DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia

We performed a systematic analysis of blood DNA methylation profiles from 4,483 participants from seven independent cohorts identifying differentially methylated positions (DMPs) associated with psychosis, schizophrenia and treatment-resistant schizophrenia. Psychosis cases were characterized by significant differences in measures of blood cell proportions and elevated smoking exposure derived from the DNA methylation data, with the largest differences seen in treatment-resistant schizophrenia patients. We implemented a stringent pipeline to meta-analyze epigenome-wide association study (EWAS) results across datasets, identifying 95 DMPs associated with psychosis and 1,048 DMPs associated with schizophrenia, with evidence of colocalization to regions nominated by genetic association studies of disease. Many schizophrenia-associated DNA methylation differences were only present in patients with treatment-resistant schizophrenia, potentially reflecting exposure to the atypical antipsychotic clozapine. Our results highlight how DNA methylation data can be leveraged to identify physiological (e.g., differential cell counts) and environmental (e.g., smoking) factors associated with psychosis and molecular biomarkers of treatment-resistant schizophrenia

Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls

Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed approximately 19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated approximately 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease-IRGM for Crohn\u27s disease, HLA for Crohn\u27s disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetes-although in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases

The Impact of Incomplete Linkage Disequilibrium and Genetic Model Choice on the Analysis and Interpretation of Genome-wide Association Studies

When conducting a genetic association study, it has previously been observed that a multiplicative risk model tends to fit better at a disease-associated marker locus than at the ungenotyped causative locus. This suggests that, while overall risk decreases as linkage disequilibrium breaks down, non-multiplicative components are more affected. This effect is investigated here, in particular the practical consequences it has on testing for trait/marker associations and the estimation of mode of inheritance and risk once an associated locus has been found. The extreme significance levels required for genome-wide association studies define a restricted range of detectable allele frequencies and effect sizes. For such parameters there is little to be gained by using a test that models the correct mode of inheritance rather than the multiplicative; thus the Cochran-Armitage trend test, which assumes a multiplicative model, is preferable to a more general model as it uses fewer degrees of freedom. Equally when estimating risk, it is likely that a multiplicative risk model will provide a good fit to the data, regardless of the underlying mode of inheritance at the true susceptibility locus. This may lead to problems in interpreting risk estimates

A two-stage meta-analysis identifies several new loci for Parkinson's disease.

A previous genome-wide association (GWA) meta-analysis of 12,386 PD cases and 21,026 controls conducted by the International Parkinson's Disease Genomics Consortium (IPDGC) discovered or confirmed 11 Parkinson's disease (PD) loci. This first analysis of the two-stage IPDGC study focused on the set of loci that passed genome-wide significance in the first stage GWA scan. However, the second stage genotyping array, the ImmunoChip, included a larger set of 1,920 SNPs selected on the basis of the GWA analysis. Here, we analyzed this set of 1,920 SNPs, and we identified five additional PD risk loci (combined p<5×10(-10), PARK16/1q32, STX1B/16p11, FGF20/8p22, STBD1/4q21, and GPNMB/7p15). Two of these five loci have been suggested by previous association studies (PARK16/1q32, FGF20/8p22), and this study provides further support for these findings. Using a dataset of post-mortem brain samples assayed for gene expression (n = 399) and methylation (n = 292), we identified methylation and expression changes associated with PD risk variants in PARK16/1q32, GPNMB/7p15, and STX1B/16p11 loci, hence suggesting potential molecular mechanisms and candidate genes at these risk loci

Synergy Disequilibrium Plots: graphical visualization of pairwise synergies and redundancies of SNPs with respect to a phenotype

Summary:We present a visualization tool applied on genome-wide association data, revealing disease-associated haplotypes, epistatically interacting loci, as well as providing visual signatures of multivariate correlations of genetic markers with respect to a phenotype

Evidence that duplications of 22q11.2 protect against schizophrenia

A number of large, rare copy number variants (CNVs) are deleterious for neurodevelopmental disorders, but large, rare, protective CNVs have not been reported for such phenotypes. Here we show in a CNV analysis of 47 005 individuals, the largest CNV analysis of schizophrenia to date, that large duplications (1.5-3.0 Mb) at 22q11.2--the reciprocal of the well-known, risk-inducing deletion of this locus--are substantially less common in schizophrenia cases than in the general population (0.014% vs 0.085%, OR=0.17, P=0.00086). 22q11.2 duplications represent the first putative protective mutation for schizophrenia

Recessive mutations in the cancer gene <i>Ataxia Telangiectasia Mutated (ATM)</i>, at a locus previously associated with metformin response, cause dysglycaemia and insulin resistance

Aim: To investigate glucose and insulin metabolism in participants with ataxia telangiectasia in the absence of a diagnosis of diabetes. Methods: A standard oral glucose tolerance test was performed in participants with ataxia telangiectasia (n = 10) and in a control cohort (n = 10). Serial glucose and insulin measurements were taken to permit cohort comparisons of glucose‐insulin homeostasis and indices of insulin secretion and sensitivity. Results: During the oral glucose tolerance test, the 2‐h glucose (6.75 vs 4.93 mmol/l; P = 0.029), insulin concentrations (285.6 vs 148.5 pmol/l; P = 0.043), incremental area under the curve for glucose (314 vs 161 mmol/l/min; P = 0.036) and incremental area under the curve for insulin (37,720 vs 18,080 pmol/l/min; P = 0.03) were higher in participants with ataxia telangiectasia than in the controls. There were no significant differences between groups in fasting glucose, insulin concentrations or insulinogenic index measurement (0.94 vs 0.95; P = 0.95). The Matsuda index, reflecting whole‐body insulin sensitivity, was lower in participants with ataxia telangiectasia (5.96 vs 11.03; P = 0.019) than in control subjects. Conclusions: Mutations in Ataxia Telangiectasia Mutated (ATM) that cause ataxia telangiectasia are associated with elevated glycaemia and low insulin sensitivity in participants without diabetes. This indicates a role of ATM in glucose and insulin metabolic pathway

Common DNA markers can account for more than half of the genetic influence on cognitive abilities

For nearly a century, twin and adoption studies have yielded substantial estimates of heritability for cognitive abilities, although it has proved difficult for genomewide-association studies to identify the genetic variants that account for this heritability (i.e., the missing-heritability problem). However, a new approach, genomewide complex-trait analysis (GCTA), forgoes the identification of individual variants to estimate the total heritability captured by common DNA markers on genotyping arrays. In the same sample of 3,154 pairs of 12-year-old twins, we directly compared twin-study heritability estimates for cognitive abilities (language, verbal, nonverbal, and general) with GCTA estimates captured by 1.7 million DNA markers. We found that DNA markers tagged by the array accounted for .66 of the estimated heritability, reaffirming that cognitive abilities are heritable. Larger sample sizes alone will be sufficient to identify many of the genetic variants that influence cognitive abilities

Autoinflammatory genes and susceptibility to psoriatic juvenile idiopathic arthritis

OBJECTIVE: To investigate the association of NLRP3, NOD2, MEFV, and PSTPIP1, genes that cause 4 of the autoinflammatory hereditary periodic fever syndromes (HPFS), with juvenile idiopathic arthritis (JIA). METHODS: Fifty-one single-nucleotide polymorphisms (SNPs) across the 4 loci were investigated using MassArray genotyping in 950 Caucasian patients with JIA living in the UK and 728 ethnically matched healthy controls. RESULTS: Prior to Bonferroni correction for multiple testing, significant genotype associations between 6 SNPs in MEFV and JIA were observed and, in subgroup analysis, associations between 12 SNPs across all 4 loci and the subgroup of patients with psoriatic JIA were found. After Bonferroni correction for multiple testing, 2 genotype associations remained significant in the subgroup of patients with psoriatic JIA (MEFV SNP rs224204 [corrected P = 0.025] and NLRP3 SNP rs3806265 [corrected P = 0.04]). CONCLUSION: These findings support the use of monogenic loci as candidates for investigating the genetic component of complex disease and provide preliminary evidence of association between SNPs in autoinflammatory genes and psoriatic JIA. Our findings raise the interesting possibility of a shared disease mechanism between the HPFS and psoriatic JIA, potentially involving abnormal production of interleukin-1beta

Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia

Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders