Improving genetic prediction by leveraging genetic correlations among human diseases and traits

Genetics of Anxiety Disorders

PURPOSE OF REVIEW

Anxiety disorders are among the most common mental disorders with a lifetime prevalence of over 20%. Clinically, anxiety is not thought of as a homogenous disorder, but is subclassified in generalized, panic, and phobic anxiety disorder. Anxiety disorders are moderately heritable. This review will explore recent genetic and epigenetic approaches to anxiety disorders explaining differential susceptibility risk.

RECENT FINDINGS

A substantial portion of the variance in susceptibility risk can be explained by differential inherited and acquired genetic and epigenetic risk. Available data suggest that anxiety disorders are highly complex and polygenic. Despite the substantial progress in genetic research over the last decade, only few risk loci for anxiety disorders have been identified so far. This review will cover recent findings from large-scale genome-wide association studies as well as newer epigenome-wide studies. Progress in this area will likely require analysis of much larger sample sizes than have been reported to date. We discuss prospects for clinical translation of genetic findings and future directions for research.

Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes

We introduce cross-trait penalized regression (CTPR), a powerful and practical approach for multi-trait polygenic risk prediction in large cohorts. Specifically, we propose a novel cross-trait penalty function with the Lasso and the minimax concave penalty (MCP) to incorporate the shared genetic effects across multiple traits for large-sample GWAS data. Our approach extracts information from the secondary traits that is beneficial for predicting the primary trait based on individual-level genotypes and/or summary statistics. Our novel implementation of a parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS data. We illustrate our method using large-scale GWAS data (~1M SNPs) from the UK Biobank (N = 456,837). We show that our multi-trait method outperforms the recently proposed multi-trait analysis of GWAS (MTAG) for predictive performance. The prediction accuracy for height by the aid of BMI improves from R² = 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data.

Information of genetic architectures of complex traits can be leveraged for predicting phenotypes. Here, the authors develop CTPR (Cross-Trait Penalized Regression), a method for multi-trait polygenic risk prediction using individual-level genotypes and/or summary statistics from large cohorts.

Crop genome-wide association study: a harvest of biological relevance

With the advent of rapid genotyping and next-generation sequencing technologies, genome-wide association study (GWAS) has become a routine strategy for decoding genotype-phenotype associations in many species. More than 1000 such studies over the last decade have revealed substantial genotype-phenotype associations in crops and provided unparalleled opportunities to probe functional genomics. Beyond the many 'hits' obtained, this review summarizes recent efforts to increase our understanding of the genetic architecture of complex traits by focusing on non-main effects including epistasis, pleiotropy, and phenotypic plasticity. We also discuss how these achievements and the remaining gaps in our knowledge will guide future studies. Synthetic association is highlighted as leading to false causality, which is prevalent but largely underestimated. Furthermore, validation evidence is appealing for future GWAS, especially in the context of emerging genome-editing technologies.

New approaches in psychiatric drug development

Numerous novel neuroscience-based drug targets have been identified in recent years. However, it remains unclear how these targets relate to the expression of symptoms in central nervous system (CNS) disorders in general and psychiatric disorders in particular. To discuss this issue, a New Frontiers Meetings of European College of Neuropsychopharmacology (ECNP) was organized to address the challenges in translational neuroscience research that are impeding the effective development of new treatments. The main aim of this meeting was to discuss scientific insights, concepts and methodologies in order to improve drug development for psychiatric disorders. The meeting was designed to bring together stakeholders from academia, pharmaceutical industry, and regulatory agencies. Here we provide a synopsis of the proceedings from the meeting entitled 'New approaches to psychiatric drug development'. New views on psychiatric drug development were presented to address the challenges and pitfalls as identified by the different stakeholders. The general conclusion of the meeting was that drug discovery could be stimulated by designing new classification and sensitive assessment tools for psychiatric disorders, which bear closer relationships to neuropharmacological and neuroscientific developments. This is in line with the vision of precision psychiatry in which patients are clustered, not merely on symptoms, but primarily on biological phenotypes that represent pathophysiological relevant and 'drugable' processes. To achieve these goals, a closer collaboration between all stakeholders in early stages of development is essential to define the research criteria together and to reach consensus on new quantitative biological methodologies and etiology-directed treatments.

South Korea: in the midst of a privacy reform centered on data sharing

With rapid developments in genomic and digital technologies, genomic data sharing has become a key issue for the achievement of precision medicine in South Korea. The legal and administrative framework for data sharing and protection in this country is currently under intense scrutiny from national and international stakeholders. Policymakers are assessing the relevance of specific restrictions in national laws and guidelines for better alignment with international approaches. This manuscript will consider key issues in international genome data sharing in South Korea, including consent, privacy, security measures, compatible adequacy and oversight, and map out an approach to genomic data sharing that recognizes the importance of patient engagement and responsible use of data in South Korea.

Three-Dimensional Electrohydrodynamic Printing and Spinning of Flexible Composite Structures for Oral Multidrug Forms

A simple method to rapidly customize and to also mass produce oral dosage forms is arguably a current bottleneck in the development of modern personalized medicine. Specifically, delayed-release mechanisms with well-controlled dosage profiles for combinations of traditional Chinese herbal extracts and Western medications are not well established. Herein, we demonstrate a novel multidrug-loaded membrane sandwich with structures infused with ibuprofen (IBU) and Ganoderma lucidum polysaccharide (GLP) using three-dimensional electrohydrodynamic printing and electrospinning techniques. The resulting flexible membrane consists of microscaled, multilayered cellulose acetate (CA) membranes loaded with IBU in the shape of either concentric squares or circles, as the top and bottom layers of a sandwich structure. In between the CA-IBU layers are randomly electrospun polyvinyl pyrrolidone (PVP) layers loaded with GLP. The complete fibrous membrane sandwich can be folded and embedded into a 0-size capsule to achieve oral compliance. Simulated in vitro testing of gastric and intestinal fluids demonstrated a triphasic release profile. There was an immediate release of GLP after gastric juices dissolved the capsule shell and the PVP, followed by the short-term release of 60% of the IBU within an hour afterward, and the remaining IBU was released in a sustained manner following a Fickian diffusion profile. In summary, this multidrug (both hydrophilic and/or hydrophobic) oral system with precision-designed structures should enable personalized therapeutic dosing.

Genomic Prediction Using Individual-Level Data and Summary Statistics from Multiple Populations

This study presents a method for genomic prediction that uses individual-level data and summary statistics from multiple populations. Genome-wide markers are nowadays widely used to predict complex traits, and genomic prediction using multi-population data are an appealing approach to achieve higher prediction accuracies. However, sharing of individual-level data across populations is not always possible. We present a method that enables integration of summary statistics from separate analyses with the available individual-level data. The data can either consist of individuals with single or multiple (weighted) phenotype records per individual. We developed a method based on a hypothetical joint analysis model and absorption of population-specific information. We show that population-specific information is fully captured by estimated allele substitution effects and the accuracy of those estimates, i.e., the summary statistics. The method gives identical result as the joint analysis of all individual-level data when complete summary statistics are available. We provide a series of easy-to-use approximations that can be used when complete summary statistics are not available or impractical to share. Simulations show that approximations enable integration of different sources of information across a wide range of settings, yielding accurate predictions. The method can be readily extended to multiple-traits. In summary, the developed method enables integration of genome-wide data in the individual-level or summary statistics from multiple populations to obtain more accurate estimates of allele substitution effects and genomic predictions.

Replicability and Prediction: Lessons and Challenges from GWAS

Since the publication of the Wellcome Trust Case Control Consortium (WTCCC) landmark study a decade ago, genome-wide association studies (GWAS) have led to the discovery of thousands of risk variants involved in disease etiology. This success story has two angles that are often overlooked. First, GWAS findings are highly replicable. This is an unprecedented phenomenon in complex trait genetics, and indeed in many areas of science, which in past decades have been plagued by false positives. At a time of increasing concerns about the lack of reproducibility, we examine the biological and methodological reasons that account for the replicability of GWAS and identify the challenges ahead. In contrast to the exemplary success of disease gene discovery, at present GWAS findings are not useful for predicting phenotypes. We close with an overview of the prospects for individualized prediction of disease risk and its foreseeable impact in clinical practice.