Flexible Analysis Pipeline Research Articles

Statistical examination of shared loci in neuropsychiatric diseases using genome-wide association study summary statistics

Continued methodological advances have enabled numerous statistical approaches for the analysis of summary statistics from genome-wide association studies. Genetic correlation analysis within specific regions enables a new strategy for identifying pleiotropy. Genomic regions with significant ‘local’ genetic correlations can be investigated further using state-of-the-art methodologies for statistical fine-mapping and variant colocalisation. We explored the utility of a genome-wide local genetic correlation analysis approach for identifying genetic overlaps between the candidate neuropsychiatric disorders, Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia, Parkinson’s disease, and schizophrenia. The correlation analysis identified several associations between traits, the majority of which were loci in the human leukocyte antigen region. Colocalisation analysis suggested that disease-implicated variants in these loci often differ between traits and, in one locus, indicated a shared causal variant between ALS and AD. Our study identified candidate loci that might play a role in multiple neuropsychiatric diseases and suggested the role of distinct mechanisms across diseases despite shared loci. The fine-mapping and colocalisation analysis protocol designed for this study has been implemented in a flexible analysis pipeline that produces HTML reports and is available at: https://github.com/ThomasPSpargo/COLOC-reporter.

A new paradigm for molecular dynamics databases: the COVID-19 database, the legacy of a titanic community effort.

Molecular dynamics (MD) simulations are keeping computers busy around the world, generating a huge amount of data that is typically not open to the scientific community. Pioneering efforts to ensure the safety and reusability of MD data have been based on the use of simple databases providing a limited set of standard analyses on single-short trajectories. Despite their value, these databases do not offer a true solution for the current community of MD users, who want a flexible analysis pipeline and the possibility to address huge non-Markovian ensembles of large systems. Here we present a new paradigm for MD databases, resilient to large systems and long trajectories, and designed to be compatible with modern MD simulations. The data are offered to the community through a web-based graphical user interface (GUI), implemented with state-of-the-art technology, which incorporates system-specific analysis designed by the trajectory providers. A REST API and associated Jupyter Notebooks are integrated into the platform, allowing fully customized meta-analysis by final users. The new technology is illustrated using a collection of trajectories obtained by the community in the context of the effort to fight the COVID-19 pandemic. The server is accessible at https://bioexcel-cv19.bsc.es/#/. It is free and open to all users and there are no login requirements. It is also integrated into the simulations section of the BioExcel-MolSSI COVID-19 Molecular Structure and Therapeutics Hub: https://covid.molssi.org/simulations/ and is part of the MDDB effort (https://mddbr.eu).

Diagnostic and clinical utility of whole genome sequencing in a cohort of undiagnosed Chinese families with rare diseases

Rare diseases are usually chronically debilitating or even life-threatening with diagnostic and therapeutic challenges in current clinical practice. It has been estimated that 80% of rare diseases are genetic in origin, and thus genome sequencing-based diagnosis offers a promising alternative for rare-disease management. In this study, 79 individuals from 16 independent families were performed for whole-genome sequencing (WGS) in an effort to identify the causative mutations for 16 distinct rare diseases that are largely clinically intractable. Comprehensive analysis of variations, including simple nucleotide variants (SNVs), copy-number variations (CNVs), and structural variations (SVs), was implemented using the WGS data. A flexible analysis pipeline that allowed a certain degree of misclassification of disease status was developed to facilitate the identification of causative variants. As a result, disease-causing variants were identified in 10 of the 16 investigated diseases, yielding a diagnostic rate of 62.5%. Additionally, new potentially pathogenic variants were discovered for two disorders, including IGF2/INS-IGF2 in mitochondrial disease and FBN3 in Klippel–Trenaunay–Weber syndrome. Our WGS analysis not only detected a CNV associated with 3p deletion syndrome but also captured a simple sequence repeat (SSR) variation associated with Machado–Joseph disease. To our knowledge, this is the first time the clinical WGS analysis of short-read sequences has been used successfully to identify a causative SSR variation that perfectly segregates with a repeat expansion disorder. After the WGS analysis, we confirmed the initial diagnosis for three of 10 established disorders and modified or corrected the initial diagnosis for the remaining seven disorders. In summary, clinical WGS is a powerful tool for the diagnosis of rare diseases, and its diagnostic clarity at molecular levels offers important benefits for the participating families.