Background

Recent genome-wide association studies have identified a large number of genetic risk factors for schizophrenia (SCZ) featuring ion channels and calcium transporters. For some of these risk factors, independent prior investigations have examined the effects of genetic alterations on the cellular electrical excitability and calcium homeostasis. In the present proof-of-concept study, we harnessed these experimental results for modeling of computational properties on layer V cortical pyramidal cells and identified possible common alterations in behavior across SCZ-related genes.

Methods

We applied a biophysically detailed multicompartmental model to study the excitability of a layer V pyramidal cell. We reviewed the literature on functional genomics for variants of genes associated with SCZ and used changes in neuron model parameters to represent the effects of these variants.

Results

We present and apply a framework for examining the effects of subtle single nucleotide polymorphisms in ion channel and calcium transporter-encoding genes on neuron excitability. Our analysis indicates that most of the considered SCZ-related genetic variants affect the spiking behavior and intracellular calcium dynamics resulting from summation of inputs across the dendritic tree.

Conclusions

Our results suggest that alteration in the ability of a single neuron to integrate the inputs and scale its excitability may constitute a fundamental mechanistic contributor to mental disease, alongside the previously proposed deficits in synaptic communication and network behavior.

Characterizing the distribution of effects from genome-wide genotyping data is crucial for understanding important aspects of the genetic architecture of complex traits, such as number or proportion of non-null loci, average proportion of phenotypic variance explained per non-null effect, power for discovery, and polygenic risk prediction. To this end, previous work has used effect-size models based on various distributions, including the normal and normal mixture distributions, among others. In this paper we propose a scale mixture of two normals model for effect size distributions of genome-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local false discovery rate, and power for discovery of a specified proportion of phenotypic variance explained from additive effects of loci surpassing a given significance threshold. We also examine the crucial issue of the impact of linkage disequilibrium (LD) on effect sizes and parameter estimates, both analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn's disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While capturing the general behavior of the data, this mixture model underestimates the tails of the CD effect size distribution. We discuss the implications of pervasive small but replicating effects in CD and SZ on genomic control and power. Finally, we conclude that, despite having very similar estimates of variance explained by genotyped SNPs, CD and SZ have a broadly dissimilar genetic architecture, due to differing mean effect size and proportion of non-null loci.

Genome-wide Association Studies (GWAS) result in millions of summary statistics ("z-scores") for single nucleotide polymorphism (SNP) associations with phenotypes. These rich datasets afford deep insights into the nature and extent of genetic contributions to complex phenotypes such as psychiatric disorders, which are understood to have substantial genetic components that arise from very large numbers of SNPs. The complexity of the datasets, however, poses a significant challenge to maximizing their utility. This is reflected in a need for better understanding the landscape of z-scores, as such knowledge would enhance causal SNP and gene discovery, help elucidate mechanistic pathways, and inform future study design. Here we present a parsimonious methodology for modeling effect sizes and replication probabilities, relying only on summary statistics from GWAS substudies, and a scheme allowing for direct empirical validation. We show that modeling z-scores as a mixture of Gaussians is conceptually appropriate, in particular taking into account ubiquitous non-null effects that are likely in the datasets due to weak linkage disequilibrium with causal SNPs. The four-parameter model allows for estimating the degree of polygenicity of the phenotype and predicting the proportion of chip heritability explainable by genome-wide significant SNPs in future studies with larger sample sizes. We apply the model to recent GWAS of schizophrenia (N = 82,315) and putamen volume (N = 12,596), with approximately 9.3 million SNP z-scores in both cases. We show that, over a broad range of z-scores and sample sizes, the model accurately predicts expectation estimates of true effect sizes and replication probabilities in multistage GWAS designs. We assess the degree to which effect sizes are over-estimated when based on linear-regression association coefficients. We estimate the polygenicity of schizophrenia to be 0.037 and the putamen to be 0.001, while the respective sample sizes required to approach fully explaining the chip heritability are 10(6) and 10(5). The model can be extended to incorporate prior knowledge such as pleiotropy and SNP annotation. The current findings suggest that the model is applicable to a broad array of complex phenotypes and will enhance understanding of their genetic architectures.

Schizophrenia (SCZ) is associated with differences in subcortical brain volumes and intracranial volume (ICV). However, little is known about the underlying etiology of these brain alterations. Here, we explored whether brain structure volumes and SCZ share genetic risk factors. Using conditional false discovery rate (FDR) analysis, we integrated genome-wide association study (GWAS) data on SCZ (n = 82315) and GWAS data on 7 subcortical brain volumes and ICV (n = 11840). By conditioning the FDR on overlapping associations, this statistical approach increases power to discover genetic loci. To assess the credibility of our approach, we studied the identified loci in larger GWAS samples on ICV (n = 26577) and hippocampal volume (n = 26814). We observed polygenic overlap between SCZ and volumes of hippocampus, putamen, and ICV. Based on conjunctional FDR < 0.05, we identified 2 loci shared between SCZ and ICV implicating genes FOXO3 (rs10457180) and ITIH4 (rs4687658), 2 loci shared between SCZ and hippocampal volume implicating SLC4A10 (rs4664442) and SPATS2L (rs1653290), and 2 loci shared between SCZ and volume of putamen implicating DCC (rs4632195) and DLG2 (rs11233632). The loci shared between SCZ and hippocampal volume or ICV had not reached significance in the primary GWAS on brain phenotypes. Proving our point of increased power, 2 loci did reach genome-wide significance with ICV (rs10457180) and hippocampal volume (rs4664442) in the larger GWAS. Three of the 6 identified loci are novel for SCZ. Altogether, the findings provide new insights into the relationship between SCZ and brain structure volumes, suggesting that their genetic architectures are not independent.

Background

Epidemiological findings suggest a relationship between multiple sclerosis (MS) and cardiovascular disease (CVD) risk factors, although the nature of this relationship is not well understood.

Objective

We used genome-wide association study (GWAS) data to identify shared genetic factors (pleiotropy) between MS and CVD risk factors.

Methods

Using summary statistics from a large, recent GWAS (total n > 250,000 individuals), we investigated overlap in single nucleotide polymorphisms (SNPs) associated with MS and a number of CVD risk factors including triglycerides (TG), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, body mass index, waist-to-hip ratio, type 2 diabetes, systolic blood pressure, and C-reactive protein level.

Results and conclusion

Using conditional enrichment plots, we found 30-fold enrichment of MS SNPs for different levels of association with LDL and TG SNPs, with a corresponding reduction in conditional false discovery rate (FDR). We identified 133 pleiotropic loci outside the extended major histocompatibility complex with conditional FDR < 0.01, of which 65 are novel. These pleiotropic loci were located on 21 different chromosomes. Our findings point to overlapping pathobiology between clinically diagnosed MS and cardiovascular risk factors and identify novel common variants associated with increased MS risk.

Schizophrenia is suggested to be a by-product of the evolution in humans, a compromise for our language, creative thinking and cognitive abilities, and thus, essentially, a human disorder. The time of its origin during the course of human evolution remains unclear. Here we investigate several markers of early human evolution and their relationship to the genetic risk of schizophrenia. We tested the schizophrenia evolutionary hypothesis by analyzing genome-wide association studies of schizophrenia and other human phenotypes in a statistical framework suited for polygenic architectures. We analyzed evolutionary proxy measures: human accelerated regions, segmental duplications, and ohnologs, representing various time periods of human evolution for overlap with the human genomic loci associated with schizophrenia. Polygenic enrichment plots suggest a higher prevalence of schizophrenia associations in human accelerated regions, segmental duplications and ohnologs. However, the enrichment is mostly accounted for by linkage disequilibrium, especially with functional elements like introns and untranslated regions. Our results did not provide clear evidence that markers of early human evolution are more likely associated with schizophrenia. While SNPs associated with schizophrenia are enriched in HAR, Ohno and SD regions, the enrichment seems to be mediated by affiliation to known genomic enrichment categories. Taken together with previous results, these findings suggest that schizophrenia risk may have mainly developed more recently in human evolution.

The genome-wide association study of the Psychiatric Genomics Consortium identified over one hundred schizophrenia susceptibility loci. The number of non-coding variants discovered suggests that gene regulation could mediate the effect of these variants on disease. Expression quantitative trait loci (eQTLs) contribute to variation in levels of mRNA. Given the co-occurrence of schizophrenia and several traits not involving the central nervous system (CNS), we investigated the enrichment of schizophrenia associations among eQTLs for four non-CNS tissues: adipose tissue, epidermal tissue, lymphoblastoid cells and blood. Significant enrichment was seen in eQTLs of all tissues: adipose (β = 0.18, p = 8.8 × 10-06), epidermal (β = 0.12, p = 3.1 × 10-04), lymphoblastoid (β = 0.19, p = 6.2 × 10-08) and blood (β = 0.19, p = 6.4 × 10-06). For comparison, we looked for enrichment of association with traits of known relevance to one or more of these tissues (body mass index, height, rheumatoid arthritis, systolic blood pressure and type-II diabetes) and found that schizophrenia enrichment was of similar scale to that observed when studying diseases in the context of a more likely causal tissue. To further investigate tissue specificity, we looked for differential enrichment of eQTLs with relevant Roadmap affiliation (enhancers and promoters) and varying distance from the transcription start site. Neither factor significantly contributed to the enrichment, suggesting that this is equally distributed in tissue-specific and cross-tissue regulatory elements. Our analyses suggest that functional correlates of schizophrenia risk are prevalent in non-CNS tissues. This could be because of pleiotropy or the effectiveness of variants affecting expression in different contexts. This suggests the utility of large, single-tissue eQTL experiments to increase eQTL discovery power in the study of schizophrenia, in addition to smaller, multiple-tissue approaches. Our results conform to the notion that schizophrenia is a systemic disorder involving many tissues.

Schizophrenia is associated with differences in personality traits, and recent studies suggest that personality traits and schizophrenia share a genetic basis. Here we aimed to identify specific genetic loci shared between schizophrenia and the Big Five personality traits using a Bayesian statistical framework. Using summary statistics from genome-wide association studies (GWAS) on personality traits in the 23andMe cohort (n = 59,225) and schizophrenia in the Psychiatric Genomics Consortium cohort (n = 82,315), we evaluated overlap in common genetic variants. The Big Five personality traits neuroticism, extraversion, openness, agreeableness and conscientiousness were measured using a web implementation of the Big Five Inventory. Applying the conditional false discovery rate approach, we increased discovery of genetic loci and identified two loci shared between neuroticism and schizophrenia and six loci shared between openness and schizophrenia. The study provides new insights into the relationship between personality traits and schizophrenia by highlighting genetic loci involved in their common genetic etiology.

There is evidence for genetic overlap between cognitive abilities and schizophrenia (SCZ), and genome-wide association studies (GWAS) demonstrate that both SCZ and general cognitive abilities have a strong polygenic component with many single-nucleotide polymorphisms (SNPs) each with a small effect. Here we investigated the shared genetic architecture between SCZ and educational attainment, which is regarded as a "proxy phenotype" for cognitive abilities, but may also reflect other traits. We applied a conditional false discovery rate (condFDR) method to GWAS of SCZ (n = 82 315), college completion ("College," n = 95 427), and years of education ("EduYears," n = 101 069). Variants associated with College or EduYears showed enrichment of association with SCZ, demonstrating polygenic overlap. This was confirmed by an increased replication rate in SCZ. By applying a condFDR threshold <0.01, we identified 18 genomic loci associated with SCZ after conditioning on College and 15 loci associated with SCZ after conditioning on EduYears. Ten of these loci overlapped. Using conjunctional FDR, we identified 10 loci shared between SCZ and College, and 29 loci shared between SCZ and EduYears. The majority of these loci had effects in opposite directions. Our results provide evidence for polygenic overlap between SCZ and educational attainment, and identify novel pleiotropic loci. Other studies have reported genetic overlap between SCZ and cognition, or SCZ and educational attainment, with negative correlation. Importantly, our methods enable identification of bi-directional effects, which highlight the complex relationship between SCZ and educational attainment, and support polygenic mechanisms underlying both cognitive dysfunction and creativity in SCZ.

There is evidence for genetic overlap between cognitive abilities and schizophrenia (SCZ), and genome-wide association studies (GWAS) demonstrate that both SCZ and general cognitive abilities have a strong polygenic component with many single-nucleotide polymorphisms (SNPs) each with a small effect. Here we investigated the shared genetic architecture between SCZ and educational attainment, which is regarded as a proxy phenotype for cognitive abilities, but may also reflect other traits. We applied a conditional false discovery rate (condFDR) method to GWAS of SCZ (n = 82 315), college completion (College, n = 95 427), and years of education (EduYears, n = 101 069). Variants associated with College or EduYears showed enrichment of association with SCZ, demonstrating polygenic overlap. This was confirmed by an increased replication rate in SCZ. By applying a condFDR threshold <0.01, we identified 18 genomic loci associated with SCZ after conditioning on College and 15 loci associated with SCZ after conditioning on EduYears. Ten of these loci overlapped. Using conjunctional FDR, we identified 10 loci shared between SCZ and College, and 29 loci shared between SCZ and EduYears. The majority of these loci had effects in opposite directions. Our results provide evidence for polygenic overlap between SCZ and educational attainment, and identify novel pleiotropic loci. Other studies have reported genetic overlap between SCZ and cognition, or SCZ and educational attainment, with negative correlation. Importantly, our methods enable identification of bi-directional effects, which highlight the complex relationship between SCZ and educational attainment, and support polygenic mechanisms underlying both cognitive dysfunction and creativity in SCZ.