Correction to: Picomolar concentrations of oligomeric alpha-synuclein sensitizes TLR4 to play an initiating role in Parkinson's disease pathogenesis.

In the original publication of this article, the author Magarida Rodrigues was written incorrectly.

Picomolar concentrations of oligomeric alpha-synuclein sensitizes TLR4 to play an initiating role in Parkinson's disease pathogenesis.

Despite the wealth of genomic and transcriptomic data in Parkinson's disease (PD), the initial molecular events are unknown. Using LD score regression analysis, we show significant enrichment in PD heritability within regulatory sites for LPS-activated monocytes and that TLR4 expression is highest within human substantia nigra, the most affected brain region, suggesting a role for TLR4 inflammatory responses. We then performed extended incubation of cells with physiological concentrations of small alpha-synuclein oligomers observing the development of a TLR4-dependent sensitized inflammatory response with time, including TNF-α production. ROS and cell death in primary neuronal cultures were significantly reduced by TLR4 antagonists revealing that an indirect inflammatory mechanism involving cytokines produced by glial cells makes a major contribution to neuronal death. Prolonged exposure to low levels of alpha-synuclein oligomers sensitizes TLR4 responsiveness in astrocytes and microglial, explaining how they become pro-inflammatory, and may be an early causative event in PD.

Relative impact of indels versus SNPs on complex disease.

It is unclear whether insertions and deletions (indels) are more likely to influence complex traits than abundant single-nucleotide polymorphisms (SNPs). We sought to understand which category of variation is more likely to impact health. Using the SardiNIA study as an exemplar, we characterized 478,876 common indels and 8,246,244 common SNPs in up to 5,949 well-phenotyped individuals from an isolated valley in Sardinia. We assessed association between 120 traits, resulting in 89 nonoverlapping-associated loci.We evaluated whether indels were enriched among credible sets of potential causal variants. These credible sets included 1,319 SNPs and 88 indels. We did not find indels to be significantly enriched. Indels were the most likely causal variant in seven loci, including one locus associated with monocyte count where an indel with causality and mechanism previously demonstrated (rs200748895:TGCTG/T) had a 0.999 posterior probability. Overall, our results show a very modest and nonsignificant enrichment for common indels in associated loci.

Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies.

In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, the linear mixed model and the recently proposed logistic mixed model, perform poorly; they produce large type I error rates when used to analyze unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE (Scalable and Accurate Implementation of GEneralized mixed model), provides accurate P values even when case-control ratios are extremely unbalanced. SAIGE uses state-of-art optimization strategies to reduce computational costs; hence, it is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 samples from white British participants with European ancestry for > 1,400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.

Long- and short-term outcomes in renal allografts with deceased donors: A large recipient and donor genome-wide association study.

Improvements in immunosuppression have modified short-term survival of deceased-donor allografts, but not their rate of long-term failure. Mismatches between donor and recipient HLA play an important role in the acute and chronic allogeneic immune response against the graft. Perfect matching at clinically relevant HLA loci does not obviate the need for immunosuppression, suggesting that additional genetic variation plays a critical role in both short- and long-term graft outcomes. By combining patient data and samples from supranational cohorts across the United Kingdom and European Union, we performed the first large-scale genome-wide association study analyzing both donor and recipient DNA in 2094 complete renal transplant-pairs with replication in 5866 complete pairs. We studied deceased-donor grafts allocated on the basis of preferential HLA matching, which provided some control for HLA genetic effects. No strong donor or recipient genetic effects contributing to long- or short-term allograft survival were found outside the HLA region. We discuss the implications for future research and clinical application.

It's All in the Brain: A Review of Available Functional Genomic Annotations.

What makes the molecular study of psychiatric and other neurological conditions particularly challenging compared with other complex traits is the difficulty of accessing the relevant tissue. The Encyclopedia of DNA Elements (ENCODE) project was one of the earliest producers of brain-derived epigenetic functional genomic data, albeit initially from only two cancerous brain cell lines for a limited number of epigenetic marks. It has only been in very recent years that such data from human brain tissue have been made available from various sources. Yet, these data are scattered throughout the literature with no central organization. This review summarizes the availability and accessibility of brain epigenetic and functional genomic data as a single resource to allow investigators to easily access available brain annotations and thus incorporate this wealth of information into their research to make important advances in the field of neuroscience.

Allele-Skewed DNA Modification in the Brain: Relevance to a Schizophrenia GWAS.

Numerous recent studies have suggested that phenotypic effects of DNA sequence variants can be mediated or modulated by their epigenetic marks, such as allele-skewed DNA modification (ASM). Using Affymetrix SNP microarrays, we performed a comprehensive search of ASM effects in human post-mortem brain and sperm samples (total n = 256) from individuals with major psychosis and control individuals. Depending on the phenotypic category of the brain samples, 1.4%-7.5% of interrogated SNPs exhibited ASM effects. Next, we investigated ASM in the context of genetic studies of schizophrenia and detected that brain ASM SNPs were significantly overrepresented among sub-threshold SNPs from a schizophrenia genome-wide association study (GWAS). Brain ASM SNPs showed a much stronger enrichment in a schizophrenia GWAS than in 17 large GWASs of non-psychiatric diseases and traits, arguing that ASM effects are at least partially tissue specific. Studies of germline and control brain ASM SNPs supported a causal association between ASM and schizophrenia. Finally, significantly higher proportions of ASM SNPs than of non-ASM SNPs were detected at loci exhibiting epigenetic signatures of enhancers and promoters, and they were overrepresented within transcription factor binding regions and DNase I hypersensitive sites. All of these findings collectively indicate that ASM SNPs should be prioritized in follow-up GWASs.

Genomics implicates adaptive and innate immunity in Alzheimer's and Parkinson's diseases.

OBJECTIVES: We assessed the current genetic evidence for the involvement of various cell types and tissue types in the etiology of neurodegenerative diseases, especially in relation to the neuroinflammatory hypothesis of neurodegenerative diseases. METHODS: We obtained large-scale genome-wide association study (GWAS) summary statistics from Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). We used multiple sclerosis (MS), an autoimmune disease of the central nervous system, as a positive control. We applied stratified LD score regression to determine if functional marks for cell type and tissue activity, and gene-set lists were enriched for genetic heritability. We compared our results to those from two gene-set enrichment methods (Ingenuity Pathway Analysis and enrichr). RESULTS: There were no significant heritability enrichments for annotations marking genes active within brain regions, but there were significant heritability enrichments for annotations marking genes active within cell types that form part of both the innate and adaptive immune systems. We found this for MS (as expected) and also for AD and PD. The strongest signals were from the adaptive immune system (e.g., T cells) for PD, and from both the adaptive (e.g., T cells) and innate (e.g., CD14: a marker for monocytes, and CD15: a marker for neutrophils) immune systems for AD. Annotations from the liver were also significant for AD. Pathway analysis provided complementary results. INTERPRETATION: For AD and PD, we found significant enrichment of heritability in annotations marking gene activity in immune cells.

Smoking Gun or Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants.

Although technology has triumphed in facilitating routine genome sequencing, new challenges have been created for the data-analyst. Genome-scale surveys of human variation generate volumes of data that far exceed capabilities for laboratory characterization. By incorporating functional annotations as predictors, statistical learning has been widely investigated for prioritizing genetic variants likely to be associated with complex disease. We compared three published prioritization procedures, which use different statistical learning algorithms and different predictors with regard to the quantity, type and coding. We also explored different combinations of algorithm and annotation set. As an application, we tested which methodology performed best for prioritizing variants using data from a large schizophrenia meta-analysis by the Psychiatric Genomics Consortium. Results suggest that all methods have considerable (and similar) predictive accuracies (AUCs 0.64-0.71) in test set data, but there is more variability in the application to the schizophrenia GWAS. In conclusion, a variety of algorithms and annotations seem to have a similar potential to effectively enrich true risk variants in genome-scale datasets, however none offer more than incremental improvement in prediction. We discuss how methods might be evolved for risk variant prediction to address the impending bottleneck of the new generation of genome re-sequencing studies.

Assessing models for genetic prediction of complex traits: a comparison of visualization and quantitative methods.

BACKGROUND: In silico models have recently been created in order to predict which genetic variants are more likely to contribute to the risk of a complex trait given their functional characteristics. However, there has been no comprehensive review as to which type of predictive accuracy measures and data visualization techniques are most useful for assessing these models. METHODS: We assessed the performance of the models for predicting risk using various methodologies, some of which include: receiver operating characteristic (ROC) curves, histograms of classification probability, and the novel use of the quantile-quantile plot. These measures have variable interpretability depending on factors such as whether the dataset is balanced in terms of numbers of genetic variants classified as risk variants versus those that are not. RESULTS: We conclude that the area under the curve (AUC) is a suitable starting place, and for models with similar AUCs, violin plots are particularly useful for examining the distribution of the risk scores.

A genome-wide association study of suicide severity scores in bipolar disorder.

BACKGROUND: Suicide claims one million lives worldwide annually, making it a serious public health concern. The risk for suicidal behaviour can be partly explained by genetic factors, as suggested by twin and family studies (reviewed in (Zai et al. 2012)). Recently, genome-wide association studies (GWASs) of suicide attempt on large samples of bipolar disorder (BD) patients from multiple sites have identified a number of novel candidate genes. GWASs of suicide behaviour severity, from suicidal ideation to serious suicide attempt, have not been reported for BD. METHODS: We conducted a GWAS of suicide behaviour severity in three independent BD samples:212 small nuclear families with BD probands from Toronto, Canada, 428 BD cases from Toronto, and 483 BD cases from the UK. We carried out imputation with 1000 Genome Project data as reference using IMPUTE2. Quality control and data analysis was conducted using PLINK and R. We conducted the quantitative analyses of suicide behaviour severity in the three samples separately, and derived an overall significance by a meta-analysis using the METAL software. RESULTS: We did not find genome-wide significant association of any tested markers in any of the BD samples, but we found a number of suggestive associations, including regions on chromosomes 8 and 10 (p < 1e-5). CONCLUSIONS: Our GWAS findings suggest that likely many gene variants of small effects contribute collectively to the risk for suicidal behaviour severity in BD. Larger independent replications are required to strengthen the findings from the GWAS presented here.

A Bayesian method to incorporate hundreds of functional characteristics with association evidence to improve variant prioritization.

The increasing quantity and quality of functional genomic information motivate the assessment and integration of these data with association data, including data originating from genome-wide association studies (GWAS). We used previously described GWAS signals ("hits") to train a regularized logistic model in order to predict SNP causality on the basis of a large multivariate functional dataset. We show how this model can be used to derive Bayes factors for integrating functional and association data into a combined Bayesian analysis. Functional characteristics were obtained from the Encyclopedia of DNA Elements (ENCODE), from published expression quantitative trait loci (eQTL), and from other sources of genome-wide characteristics. We trained the model using all GWAS signals combined, and also using phenotype specific signals for autoimmune, brain-related, cancer, and cardiovascular disorders. The non-phenotype specific and the autoimmune GWAS signals gave the most reliable results. We found SNPs with higher probabilities of causality from functional characteristics showed an enrichment of more significant p-values compared to all GWAS SNPs in three large GWAS studies of complex traits. We investigated the ability of our Bayesian method to improve the identification of true causal signals in a psoriasis GWAS dataset and found that combining functional data with association data improves the ability to prioritise novel hits. We used the predictions from the penalized logistic regression model to calculate Bayes factors relating to functional characteristics and supply these online alongside resources to integrate these data with association data.

Protein kinase cAMP-dependent regulatory type II beta (PRKAR2B) gene variants in antipsychotic-induced weight gain.

OBJECTIVE: Antipsychotics are effective in treating schizophrenia symptoms. However, the use of clozapine and olanzapine in particular are associated with significant weight gain. Mouse and human studies suggest that the protein kinase cAMP-dependent regulatory type II beta (PRKAR2B) gene may be involved in energy metabolism, and there is evidence that it is associated with clozapine's effects on triglyceride levels. We aimed at assessing PRKAR2B's role in antipsychotic-induced weight gain in schizophrenia patients. METHODS: DNA samples from adult schizophrenia or schizoaffective disorder patients of mixed ancestry were genotyped, and weight gain was assessed. We analyzed 16 tag single-nucleotide polymorphisms across the PRKAR2B gene in a Caucasian subset treated either with clozapine or olanzapine (N = 99). Linear regression based on an additive model was performed with the inclusion of relevant covariates. RESULTS: Normalized per cent weight change was analyzed, revealing that patients with the minor allele at rs9656135 had a mean weight increase of 4.1%, whereas patients without this allele had an increase of 3.4%. This association is not significant after correcting for multiple testing. CONCLUSIONS: Because of limited power, PRKAR2B's role in antipsychotic-induced weight gain is unclear, but biological evidence suggests that PRKAR2B may be involved. Further research in larger sample sizes is warranted.