1000 Genomes Project panel and
found similar results to ours; they observed that
SCZ:PRS could be deviated based on the main popula-
tion’s ancestry. In their analysis, they also reported
Figure 2. Analysis of the correlation of schizophrenia (SCZ) and major depressive disorder (MDD) polygenic risk scores (PRSs)
with global ancestry components. Triangles represent patients with bipolar disorder, circles represent patients with SCZ, and
squares represent non-psychiatric controls. Each triangle, circle, or square represents any of the 195 individuals included in the
analysis. The individuals’ graph in both plots is the same in the same position, the only change is the gradient of SCZ:PRS or
MDD:PRS value. (A) Correlation of SCZ:PRS with global ancestry components. The gradient represents the SCZ-PRS, red color
means a higher SCZ:PRS, and blue means a lower SCZ:PRS. Individuals with a higher degree of global Mexican Amerindian an-
cestry are grouped in values lower than zero in both principal components’ axes. The high correlation between the ancestry
components and the SCZ:PRS is shown. (B) Correlation of MDD:PRS with global ancestry components. The gradient represents
the MDD:PRS, red color means a higher MDD:PRS, and blue means a lower MDD:PRS. Individuals with a higher degree of global
Mexican Amerindian ancestry are grouped in values lower than zero in both principal components axes. The low correlation
between the ancestry components and the MDD:PRS is shown.
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REV INVEST CLIN. 2019;71:321-9
that it was not possible to predict how PRS could
change according to ancestry. In this sense, we think
that the application of PRS in different populations,
with distinct admixtures and diverse phenotypes,
could give us more information on the use of PRS for
psychiatric disorders as a translational risk prediction
The results obtained from this study should be con-
sidered as preliminary due to the small sample size; it
will be necessary to increase the sample size to have
a better understanding of both the utility of PRS to
determine dual diagnosis risk in BD and SCZ, and to
assess how to correct for genetic population factors
that influence PRS. This study is among the first ones
looking at how PRSs for psychiatric disorders perform
as markers of dual diagnosis in admixed populations.
Nevertheless, we found that dual diagnosis had a
shared etiology with MDD and SCZ. The present
study can help reduce disparities in what is known
about the PRSs in different populations.
This work was supported by the Instituto Nacional de
Medicina Genómica with a grant to Humberto Nicolini
(Grant number 23/2015/I). We want to thank all the
participants in the present study. We are grateful to
Samuel Canizales-Quinteros for the critical revision of
the manuscript. José Jaime Martínez-Magaña is a
doctoral student at the Doctorado en Ciencias Bio-
médicas, Universidad Juárez Autonóma de Tabasco
(UJAT) and was supported by the National Council of
Science and Technology (CONACYT) of Mexico.
1. Mitchell KJ. What is complex about complex disorders? Genome
Biol. 2012;13:237.
2. Avramopoulos D. Genetics of psychiatric disorders methods:
molecular approaches. Psychiatr Clin North Am. 2010;33:1-3.
3. Psychiatric GWAS Consortium Bipolar Disorder Working Group.
Large-scale genome-wide association analysis of bipolar disor-
der identifies a new susceptibility locus near ODZ4. Nat Genet
4. Stahl EA, Breen G, Forstner AJ, McQuillin A, Ripke S, Trubetskoy
V, et al. Genome-wide association study identifies 30 loci as-
sociated with bipolar disorder. Nat Genet. 2019;51:793-803.
5. Ritter ML, Guo W, Samuels JF, Wang Y, Nestadt PS, Krasnow J,
et al. Genome wide association study (GWAS) between atten-
tion deficit hyperactivity disorder (ADHD) and obsessive com-
pulsive disorder (OCD). Front Mol Neurosci. 2017;10:83.
6. Lambert JC, Ibrahim-Verbaas CA, Harold D, Naj AC, Sims R, Bel-
lenguez C, et al. Meta-analysis of 74,046 individuals identifies
11 new susceptibility loci for Alzheimer’s disease. Nat Genet.
7. Schork AJ, Brown TT, Hagler DJ, Thompson WK, Chen CH, Dale
AM, et al. Polygenic risk for psychiatric disorders correlates with
executive function in typical development. Genes Brain Behav.
8. Cross-Disorder Group of the Psychiatric Genomics Consortium.
Identification of risk loci with shared effects on five major psy-
chiatric disorders: a genome-wide analysis. Lancet. 2013;
9. Euesden J, Lewis CM, O’Reilly PF. PRSice: polygenic risk score
software. Bioinformatics. 2015;31:1466-8.
10. Lewis CM, Vassos E. Prospects for using risk scores in poly-
genic medicine. Genome Med. 2017;9:96.
11. Vassos E, Di Forti M, Coleman J, Iyegbe C, Prata D, Euesden J, et
al. An examination of polygenic score risk prediction in individu-
als with first-episode psychosis. Biol Psychiatry. 2017;81:470-7.
12. Torkamani A, Wineinger NE, Topol EJ. The personal and clinical
utility of polygenic risk scores. Nat Rev Genet. 2018;19:581-90.
13. Wong CC, Schumann G. Review. Genetics of addictions: strategies
for addressing heterogeneity and polygenicity of substance use
disorders. Philos Trans R Soc Lond B Biol Sci. 2008;363:3213-22.
14. Maher BS, Marazita ML, Zubenko WN, Kaplan BB, Zubenko GS.
Genetic segregation analysis of alcohol and other substance-
use disorders in families with recurrent, early-onset major de-
pression. Am J Drug Alcohol Abuse. 2002;28:711-31.
15. Kessler RC. The epidemiology of dual diagnosis. Biol Psychiatry.
16. Buckley PF, Brown ES. Prevalence and consequences of dual di-
agnosis. J Clin Psychiatry. 2006;67:e01.
17. Breslau N. Psychiatric comorbidity of smoking and nicotine de-
pendence. Behav Genet. 1995;25:95-101.
18. Gattamorta KA, Mena MP, Ainsley JB, Santisteban DA. The co-
morbidity of psychiatric and substance use disorders among
Hispanic adolescents. J Dual Diagn. 2017;13:254-63.
19. Taukoor B, Paruk S, Karim E, Burns JK. Substance use in adoles-
cents with mental illness in Durban, South Africa. J Child Adolesc
Ment Health. 2017;29:51-61.
20. Wilton G, Stewart LA. Outcomes of offenders with co-occurring
substance use disorders and mental disorders. Psychiatr Serv.
21. Di Lorenzo R, Galliani A, Guicciardi A, Landi G, Ferri P. A retro-
spective analysis focusing on a group of patients with dual di-
agnosis treated by both mental health and substance use ser-
vices. Neuropsychiatr Dis Treat. 2014;10:1479-88.
22. Levran O, Randesi M, da Rosa JC, Ott J, Rotrosen J, Adelson M,
et al. Overlapping dopaminergic pathway genetic susceptibility
to heroin and cocaine addictions in African Americans. Ann Hum
Genet. 2015;79:188-98.
23. Kendler KS, Karkowski LM, Neale MC, Prescott CA. Illicit psycho-
active substance use, heavy use, abuse, and dependence in a US
population-based sample of male twins. Arch Gen Psychiatry.
24. Hartz SM, Horton AC, Oehlert M, Carey CE, Agrawal A, Bogdan
R, et al. Association between substance use disorder and poly-
genic liability to schizophrenia. Biol Psychiatry. 2017;82:709-15.
25. Carey CE, Agrawal A, Bucholz KK, Hartz SM, Lynskey MT, Nelson
EC, et al. Associations between polygenic risk for psychiatric
disorders and substance involvement. Front Genet. 2016;7:149.
26. Palmer RH, Brick L, Nugent NR, Bidwell LC, McGeary JE, Knopik
VS, et al. Examining the role of common genetic variants on al-
cohol, tobacco, cannabis and illicit drug dependence: genetics of
vulnerability to drug dependence. Addiction. 2015;110:530-7.
27. Maher BS. Polygenic scores in epidemiology: risk prediction, eti-
ology, and clinical utility. Curr Epidemiol Rep. 2015;2:239-44.
28. Vink JM, Hottenga JJ, de Geus EJ, Willemsen G, Neale MC, Fur-
berg H, et al. Polygenic risk scores for smoking: predictors for
alcohol and cannabis use? Addiction. 2014;109:1141-51.
29. Reginsson GW, Ingason A, Euesden J, Bjornsdottir G, Olafsson
S, Sigurdsson E, et al. Polygenic risk scores for schizophrenia and
bipolar disorder associate with addiction. Addict Biol. 2018;
30. Gurriarán X, Rodríguez-López J, Flórez G, Pereiro C, Fernández
JM, Fariñas E, et al. Relationships between substance abuse/
dependence and psychiatric disorders based on polygenic scores.
Genes Brain Behav. 2019;18:e12504.
31. Moreno-Estrada A, Gignoux CR, Fernández-López JC, Zakharia
F, Sikora M, Contreras AV, et al. Human genetics. The genetics
No part of this publication may be reproduced or photocopying without the prior written permission of the publisher. © Permanyer 2019
of Mexico recapitulates native American substructure and af-
fects biomedical traits. Science. 2014;344:1280-5.
32. Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Grav-
el S, et al. Human demographic history impacts genetic risk
prediction across diverse populations. Am J Hum Genet. 2017;
33. Kim MS, Patel KP, Teng AK, Berens AJ, Lachance J. Genetic
disease risks can be misestimated across global populations.
Genome Biol. 2018;19:179.
34. De La Vega FM, Bustamante CD. Polygenic risk scores: a biased
prediction? Genome Med. 2018;10:100.
35. Nurnberger JI Jr., Blehar MC, Kaufmann CA, York-Cooler C, Simp-
son SG, Harkavy-Friedman J, et al. Diagnostic interview for ge-
netic studies. Rationale, unique features, and training. NIMH
genetics initiative. Arch Gen Psychiatry. 1994;51:849-59.
36. Volkow ND. Substance use disorders in schizophrenia clinical
implications of comorbidity. Schizophr Bull. 2009;35:469-72.
37. Sullivan PF, Daly MJ, O’Donovan M. Genetic architectures of
psychiatric disorders: the emerging picture and its implications.
Nat Rev Genet. 2012;13:537-51.
38. Autism Spectrum Disorders Working Group of the Psychiatric
Genomics Consortium. Meta-analysis of GWAS of over 16,000
individuals with autism spectrum disorder highlights a novel
locus at 10q24.32 and a significant overlap with schizophrenia.
Mol Autism. 2017;8:21.
39. Neale BM, Medland SE, Ripke S, Asherson P, Franke B, Lesch KP,
et al. Meta-analysis of genome-wide association studies of at-
tention-deficit/hyperactivity disorder. J Am Acad Child Adolesc
Psychiatry. 2010;49:884-97.
40. Major Depressive Disorder Working Group of the Psychiatric
GWAS Consortium, Ripke S, Wray NR, Lewis CM, Hamilton SP,
Weissman MM, et al. A mega-analysis of genome-wide asso-
ciation studies for major depressive disorder. Mol Psychiatry.
41. Schizophrenia Working Group of the Psychiatric Genomics Con-
sortium. Biological insights from 108 schizophrenia-associated
genetic loci. Nature. 2014;511:421-7.
42. Browning SR, Browning BL. Rapid and accurate haplotype phas-
ing and missing-data inference for whole-genome association
studies by use of localized haplotype clustering. Am J Hum
Genet. 2007;81:1084-97.
43. Browning BL, Browning SR. Genotype imputation with millions
of reference samples. Am J Hum Genet. 2016;98:116-26.
44. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin
RM, Garrison EP, Kang HM, et al. A global reference for human
genetic variation. Nature. 2015;526:68-74.
45. Verma SS, de Andrade M, Tromp G, Kuivaniemi H, Pugh E,
Namjou-Khales B, et al. Imputation and quality control steps
for combining multiple genome-wide datasets. Front Genet.
46. International Schizophrenia Consortium, Purcell SM, Wray NR,
Stone JL, Visscher PM, O’Donovan MC, et al. Common poly-
genic variation contributes to risk of schizophrenia and bipolar
disorder. Nature. 2009;460:748-52.
47. Shi H, Medway C, Brown K, Kalsheker N, Morgan K. Using fisher’s
method with PLINK ‘LD clumped’ output to compare SNP effects
across genome-wide association study (GWAS) datasets. Int J
Mol Epidemiol Genet. 2011;2:30-5.
48. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for
genome-wide complex trait analysis. Am J Hum Genet. 2011;
49. Galanter JM, Fernandez-Lopez JC, Gignoux CR, Barnholtz-
Sloan J, Fernandez-Rozadilla C, Via M, et al. Development of
a panel of genome-wide ancestry informative markers to
study admixture throughout the Americas. PLoS Genet. 2012;
50. Nagelkerke NJ. A note on a general definition of the coefficient
of determination. Biometrika. 1991;78:691-2.
51. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obu-
chowski N, et al. Assessing the performance of prediction mod-
els: a framework for traditional and novel measures. Epidemiol-
ogy. 2010;21:128-38.
52. Team RDC. R: a Language and Environment for Statistical Com-
puting. R Dev Core Team. R Foundation for Statistical Comput-
ing. Vienna, Austria: Team RDC; 2008.
53. Dzafic I, Burianová H, Periyasamy S, Mowry B. Association
between schizophrenia polygenic risk and neural correlates of
emotion perception. Psychiatry Res Neuroimaging. 2018;
54. Poletti M, Raballo A. Polygenic risk score and the (neuro)devel-
opmental ontogenesis of the schizophrenia spectrum vulnerabil-
ity phenotypes. Schizophr Res. 2018;202:389-90.
55. Andersen AM, Pietrzak RH, Kranzler HR, Ma L, Zhou H, Liu X, et
al. Polygenic scores for major depressive disorder and risk of
alcohol dependence. JAMA Psychiatry. 2017;74:1153-60.
56. Florez JC, Price AL, Campbell D, Riba L, Parra MV, Yu F, et al.
Strong association of socioeconomic status with genetic ances-
try in latinos: implications for admixture studies of Type 2 dia-
betes. Diabetologia. 2009;52:1528-36.
57. Curtis D. Polygenic risk score for schizophrenia is more strongly
associated with ancestry than with schizophrenia. Psychiatr
Genet. 2018;28:85-9.
No part of this publication may be reproduced or photocopying without the prior written permission of the publisher. © Permanyer 2019
... We found significant associations between lifetime AUD and MDD, bipolar disorder, OCD, suicidality, and nicotine dependence even after we controlled for sociodemographic characteristics. AUD and other mental disorders might be related in several ways, including common genetic susceptibility [35] and shared psychosocial factors that have a role in the development of these disorders [36]. These associations would also explain the association of AUD with lower scores in the MCS component of HRQOL. ...
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Introduction: The Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) criteria for alcohol use disorders (AUD) was a significant shift from the historical DSM-IV Text Revised version. Following this shift in diagnostic criteria, a difference in the prevalence of AUD was expected. The current study aimed to evaluate the consequences of the modification of the diagnostic criteria from DSM-IV to DSM-5 AUD using lifetime diagnosis in Singapore's multi-ethnic population using data from a nationwide epidemiological study. Methods: Respondents were assessed for lifetime mental disorders using the Composite International Diagnostic Interview (CIDI) administered through face-to-face interviews. Lifetime DSM-IV AUD diagnoses were compared with DSM-5 AUD diagnoses generated by modifying the criteria and the addition of the craving criterion. Sociodemographic correlates of lifetime DSM-IV/DSM-5 AUD were examined using multiple logistic regression analysis. Associations between DSM-IV/DSM-5 AUD, chronic conditions, and the HRQOL summary scores were examined using logistic and linear regression after controlling for significant sociodemographic factors. Results: The lifetime prevalence of DSM-IV AUD was 4.6% (0.5% for dependence and 4.1% for abuse) in the adult population, while the lifetime prevalence of DSM-5 AUD was 2.2%. Younger age, male gender, and lower education were associated with higher odds of both DSM-IV and DSM-5 AUD. However, those who were economically inactive (versus employed) (AOR, 0.4; 95% CI 0.2-0.9) and had a higher monthly household income (SGD 4000-5999 versus below SGD 2000) had lower odds of DSM-IV AUD (AOR, 0.4; 95% CI 0.2-0.7), but this was not observed among those with DSM-5 AUD. Both DSM-IV and DSM-5 AUD were associated with significant comorbidities in terms of other mental disorders; however, those diagnosed with lifetime GAD had significantly higher odds of having DSM-5 AUD (AOR, 5; 95% CI 1.9-13.2) but not DSM-IV AUD. Conclusions: While using the DSM-5 criteria would result in a lower prevalence of AUD in Singapore, it remains a highly comorbid condition associated with a poor health-related quality of life that is largely untreated, which makes it a significant public health concern.
... 2019 [73] 192 individuals of Mexican Ancestry (125 cases and 67 controls). 72 of the cases had e lifetime DSM-IV schizophrenia (SCZ), while 53 (m = 25) were with BPD diagnosis. ...
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(1) Background: Comorbidity between Alcohol Use Disorders (AUD), mood, and anxiety disorders represents a significant health burden, yet its neurobiological underpinnings are still elusive. The current paper reviews all genome-wide association studies conducted in the past ten years, sampling those on AUD and mood or anxiety disorders. (2) Methods: In keeping with PRISMA guidelines, we searched EMBASE, Medline/PUBMED, and PsycINFO databases (January 2010 to December 2020), including references of enrolled studies. Study selection was based on predefined criteria and data underwent a multistep revision process. (3) Results: 15 studies were included. Some of them explored dual diagnoses phenotypes directly while others employed correlational analysis based on polygenic risk score approach. Their results support the significant overlap of genetic factors involved in AUDs and mood and anxiety disorders. Comorbidity risk seems to be conveyed by genes engaged in neuronal development, connectivity, and signaling although the precise neuronal pathways and mechanisms remain unclear. (4) Conclusion: given that genes associated with complex traits including comorbid clinical presentations are of small effect, and individually responsible for a very low proportion of total variance, larger samples consisting of multiple refined comorbid combinations confirmed by re-sequencing approaches will be necessary to disentangle the genetic architecture of dual diagnosis.
... A recent study in India, for example, found an association for schizophrenia that had not previously been reported in European populations 20 . Other studies have identified differences in non-Europeans from the polygenic risk scores (PRSs) derived from genetic associations based on European ancestry 16,21 . Because of the high degree of genetic mixing in Mexico, there is a need to find loci associated with the comorbidity. ...
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The combination of substance use and psychiatric disorders is one of the most common comorbidities. The objective of this study was to perform a genome-wide association study of this comorbidity (Com), substance use alone (Subs), and psychiatric symptomatology alone (Psych) in the Mexican population. The study included 3914 individuals of Mexican descent. Genotyping was carried out using the PsychArray microarray and genome-wide correlations were calculated. Genome-wide associations were analyzed using multiple logistic models, polygenic risk scores (PRSs) were evaluated using multinomial models, and vertical pleiotropy was evaluated by generalized summary-data-based Mendelian randomization. Brain DNA methylation quantitative loci (brain meQTL) were also evaluated in the prefrontal cortex. Genome-wide correlation and vertical pleiotropy were found between all traits. No genome-wide association signals were found, but 64 single-nucleotide polymorphism (SNPs) reached nominal associations (p < 5.00e−05). The SNPs associated with each trait were independent, and the individuals with high PRSs had a higher prevalence of tobacco and alcohol use. In the multinomial models all of the PRSs (Subs-PRS, Com-PRS, and Psych-PRS) were associated with all of the traits. Brain meQTL of the Subs-associated SNPs had an effect on the genes enriched in insulin signaling pathway, and that of the Psych-associated SNPs had an effect on the Fc gamma receptor phagocytosis pathway. End
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Background: Ancestry is often viewed as a more objective and less objectionable population descriptor than race or ethnicity. Perhaps reflecting this, usage of the term “ancestry” is rapidly growing in genetics research, with ancestry groups referenced in many situations. The appropriate usage of population descriptors in genetics research is an ongoing source of debate. Sound normative guidance should rest on an empirical understanding of current usage; in the case of ancestry, questions about how researchers use the concept, and what they mean by it, remain unanswered. Methods: Systematic literature analysis of 205 articles at least tangentially related to human health from diverse disciplines that use the concept of ancestry, and semi-structured interviews with 44 lead authors of some of those articles. Results: Ancestry is relied on to structure research questions and key methodological approaches. Yet researchers struggle to define it, and/or offer diverse definitions. For some ancestry is a genetic concept, but for many—including geneticists—ancestry is only tangentially related to genetics. For some interviewees, ancestry is explicitly equated to ethnicity; for others it is explicitly distanced from it. Ancestry is operationalized using multiple data types (including genetic variation and self-reported identities), though for a large fraction of articles (26%) it is impossible to tell which data types were used. Across the literature and interviews there is no consistent understanding of how ancestry relates to genetic concepts (including genetic ancestry and population structure), nor how these genetic concepts relate to each other. Beyond this conceptual confusion, practices related to summarizing patterns of genetic variation often rest on uninterrogated conventions. Continental labels are by far the most common type of label applied to ancestry groups. We observed many instances of slippage between reference to ancestry groups and racial groups. Conclusion: Ancestry is in practice a highly ambiguous concept, and far from an objective counterpart to race or ethnicity. It is not uniquely a “biological” construct, and it does not represent a “safe haven” for researchers seeking to avoid evoking race or ethnicity in their work. Distinguishing genetic ancestry from ancestry more broadly will be a necessary part of providing conceptual clarity.
Obsessive-compulsive disorder (OCD) is frequent and often disabling. Yet, correct diagnosis and appropriate treatment implementation are usually delayed, with undesirable consequences. In this paper we review the rationale for early intervention in OCD and provide recommendations for early intervention services. Two scenarios are discussed, i.e., subclinical (prodromal) obsessive-compulsive symptoms (OCS) and full-blown OCD. Although the typical patient with OCD reports a long history of subclinical OCS, longitudinal studies suggest most individuals with OCS in the community do not convert to full-blown OCD. Thus, research on “at risk” phenotypes for OCD and how they should incorporate different risk factors (e.g., polygenic risk scores) are badly needed. For this specific scenario, preventative treatments that are cheap, well tolerated and highly scalable (e.g., lifestyle interventions) are of major interest. Increasing evidence suggests OCD to be a progressive disorder and that severity and duration of illness are associated with both biological changes and increased clinical complexity, as exemplified by greater number of physical and psychiatric comorbidities, increased family accommodation and worse treatment response. Therefore, correct identification and early treatment implementation for full-blown OCD are critical for ethical, clinical and therapeutic reasons. Based on the existing findings, we argue that, regardless of focusing on clinical OCD or subclinical OCS, early intervention services need to target a childhood age group. In addition to delivering well established treatments to people with full-blown OCD early on their illness, early intervention services also need to provide psychoeducation for patients, families and teachers.
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Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10−4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10−8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder. Genome-wide analysis identifies 30 loci associated with bipolar disorder, allowing for comparisons of shared genes and pathways with other psychiatric disorders, including schizophrenia and depression.
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Abstract A new study highlights the biases and inaccuracies of polygenic risk scores (PRS) when predicting disease risk in individuals from populations other than those used in their derivation. The design bias of workhorse tools used for research, particularly genotyping arrays, contributes to these distortions. To avoid further inequities in health outcomes, the inclusion of diverse populations in research, unbiased genotyping, and methods of bias reduction in PRS are critical.
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Background Accurate assessment of health disparities requires unbiased knowledge of genetic risks in different populations. Unfortunately, most genome-wide association studies use genotyping arrays and European samples. Here, we integrate whole genome sequence data from global populations, results from thousands of genome-wide association studies (GWAS), and extensive computer simulations to identify how genetic disease risks can be misestimated. Results In contrast to null expectations, we find that risk allele frequencies at known disease loci are significantly different for African populations compared to other continents. Strikingly, ancestral risk alleles are found at 9.51% higher frequency in Africa, and derived risk alleles are found at 5.40% lower frequency in Africa. By simulating GWAS with different study populations, we find that non-African cohorts yield disease associations that have biased allele frequencies and that African cohorts yield disease associations that are relatively free of bias. We also find empirical evidence that genotyping arrays and SNP ascertainment bias contribute to continental differences in risk allele frequencies. Because of these causes, polygenic risk scores can be grossly misestimated for individuals of African descent. Importantly, continental differences in risk allele frequencies are only moderately reduced if GWAS use whole genome sequences and hundreds of thousands of cases and controls. Finally, comparisons between uncorrected and corrected genetic risk scores reveal the benefits of considering whether risk alleles are ancestral or derived. Conclusions Our results imply that caution must be taken when extrapolating GWAS results from one population to predict disease risks in another population. Electronic supplementary material The online version of this article (10.1186/s13059-018-1561-7) contains supplementary material, which is available to authorized users.
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Initial expectations for genome-wide association studies were high, as such studies promised to rapidly transform personalized medicine with individualized disease risk predictions, prevention strategies and treatments. Early findings, however, revealed a more complex genetic architecture than was anticipated for most common diseases - complexity that seemed to limit the immediate utility of these findings. As a result, the practice of utilizing the DNA of an individual to predict disease has been judged to provide little to no useful information. Nevertheless, recent efforts have begun to demonstrate the utility of polygenic risk profiling to identify groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to disease. In this context, we review the evidence supporting the personal and clinical utility of polygenic risk profiling.
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Editorial summary Genome-wide association studies have made strides in identifying common variation associated with disease. The modest effect sizes preclude risk prediction based on single genetic variants, but polygenic risk scores that combine thousands of variants show some predictive ability across a range of complex traits and diseases, including neuropsychiatric disorders. Here, we consider the potential for translation to clinical use.
Background: The polygenic risk score (PRS) for schizophrenia, derived from very large numbers of weakly associated genetic markers, has been repeatedly shown to be robustly associated with schizophrenia in independent samples and also with other diseases and traits. Aim: This study aims to explore the distribution of the schizophrenia PRS in subjects of different ancestry. Methods: The schizophrenia PRS derived from the large genome-wide association study carried out by the Psychiatric Genetics Consortium was calculated using the downloaded genotypes of HapMap subjects from 11 different ancestral groups. It was also calculated using downloaded genotypes of European schizophrenia cases and controls from the CommonMind Consortium. Results: The PRS for schizophrenia varied significantly between ancestral groups (P<2×10) and was much higher in African than European HapMap subjects. The mean difference between these groups was 10 times as high as the mean difference between European schizophrenia cases and controls. The distributions of scores for African and European subjects hardly overlapped. Conclusion: The PRS cannot be regarded as simply a measure of the polygenic contribution to risk of schizophrenia and clearly contains a strong ancestry component. It is possible that this could be controlled to some extent by incorporating principal components as covariates, but doubts remain as to how it should be interpreted. The PRS derived from European subjects cannot be applied to non-Europeans, limiting its potential usefulness in clinical settings and raising issues of inequity in health provision. Previous studies that have used the PRS should be re-examined in the light of these findings.
Genetic susceptibility to substance use disorders (SUDs) is partially shared between substances. Heritability of any substance dependence, estimated as 54%, is partly explained by additive effects of common variants. Comorbidity between SUDs and other psychiatric disorders is frequent. The present study aims to analyze the additive role of common variants in this comorbidity using polygenic scores (PGSs) based on genome‐wide association study (GWAS) discovery samples of schizophrenia, bipolar disorder, attention‐deficit/hyperactivity disorder, autism spectrum disorder, major depressive disorder, and anxiety disorders, available from large consortia. PGSs were calculated for 534 patients meeting DSM‐IV criteria for dependence of a substance and abuse/dependence of another substance between alcohol, tobacco, cannabis, cocaine, opiates, hypnotics, stimulants, hallucinogens and solvents; and 587 blood donors from the same population, Iberians from Galicia, as controls. Significance of the PGS and percentage of variance explained were calculated by logistic regression. Using discovery samples of similar size, significant associations with SUDs were detected for schizophrenia PGS. Schizophrenia PGS explained more variance in SUDs than in most psychiatric disorders. Cross‐disorder PGS based on five psychiatric disorders was significant after adjustment for the effect of schizophrenia PGS. Schizophrenia PGS was significantly higher in women than in men abusing alcohol. Our findings indicate that SUDs share genetic susceptibility with schizophrenia to a greater extent than with other psychiatric disorders, including externalizating disorders such as attention‐deficit/hyperactivity disorder. Women have lower probability to develop substance abuse/dependence than men at similar PGS probably because of a higher social pressure against excessive drug use in women.
The neural correlates of emotion perception have been shown to be significantly altered in schizophrenia (SCZ) patients as well as their healthy relatives, possibly reflecting genetic susceptibility to the disease. The aim of the study was to investigate the association between SCZ polygenic risk and brain activity whilst testing perception of multisensory, dynamic emotional stimuli. We created SCZ polygenic risk scores (PRS) for a sample of twenty-eight healthy individuals. The PRS was based on data from the Psychiatric Genomics Consortium and was used as a regressor score in the neuroimaging analysis. The results of a multivariate brain-behaviour analysis show that higher SCZ PRS are related to increased activity in brain regions critical for emotion during the perception of threatening (angry) emotions. These results suggest that individuals with higher SCZ PRS over-activate the neural correlates underlying emotion during perception of threat, perhaps due to an increased experience of fear or neural inefficiency in emotion-regulation areas. Moreover, over-recruitment of emotion regulation regions might function as a compensation to maintain normal emotion regulation during threat perception. If replicated in larger studies, these findings may have important implications for understanding the neurophysiological biomarkers relevant in SCZ.
Executive functions are a diverse and critical suite of cognitive abilities that are often disrupted in individuals with psychiatric disorders. Despite their moderate to high heritability, little is known about the molecular genetic factors that contribute to variability in executive function and how these factors may be related to those that predispose to psychiatric illness. We examined the relationship between polygenic risk scores built from large genome‐wide association studies of psychiatric illness and executive functioning in typically developing children. In our discovery sample (N=417), consistent with previous reports on general cognitive abilities, polygenic risk for autism spectrum disorder was associated with better performance on the Dimensional Change Card Sort test from the NIH Cognition Toolbox, with the largest effect in the youngest children. Polygenic risk for major depressive disorder was associated with poorer performance on the Flanker test in the same sample. This second association replicated for performance on the Penn Conditional Exclusion Test in an independent cohort (N=3,681). Our results suggest that the molecular genetic factors contributing to variability in executive function during typical development are at least partially overlapping with those associated with psychiatric disorders, although larger studies and further replication are needed.