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

Descriptive Characteristics and Risk Factors for Trauma: An Evidence-Based Practice Project

This Evidence-Based Practice (EBP) project examined the following question: What are the perspectives, experiences, and self-reports of adult individuals, groups, or populations who have MCI or report problems with Functional Cognition (and / or their caregivers)

Efficient estimation algorithms for large and complex data sets

The recent world-wide surge in available data allows the investigation of many new and sophisticated questions that were inconceivable just a few years ago. However, two types of data sets often complicate the subsequent analysis: Data that is simple in structure but large in size, and data that is small in size but complex in structure. These two kinds of problems also apply to biological data. For example, data sets acquired from family studies, where the data can be visualized as pedigrees, are small in size but, because of the dependencies within families, they are complex in structure. By comparison, next-generation sequencing data, such as data from chromatin immunoprecipitation followed by deep sequencing (ChIP-Seq), is simple in structure but large in size. Even though the available computational power is increasing steadily, it often cannot keep up with the massive amounts of new data that are being acquired. In these situations, ordinary methods are no longer applicable or scale badly with increasing sample size. The challenge in today’s environment is then to adapt common algorithms for modern data sets. This dissertation considers the challenge of performing inference on modern data sets, and approaches the problem in two parts: first using a problem in the field of genetics, and then using one from molecular biology. In the first part, we focus on data of a complex nature. Specifically, we analyze data from a family study on colorectal cancer (CRC). To model familial clusters of increased cancer risk, we assume inheritable but latent variables for a risk factor that increases the hazard rate for the occurrence of CRC. During parameter estimation, the inheritability of this latent variable necessitates a marginalization of the likelihood that is costly in time for large families. We first approached this problem by implementing computational accelerations that reduced the time for an optimization by the Nelder-Mead method to about 10% of a naive implementation. In a next step, we developed an expectation-maximization (EM) algorithm that works on data obtained from pedigrees. To achieve this, we used factor graphs to factorize the likelihood into a product of “local” functions, which enabled us to apply the sum-product algorithm in the E-step, reducing the computational complexity from exponential to linear. Our algorithm thus enables parameter estimation for family studies in a feasible amount of time. In the second part, we turn to ChIP-Seq data. Previously, practitioners were required to assemble a set of tools based on different statistical assumptions and dedicated to specific applications such as calling protein occupancy peaks or testing for differential occupancies between experimental conditions. In order to remove these restrictions and create a unified framework for ChIP-Seq analysis, we developed GenoGAM (Genome-wide Generalized Additive Model), which extends generalized additive models to efficiently work on data spread over a long x axis by reducing the scaling from cubic to linear and by employing a data parallelism strategy. Our software makes the well-established and flexible GAM framework available for a number of genomic applications. Furthermore, the statistical framework allows for significance testing for differential occupancy. In conclusion, I show how developing algorithms of lower complexity can open the door for analyses that were previously intractable. On this basis, it is recommended to focus subsequent research efforts on lowering the complexity of existing algorithms and design new, lower-complexity algorithms

Strategies For Improving Epistasis Detection And Replication

Genome-wide association studies (GWAS) have been extensively critiqued for their perceived inability to adequately elucidate the genetic underpinnings of complex disease. Of particular concern is “missing heritability,” or the difference between the total estimated heritability of a phenotype and that explained by GWAS-identified loci. There are numerous proposed explanations for this missing heritability, but a frequently ignored and potentially vastly informative alternative explanation is the ubiquity of epistasis underlying complex phenotypes. Given our understanding of how biomolecules interact in networks and pathways, it is not unreasonable to conclude that the effect of variation at individual genetic loci may non-additively depend on and should be analyzed in the context of their interacting partners. It has been recognized for over a century that deviation from expected Mendelian proportions can be explained by the interaction of multiple loci, and the epistatic underpinnings of phenotypes in model organisms have been extensively experimentally quantified. Therefore, the dearth of inspiring single locus GWAS hits for complex human phenotypes (and the inconsistent replication of these between populations) should not be surprising, as one might expect the joint effect of multiple perturbations to interacting partners within a functional biological module to be more important than individual main effects. Current methods for analyzing data from GWAS are not well-equipped to detect epistasis or replicate significant interactions. The multiple testing burden associated with testing each pairwise interaction quickly becomes nearly insurmountable with increasing numbers of loci. Statistical and machine learning approaches that have worked well for other types of high-dimensional data are appealing and may be useful for detecting epistasis, but potentially require tweaks to function appropriately. Biological knowledge may also be leveraged to guide the search for epistasis candidates, but requires context-appropriate application (as, for example, two loci with significant main effects may not have a significant interaction, and vice versa). Rather than renouncing GWAS and the wealth of associated data that has been accumulated as a failure, I propose the development of new techniques and incorporation of diverse data sources to analyze GWAS data in an epistasis-centric framework

Statistical modelling strategies in molecular epidemiology, with an application to attention-deficit hyperactivity disorder

Since 1990s, the technological developments in measuring molecular data have been instrumental in advancing molecular epidemiology. A consequential challenge is to integrate the evidence from multiple molecular datasets for a better understanding of the biological mechanisms underlying complex traits. In this thesis, I applied different statistical modelling approaches to investigate the biological background of attention-deficit/hyperactivity disorder (ADHD), a common neurodevelopmental disorder with an early onset, high persistence and a notable impact on the global burden of disease. I examined the putative impact of exposure to maternal smoking during pregnancy on the risk of ADHD and other adverse outcomes in the offspring related to epigenetic modifications and other molecular changes. The potential causality between ADHD and obesity was analysed using genetically informative methods. The link between systemic chronic inflammation, which can be triggered by smoking or obesity, and common psychiatric outcomes was also investigated. The main dataset used was the Northern Finland Birth Cohort 1986 (N = 6,728 for ADHD symptoms, N = 432 for phenotype and full omics data available), with complementary data from other European cohorts and publicly available summary statistics. I used integrative statistical approaches that leverage evidence from different omics datasets. Regression modelling and Mendelian Randomisation techniques were applied throughout, and a recently published network method based on sparse canonical correlation analysis was also used. The results showed evidence for a long-term impact of intrauterine smoke exposure on offspring DNA methylation, and some indication that DNA methylation mediates the effect of the smoke exposure on offspring later life health outcomes. There was also suggestive bidirectional causality between ADHD and obesity, and evidence for an inflammatory component in the aetiology of psychiatric outcomes. This thesis adds to the literature by a thorough investigation of different omics datasets and integrative statistical approaches applied to ADHD and other psychiatric outcomes.Open Acces