Refining phenotype characterization in genetic linkage studies of schizophrenia

Abstract

A definitive replicable genetic linkage for a major locus underlying the susceptibility to schizophrenia has not been identified to date. Although there are several possible explanations for the failure to find linkage in schizophrenia, one major problem is that the range of phenotypic expressions of the genes for schizophrenia has not been clarified. A more refined understanding of the various phenotypic expressions of a gene related to schizophrenia would enhance the power of studies designed to detect a genetic linkage with a major chromosomal locus and would benefit other strategies for understanding the etiology of schizophrenia.

The genes for schizophrenia may be frequently expressed in relatives of schizophrenic patients, although with less severe symptoms than those of chronic schizophrenia. Two series of findings support this notion. Nonschizophrenic relatives of schizophrenic patients demonstrate an increased incidence of nonpsychotic schizophrenia‐like symptoms and traits, and they manifest deficit performances on several different laboratory tests of neurocognitive functioning. A more refined phenotypic expression of a schizophrenia‐related gene may thus be indicated by personality traits and subclinical neurocognitive deficits.

These personality traits and neurocognitive deficits are considered here as possible aids in the identification of affected cases in genetic linkage studies of schizophrenia. Terminology for different indicators of neurocognitive deficits is introduced, and the relative utility of personality traits and indicators of neurocognitive deficit for genetic linkage studies is discussed. As specific examples, schizophrenia‐related personality traits that are unrelated to affective symptoms and performance deficits on tasks of eye tracking and continuous attention are considered for strategies for broadening phenotype characterization without reducing the specificity of affected case identification.     

Auditory Mismatch Negativity and Repetition Suppression Deficits in Schizophrenia Explained by Irregular Computation of Prediction Error

BackgroundThe predictive coding model is rapidly gaining attention in schizophrenia research. It posits the neuronal computation of residual variance (‘prediction error’) between sensory information and top-down expectation through multiple hierarchical levels. Event-related potentials (ERP) reflect cortical processing stages that are increasingly interpreted in the light of the predictive coding hypothesis. Both mismatch negativity (MMN) and repetition suppression (RS) measures are considered a prediction error correlates based on error detection and error minimization, respectively.MethodsTwenty-five schizophrenia patients and 25 healthy controls completed auditory tasks designed to elicit MMN and RS responses that were investigated using repeated measures models and strong spatio-temporal a priori hypothesis based on previous research. Separate correlations were performed for controls and schizophrenia patients, using age and clinical variables as covariates.ResultsMMN and RS deficits were largely replicated in our sample of schizophrenia patients. Moreover, MMN and RS measures were strongly correlated in healthy controls, while no correlation was found in schizophrenia patients. Single-trial analyses indicated significantly lower signal-to-noise ratio during prediction error computation in schizophrenia.ConclusionsThis study provides evidence that auditory ERP components relevant for schizophrenia research can be reconciled in the light of the predictive coding framework. The lack of any correlation between the investigated measures in schizophrenia patients suggests a disruption of predictive coding mechanisms in general. More specifically, these results suggest that schizophrenia is associated with an irregular computation of residual variance between sensory input and top-down models, i.e. prediction error.     

Potassium channel gene associations with joint processing speed and white matter impairments in schizophrenia

Patients with schizophrenia show decreased processing speed on neuropsychological testing and decreased white matter integrity as measured by diffusion tensor imaging, two traits shown to be both heritable and genetically associated indicating that there may be genes that influence both traits as well as schizophrenia disease risk. The potassium channel gene family is a reasonable candidate to harbor such a gene given the prominent role potassium channels play in the central nervous system in signal transduction, particularly in myelinated axons. We genotyped members of the large potassium channel gene family focusing on putatively functional single nucleotide polymorphisms (SNPs) in a population of 363 controls, 194 patients with schizophrenia spectrum disorder (SSD) and 28 patients with affective disorders with psychotic features who completed imaging and neuropsychological testing. We then performed three association analyses using three phenotypes – processing speed, whole‐brain white matter fractional anisotropy (FA) and schizophrenia spectrum diagnosis. We extracted SNPs showing an association at a nominal P value of <0.05 with all three phenotypes in the expected direction: decreased processing speed, decreased FA and increased risk of SSD. A single SNP, rs8234, in the 3′ untranslated region of voltage‐gated potassium channel subfamily Q member 1 (KCNQ1) was identified. Rs8234 has been shown to affect KCNQ1 expression levels, and KCNQ1 levels have been shown to affect neuronal action potentials. This exploratory analysis provides preliminary data suggesting that KCNQ1 may contribute to the shared risk for diminished processing speed, diminished white mater integrity and increased risk of schizophrenia.     

Schizophrenia-associated MicroRNA–Gene Interactions in the Dorsolateral Prefrontal Cortex

Schizophrenia-associated anomalies in gene expression in postmortem brain can be attributed to a combination of genetic and environmental influences. Given the small effect size of common variants, it is likely that we may only see the combined impact of some of these at the pathway level in small postmortem studies. At the gene level, however, there may be more impact from common environmental exposures mediated by influential epigenomic modifiers, such as microRNA (miRNA). We hypothesise that dysregulation of miRNAs and their alteration of gene expression have significant implications in the pathophysiology of schizophrenia. In this study, we integrate changes in cortical gene and miRNA expression to identify regulatory interactions and networks associated with the disorder. Gene expression analysis in post-mortem prefrontal dorsolateral cortex (BA 46) (n = 74 matched pairs of schizophrenia, schizoaffective, and control samples) was integrated with miRNA expression in the same cohort to identify gene–miRNA regulatory networks. A significant gene–miRNA interaction network was identified, including miR-92a, miR-495, and miR-134, which converged with differentially expressed genes in pathways involved in neurodevelopment and oligodendrocyte function. The capacity for miRNA to directly regulate gene expression through respective binding sites in BCL11A, PLP1, and SYT11 was also confirmed to support the biological relevance of this integrated network model. The observations in this study support the hypothesis that miRNA dysregulation is an important factor in the complex pathophysiology of schizophrenia.     

Genome-wide association study of schizophrenia in Japanese population

Schizophrenia is a devastating neuropsychiatric disorder with genetically complex traits. Genetic variants should explain a considerable portion of the risk for schizophrenia, and genome-wide association study (GWAS) is a potentially powerful tool for identifying the risk variants that underlie the disease. Here, we report the results of a three-stage analysis of three independent cohorts consisting of a total of 2,535 samples from Japanese and Chinese populations for searching schizophrenia susceptibility genes using a GWAS approach. Firstly, we examined 115,770 single nucleotide polymorphisms (SNPs) in 120 patient-parents trio samples from Japanese schizophrenia pedigrees. In stage II, we evaluated 1,632 SNPs (1,159 SNPs of p<0.01 and 473 SNPs of p<0.05 that located in previously reported linkage regions). The second sample consisted of 1,012 case-control samples of Japanese origin. The most significant p value was obtained for the SNP in the ELAVL2 [(embryonic lethal, abnormal vision, Drosophila)-like 2] gene located on 9p21.3 (p = 0.00087). In stage III, we scrutinized the ELAVL2 gene by genotyping gene-centric tagSNPs in the third sample set of 293 family samples (1,163 individuals) of Chinese descent and the SNP in the gene showed a nominal association with schizophrenia in Chinese population (p = 0.026). The current data in Asian population would be helpful for deciphering ethnic diversity of schizophrenia etiology.     

Association of AKT1 gene variants and protein expression in both schizophrenia and bipolar disorder

The AKT1 gene has been associated with the genetic aetiology of schizophrenia. Following the overlap model of bipolar disorder and schizophrenia, we aimed to investigate AKT1 genetic variants and protein expression in both diseases. A total of 679 subjects with European ancestry were included: 384 with schizophrenia, 130 with bipolar disorder and 165 controls. Six single nucleotide polymorphisms (SNPs) were investigated for association with the diseases using single‐ and multi‐locus analyses. AKT1 and AKT2 protein levels were measured in post‐mortem brain tissues from ante‐mortem diagnosed schizophrenia (n = 30) and bipolar disorder subjects (n = 12) and matched controls. The analysis identified a significant global distortion in schizophrenia (P = 0.0026) and a weak association in bipolar disorder (P = 0.046). A sliding window procedure showed a five‐SNP haplotype (TCGAG) to be associated with schizophrenia (P = 1.22 × 10^?4) and bipolar disorder (P = 0.0041) and a four‐SNP haplotype (TCGA) with the combined sample (1.73 × 10^?5). On the basis of selected genotypes, a significant difference in protein expression emerged between subjects (P < 0.02). In conclusion, our findings, by showing the involvement of the AKT1 gene in both schizophrenia and bipolar disorder, support the role of AKT1 in the genetics of both disorders and add support to the view that there is some genetic overlap between them.     

No association between the PPARG gene and schizophrenia in a British population

It has consistently been reported that patients with schizophrenia have an increased risk of type-2 diabetes. To investigate a genetic link between these two diseases, the combined effects of the PLA2G4A, PTGS2 and PPARG genes were tested among 221 British nuclear families consisting of fathers, mothers and affected offspring with schizophrenia. A total of 10 single nucleotide polymorphisms (SNPs) were tested and the likelihood-based association analysis for nuclear families was used to analyse the genotyping data. Eight SNPs detected across the PPARG gene did not show allelic association with schizophrenia; a weak association was detected at rs2745557 in the PTGS2 locus (χ²=4.19, p=0.041) and rs10798059 in the PLA2G4A locus (χ²=4.28, p=0.039) but these associations did not survive after 10,000 permutations to correct the p-value (global p=0.246). The gene–gene interaction test did not show any evidence of either cis-phase interactions for the PLA2G4A and PTGS2 combinations or a trans-phase interaction for the PLA2G4A and PPARG combinations. The PPARG gene has been reported to be strongly associated with type-2 diabetes, but the present study did not support the hypothesis that the PPARG gene may also play an important role in the development of schizophrenia.     

Gray matter deficits in bipolar disorder are associated with genetic variability at interleukin‐1 beta gene (2q13)

Twin, family and recent molecular studies support the hypothesis of genetic overlapping between schizophrenia and bipolar disorder. Brain structural features shared by both psychiatric disorders might be the phenotypic expression of a common genetic risk background. Interleukin‐1 (IL‐1) cluster (chromosome 2q13) genetic variability, previously associated with an increased risk both for schizophrenia and for bipolar disorder, has been also associated with gray matter (GM) deficits, ventricular enlargement and hypoactivity of prefrontal cortex in schizophrenia. The aim of the present study was to analyze the influence of IL‐1 cluster on brain morphology in bipolar disorder. Genetic variability at IL‐1B and IL‐1RN genes was analyzed in 20 DSM‐IV ( Diagnostic and Statistical Manual of Mental Disorders ‐Fourth Edition) bipolar patients. Magnetic resonance imaging (MRI) measurements were obtained for whole‐brain GM and white matter, dorsolateral prefrontal cortex (DLPFC), superior temporal gyrus, hippocampus and lateral ventricles. MRI data were corrected for age and cranial size using regression parameters from a group of 45 healthy subjects. A ?511C/T polymorphism (rs16944) of IL‐1B gene was associated with whole‐brain GM deficits (P = 0.031) and left DLPFCGM deficits (P = 0.047) in bipolar disorder patients. These findings support the hypothesis of IL‐1 cluster variability as a shared genetic risk factor contributing to GM deficits both in bipolar disorder and in schizophrenia. Independent replication in larger samples would be of interest to confirm these results.     

A Preliminary Genetic Analysis of Complement 3 Gene and Schizophrenia

Complement pathway activation was found to occur frequently in schizophrenia, and complement 3 (C3) plays a major role in this process. Previous studies have provided evidence for the possible role of C3 in the development of schizophrenia. In this study, we hypothesized that the gene encoding C3 (C3) may confer susceptibility to schizophrenia in Han Chinese. We analyzed 7 common single nucleotide polymorphisms (SNPs) of C3 in 647 schizophrenia patients and 687 healthy controls. Peripheral C3 mRNA expression level was measured in 23 drug-naïve patients with schizophrenia and 24 controls. Two SNPs (rs1047286 and rs2250656) that deviated from Hardy-Weinberg equilibrium were excluded for further analysis. Among the remaining 5 SNPs, there was no significant difference in allele and genotype frequencies between the patient and control groups. Logistic regression analysis showed no significant SNP-gender interaction in either dominant model or recessive model. There was no significant difference in the level of peripheral C3 expression between the drug-naïve schizophrenia patients and healthy controls. In conclusion, the results of this study do not support C3 as a major genetic susceptibility factor in schizophrenia. Other factors in AP may have critical roles in schizophrenia and be worthy of further investigation.     

Decreased binding of [11C]NNC112 and [11C]SCH23390 in patients with chronic schizophrenia

AimsAbnormality of cognitive function in schizophrenia has been suggested to be related to dopamine D₁ receptor. However, the results of previous positron emission tomography (PET) studies of dopamine D₁ receptor in schizophrenia were not consistent.Main methodsIn this study, six patients with schizophrenia in severe residual phase with chronic antipsychotic treatment and twelve healthy age-matched controls participated. Two different radioligands, [¹¹C]NNC112 and [¹¹C]SCH23390, for dopamine D₁ receptor were used on the same subjects. Binding of the ligands was measured by PET, and statistical analysis was performed using one-way analysis of covariate (ANCOVA) with age as covariate.Key findingsGood correlations between binding potential values (BP_ND) and age were observed in all regions of interest (ROIs) with both ligands. ANCOVA with age as covariate of BP_ND values of all ROIs revealed that the patient group showed significantly lower BP_ND value compared with the control group in both ligands.SignificanceIn patients with chronic schizophrenia in severe residual phase with chronic antipsychotic treatment, the binding potential values of both ligands were significantly lower in the striatum and cortical regions than those of healthy controls.     

A novel permutation testing method implicates sixteen nicotinic acetylcholine receptor genes as risk factors for smoking in schizophrenia families

Smoking is a common correlate of schizophrenia, which leads to medical morbidity. Although twin and adoption studies have consistently implicated genes in the etiology of both smoking and schizophrenia, finding genes has been difficult. Several authors have suggested that clinical or neurobiological features associated with schizophrenia, such as smoking, might improve the ability to detect schizophrenia susceptibility genes by identifying genes related to the etiology of that feature. The objective of this study is to assess evidence for linkage of sixteen nicotinic acetylcholine receptor genes and smoking in schizophrenia families, using data from the NIMH Genetics Initiative for schizophrenia. Sixteen nicotinic acetylcholine receptor genes were selected prior to analysis. We used a multipoint sibling pair linkage analysis program, SIBPAL2, with a smoking trait in schizophrenia families. The significance of the group of candidate genes, in addition to each individual candidate gene, was assessed using permutation testing, which adjusted for multiple comparisons. The group of genes showed significant linkage to the smoking trait after adjusting for multiple comparisons through permutation testing (p = 0.039). In addition, two of the individual candidate genes were significant (CHRNA2, p = 0.044) and (CHRNB2, p = 0.015) and two genes were marginally significant (CHRNA7, p = 0.095; CHRNA1, p = 0.076). The significance of the complex hypothesis, involving sixteen genes, implicates the nicotinic system in smoking for schizophrenic families. Individual gene analysis suggests that CHRNA2 and CHRNB2 may play a particular role in this involvement. Such findings help prioritize genes for future case control studies. In addition, we provide a novel permutation method that is useful in future analyses involving a single hypothesis, with multiple candidate genes.     

Single point mutation on the gene encoding dysbindin results in recognition deficits

The dystrobrevin‐binding protein 1 (DTNBP1) gene is a candidate risk factor for schizophrenia and has been associated with cognitive ability in both patient populations and healthy controls. DTNBP1 encodes dysbindin protein, which is localized to synaptic sites and is reduced in the prefrontal cortex and hippocampus of patients with schizophrenia, indicating a potential role in schizophrenia etiology. Most studies of dysbindin function have focused on the sandy (sdy) mice that lack dysbindin protein and have a wide range of abnormalities. In this study, we examined dysbindin salt and pepper (spp) mice that possess a single point mutation on the Dtnbp1 gene predicted to reduce, but not eliminate, dysbindin expression. By western blot analysis, we found that spp homozygous (spp ?/?) mutants had reduced dysbindin and synaptosomal‐associated protein 25 (SNAP‐25) in the prefrontal cortex, but unaltered levels in hippocampus. Behaviorally, spp mutants performed comparably to controls on a wide range of tasks assessing locomotion, anxiety, spatial recognition and working memory. However, spp ?/? mice had selective deficits in tasks measuring novel object recognition and social novelty recognition. Our results indicate that reduced dysbindin and SNAP‐25 protein in the prefrontal cortex of spp ?/? is associated with selective impairments in recognition processing. These spp mice may prove useful as a novel mouse model to study cognitive deficits linked to dysbindin alterations. Our findings also suggest that aspects of recognition memory may be specifically influenced by DTNBP1 single nucleotide polymorphisms or risk haplotypes in humans and this connection should be further investigated.     

Frequency distributions for chloroplast genes in Chlamydomonas zygote clones: Evidence for random drift

Mitochondria and chloroplasts of eucaryotic cells contain populations of DNA molecules. In certain cases, e.g., the chloroplasts of Chlamydomonas reinhardtii and the mitochondria of Saccharomyces cerevisiae, organelles contributed by the two parents are known to fuse in the zygote, creating a single population of DNA molecules. In a cross, this population will include molecules of both parental genotypes. There is reason to suspect that organelle DNA molecules in this population are selected randomly for replication and recombination. This would result in random changes in the frequency of a particular allele or genotype within the organelle gene pool of a single zygote and also within its clone of progeny cells. A given gene frequency would increase in some zygote clones and decrease in others, analogous to random drift of gene frequencies in small Mendelian populations. To test this, we have examined the distribution of chloroplast gene frequencies among the zygote clones produced in each of a number of crosses of Chlamydomonas. These distributions are typically U or L shaped as predicted by the random drift hypothesis. They include uniparental zygote clones, in which a chloroplast allele from one parent has been fixed (frequency 100%) and the alternative allele from the other parent has been lost (frequency 0%). Among the remaining (biparental) zygote clones, there is a linear distribution of allele frequencies, showing a great increase in variance over the input frequencies. In these experiments both biparental and uniparental zygotes show a bias favoring chloroplast alleles from the mt⁺ (maternal) parent, and there is no statistically significant mode at the allele frequency of 0.5 corresponding to the equal input of alleles from the maternal and paternal (mt^?) parents. The observed distributions support the hypothesis that both uniparental inheritance and the high variance of allele frequencies among zygote clones are due to random drift of allele frequencies, coupled with a directional force which favors fixation of the maternal allele. In addition, statistical analysis of the data shows a strong but incomplete tendency for linked chloroplast markers to be fixed or lost together in uniparental zygotes. Possible cellular and molecular mechanisms for these observations are discussed.     

Complexin2 null mutation requires a ‘second hit’ for induction of phenotypic changes relevant to schizophrenia

Schizophrenia is a devastating disease that affects approximately 1% of the population across cultures. Its neurobiological underpinnings are still unknown. Accordingly, animal models of schizophrenia often lack construct validity. As concordance rate in monozygotic twins amounts to only 50%, environmental risk factors (e.g. neurotrauma, drug abuse, psychotrauma) likely act as necessary ‘second hit' to trigger/drive the disease process in a genetically predisposed individual. Valid animal models would have to consider this genetic–environmental interaction. Based on this concept, we designed an experimental approach for modeling a schizophrenia‐like phenotype in mice. As dysfunction in synaptic transmission plays a key role in schizophrenia, and complexin2 (CPLX2) gene expression is reduced in hippocampus of schizophrenic patients, we developed a mouse model with Cplx2 null mutation as genetic risk factor and a mild parietal neurotrauma, applied during puberty, as environmental ‘second hit'. Several months after lesion, Cplx2 null mutants showed reduced pre‐pulse inhibition, deficit of spatial learning and loss of inhibition after MK‐801 challenge. These abnormalities were largely absent in lesioned wild‐type mice and non‐lesioned Cplx2 null mutants. Forced alternation in T‐maze, object recognition, social interaction and elevated plus maze tests were unaltered in all groups. The previously reported mild motor phenotype of Cplx2 null mutants was accentuated upon lesion. MRI volumetrical analysis showed a decrease of hippocampal volume exclusively in lesioned Cplx2 null mutants. These findings provide suggestive evidence for the ‘second hit' hypothesis of schizophrenia and may offer new tools for the development of advanced treatment strategies.     

Association between ST8SIA2 and the Risk of Schizophrenia and Bipolar I Disorder across Diagnostic Boundaries

Background

Findings from family studies and recent genome-wide association studies have indicated overlap in the risk genes between schizophrenia and bipolar disorder (BD). After finding a linkage between the ST8SIA2 (ST8 alpha-N-acetyl-neuraminide alpha-2, 8-sicalyltransferase 2 gene) locus (15q26) and mixed families with schizophrenia and BD, several studies have reported a significant association between this gene and schizophrenia or BD. We investigated the genetic association between ST8SIA2 and both schizophrenia and BD in the Korean population.

Methods

A total of 582 patients with schizophrenia, 339 patients with BD, and 502 healthy controls were included. Thirty-one tag single nucleotide polymorphisms (SNPs) across the ST8SIA2 region and three other SNPs showing significant associations in previous studies were genotyped. The associations were evaluated by logistic regression analysis using additive, dominant, and recessive genetic models.

Results

Fourteen of 34 SNPs showed a nominally significant association (p < 0.05) with at least one diagnostic group. These association trends were strongest for the schizophrenia and combined schizophrenia and bipolar I disorder (BD-I) groups. The strongest association was observed in rs11637898 for schizophrenia (p = 0.0033) and BD-I (p = 0.0050) under the dominant model. The association between rs11637898 and the combined schizophrenia and BD-I group (p = 0.0006, under the dominant model) remained significant after correcting for multiple testing.

Discussion

We identified a possible role of ST8SIA2 in the common susceptibility of schizophrenia and BD-I. However, no association trend was observed for bipolar II disorder. Further efforts are needed to identify a specific phenotype associated with this gene crossing the current diagnostic categories.     

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

BackgroundThe prediction of breast cancer subtypes plays a key role in the diagnosis and prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in the intelligent prediction of breast cancer subtypes. However, most of the traditional DL models use single modality data, which can just extract a few features, so it cannot establish a stable relationship between patient characteristics and breast cancer subtypes.DatasetWe used the TCGA-BRCA dataset as a sample set for molecular subtype prediction of breast cancer. It is a public dataset that can be obtained through the following link: https://portal.gdc.cancer.gov/projects/TCGA-BRCAMethodsIn this paper, a Hybrid DL model based on the multimodal data is proposed. We combine the patient's gene modality data with image modality data to construct a multimodal fusion framework. According to the different forms and states, we set up feature extraction networks respectively, and then we fuse the output of the two feature networks based on the idea of weighted linear aggregation. Finally, the fused features are used to predict breast cancer subtypes. In particular, we use the principal component analysis to reduce the dimensionality of high-dimensional data of gene modality and filter the data of image modality. Besides, we also improve the traditional feature extraction network to make it show better performance.ResultsThe results show that compared with the traditional DL model, the Hybrid DL model proposed in this paper is more accurate and efficient in predicting breast cancer subtypes. Our model achieved a prediction accuracy of 88.07% in 10 times of 10-fold cross-validation. We did a separate AUC test for each subtype, and the average AUC value obtained was 0.9427. In terms of subtype prediction accuracy, our model is about 7.45% higher than the previous average.     

The potential shared role of inflammation in insulin resistance and schizophrenia: A bidirectional two-sample mendelian randomization study

BackgroundInsulin resistance predisposes to cardiometabolic disorders, which are commonly comorbid with schizophrenia and are key contributors to the significant excess mortality in schizophrenia. Mechanisms for the comorbidity remain unclear, but observational studies have implicated inflammation in both schizophrenia and cardiometabolic disorders separately. We aimed to examine whether there is genetic evidence that insulin resistance and 7 related cardiometabolic traits may be causally associated with schizophrenia, and whether evidence supports inflammation as a common mechanism for cardiometabolic disorders and schizophrenia.Methods and findingsWe used summary data from genome-wide association studies of mostly European adults from large consortia (Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) featuring up to 108,557 participants; Diabetes Genetics Replication And Meta-analysis (DIAGRAM) featuring up to 435,387 participants; Global Lipids Genetics Consortium (GLGC) featuring up to 173,082 participants; Genetic Investigation of Anthropometric Traits (GIANT) featuring up to 339,224 participants; Psychiatric Genomics Consortium (PGC) featuring up to 105,318 participants; and Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium featuring up to 204,402 participants). We conducted two-sample uni- and multivariable mendelian randomization (MR) analysis to test whether (i) 10 cardiometabolic traits (fasting insulin, high-density lipoprotein and triglycerides representing an insulin resistance phenotype, and 7 related cardiometabolic traits: low-density lipoprotein, fasting plasma glucose, glycated haemoglobin, leptin, body mass index, glucose tolerance, and type 2 diabetes) could be causally associated with schizophrenia; and (ii) inflammation could be a shared mechanism for these phenotypes. We conducted a detailed set of sensitivity analyses to test the assumptions for a valid MR analysis. We did not find statistically significant evidence in support of a causal relationship between cardiometabolic traits and schizophrenia, or vice versa. However, we report that a genetically predicted inflammation-related insulin resistance phenotype (raised fasting insulin (raised fasting insulin (Wald ratio OR = 2.95, 95% C.I, 1.38–6.34, Holm-Bonferroni corrected p-value (p) = 0.035) and lower high-density lipoprotein (Wald ratio OR = 0.55, 95% C.I., 0.36–0.84; p = 0.035)) was associated with schizophrenia. Evidence for these associations attenuated to the null in multivariable MR analyses after adjusting for C-reactive protein, an archetypal inflammatory marker: (fasting insulin Wald ratio OR = 1.02, 95% C.I, 0.37–2.78, p = 0.975), high-density lipoprotein (Wald ratio OR = 1.00, 95% C.I., 0.85–1.16; p = 0.849), suggesting that the associations could be fully explained by inflammation. One potential limitation of the study is that the full range of gene products from the genetic variants we used as proxies for the exposures is unknown, and so we are unable to comment on potential biological mechanisms of association other than inflammation, which may also be relevant.ConclusionsOur findings support a role for inflammation as a common cause for insulin resistance and schizophrenia, which may at least partly explain why the traits commonly co-occur in clinical practice. Inflammation and immune pathways may represent novel therapeutic targets for the prevention or treatment of schizophrenia and comorbid insulin resistance. Future work is needed to understand how inflammation may contribute to the risk of schizophrenia and insulin resistance.

In a Mendelian randomization study, Benjamin Perry and colleagues investigate the genetic evidence supporting relationships between inflammation, insulin resistance, and schizophrenia.     

Pathway analysis of a genome-wide association study in schizophrenia

Objective

The aim of this study was to identify the candidate single nucleotide polymorphisms (SNPs) and candidate mechanisms that contribute to schizophrenia susceptibility and to generate a SNP to gene to pathway hypothesis using an analytical pathway-based approach.

Methods

We used schizophrenia GWAS data of the genotypes of 660,259 SNPs in 1378 controls and 1351 cases of European descent after quality control filtering. ICSNPathway (Identify candidate Causal SNPs and Pathways) analysis was applied to the schizophrenia GWAS dataset. The first stage involved the pre-selection of candidate SNPs by linkage disequilibrium analysis and the functional SNP annotation of the most significant SNPs found. The second stage involved the annotation of biological mechanisms for the pre-selected candidate SNPs using improved-gene set enrichment analysis.

Results

ICSNPathway analysis identified fifteen candidate SNPs, ten candidate pathways, and nine hypothetical biological mechanisms. The most strongly associated potential pathways were as follows. First, rs1644731 and rs1644730 to RDH8 to estrogen biosynthetic process (p < 0.001, FDR < 0.001). The genes involved in this pathway are RDH8 and HSD3B1 (p < 0.05). All-trans-retinol dehydrogenase (RDH8) is a visual cycle enzyme that reduces all-trans-retinal to all-trans-retinol in the presence of NADPH. The chemical reactions and pathways involved result in the formation of estrogens, which are C18 steroid hormones that can stimulate the development of female sexual characteristics. Second, rs1146031 to ACVR1 to mesoderm formation and activin binding (p < 0.001, FDR = 0.032, 0.034). Two of 15 candidate genes are known genes associated with schizophrenia: KCNQ2 and APOL2. One of the 10 candidate pathways, estrogen biosynthetic process, is known to be associated with schizophrenia (p < 0.001, FDR < 0.001). However, 13 of candidate genes (RDH8, ACVR1, PSMD9, KCNAB1, SLC17A3, ARCN1, COG7, STAB2, LRPAP1, STAB1, CXCL16, COL4A4, EXOSC3) and 9 of candidate pathways were novel.

Conclusion

By applying ICSNPathway analysis to schizophrenia GWAS data, we identified candidate SNPs, genes like KCNQ2 and APOL2 and pathways involving the estrogen biosynthetic process may contribute to schizophrenia susceptibility. Further analyses are needed to validate the results of this analysis.