Sarah A. Gagliano

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.

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

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.

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.

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.

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

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 clozapines effects on triglyceride levels. We aimed at assessing PRKAR2Bs role in antipsychotic‐induced weight gain in schizophrenia patients.

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.

Rapporteur summaries of plenary, symposia, and oral sessions from the XXIIIrd World Congress of Psychiatric Genetics Meeting in Toronto, Canada, 16-20 October 2015

The XXIIIrd World Congress of Psychiatric Genetics meeting, sponsored by the International Society of Psychiatric Genetics, was held in Toronto, ON, Canada, on 16–20 October 2015. Approximately 700 participants attended to discuss the latest state-of-the-art findings in this rapidly advancing and evolving field. The following report was written by trainee travel awardees. Each was assigned one session as a rapporteur. This manuscript represents the highlights and topics that were covered in the plenary sessions, symposia, and oral sessions during the conference, and contains major notable and new findings.

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.SAIGE (Scalable and Accurate Implementation of GEneralized mixed model) is a generalized mixed model association test that can efficiently analyze large data sets while controlling for unbalanced case-control ratios and sample relatedness, as shown by applying SAIGE to the UK Biobank data for > 1,400 binary phenotypes.

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

Objectives We assessed the current genetic evidence for the involvement of various cell types and tissue types in the aetiology 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 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 Alzheimer’s and Parkinson’s disease, we found significant enrichment of heritability in annotations marking gene activity in immune cells.Neurodegenerative disorders are devastating diseases with a worldwide health-care burden. Studies have demonstrated enrichment of disease-associated genetic variants with functional genomic annotations. Determining associated cell-types is important to understand pathogenicity. We obtained GWAS summary statistics from Parkinsons disease (PD), Alzheimers disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and frontotemporal dementia (FTD). We applied stratified LD score regression to determine if functional categories are enriched for heritability. There was little enrichment of brain annotations, but annotations from both the innate and adaptive immune systems were enriched for MS (as expected), AD, and PD, in decreasing order of statistical significance.

Circumstantial Evidence? Comparison of Statistical Learning Methods using Functional Annotations for Prioritizing Risk Variants

Although technology has triumphed in facilitating routine genome re-sequencing, new challenges have been created for the data analyst. Genome scale surveys of human disease variation generate volumes of data that far exceed capabilities for laboratory characterization, and importantly also create a substantial burden of type I error. By incorporating a variety of functional annotations as predictors, such as regulatory and protein coding elements, statistical learning has been widely investigated as a mechanism for the prioritization of genetic variants that are more likely to be associated with complex disease. These methods offer a hope of identification of sufficiently large numbers of truly associated variants, to make cost-effective the large-scale functional characterization necessary to progress genome scale experiments. We compared the results from three published prioritization procedures which use different statistical learning algorithms and different predictors with regard to the quantity, type and coding of the functional annotations. In this paper we also explore different combinations of algorithm and annotation set. We train the models in 60% of the data and reserve the remainder for testing the accuracy. As an application, we tested which methodology performed the best for prioritizing sub-genome-wide-significant variants (5×10-8<p<1×10-6) using data from the first and second rounds of 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). However, predictive accuracy results obtained from the test set do not always reflect results obtained from the application to the schizophrenia meta-analysis. 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 towards the step change in the risk variant prediction required to address the impending bottleneck of the new generation of genome re-sequencing studies.