Shared trans-ancestry genetic etiology between panic disorder and anxiety disorders

Genome-wide association study and polygenic risk score analysis for schizophrenia in a Korean population

Although large-scale genome-wide association studies (GWASs) have revealed the genetic architecture of schizophrenia, these studies have mainly focused on populations of European ancestry. This study aimed to identify common genetic variants associated with schizophrenia in the Korean population and evaluate the performance of polygenic risk scores (PRSs) derived from large-scale GWASs across ancestries. In the Korean psychiatric GWAS project (KPGP), seven academic institutes and their affiliated hospitals across South Korea recruited a cohort of 1670 patients with DSM-IV-defined schizophrenia and 2271 healthy controls. A total of 6690,822 SNPs were tested for association with schizophrenia. We identified one previously unreported genome-wide significant locus rs2423464 (P = 2.83 × 10-11; odds ratio = 1.65; 95 % confidence interval = 1.43-1.91, minor allele frequency = 0.126). This variant was also associated with increased lysosomal-associated membrane protein family member 5 (LAMP5) gene expression. The PRS derived from the meta-analysis results of East Asian and European GWASs explained a larger proportion of the phenotypic variance in the Korean schizophrenia sample than the PRS of an East Asian or European GWAS. (R2 = 0.073 for meta-analysis; 0.028 for East Asian GWAS; 0.037 for European GWAS). GWASs involving diverse ethnic groups will expand our understanding of the genetic architecture of schizophrenia.

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.

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.

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.