Summaries of plenary, symposia, and oral sessions at the XXII World Congress of Psychiatric Genetics, Copenhagen, Denmark, 12-16 October 2014

The XXII World Congress of Psychiatric Genetics, sponsored by the International Society of Psychiatric Genetics, took place in Copenhagen, Denmark, on 12-16 October 2014. A total of 883 participants gathered to discuss the latest findings in the field. The following report was written by student and postdoctoral attendees. Each was assigned one or more sessions as a rapporteur. This manuscript represents topics covered in most, but not all of the oral presentations during the conference, and contains some of the major notable new findings reported

Association Analysis Using Set-Based Approaches in the Post-GWAS Era

Genotyping arrays have greatly facilitated genetic epidemiological studies into genetic risk factors for numerous complex diseases such as psychiatric disorders. The use of genome-wide association analysis (GWAS) is unequivocally established. More recently, DNA methylation arrays have enabled genome-wide profiling of the methylome, in addition to contemporary genetic epidemiology study design. An example of one such study is the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Lipidomics Study, which identified methylation markers (CpG markers) and single nucleotide polymorphisms (SNPs), associated with the change in triglyceride levels after drug intervention. Genotyping and methylation arrays assay several hundred thousand markers; however, single-marker association analysis suffers greatly from the burden of multiple testing. Set-based (SNP or CpG set) association approaches offer great flexibility, thus allowing the joint testing of a set of variants. For instance, a polygenic risk score (PRS) is a set-based approach, which, in addition to the strongly associated SNPs identified by large-scale GWAS, recruits SNPs with moderate to weak effects. The genotype information of the SNP set in the PRS is taken from an independent sample (target sample) and is then weighted by individual SNP effects derived from a relevant GWAS performed on a separate sample (discovery sample) into a cumulative score for each individual in the target sample. The resulting score, based on a SNP set or the PRS, is then regressed on the target phenotype. Such a regression model is evaluated by the amount of variance explained (R2) by the PRS in the target phenotype. Another strategy of set-based association analysis is kernel machine regression (KMR): a semi-parametric regression approach, in which the effects of markers within a set (CpG set or SNP set) are modelled via a kernel function and thus evaluated by a single-component variance test. A kernel function computes pairwise genomic similarity between the individuals, that is, the inner product of a set of variants under analysis, maybe comprising a gene or a biological pathway. For my first article, I performed a simulation study to evaluate the performance of PRS in correlated discovery and target traits by considering various sample sizes of the target sample, namely n=200, 500, and 1000. The PRS for correlated traits can be viewed as a situation of calculating schizophrenia-PRS for psychosocial endophenotypes such as global assessment functioning (GAF) score or positive and negative syndrome scale (PANSS) score. Considering such a situation, I simulated four correlated target traits that had varying degrees of correlation (r2) with the discovery trait, i.e., r2= 1.00, 0.8, 0.6, and 0.4. The results demonstrated that the average R2 estimates by the PRS roughly decreased by the square of the correlation between the target traits. In addition, the range of estimated R2 is most inflated in the sample size of the target trait n=200. Thus, the simulation findings alert researchers conducting clinical studies with endophenotypes to the fact that they need to pay attention to two important factors: first, the sample size of the target trait and secondly, the shared amount of genetic correlation between the target and discovery traits. In my second article, I implemented a KMR approach for set-based association testing of a CpG set. KMR has been successfully employed on SNP sets. In preparation of the second article, I used real and simulated datasets (based on a real dataset) provided by the Genetic Analysis Workshop 20 (GAW20) from the GOLDN study. GOLDN is a longitudinal study with individuals recruited from pedigrees. In my analysis, I only used independent individuals, which restricted the sample size in the real and simulated datasets to n<200. CpG sets were devised using the evidence of association reported by the GOLDN study in the real data set. For simulated datasets, true causal CpGs were provided by GAW20. Thus, I formulated candidate genomic regions of varying lengths while keeping the associated CpG(s) inside the region. The results replicated the evidence of association reported by GOLDN in the real data, and in simulated datasets albeit nominally. Moreover, in the simulated data, causal SNPs exert their full effect on the phenoytpes given when the causal CpG loci had no methylation (B-value=0). Thus, I also considered modelling an interaction term along with the main effects. The results yielded significant association. As part of the discussion, simulation results on the performance of the linear kernel for a CpG set with original (B-values) and logit transformed methylation values (M-values) indicated that logit transformation results in a loss of power. There, I also considered analysing an additive kernel that combines the genotype kernel and the methylation kernel and then tests for association with the phenotype. The initial simulations suggest that an additive kernel with a CpG set including hypo, semi, and hypermethylated sites simultaneously might not improve the model over only including a SNP set. However, it appears fruitful to investigate further the situation in which only one type of methylation state is present in a CpG set

Integration of evidence across human and model organism studies: A meeting report.

The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting\u27s objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and \u27omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs

Systems modeling of white matter microstructural abnormalities in Alzheimer's disease

INTRODUCTION: Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD). However, it is unclear which brain regions have the strongest WM changes in presymptomatic AD and what biological processes underlie WM abnormality during disease progression. METHODS: We developed a systems biology framework to integrate matched diffusion tensor imaging (DTI), genetic and transcriptomic data to investigate regional vulnerability to AD and identify genetic risk factors and gene subnetworks underlying WM abnormality in AD. RESULTS: We quantified regional WM abnormality and identified most vulnerable brain regions. A SNP rs2203712 in CELF1 was most significantly associated with several DTI-derived features in the hippocampus, the top ranked brain region. An immune response gene subnetwork in the blood was most correlated with DTI features across all the brain regions. DISCUSSION: Incorporation of image analysis with gene network analysis enhances our understanding of disease progression and facilitates identification of novel therapeutic strategies for AD

Mapping Genetic Influence on Brain Structure

Neuroimaging is playing an increasingly crucial role in delineating pathological conditions that cannot be typically defined by non-specific clinical symptom. The goal of this thesis was to characterize the genetic influence on grey and white matter indices and evaluate their potential as a reliable “structural MRI signatures”. We first assessed the effects of spatial resolution and smoothing on heritability estimation (Chapter 3). We then investigated heritability patterns of MRI measures of grey and white matter (Chapters 4-5). We then performed a cross-sectional evaluation of how heritability changes over the lifespan for both grey and white matter (Chapter 6). Finally, multivariate structural equation modeling was used to investigate the genetic correlation between grey matter structure and white matter connectivity (Chapter 7), in the default mode network (DMN). Our results show that several key brain structures were moderate to highly heritable and that this heritability was both spatially and temporally heterogeneous. At a network level, the DMN was found to have distinct genetic factors that modulated the grey matter regions and white matter tracts separately. We conclude that the spatial and temporal heterogeneity are likely to reflect gene expression patterns that are related to the developmental of specific brain regions and circuits over time

Missing heritability paradox in schizophrenia: hypothesis and plausible clues

Genetic research on schizophrenia, a common psychiatric disease with complex etiology and high (56-80%) heritability, has failed to identify causal genes, variants or causative mechanisms. Given the extensive effort and limited success to date, it is imperative to review potential reasons for this missing heritability. We argue that a successful elucidation of hereditary mechanisms in schizophrenia will likely involve; the identification of discrete endophenotypes; attention to the role of neurodevelopment and cell differentiation; consideration of the genome structure including temporal and spatial patterns, accommodation of environmental effects at the level of gene expression including any sex differences and pattern of mutations including de novo events and the use of analytic techniques that go beyond genome wide association studies. Identification of the heritable component of schizophrenia and sources of “missing heritability” is needed to understand the cause/s of the disorder and to facilitate the development of effective corrective and possibly preventive measures

Pathways to dementia: genetic predictors of cognitive and brain imaging endophenotypes in Alzheimer's disease

Indiana University-Purdue University Indianapolis (IUPUI)Alzheimer's disease (AD) is a national priority, with nearly six million Americans affected at an annual cost of $200 billion and no available cure. A better understanding of the mechanisms underlying AD is crucial to combat its high and rising incidence and burdens. Most cases of AD are thought to have a complex etiology with numerous genetic and environmental factors influencing susceptibility. Recent genome-wide association studies (GWAS) have confirmed roles for several hypothesized genes and have discovered novel loci associated with disease risk. However, most GWAS-implicated genetic variants have displayed modest individual effects on disease risk and together leave substantial heritability and pathophysiology unexplained. As a result, new paradigms focusing on biological pathways have emerged, drawing on the hypothesis that complex diseases may be influenced by collective effects of multiple variants – of a variety of effect sizes, directions, and frequencies – within key biological pathways. A variety of tools have been developed for pathway-based statistical analysis of GWAS data, but consensus approaches have not been systematically determined. We critically review strategies for genetic pathway analysis, synthesizing extant concepts and methodologies to guide application and future development. We then apply pathway-based approaches to complement GWAS of key AD-related endophenotypes, focusing on two early, hallmark features of disease, episodic memory impairment and brain deposition of amyloid-β. Using GWAS and pathway analysis, we confirmed the association of APOE (apolipoprotein E) and discovered additional genetic modulators of memory functioning and amyloid-β deposition in AD, including pathways related to long-term potentiation, cell adhesion, inflammation, and NOTCH signaling. We also identified genetic associations to amyloid-β deposition that have classically been understood to mediate learning and memory, including the BCHE gene and signaling through the epidermal growth factor receptor. These findings validate the use of pathway analysis in complex diseases and illuminate novel genetic mechanisms of AD, including several pathways at the intersection of disease-related pathology and cognitive decline which represent targets for future studies. The complexity of the AD genetic architecture also suggests that biomarker and treatment strategies may require simultaneous targeting of multiple pathways to effectively combat disease onset and progression

Genome-wide significant linkage of schizophrenia-related neuroanatomical trait to 12q24

The insula and medial prefrontal cortex (mPFC) share functional, histological, transcriptional and developmental characteristics and they serve higher cognitive functions of theoretical relevance to schizophrenia and related disorders. Meta-analyses and multivariate analysis of structural magnetic resonance imaging (MRI) scans indicate that gray matter density and volume reductions in schizophrenia are the most consistent and pronounced in a network primarily composed of the insula and mPFC. We used source-based morphometry, a multivariate technique optimized for structural MRI, in a large sample of randomly ascertained pedigrees (N = 887) to derive an insula-mPFC component and to investigate its genetic determinants. Firstly, we replicated the insula-mPFC gray matter component as an independent source of gray matter variation in the general population, and verified its relevance to schizophrenia in an independent case-control sample. Secondly, we showed that the neuroanatomical variation defined by this component is largely determined by additive genetic variation (h2 = 0.59), and genome-wide linkage analysis resulted in a significant linkage peak at 12q24 (LOD = 3.76). This region has been of significant interest to psychiatric genetics as it contains the Darier’s disease locus and other proposed susceptibility genes (e.g. DAO, NOS1), and it has been linked to affective disorders and schizophrenia in multiple populations. Thus, in conjunction with previous clinical studies, our data imply that one or more psychiatric risk variants at 12q24 are co-inherited with reductions in mPFC and insula gray matter concentration

Imaging genetics : Methodological approaches to overcoming high dimensional barriers

Imaging genetics is still a quite novel area of research which attempts to discover how genetic factors affect brain structures and functions. In this thesis, using a various methodological approaches I showed how it can contribute to our understanding of the complex genetic architecture of the human brain