Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery

Endophenotype 2.0: updated definitions and criteria for endophenotypes of psychiatric disorders, incorporating new technologies and findings

Recent genetic studies have linked numerous loci to psychiatric disorders. However, the biological pathways that connect these genetic associations to psychiatric disorders' specific pathophysiological processes are largely unclear. Endophenotypes, first defined over five decades ago, are heritable traits, independent of disease state that are associated with a disease, encompassing a broad range of neurophysiological, biochemical, endocrinological, neuroanatomical, cognitive, and neuropsychological characteristics. Considering the advancements in genetics and genomics over recent decades, we propose a revised definition of endophenotypes as 'genetically influenced phenotypes linked to disease or treatment characteristics and their related events.' We also updated endophenotype criteria to include (1) reliable measurement, (2) association with the disease or its related events, and (3) genetic mediation. 'Genetic mediation' is introduced to differentiate between causality and pleiotropic effects and allows non-linear relationships. Furthermore, this updated Endophenotype 2.0 framework expands to encompass genetically regulated responses to disease-related factors, including environmental risks, illness progression, treatment responses, and resilience phenotypes, which may be state-dependent. This broadened definition paves the way for developing new endophenotypes crucial for genetic analyses in psychiatric disorders. Integrating genetics, genomics, and diverse endophenotypes into multi-dimensional mechanistic models is vital for advancing our understanding of psychiatric disorders. Crucially, elucidating the biological underpinnings of endophenotypes will enhance our grasp of psychiatric genetics, thereby improving disease risk prediction and treatment approaches.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

From bugs to bedside: functional annotation of human genetic variation for neurological disorders using invertebrate models

The exponential accumulation of DNA sequencing data has opened new avenues for discovering the causative roles of single-nucleotide polymorphisms (SNPs) in neurological diseases. The opportunities emerging from this are staggering, yet only as good as our abilities to glean insights from this surplus of information. Whereas computational biology continues to improve with respect to predictions and molecular modeling, the differences between in silico and in vivo analysis remain substantial. Invertebrate in vivo model systems represent technically advanced, experimentally mature, high-throughput, efficient and cost-effective resources for investigating a disease. With a decades-long track record of enabling investigators to discern function from DNA, fly (Drosophila) and worm (Caenorhabditis elegans) models have never been better poised to serve as living engines of discovery. Both of these animals have already proven useful in the classification of genetic variants as either pathogenic or benign across a range of neurodevelopmental and neurodegenerative disorders-including autism spectrum disorders, ciliopathies, amyotrophic lateral sclerosis, Alzheimer's and Parkinson's disease. Pathogenic SNPs typically display distinctive phenotypes in functional assays when compared with null alleles and frequently lead to protein products with gain-of-function or partial loss-of-function properties that contribute to neurological disease pathogenesis. The utility of invertebrates is logically limited by overt differences in anatomical and physiological characteristics, and also the evolutionary distance in genome structure. Nevertheless, functional annotation of disease-SNPs using invertebrate models can expedite the process of assigning cellular and organismal consequences to mutations, ascertain insights into mechanisms of action, and accelerate therapeutic target discovery and drug development for neurological conditions.