Polygenic Risk Scores Differentiating Schizophrenia From Bipolar Disorder Are Associated With Premorbid Intelligence in Schizophrenia Patients and Healthy Subjects

Discrimination between healthy participants and people with panic disorder based on polygenic scores for psychiatric disorders and for intermediate phenotypes using machine learning

OBJECTIVE

Panic disorder is a modestly heritable condition. Currently, diagnosis is based only on clinical symptoms; identifying objective biomarkers and a more reliable diagnostic procedure is desirable. We investigated whether people with panic disorder can be reliably diagnosed utilizing combinations of multiple polygenic scores for psychiatric disorders and their intermediate phenotypes, compared with single polygenic score approaches, by applying specific machine learning techniques.

METHODS

Polygenic scores for 48 psychiatric disorders and intermediate phenotypes based on large-scale genome-wide association studies (n = 7556-1,131,881) were calculated for people with panic disorder (n = 718) and healthy controls (n = 1717). Discrimination between people with panic disorder and healthy controls was based on the 48 polygenic scores using five methods for classification: logistic regression, neural networks, quadratic discriminant analysis, random forests and a support vector machine. Differences in discrimination accuracy (area under the curve) due to an increased number of polygenic score combinations and differences in the accuracy across five classifiers were investigated.

RESULTS

All five classifiers performed relatively well for distinguishing people with panic disorder from healthy controls by increasing the number of polygenic scores. Of the 48 polygenic scores, the polygenic score for anxiety UK Biobank was the most useful for discrimination by the classifiers. In combinations of two or three polygenic scores, the polygenic score for anxiety UK Biobank was included as one of polygenic scores in all classifiers. When all 48 polygenic scores were used in combination, the greatest areas under the curve significantly differed among the five classifiers. Support vector machine and logistic regression had higher accuracy than quadratic discriminant analysis and random forests. For each classifier, the greatest area under the curve was 0.600 ± 0.030 for logistic regression (polygenic score combinations N = 14), 0.591 ± 0.039 for neural networks (N = 9), 0.603 ± 0.033 for quadratic discriminant analysis (N = 10), 0.572 ± 0.039 for random forests (N = 25) and 0.617 ± 0.041 for support vector machine (N = 11). The greatest areas under the curve at the best polygenic score combination significantly differed among the five classifiers. Random forests had the lowest accuracy among classifiers. Support vector machine had higher accuracy than neural networks.

CONCLUSIONS

These findings suggest that increasing the number of polygenic score combinations up to approximately 10 effectively improved the discrimination accuracy and that support vector machine exhibited greater accuracy among classifiers. However, the discrimination accuracy for panic disorder, when based solely on polygenic score combinations, was found to be modest.

Studying the relationship between intelligence quotient and schizophrenia polygenic scores in a family design with first-episode psychosis population

BACKGROUND

The intelligence quotient (IQ) of patients with first-episode psychosis (FEP) and their unaffected relatives may be related to the genetic burden of schizophrenia (SCZ). The polygenic score approach can be useful for testing this question.

AIM

To assess the contribution of the polygenic risk scores for SCZ (PGS-SCZ) and polygenic scores for IQ (PGS-IQ) to the individual IQ and its difference from the mean IQ of the family (named family-IQ) through a family-based design in an FEP sample.

METHODS

The PAFIP-FAMILIES sample (Spain) consists of 122 FEP patients, 131 parents, 94 siblings, and 176 controls. They all completed the WAIS Vocabulary subtest for IQ estimation and provided a DNA sample. We calculated PGS-SCZ and PGS-IQ using the continuous shrinkage method. To account for relatedness in our sample, we performed linear mixed models. We controlled for covariates potentially related to IQ, including age, years of education, sex, and ancestry principal components.

RESULTS

FEP patients significantly deviated from their family-IQ. FEP patients had higher PGS-SCZ than other groups, whereas the relatives had intermediate scores between patients and controls. PGS-IQ did not differ between groups. PGS-SCZ significantly predicted the deviation from family-IQ, whereas PGS-IQ significantly predicted individual IQ.

CONCLUSIONS

PGS-SCZ discriminated between different levels of genetic risk for the disorder and was specifically related to patients' lower IQ in relation to family-IQ. The genetic background of the disorder may affect neurocognition through complex pathological processes interacting with environmental factors that prevent the individual from reaching their familial cognitive potential.

Genome-wide DNA methylation risk scores for schizophrenia derived from blood and brain tissues further explain the genetic risk in patients stratified by polygenic risk scores for schizophrenia and bipolar disorder

BACKGROUND

Genetic and environmental factors contribute to the pathogenesis of schizophrenia (SZ) and bipolar disorder (BD). Among genetic risk groups stratified by combinations of Polygenic Risk Score (PRS) deciles for SZ, BD and SZ versus BD, genetic SZ risk groups had high SZ risk and prominent cognitive impairments. Furthermore, epigenetic alterations are implicated in these disorders. However, it was unclear whether DNA Methylation Risk Scores (MRSs) for SZ risk derived from blood and brain tissues were associated with SZ risk, particularly the PRS-stratified genetic SZ risk group.

METHODS

Epigenome-wide association studies (EWASs) of SZ risk in whole blood were preliminarily conducted between 66 SZ patients and 30 healthy controls (HCs) and among genetic risk groups (individuals with low genetic risk for SZ and BD in HCs (n=30) and in SZ patients (n=11), genetic BD risk in SZ patients (n=25) and genetic SZ risk in SZ patients (n=30)) stratified by combinations of PRSs for SZ, BD and SZ versus BD. Next, differences in MRSs based on independent EWASs of SZ risk in whole blood, postmortem frontal cortex (FC) and superior temporal gyrus (STG) were investigated among our case‒control and PRS-stratified genetic risk status groups.

RESULTS

Among case‒control and genetic risk status groups, 33 and 351 genome-wide significant differentially methylated positions (DMPs) associated with SZ were identified, respectively, many of which were hypermethylated. Compared with the low genetic risk in HCs group, the genetic SZ risk in SZ group had 39 genome-wide significant DMPs, while the genetic BD risk in SZ group had only six genome-wide significant DMPs. The MRSs for SZ risk derived from whole blood, FC and STG were higher in our SZ patients than in HCs in whole blood and were particularly higher in the genetic SZ risk in SZ group than in the low genetic risk in HCs and genetic BD risk in SZ groups. Conversely, the MRSs for SZ risk based on our whole-blood EWASs among genetic risk groups were also associated with SZ in the FC and STG. There were no correlations between the MRSs and PRSs.

CONCLUSIONS

These findings suggest that the MRS is a potential genetic marker in understanding SZ, particularly in patients with a genetic SZ risk.

Olfactory identification ability among schizophrenia patients, their first-degree relatives and healthy subjects

OBJECTIVE

Olfactory impairments, including identification, have been reported in patients with schizophrenia, while few studies have examined the olfactory function of unaffected first-degree relatives of patients with schizophrenia, and the sample sizes of first-degree relatives were relatively small. Here, we investigated olfactory identification ability among patients with schizophrenia, first-degree relatives and healthy controls (HCs) using relatively large sample sizes at a single institute.

METHODS

To assess olfactory identification ability, the open essence odorant identification test was administered to 172 schizophrenia patients, 75 first-degree relatives and 158 healthy controls. Differences in olfactory identification and correlations between olfactory ability and clinical variables were examined among these participants.

RESULTS

We found a significant difference in olfactory identification ability among the diagnostic groups (p = 7.65 × 10-16). Schizophrenia patients displayed lower olfactory identification ability than first-degree relatives (Cohen's d = -0.57, p = 3.13 × 10-6) and healthy controls (d = -1.00, p = 2.19 × 10-16). Furthermore, first-degree relatives had lower olfactory identification ability than healthy controls (d = -0.29, p = 0.039). Olfactory identification ability moderately and negatively correlated with the duration of illness (r = -0.41, p = 1.88 × 10-8) and negative symptoms (r = -0.28, p = 1.99 × 10-4) in schizophrenia patients, although the correlation with the duration of illness was affected by aging (r = -0.24).

CONCLUSIONS

Our results demonstrated that schizophrenia patients have impaired olfactory identification ability compared with first-degree relatives and healthy controls, and the impaired olfactory identification ability of first-degree relatives was intermediate between those in schizophrenia patients and healthy controls. Olfactory identification ability was relatively independent of clinical variables. Therefore, olfactory identification ability might be an intermediate phenotype for schizophrenia.

Mitochondrial genetic variants associated with bipolar disorder and Schizophrenia in a Japanese population

BACKGROUND

Bipolar disorder (BD) and schizophrenia (SZ) are complex psychotic disorders (PSY), with both environmental and genetic factors including possible maternal inheritance playing a role. Some studies have investigated whether genetic variants in the mitochondrial chromosome are associated with BD and SZ. However, the genetic variants identified as being associated are not identical among studies, and the participants were limited to individuals of European ancestry. Here, we investigate associations of genome-wide genetic variants in the mitochondrial chromosome with BD, SZ, and PSY in a Japanese population.

METHODS

After performing quality control for individuals and genetic variants, we investigated whether mitochondrial genetic variants [minor allele frequency (MAF) > 0.01, n = 45 variants) are associated with BD, SZ, and PSY in 420 Japanese individuals consisting of patients with BD (n = 51), patients with SZ (n = 172), and healthy controls (HCs, n = 197).

RESULTS

Of mitochondrial genetic variants, three (rs200478835, rs200044200 and rs28359178 on or near NADH dehydrogenase) and one (rs200478835) were significantly associated with BD and PSY, respectively, even after correcting for multiple comparisons (PGC=0.045-4.9 × 10- 3). In particular, individuals with the minor G-allele of rs200044200, a missense variant, were only observed among patients with BD (MAF = 0.059) but not HCs (MAF = 0) (odds ratio=∞). Three patients commonly had neuropsychiatric family histories.

CONCLUSIONS

We suggest that mitochondrial genetic variants in NADH dehydrogenase-related genes may contribute to the pathogenesis of BD and PSY in the Japanese population through dysfunction of energy production.

Cognitive performances across individuals at high genetic risk for schizophrenia, high genetic risk for bipolar disorder, and low genetic risks: a combined polygenic risk score approach

BACKGROUND

Individuals with schizophrenia (SCZ) and bipolar disorder (BD) display cognitive impairments, but the impairments in those with SCZ are more prominent, supported by genetic overlap between SCZ and cognitive impairments. However, it remains unclear whether cognitive performances differ between individuals at high and low genetic risks for SCZ or BD.

METHODS

Using the latest Psychiatric Genomics Consortium (PGC) data, we calculated PGC3 SCZ-, PGC3 BD-, and SCZ v. BD polygenic risk scores (PRSs) in 173 SCZ patients, 70 unaffected first-degree relatives (FRs) and 196 healthy controls (HCs). Based on combinations of three PRS deciles, individuals in the genetic SCZ, genetic BD and low genetic risk groups were extracted. Cognitive performance was assessed by the Brief Assessment of Cognition in Schizophrenia.

RESULTS

SCZ-, BD-, SCZ v. BD-PRSs were associated with case-control status (R2 = 0.020-0.061), and SCZ-PRS was associated with relative-control status (R2 = 0.023). Furthermore, individuals in the highest decile for SCZ PRSs had elevated BD-PRSs [odds ratio (OR) = 6.33] and SCZ v. BD-PRSs (OR = 1.86) compared with those in the lowest decile. Of the three genetic risk groups, the low genetic risk group contained more HCs, whereas the genetic BD and SCZ groups contained more SCZ patients (p < 0.05). SCZ patients had widespread cognitive impairments, and FRs had cognitive impairments that were between those of SCZ patients and HCs (p < 0.05). Cognitive differences between HCs in the low genetic risk group and SCZ patients in the genetic BD or genetic SCZ groups were more prominent (Cohen's d > -0.20) than those between HCs and SCZ patients in the no genetic risk group. Furthermore, SCZ patients in the genetic SCZ group displayed lower scores in verbal fluency and attention than those in the genetic BD group (d > -0.20).

CONCLUSIONS

Our findings suggest that cognitive impairments in SCZ are partially mediated through genetic loadings for SCZ but not BD.

The genetic architecture of schizophrenia: review of large-scale genetic studies

Schizophrenia is a complex and often chronic psychiatric disorder with high heritability. Diagnosis of schizophrenia is still made clinically based on psychiatric symptoms; no diagnostic tests or biomarkers are available. Pathophysiology-based diagnostic scheme and treatments are also not available. Elucidation of the pathogenesis is needed for development of pathology-based diagnostics and treatments. In the past few decades, genetic research has made substantial advances in our understanding of the genetic architecture of schizophrenia. Rare copy number variations (CNVs) and rare single-nucleotide variants (SNVs) detected by whole-genome CNV analysis and whole-genome/-exome sequencing analysis have provided the great advances. Common single-nucleotide polymorphisms (SNPs) detected by large-scale genome-wide association studies have also provided important information. Large-scale genetic studies have been revealed that both rare and common genetic variants play crucial roles in this disorder. In this review, we focused on CNVs, SNVs, and SNPs, and discuss the latest research findings on the pathogenesis of schizophrenia based on these genetic variants. Rare variants with large effect sizes can provide mechanistic hypotheses. CRISPR-based genetics approaches and induced pluripotent stem cell technology can facilitate the functional analysis of these variants detected in patients with schizophrenia. Recent advances in long-read sequence technology are expected to detect variants that cannot be detected by short-read sequence technology. Various studies that bring together data from common variant and transcriptomic datasets provide biological insight. These new approaches will provide additional insight into the pathophysiology of schizophrenia and facilitate the development of pathology-based therapeutics.

Higher polygenic risk scores for anxiety disorders are associated with reduced area in the anterior cingulate gyrus

Anxiety disorders are heterogeneous, show a moderate genetic contribution and are associated with inconsistent cortical structure alterations. Here, we investigated whether genetic factors for anxiety disorders contribute to cortical alterations by conducting polygenic risk score (PRS) analyses. We calculated PRSs for anxiety disorders at several P value thresholds (from P_T ≤ 5.0 × 10^-8 to P_T ≤ 1.0) based on the latest large-scale genome-wide association study of anxiety disorders from the UK biobank (25,453 cases; 58,113 controls) in an independent sample of psychiatrically and physically healthy subjects (n = 174). Using regression after adjusting for confounding factors, we tested whether these PRSs were associated with the surface area and cortical thickness in 34 bilateral brain regions extracted using FreeSurfer. A higher PRS for anxiety disorders at P_T ≤ 1.0 was significantly associated with a reduced right caudal anterior cingulate area (beta = -0.25, p_uncorrected = 9.51 × 10^-4, p_corrected = 0.032). PRSs based on more common SNPs, especially from P_T ≤ 0.01 to P_T ≤ 1.0, were associated with the right caudal anterior cingulate area (a maximum at P_T ≤ 0.5: R² = 0.066, beta = -0.27, p_uncorr = 3.81 × 10^-4, p_corr = 0.013). Furthermore, individuals in the highest quartile for anxiety disorder PRS had lower surface area and volume in the right anterior cingulate gyrus than those in the lowest quartile. We suggest a shared genetic etiology between anxiety disorders and structural features of the anterior cingulate gyrus, possibly contributing to the pathogenesis of anxiety disorders via emotional dysregulations. Our findings suggest the potential usefulness of PRS to reduce pathological heterogeneity among anxiety disorders.

Overlap between genetic variants associated with schizophrenia spectrum disorders and intelligence quotient: a systematic review

BACKGROUND

To study whether there is genetic overlap underlying the risk for schizophrenia spectrum disorders (SSDs) and low intelligence quotient (IQ), we reviewed and summarized the evidence on genetic variants associated with both traits.

METHODS

We performed this review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and preregistered it in PROSPERO. We searched the Medline databases via PubMed, PsycInfo, Web of Science and Scopus. We included studies in adults with a diagnosis of SSD that explored genetic variants (single nucleotide polymorphisms [SNPs], copy number variants [CNVs], genomic insertions or genomic deletions), estimated IQ and studied the relationship between genetic variability and both traits (SSD and IQ). We synthesized the results and assessed risk of bias using the Quality of Genetic Association Studies (Q-Genie) tool.

RESULTS

Fifty-five studies met the inclusion criteria (45 case-control, 9 cross-sectional, 1 cohort), of which 55% reported significant associations for genetic variants involved in IQ and SSD. The SNPs more frequently explored through candidate gene studies were in COMT, DTNBP1, BDNF and TCF4. Through genome-wide association studies, 2 SNPs in CHD7 and GATAD2A were associated with IQ in patients with SSD. The studies on CNVs suggested significant associations between structural variants and low IQ in patients with SSD.

LIMITATIONS

Overall, primary studies used heterogeneous IQ measurement tools and had small samples. Grey literature was not screened.

CONCLUSION

Genetic overlap between SSD and IQ supports the neurodevelopmental hypothesis of schizophrenia. Most of the risk polymorphisms identified were in genes relevant to brain development, neural proliferation and differentiation, and synaptic plasticity.