Bipolar disorder (BD) is a heritable mental illness with complex etiology. We performed a genome-wide association study (GWAS) of 41,917 BD cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. BD risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics, and anesthetics. Integrating eQTL data implicated 15 genes robustly linked to BD via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of BD subtypes indicated high but imperfect genetic correlation between BD type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of BD, identify novel therapeutic leads, and prioritize genes for functional follow-up studies.

Microglia have emerged as important players in brain aging and pathology. To understand how genetic risk for neurological and psychiatric disorders is related to microglial function, large transcriptome studies are essential. Here, we describe the transcriptome analysis of 255 primary human microglia samples isolated at autopsy from multiple brain regions of 100 human subjects. We performed systematic analyses to investigate various aspects of microglial heterogeneities, including brain region and aging. We mapped expression and splicing quantitative trait loci and showed that many neurological disease susceptibility loci are mediated through gene expression or splicing in microglia. Fine-mapping of these loci nominated candidate causal variants that are within microglia-specific enhancers, finding associations with microglia expression of USP6NL for Alzheimer’s disease and P2RY12 for Parkinson’s disease. We have built the most comprehensive catalog to date of genetic effects on the microglia transcriptome and propose candidate functional variants in neurological and psychiatric disorders.

While gene expression data at the mRNA level can be globally and accurately measured, profiling the activity of cell signaling pathways is currently much more difficult. eXpression2Kinases (X2K) computationally predicts involvement of upstream cell signaling pathways, given a signature of differentially expressed genes. X2K first computes enrichment for transcription factors likely to regulate the expression of the differentially expressed genes. The next step of X2K connects these enriched transcription factors through known protein–protein interactions (PPIs) to construct a subnetwork. The final step performs kinase enrichment analysis on the members of the subnetwork. X2K Web is a new implementation of the original eXpression2Kinases algorithm with important enhancements. X2K Web includes many new transcription factor and kinase libraries, and PPI networks. For demonstration, thousands of gene expression signatures induced by kinase inhibitors, applied to six breast cancer cell lines, are provided for fetching directly into X2K Web. The results are displayed as interactive downloadable vector graphic network images and bar graphs. Benchmarking various settings via random permutations enabled the identification of an optimal set of parameters to be used as the default settings in X2K Web. X2K Web is freely available from http://X2K.cloud.

The frequency by which genes are studied correlates with the prior knowledge accumulated about them. This leads to an imbalance in research attention where some genes are highly investigated while others are ignored. Geneshot is a search engine developed to illuminate this gap and to promote attention to the under-studied genome. Through a simple web interface, Geneshot enables researchers to enter arbitrary search terms, to receive ranked lists of genes relevant to the search terms. Returned ranked gene lists contain genes that were previously published in association with the search terms, as well as genes predicted to be associated with the terms based on data integration from multiple sources. The search results are presented with interactive visualizations. To predict gene function, Geneshot utilizes gene–gene similarity matrices from processed RNA-seq data, or from gene–gene co-occurrence data obtained from multiple sources. In addition, Geneshot can be used to analyze the novelty of gene sets and augment gene sets with additional relevant genes. The Geneshot web-server and API are freely and openly available from https://amp.pharm.mssm.edu/geneshot.

In contrast to other primates, human children's imitation performance goes from low to high fidelity soon after infancy. Are such changes associated with the development of other forms of learning? We addressed this question by testing 215 children (26-59 months) on two social conditions (imitation, emulation) - involving a demonstration - and two asocial conditions (trial-and-error, recall) - involving individual learning - using two touchscreen tasks. The tasks required responding to either three different pictures in a specific picture order (Cognitive: Airplane→Ball→Cow) or three identical pictures in a specific spatial order (Motor-Spatial: Up→Down→Right). There were age-related improvements across all conditions and imitation, emulation and recall performance were significantly better than trial-and-error learning. Generalized linear models demonstrated that motor-spatial imitation fidelity was associated with age and motor-spatial emulation performance, but cognitive imitation fidelity was only associated with age. While this study provides evidence for multiple imitation mechanisms, the development of one of those mechanisms - motor-spatial imitation - may be bootstrapped by the development of another social learning skill - motor-spatial emulation. Together, these findings provide important clues about the development of imitation, which is arguably a distinctive feature of the human species.

Motivation Genome-wide association studies (GWAS) summary statistics have popularised and accelerated genetic research. However, a lack of standardisation of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies. Results To address this issue, we have developed MungeSumstats, a Bioconductor R package for the standardisation and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardised, tabular summary statistic file, VCF or R native data object. Availability MungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats Supplementary information The analysis deriving the most common summary statistic formats is available at: https://al-murphy.github.io/SumstatFormats

As more digital resources are produced by the research community, it is becoming increasingly important to harmonize and organize them for synergistic utilization. The findable, accessible, interoperable, and reusable (FAIR) guiding principles have prompted many stakeholders to consider strategies for tackling this challenge. The FAIRshake toolkit was developed to enable the establishment of community-driven FAIR metrics and rubrics paired with manual and automated FAIR assessments. FAIR assessments are visualized as an insignia that can be embedded within digital-resources-hosting websites. Using FAIRshake, a variety of biomedical digital resources were manually and automatically evaluated for their level of FAIRness.

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