Abstract
Schizophrenia and bipolar disorder display clinical similarities and dissimilarities. We investigated whether the genetic factor differentiating schizophrenia from bipolar disorder is genetically associated with cognitive phenotypes and hippocampal volumes. We revealed genetic overlaps of the genetic differentiating factor with low general cognitive ability, low childhood IQ, low educational attainment and reduced hippocampal volumes. The genetic correlations with low general cognitive ability and reduced hippocampal volumes were associated with risk of schizophrenia, whereas the genetic correlations with high childhood IQ and educational attainment were associated with risks of bipolar disorder. These findings suggest these disorders have disorder-specific genetic factors related to clinical phenotypes.
Keywords: Genetic correlation, schizophrenia, bipolar disorder, cognitive function, hippocampus
Schizophrenia and bipolar disorder are highly heritable disorders with clinical similarities and a complex, overlapping polygenic architecture.1,2 In contrast, a large-scale genome-wide association study (GWAS) identified two genome-wide significant loci differentiating schizophrenia from bipolar disorder.3 Although schizophrenia displays cognitive dysfunctions and reduced hippocampal volumes,4 there are somewhat limited data on these impairments in bipolar disorder. Genetic overlaps of risk for schizophrenia with cognitive impairments and reduced hippocampal volumes have been reported.2,5,6 These findings suggest that the two disorders would be distinct diagnoses, with disorder-specific genetic factors related to clinical phenotypes. However, it remains unknown whether a genetic factor differentiating schizophrenia from bipolar disorder can explain the dissimilarities in cognitive functions and hippocampal volumes. Here, we explored whether the genetic factor differentiating component is genetically associated with psychiatric disorders, cognitive phenotypes and hippocampal volumes.
Method
To calculate genetic correlations attributable to genome-wide single nucleotide polymorphisms (SNPs) (polygenicity; many small genetic effects) between the genetic factor differentiating schizophrenia from bipolar disorder and psychiatric disorders, cognitive phenotypes and hippocampal volumes, we obtained GWAS summary statistics for the following: schizophrenia versus bipolar disorder,3 Psychiatric Genomics Consortium 2 (PGC2) for schizophrenia,7 PGC2 for bipolar disorder,8 major depression disorder (MDD), autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), general cognitive ability,12 childhood IQ, educational attainment and hippocampal volume.15 These data were extracted from the PGC (https://www.med.unc.edu/pgc/results-and-downloads), the Centre for Cognitive Ageing and Cognitive Epidemiology at the University of Edinburgh (http://www.ccace.ed.ac.uk/node/335), the Social Science Genetic Association Consortium (https://www.thessgac.org/data) and the Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA2; http://enigma.ini.usc.edu/research/download-enigma-gwas-results/) (Table 1).
Table 1.
Demographic information of each genome-wide association study
| Sample sizes | ||||||
|---|---|---|---|---|---|---|
| Genome-wide significant loci | Total | Cases | Controlsa | Single nucleotide polymorphisms, n | ||
| Schizophrenia versus bipolar disorder | Ruderfer et al (2018)3 | 2 | 38 855 | 23 585b | 15 270c | 1 064 728 |
| Schizophrenia | Ripke et al (2014)7 | 108 | 82 315 | 35 476 | 46 839 | 1 125 108 |
| Bipolar disorder | Stahl et al (2019)8 | 30 | 51 710 | 20 352 | 31 358 | 1 110 247 |
| Major depression disorder | Wray et al (2018)9 | 44 | 173 005 | 59 851 | 113 154 | 1 110 699 |
| Autism spectrum disorder | Grove et al (2019)10 | 5 | 46 350 | 18 381 | 27 969 | 1 050 225 |
| Attention-deficit hyperactivity disorder | Demontis et al (2019)11 | 12 | 55 374 | 20 183 | 35 191 | 1 069 649 |
| General cognitive ability | Davies et al (2018)12 | 148 | 282 014 | − | − | 1 204 339 |
| Childhood IQ | Benyamin et al (2014)13 | 0 | 12 441 | − | − | 804 043 |
| Educational attainment | Lee et al (2018)14 | 1271 | 1 131 881 | − | − | 1 177 103 |
| Hippocampal volume | Hibar et al (2015)15 | 2 | 13 163 | − | − | 1 161 732 |
Controls or pseudocontrols from family trio samples.
Schizophrenia.
Bipolar disorder.
Linkage disequilibrium score regression (LDSC) analysis can estimate the genetic SNP correlations (rg) from GWASs.5,16,17 For each GWAS, an LDSC was carried out by regressing the GWAS test statistics (χ2) onto each SNP's linkage disequilibrium score. Genetic correlations were calculated by LDSC. This study was approved by each local ethical committee of the relevant institutions. Informed consent was obtained from all participants and/or their families in each study cohort. The detailed information in each GWAS and LDSC analysis have been described previously, and are briefly summarised in the Supplementary Material available at https://doi.org/10.1192/bjo.2021.1086.
Results
As expected, the genetic component differentiating schizophrenia from bipolar disorder was positively correlated with the risk of schizophrenia (rg ± s.e. = 0.53 ± 0.03, P = 1.21 × 10−82), and negatively correlated with the risk of bipolar disorder (rg ± s.e. = −0.28 ± 0.04, P = 5.04 × 10−13) (Fig. 1(a)). Among other psychiatric disorders, there was positive genetic correlation between the differentiating genetic factor and ASD (rg ± s.e. = 0.16 ± 0.05, P = 2.90 × 10−3). There were no significant genetic correlations of the differentiating factor with MDD or ADHD (P > 0.05). The genetic factor differentiating schizophrenia from bipolar disorder was genetically negatively correlated with all examined cognitive phenotypes and hippocampal volumes (Fig. 1(a); general cognitive function (rg ± s.e. = −0.23 ± 0.04, P = 6.80 × 10−11), childhood IQ (rg ± s.e. = −0.21 ± 0.08, P = 7.30 × 10−3), educational attainment (rg ± s.e. = −0.13 ± 0.03, P = 2.66 × 10−5) and hippocampal volume (rg ± s.e. = −0.22 ± 0.10, P = 0.031)). As shown in genetic correlations across phenotypes (Fig. 1(b)), genetic correlations of the genetic differentiating factor with general cognitive function and hippocampal volume would be derived from genetic correlations of schizophrenia with these phenotypes. In contrast, genetic correlations of the genetic differentiating feature with childhood IQ and educational attainment would be derived from genetic correlations of bipolar disorder and/or ASD with these phenotypes.
Fig. 1.
(a) Genetic correlations (rg) of genetic factor differentiating schizophrenia from bipolar disorder with psychiatric disorders, cognitive functions and hippocampal volumes. Error bars show s.e. of the rg. (b) Genetic correlations (rg) across genome-wide association study results. The colour scale represents the rg values. Genetic correlations were estimated with linkage disequilibrium score regression. *P < 0.05, **P < 0.01, ***P < 0.001.
Discussion
We revealed genetic overlaps of the genetic variants differentiating schizophrenia from bipolar disorder with risk of ASD, low general cognitive ability, low childhood IQ, low educational attainment and reduced hippocampal volumes. These genetic overlaps may be attributed to genetic risks for schizophrenia or bipolar disorder, or both. We further found that the genetic correlations of the genetic factor differentiating schizophrenia from bipolar disorder with low general cognitive ability and reduced hippocampal volumes are associated with risk of schizophrenia, and the genetic correlations of the genetic factor differentiating bipolar disorder from schizophrenia with high childhood IQ and educational attainment are associated with risks of bipolar disorder and/or ASD. The disorder-specific genetic liability could contribute to the clinical dissimilarities between schizophrenia and bipolar disorder. Current schizophrenia diagnoses may aggregate at least two subtypes:18 patients who resemble high intelligence and bipolar disorder (similarities), and patients who show cognitive impairments that are independent of bipolar disorder (dissimilarities). However, it remained unclear whether low intelligence causes schizophrenia or schizophrenia causes intelligence decline. Using summary data-based Mendelian randomisation,19 we recently demonstrated that low intelligence was bidirectionally associated with a high risk of schizophrenia, whereas the schizophrenia-specific genetic factors might be mainly affected by impairment in premorbid intelligence.20 Future study is required to reveal causal association between reduced hippocampal volumes and risk of schizophrenia.
Interestingly, there were no significant correlations between the genetic factor differentiating schizophrenia from bipolar disorder and MDD or ADHD. Comparing genetic correlations between schizophrenia and MDD with those between bipolar disorder and MDD, and genetic correlations between schizophrenia and ADHD with those between bipolar disorder and ADHD, both schizophrenia and bipolar disorder correlations with MDD and ADHD were similar.2 The absence of MDD or ADHD correlations with the differentiating factor might reflect similar degrees of these genetic correlations with schizophrenia and bipolar disorder.
Our findings suggest that cognitive impairments and reduced hippocampal volumes could genetically distinguish schizophrenia from bipolar disorder, and may be useful for improving diagnosis and treatment.
Acknowledgements
We would like to thank all individuals who participated in this study.
Author contributions
K.O. supervised the entire project, collected the data, wrote the manuscript and was critically involved in the design, analysis and interpretation of the data. All authors were responsible for performing the literature review. All authors were intellectually contributed to data interpretation and approved the final manuscript for publication.
Funding
This work was supported by Grants-in-Aid for Scientific Research (C) (19K08081) from the Japan Society for the Promotion of Science (JSPS) and SENSHIN Medical Research Foundation.
Supplementary material
For supplementary material accompanying this paper visit http://doi.org/10.1192/bjo.2021.1086.
click here to view supplementary material
Data availability
The data that support the findings of this study are available from the corresponding author, K.O., on reasonable request.
Declaration of interest
None.
Reference
- 1.Ohi K, Nishizawa D, Shimada T, Kataoka Y, Hasegawa J, Shioiri T, et al. Polygenetic risk scores for major psychiatric disorders among schizophrenia patients, their first-degree relatives, and healthy participants. Int J Neuropsychopharmacol 2020; 23: 157–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, Duncan L, et al. Analysis of shared heritability in common disorders of the brain. Science 2018; 360(6395): eaap8757. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ruderfer D, Ripke S, McQuillin A, Boocock J, Stahl E, Pavlides J, et al. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell 2018; 173: 1705–15.e16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Weinberg D, Lenroot R, Jacomb I, Allen K, Bruggemann J, Wells R, et al. Cognitive subtypes of schizophrenia characterized by differential brain volumetric reductions and cognitive decline. JAMA Psychiatry 2016; 73: 1251–59. [DOI] [PubMed] [Google Scholar]
- 5.Ohi K, Shimada T, Kataoka Y, Yasuyama T, Kawasaki Y, Shioiri T, et al. Genetic correlations between subcortical brain volumes and psychiatric disorders. Br J Psychiatry 2020; 216: 280–3. [DOI] [PubMed] [Google Scholar]
- 6.Ohi K, Sumiyoshi C, Fujino H, Yasuda Y, Yamamori H, Fujimoto M, et al. Genetic overlap between general cognitive function and schizophrenia: a review of cognitive GWASs. Int J Mol Sci 2018; 19: 3822. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Ripke S, Neale BM, Corvin A, Walters JT, Farh KH, Holmans PA, et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 2014; 511: 421–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Stahl EA, Breen G, Forstner AJ, McQuillin A, Ripke S, Trubetskoy V, et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet 2019; 51: 793–803. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet 2018; 50(5): 668–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet 2019; 51(3): 431–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet 2019; 51: 63–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Davies G, Lam M, Harris SE, Trampush JW, Luciano M, Hill WD, et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat Commun 2018; 9: 2098. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Benyamin B, Pourcain B, Davis OS, Davies G, Hansell NK, Brion M-JA, et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol Psychiatry 2014; 19(2): 253–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet 2018; 50(8): 1112–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Hibar DP, Stein JL, Renteria ME, Arias-Vasquez A, Desrivieres S, Jahanshad N, et al. Common genetic variants influence human subcortical brain structures. Nature 2015; 520: 224–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 2015; 47: 291–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ohi K, Otowa T, Shimada M, Sasaki T, Tanii H. Shared genetic etiology between anxiety disorders and psychiatric and related intermediate phenotypes. Psychol Med 2020; 50: 692–704. [DOI] [PubMed] [Google Scholar]
- 18.Bansal V, Mitjans M, Burik CAP, Linner RK, Okbay A, Rietveld CA, et al. Genome-wide association study results for educational attainment aid in identifying genetic heterogeneity of schizophrenia. Nat Commun 2018; 9: 3078. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018; 50: 693–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Ohi K, Takai K, Kuramitsu A, Sugiyama S, Soda M, Kitaichi K, et al. Causal associations of intelligence with schizophrenia and bipolar disorder: a Mendelian randomization analysis. Eur Psychiatry 2021; 64: e61. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
For supplementary material accompanying this paper visit http://doi.org/10.1192/bjo.2021.1086.
click here to view supplementary material
Data Availability Statement
The data that support the findings of this study are available from the corresponding author, K.O., on reasonable request.