Network-Assisted Investigation of Combined Causal Signals from Genome-Wide Association Studies in Schizophrenia

With the recent success of genome-wide association studies (GWAS), a wealth of association data has been accomplished for more than 200 complex diseases/traits, proposing a strong demand for data integration and interpretation. A combinatory analysis of multiple GWAS datasets, or an integrative analysis of GWAS data and other high-throughput data, has been particularly promising. In this study, we proposed an integrative analysis framework of multiple GWAS datasets by overlaying association signals onto the protein-protein interaction network, and demonstrated it using schizophrenia datasets. Building on a dense module search algorithm, we first searched for significantly enriched subnetworks for schizophrenia in each single GWAS dataset and then implemented a discovery-evaluation strategy to identify module genes with consistent association signals. We validated the module genes in an independent dataset, and also examined them through meta-analysis of the related SNPs using multiple GWAS datasets. As a result, we identified 205 module genes with a joint effect significantly associated with schizophrenia; these module genes included a number of well-studied candidate genes such as DISC1, GNA12, GNA13, GNAI1, GPR17, and GRIN2B. Further functional analysis suggested these genes are involved in neuronal related processes. Additionally, meta-analysis found that 18 SNPs in 9 module genes had PmetaHLA-DQA1 located in the MHC region on chromosome 6, which was reported in previous studies using the largest cohort of schizophrenia patients to date. These results demonstrated our bi-directional network-based strategy is efficient for identifying disease-associated genes with modest signals in GWAS datasets. This approach can be applied to any other complex diseases/traits where multiple GWAS datasets are available

Methods for detecting associations between phenotype and aggregations of rare variants

Although genome-wide association studies have uncovered variants associated with more than 150 traits, the percentage of phenotypic variation explained by these associations remains small. This has led to the search for the dark matter that explains this missing genetic component of heritability. One potential explanation for dark matter is rare variants, and several statistics have been devised to detect associations resulting from aggregations of rare variants in relatively short regions of interest, such as candidate genes. In this paper we investigate the feasibility of extending this approach in an agnostic way, in which we consider all variants within a much broader region of interest, such as an entire chromosome or even the entire exome. Our method searches for subsets of variant sites using either Markov chain Monte Carlo or genetic algorithms. The analysis was performed with knowledge of the Genetic Analysis Workshop 17 answers

Familial aggregation and heritability of schizophrenia and co-aggregation of psychiatric illnesses in affected families

Strong familial aggregation of schizophrenia has been reported but there is uncertainty concerning the degree of genetic contribution to the phenotypic variance of the disease. This study aimed to examine the familial aggregation and heritability of schizophrenia, and the relative risks (RRs) of other psychiatric diseases, in relatives of people with schizophrenia using the Taiwan National Health Insurance Database. The study population included individuals with affected first-degree or second-degree relatives identified from all beneficiaries (n = 23 422 955) registered in 2013. Diagnoses of schizophrenia made by psychiatrists were ascertained between January 1, 1996 and December 31, 2013. Having an affected co-twin, first-degree relative, second-degree relative, or spouse was associated with an adjusted RR (95% CI) of 37.86 (30.55-46.92), 6.30 (6.09-6.53), 2.44 (1.91-3.12), and 1.88 (1.64-2.15), respectively. Compared with the general population, individuals with one affected first-degree relative had a RR (95% CI) of 6.00 (5.79-6.22) and those with 2 or more had a RR (95% CI) of 14.66 (13.00-16.53) for schizophrenia. The accountability for the phenotypic variance of schizophrenia was 47.3% for genetic factors, 15.5% for shared environmental factors, and 37.2% for non-shared environmental factors. The RR (95% CI) in individuals with a first-degree relative with schizophrenia was 3.49 (3.34-3.64) for mood disorders and 3.91 (3.35-4.57) for delusional disorders. A family history of schizophrenia is therefore associated with a higher risk of developing schizophrenia, mood disorders, and delusional disorders. Heritability and environmental factors each account for half of the phenotypic variance of schizophrenia

Sex-specific manifestation of genetic risk for attention deficit hyperactivity disorder in the general population

Background: Attention deficit hyperactivity disorder (ADHD) is more commonly diagnosed in males than in females. A growing body of research suggests that females with ADHD might be underdiagnosed or receive alternative diagnoses, such as anxiety or depression. Other lines of reasoning suggest that females might be protected from developing ADHD, requiring a higher burden of genetic risk to manifest the disorder. Methods: We tested these two hypotheses, using common variant genetic data from two population-based cohorts. First, we tested whether females and males diagnosed with anxiety or depression differ in terms of their genetic risk for ADHD, assessed as polygenic risk scores (PRS). Second, we tested whether females and males with ADHD differed in ADHD genetic risk burden. We used three different diagnostic definitions: registry-based clinical diagnoses, screening-based research diagnoses and algorithm-based research diagnoses, to investigate possible referral biases. Results: In individuals with a registry-based clinical diagnosis of anxiety or depression, females had higher ADHD PRS than males [OR(CI) = 1.39 (1.12–1.73)] but there was no sex difference for screening-based [OR(CI) = 1.15 (0.94–1.42)] or algorithm-based [OR(CI) = 1.04 (0.89–1.21)] diagnoses. There was also no sex difference in ADHD PRS in individuals with ADHD diagnoses that were registry-based [OR(CI) = 1.04 (0.84–1.30)], screening-based [OR(CI) = 0.96 (0.85–1.08)] or algorithm-based [OR(CI) = 1.15 (0.78–1.68)]. Conclusions: This study provides genetic evidence that ADHD risk may be more likely to manifest or be diagnosed as anxiety or depression in females than in males. Contrary to some earlier studies, the results do not support increased ADHD genetic risk in females with ADHD as compared to affected males

The Impact of Errors in Copy Number Variation Detection Algorithms on Association Results

The inaccuracy of copy number variation (CNV) detection on single nucleotide polymorphism (SNP) arrays has recently been brought to attention. Such high error rates will undoubtedly have ramifications on downstream association testing. We examined this effect for a wide range of scenarios and found a noticeable decrease in power for error rates typical of CNV calling algorithms. We compared power using CNV calls to the log relative ratio and found the latter to be superior when error rates are moderate to large or when the CNV size is small. It is our recommendation that CNV researchers use intensity measurements as an alternative to CNV calls in these scenarios

A framework for interpreting genome-wide association studies of psychiatric disorders

Genome-wide association studies (GWAS) have yielded a plethora of new findings in the past 3 years. By early 2009, GWAS on 47 samples of subjects with attention-deficit hyperactivity disorder, autism, bipolar disorder, major depressive disorder and schizophrenia will be completed. Taken together, these GWAS constitute the largest biological experiment ever conducted in psychiatry (59 000 independent cases and controls, 7700 family trios and >40 billion genotypes). We know that GWAS can work, and the question now is whether it will work for psychiatric disorders. In this review, we describe these studies, the Psychiatric GWAS Consortium for meta-analyses of these data, and provide a logical framework for interpretation of some of the conceivable outcomes

Genetic pleiotropy between multiple sclerosis and schizophrenia but not bipolar disorder : differential involvement of immune-related gene loci

J. Saarela työryhmäjäsen.Peer reviewe

Stimulation of synaptic vesicle exocytosis by the mental disease gene DISC1 is mediated by N-Type voltage-gated calcium channels

Lesions and mutations of the DISC1 (Disrupted-in-schizophrenia-1) gene have been linked to major depression, schizophrenia, bipolar disorder and autism, but the influence of DISC1 on synaptic transmission remains poorly understood. Using two independent genetic approaches-RNAi and a DISC1 KO mouse-we examined the impact of DISC1 on the synaptic vesicle (SV) cycle by population imaging of the synaptic tracer vGpH in hippocampal neurons. DISC1 loss-of-function resulted in a marked decrease in SV exocytic rates during neuronal stimulation and was associated with reduced Ca(2+) transients at nerve terminals. Impaired SV release was efficiently rescued by elevation of extracellular Ca(2+), hinting at a link between DISC1 and voltage-gated Ca(2+) channels. Accordingly, blockade of N-type Cav2.2 channels mimics and occludes the effect of DISC1 inactivation on SV exocytosis, and overexpression of DISC1 in a heterologous system increases Cav2.2 currents. Collectively, these results show that DISC1-dependent enhancement of SV exocytosis is mediated by Cav2.2 and point to aberrant glutamate release as a probable endophenotype of major psychiatric disorders

Copy number variations and risk for schizophrenia in 22q11.2 deletion syndrome

22q11.2 Deletion Syndrome (22q11.2DS) is a common microdeletion syndrome with congenital and late-onset features. Testing for the genomic content of copy number variations (CNVs) may help elucidate the 22q11.2 deletion mechanism and the variable clinical expression of the syndrome including the high (25%) risk for schizophrenia. We used genome-wide microarrays to assess CNV content and the parental origin of 22q11.2 deletions in a cohort of 100 adults with 22q11.2DS (44 with schizophrenia) and controls. 22q11.2DS subjects with schizophrenia failed to exhibit de novo CNVs or any excess of novel inherited CNVs outside the 22q11.2 region. There were no significant effects of parental origin of the 22q11.2 deletion, deletion length, parental age or family history on expression of schizophrenia. There was no evidence for a general increase of de novo CNVs in 22q11.2DS. A novel finding was the relative paucity of males with de novo 22q11.2 deletions of paternal origin (P = 0.019). The Y chromosome may play a mediating role in the mechanism of 22q11.2 deletion events during spermatogenesis, resulting in the previously observed excess of maternal de novo 22q11.2 deletions. Hemizygosity of the 22q11.2 region appears to be the major CNV-related risk factor for schizophrenia in 22q11.2DS. The results reinforce the need for further efforts to identify specific molecular mechanisms underlying this expression and to identify the 1% of patients with schizophrenia who carry 22q11.2 deletions