Mouse Genome Informatics (MGI): latest news from MGD and GXD

The 36th International Mammalian Genome Conference: A scientific gathering under the cherry blossoms in Tsukuba

The 36th International Mammalian Genome Conference (IMGC) was held in a hybrid format at the Tsukuba International Congress Center in Tsukuba, Ibaraki, Japan, for 4 days from March 28 to 31, 2023. This international conference on functional genomics of mouse, human, and other mammalian species attracted 246 participants in total, of which 129 were from outside Japan, including Europe, the United States and Asia, and 117 participants were from Japan. The conference included three technical workshops, keynote lectures by domestic researchers, commemorative lectures for the conference awards, 57 oral presentations, and 97 poster presentations. The event was a great success. Topics included the establishment and analysis of disease models using genetically engineered or spontaneous mutant mice, systems genetic analysis using mouse strains such as wild-derived mice and recombinant inbred mouse strains, infectious diseases, immunology, and epigenetics. In addition, as a joint program, a two-day RIKEN Symposium was held, and active discussions continued over the four-day period. Also, there was a trainee symposium, in which young researchers were encouraged to participate, and excellent papers were selected as oral presentations in the main session.

Mouse Genome Informatics: an integrated knowledgebase system for the laboratory mouse

Mouse Genome Informatics (MGI) is a federation of expertly curated information resources designed to support experimental and computational investigations into genetic and genomic aspects of human biology and disease using the laboratory mouse as a model system. The Mouse Genome Database (MGD) and the Gene Expression Database (GXD) are core MGI databases that share data and system architecture. MGI serves as the central community resource of integrated information about mouse genome features, variation, expression, gene function, phenotype, and human disease models acquired from peer-reviewed publications, author submissions, and major bioinformatics resources. To facilitate integration and standardization of data, biocuration scientists annotate using terms from controlled metadata vocabularies and biological ontologies (e.g. Mammalian Phenotype Ontology, Mouse Developmental Anatomy, Disease Ontology, Gene Ontology, etc.), and by applying international community standards for gene, allele, and mouse strain nomenclature. MGI serves basic scientists, translational researchers, and data scientists by providing access to FAIR-compliant data in both human-readable and compute-ready formats. The MGI resource is accessible at https://informatics.jax.org. Here, we present an overview of the core data types represented in MGI and highlight recent enhancements to the resource with a focus on new data and functionality for MGD and GXD.

Updates to the Alliance of Genome Resources central infrastructure

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML).

SNP-SVant: A Computational Workflow to Predict and Annotate Genomic Variants in Organisms Lacking Benchmarked Variants

Whole-genome sequencing is widely used to investigate population genomic variation in organisms of interest. Assorted tools have been independently developed to call variants from short-read sequencing data aligned to a reference genome, including single nucleotide polymorphisms (SNPs) and structural variations (SVs). We developed SNP-SVant, an integrated, flexible, and computationally efficient bioinformatic workflow that predicts high-confidence SNPs and SVs in organisms without benchmarked variants, which are traditionally used for distinguishing sequencing errors from real variants. In the absence of these benchmarked datasets, we leverage multiple rounds of statistical recalibration to increase the precision of variant prediction. The SNP-SVant workflow is flexible, with user options to tradeoff accuracy for sensitivity. The workflow predicts SNPs and small insertions and deletions using the Genome Analysis ToolKit (GATK) and predicts SVs using the Genome Rearrangement IDentification Software Suite (GRIDSS), and it culminates in variant annotation using custom scripts. A key utility of SNP-SVant is its scalability. Variant calling is a computationally expensive procedure, and thus, SNP-SVant uses a workflow management system with intermediary checkpoint steps to ensure efficient use of resources by minimizing redundant computations and omitting steps where dependent files are available. SNP-SVant also provides metrics to assess the quality of called variants and converts between VCF and aligned FASTA format outputs to ensure compatibility with downstream tools to calculate selection statistics, which are commonplace in population genomics studies. By accounting for both small and large structural variants, users of this workflow can obtain a wide-ranging view of genomic alterations in an organism of interest. Overall, this workflow advances our capabilities in assessing the functional consequences of different types of genomic alterations, ultimately improving our ability to associate genotypes with phenotypes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Predicting single nucleotide polymorphisms and structural variations Support Protocol 1: Downloading publicly available sequencing data Support Protocol 2: Visualizing variant loci using Integrated Genome Viewer Support Protocol 3: Converting between VCF and aligned FASTA formats.

RNA-seq analysis reveals modulation of inflammatory pathways by an enriched-triterpene natural extract in mouse and human macrophage cell lines

Chronic inflammation is crucial in developing insulin resistance and type 2 diabetes. Previous studies have shown that a leaf extract of Eucalyptus tereticornis, with ursolic acid (UA), oleanolic acid (OA), and ursolic acid lactone (UAL) as the main molecules (78 %) mixed with unknown minor metabolites (22 %), provided superior anti-inflammatory, hypoglycemic, and hypolipidemic effects than reconstituted triterpenoid mixtures in macrophage cell lines and a pre-diabetic mouse model. Further identification of the molecular mechanisms of action of this mixture of triterpenes is required. This study aims to analyse the RNA expression profiles of mouse and human macrophage cell lines treated with the natural extract and its components. Activated macrophage cell lines were treated with the natural extract, UA, OA, UAL or a triterpene mixture (M1). RNA was extracted and sequenced using the DNBseq platform and the EnrichR software to perform gene enrichment analysis using the Gene Ontology database, Kyoto Encyclopedia of Genes and Genomes, and Reactome. To conduct clustering analysis, we standardised the normalised counts of each gene and applied k-means clustering. The combination of molecules in the natural extract has an additive or synergic effect that affects the expression of up-regulated genes by macrophage activation. Triterpenes (M1) regulated 76 % of human and 68 % of mouse genes, while uncharacterised minority molecules could regulate 24 % of human and 32 % of mouse genes. The extract inhibited the expression of many cytokines (IL6, IL1, OSM), chemokines (CXCL3), inflammatory mediators (MMP8 and MMP13) and the JAK-STAT signalling pathway in both models. The natural extract has a more powerful immunomodulatory effect than the triterpene mixture, increasing the number of genes regulated in mouse and human models. Our study shows that Eucalyptus tereticornis extract is a promising option for breaking the link between inflammation and insulin resistance.

The alliance of genome resources: transforming comparative genomics

Comparing genomic and biological characteristics across multiple species is essential to using model systems to investigate the molecular and cellular mechanisms underlying human biology and disease and to translate mechanistic insights from studies in model organisms for clinical applications. Building a scalable knowledge commons platform that supports cross-species comparison of rich, expertly curated knowledge regarding gene function, phenotype, and disease associations available for model organisms and humans is the primary mission of the Alliance of Genome Resources (the Alliance). The Alliance is a consortium of seven model organism knowledgebases (mouse, rat, yeast, nematode, zebrafish, frog, fruit fly) and the Gene Ontology resource. The Alliance uses a common set of gene ortholog assertions as the basis for comparing biological annotations across the organisms represented in the Alliance. The major types of knowledge associated with genes that are represented in the Alliance database currently include gene function, phenotypic alleles and variants, human disease associations, pathways, gene expression, and both protein-protein and genetic interactions. The Alliance has enhanced the ability of researchers to easily compare biological annotations for common data types across model organisms and human through the implementation of shared programmatic access mechanisms, data-specific web pages with a unified "look and feel", and interactive user interfaces specifically designed to support comparative biology. The modular infrastructure developed by the Alliance allows the resource to serve as an extensible "knowledge commons" capable of expanding to accommodate additional model organisms.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Updates to the Alliance of Genome Resources Central Infrastructure Alliance of Genome Resources Consortium

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively-studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, C. elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and APIs. Here we focus on developments over the last two years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse. We describe our progress towards a central persistent database to support curation, the data modeling that underpins harmonization, and progress towards a state-of-the art literature curation system with integrated Artificial Intelligence and Machine Learning (AI/ML).

Optimal reference genes for RNA tissue analysis in small animal models of hemorrhagic fever viruses

Reverse-transcription quantitative polymerase chain reaction assays are frequently used to evaluate gene expression in animal model studies. Data analyses depend on normalization using a suitable reference gene (RG) to minimize effects of variation due to sample collection, sample processing, or experimental set-up. Here, we investigated the suitability of nine potential RGs in laboratory animals commonly used to study viral hemorrhagic fever infection. Using tissues (liver, spleen, gonad [ovary or testis], kidney, heart, lung, eye, brain, and blood) collected from naïve animals and those infected with Crimean-Congo hemorrhagic fever (mice), Nipah (hamsters), or Lassa (guinea pigs) viruses, optimal species-specific RGs were identified based on five web-based algorithms to assess RG stability. Notably, the Ppia RG demonstrated stability across all rodent tissues tested. Optimal RG pairs that include Ppia were determined for each rodent species (Ppia and Gusb for mice; Ppia and Hrpt for hamsters; and Ppia and Gapdh for guinea pigs). These RG pair assays were multiplexed with viral targets to improve assay turnaround time and economize sample usage. Finally, a pan-rodent Ppia assay capable of detecting Ppia across multiple rodent species was developed and successfully used in ecological investigations of field-caught rodents, further supporting its pan-species utility.

Peripheral blood DNA methylation and neuroanatomical responses to HDACi treatment that rescues neurological deficits in a Kabuki syndrome mouse model

BACKGROUND

Recent findings from studies of mouse models of Mendelian disorders of epigenetic machinery strongly support the potential for postnatal therapies to improve neurobehavioral and cognitive deficits. As several of these therapies move into human clinical trials, the search for biomarkers of treatment efficacy is a priority. A potential postnatal treatment of Kabuki syndrome type 1 (KS1), caused by pathogenic variants in KMT2D encoding a histone-lysine methyltransferase, has emerged using a mouse model of KS1 (Kmt2d+/βGeo). In this mouse model, hippocampal memory deficits are ameliorated following treatment with the histone deacetylase inhibitor (HDACi), AR-42. Here, we investigate the effect of both Kmt2d+/βGeo genotype and AR-42 treatment on neuroanatomy and on DNA methylation (DNAm) in peripheral blood. While peripheral blood may not be considered a "primary tissue" with respect to understanding the pathophysiology of neurodevelopmental disorders, it has the potential to serve as an accessible biomarker of disease- and treatment-related changes in the brain.

METHODS

Half of the KS1 and wildtype mice were treated with 14 days of AR-42. Following treatment, fixed brain samples were imaged using MRI to calculate regional volumes. Blood was assayed for genome-wide DNAm at over 285,000 CpG sites using the Illumina Infinium Mouse Methylation array. DNAm patterns and brain volumes were analyzed in the four groups of animals: wildtype untreated, wildtype AR-42 treated, KS1 untreated and KS1 AR-42 treated.

RESULTS

We defined a DNAm signature in the blood of KS1 mice, that overlapped with the human KS1 DNAm signature. We also found a striking 10% decrease in total brain volume in untreated KS1 mice compared to untreated wildtype, which correlated with DNAm levels in a subset KS1 signature sites, suggesting that disease severity may be reflected in blood DNAm. Treatment with AR-42 ameliorated DNAm aberrations in KS1 mice at a small number of signature sites.

CONCLUSIONS

As this treatment impacts both neurological deficits and blood DNAm in mice, future KS clinical trials in humans could be used to assess blood DNAm as an early biomarker of therapeutic efficacy.

Biochemical pathways represented by Gene Ontology-Causal Activity Models identify distinct phenotypes resulting from mutations in pathways

Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a causally connected way. To demonstrate that individual variant genes from connected pathways result in similar but distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of 2 related but distinct pathways, gluconeogenesis and glycolysis, we show that individual causal paths in gene networks give rise to discrete phenotypic outcomes resulting from perturbations of glycolytic and gluconeogenic genes. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes.

Genetics of evolved load resistance in the skeletons of unusually large mice from Gough Island

A primary function of the skeleton is to resist the loads imparted by body weight. Genetic analyses have identified genomic regions that contribute to differences in skeletal load resistance between laboratory strains of mice, but these studies are usually restricted to 1 or 2 bones and leave open the question of how load resistance evolves in natural populations. To address these challenges, we examined the genetics of bone structure using the largest wild house mice on record, which live on Gough Island (GI). We measured structural traits connected to load resistance in the femur, tibia, scapula, humerus, radius, ulna, and mandible of GI mice, a smaller-bodied reference strain from the mainland, and 760 of their F2s. GI mice have bone geometries indicative of greater load resistance abilities but show no increase in bone mineral density compared to the mainland strain. Across traits and bones, we identified a total of 153 quantitative trait loci (QTL) that span all but one of the autosomes. The breadth of QTL detection ranges from a single bone to all 7 bones. Additive effects of QTL are modest. QTL for bone structure show limited overlap with QTL for bone length and width and QTL for body weight mapped in the same cross, suggesting a distinct genetic architecture for load resistance. Our findings provide a rare genetic portrait of the evolution of load resistance in a natural population with extreme body size.

CCIDB: a manually curated cell-cell interaction database with cell context information

Cell-cell interaction (CCI) is a crucial event in the development and function of multicellular organisms. The development of CCI databases is beneficial for researchers who want to analyze single-cell sequencing data or study CCI through molecular experiments. CCIs are known to act differently according to cellular and biological contexts such as cell types, gene mutations or disease status; however, previous CCI databases do not completely provide this contextual information pertaining to CCIs. We constructed a cell-cell interaction database (CCIDB) containing the biological and clinical contexts involved in each interaction. To build a database of cellular and tissue contexts, we collected 38 types of context features, which were categorized into seven categories, including 'interaction', 'cell type', 'cofactor', 'effector', 'phenotype', 'pathology' and 'reference'. CCIs were manually retrieved from 272 studies published recently (less than 6 years ago). In the current version of CCIDB, 520 CCIs and their 38 context features have been manually collected and curated by biodata engineers. We suggest that CCIDB is a manually curated CCI resource that is highly useful, especially for analyzing context-dependent alterations in CCIs. Database URL https://ccidb.sysmed.kr/.

Improving the classification of cardinality phenotypes using collections

MOTIVATION

Phenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize disease, and is also collected in model organisms and stored in model organism databases where they are used to understand gene functions. Phenotype data is also used in computational data analysis and machine learning methods to provide novel insights into disease mechanisms and support personalized diagnosis of disease. For mammalian organisms and in a clinical context, ontologies such as the Human Phenotype Ontology and the Mammalian Phenotype Ontology are widely used to formally and precisely describe phenotypes. We specifically analyze axioms pertaining to phenotypes of collections of entities within a body, and we find that some of the axioms in phenotype ontologies lead to inferences that may not accurately reflect the underlying biological phenomena.

RESULTS

We reformulate the phenotypes of collections of entities using an ontological theory of collections. By reformulating phenotypes of collections in phenotypes ontologies, we avoid potentially incorrect inferences pertaining to the cardinality of these collections. We apply our method to two phenotype ontologies and show that the reformulation not only removes some problematic inferences but also quantitatively improves biological data analysis.

Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources

Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms.

AVAILABILITY AND IMPLEMENTATION

Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame.

Biochemical Pathways Represented by Gene Ontology Causal Activity Models Identify Distinct Phenotypes Resulting from Mutations in Pathways

Gene inactivation can affect the process(es) in which that gene acts and causally downstream ones, yielding diverse mutant phenotypes. Identifying the genetic pathways resulting in a given phenotype helps us understand how individual genes interact in a functional network. Computable representations of biological pathways include detailed process descriptions in the Reactome Knowledgebase, and causal activity flows between molecular functions in Gene Ontology-Causal Activity Models (GO-CAMs). A computational process has been developed to convert Reactome pathways to GO-CAMs. Laboratory mice are widely used models of normal and pathological human processes. We have converted human Reactome GO-CAMs to orthologous mouse GO-CAMs, as a resource to transfer pathway knowledge between humans and model organisms. These mouse GO-CAMs allowed us to define sets of genes that function in a connected and well-defined way. To test whether individual genes from well-defined pathways result in similar and distinguishable phenotypes, we used the genes in our pathway models to cross-query mouse phenotype annotations in the Mouse Genome Database (MGD). Using GO-CAM representations of two related but distinct pathways, gluconeogenesis and glycolysis, we can identify causal paths in gene networks that give rise to discrete phenotypic outcomes for perturbations of glycolysis and gluconeogenesis. The accurate and detailed descriptions of gene interactions recovered in this analysis of well-studied processes suggest that this strategy can be applied to less well-understood processes in less well-studied model systems to predict phenotypic outcomes of novel gene variants and to identify potential gene targets in altered processes.

Analysis of SNHG14: A Long Non-Coding RNA Hosting SNORD116, Whose Loss Contributes to Prader-Willi Syndrome Etiology

The Small Nucleolar Host Gene 14 (SNHG14) is a host gene for small non-coding RNAs, including the SNORD116 small nucleolar C/D box RNA encoding locus. Large deletions of the SNHG14 locus, as well as microdeletions of the SNORD116 locus, lead to the neurodevelopmental genetic disorder Prader-Willi syndrome. This review will focus on the SNHG14 gene, its expression patterns, its role in human cancer, and the possibility that single nucleotide variants within the locus contribute to human phenotypes in the general population. This review will also include new in silico data analyses of the SNHG14 locus and new in situ RNA expression patterns of the Snhg14 RNA in mouse midbrain and hindbrain regions.

OrthoDB v11: annotation of orthologs in the widest sampling of organismal diversity

OrthoDB provides evolutionary and functional annotations of genes in a diverse sampling of eukaryotes, prokaryotes, and viruses. Genomics continues to accelerate our exploration of gene diversity and orthology is the most precise way of bridging gene functional knowledge with the rapidly expanding universe of genomic sequences. OrthoDB samples the most diverse organisms with the best quality genomics data to provide the leading coverage of species diversity. This update of the underlying data to over 18 000 prokaryotes and almost 2000 eukaryotes with over 100 million genes propels the coverage to another level. This achievement also demonstrates the scalability of the underlying OrthoLoger software for delineation of orthologs, freely available from https://orthologer.ezlab.org. In addition to the ab-initio computations of gene orthology used for the OrthoDB release, the OrthoLoger software allows mapping of novel gene sets to precomputed orthologs and thereby links to their annotations. The LEMMI-style benchmarking of OrthoLoger ensures its state-of-the-art performance and is available from https://lemortho.ezlab.org. The OrthoDB web interface has been further developed to include a pairwise orthology view from any gene to any other sampled species. OrthoDB-computed evolutionary annotations as well as extensively collated functional annotations can be accessed via REST API or SPARQL/RDF, downloaded or browsed online from https://www.orthodb.org.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

How much do model organism phenotypes contribute to the computational identification of human disease genes?

Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene–disease associations. We found that mouse genotype–phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms' data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

This article has an associated First Person interview with the first author of the paper.

Editor's choice: We investigated the use of model organism phenotypes in the computational identification of disease genes, identifying several data biases and concluding that mouse model phenotypes contribute most to computational disease gene identification.

Genetically modified mice for research on human diseases: A triumph for Biotechnology or a work in progress?

Genetically modified mice are engineered as models for human diseases. These mouse models include inbred strains, mutants, gene knockouts, gene knockins, and ‘humanized’ mice. Each mouse model is engineered to mimic a specific disease based on a theory of the genetic basis of that disease. For example, to test the amyloid theory of Alzheimer’s disease, mice with amyloid precursor protein genes are engineered, and to test the tau theory, mice with tau genes are engineered. This paper discusses the importance of mouse models in basic research, drug discovery, and translational research, and examines the question of how to define the “best” mouse model of a disease. The critiques of animal models and the caveats in translating the results from animal models to the treatment of human disease are discussed. Since many diseases are heritable, multigenic, age-related and experience-dependent, resulting from multiple gene-gene and gene-environment interactions, it will be essential to develop mouse models that reflect these genetic, epigenetic and environmental factors from a developmental perspective. Such models would provide further insight into disease emergence, progression and the ability to model two-hit and multi-hit theories of disease. The summary examines the biotechnology for creating genetically modified mice which reflect these factors and how they might be used to discover new treatments for complex human diseases such as cancers, neurodevelopmental and neurodegenerative diseases.

Harmonizing model organism data in the Alliance of Genome Resources

The Alliance of Genome Resources (the Alliance) is a combined effort of 7 knowledgebase projects: Saccharomyces Genome Database, WormBase, FlyBase, Mouse Genome Database, the Zebrafish Information Network, Rat Genome Database, and the Gene Ontology Resource. The Alliance seeks to provide several benefits: better service to the various communities served by these projects; a harmonized view of data for all biomedical researchers, bioinformaticians, clinicians, and students; and a more sustainable infrastructure. The Alliance has harmonized cross-organism data to provide useful comparative views of gene function, gene expression, and human disease relevance. The basis of the comparative views is shared calls of orthology relationships and the use of common ontologies. The key types of data are alleles and variants, gene function based on gene ontology annotations, phenotypes, association to human disease, gene expression, protein-protein and genetic interactions, and participation in pathways. The information is presented on uniform gene pages that allow facile summarization of information about each gene in each of the 7 organisms covered (budding yeast, roundworm Caenorhabditis elegans, fruit fly, house mouse, zebrafish, brown rat, and human). The harmonized knowledge is freely available on the alliancegenome.org portal, as downloadable files, and by APIs. We expect other existing and emerging knowledge bases to join in the effort to provide the union of useful data and features that each knowledge base currently provides.

GOing Forward With the Cardiac Conduction System Using Gene Ontology

The cardiac conduction system (CCS) comprises critical components responsible for the initiation, propagation, and coordination of the action potential. Aberrant CCS development can cause conduction abnormalities, including sick sinus syndrome, accessory pathways, and atrioventricular and bundle branch blocks. Gene Ontology (GO; http://geneontology.org/) is an invaluable global bioinformatics resource which provides structured, computable knowledge describing the functions of gene products. Many gene products are known to be involved in CCS development; however, this information is not comprehensively captured by GO. To address the needs of the heart development research community, this study aimed to describe the specific roles of proteins reported in the literature to be involved with CCS development and/or function. 14 proteins were prioritized for GO annotation which led to the curation of 15 peer-reviewed primary experimental articles using carefully selected GO terms. 152 descriptive GO annotations, including those describing sinoatrial node and atrioventricular node development were created and submitted to the GO Consortium database. A functional enrichment analysis of 35 key CCS development proteins confirmed that this work has improved the in-silico interpretation of this CCS dataset. This work may improve future investigations of the CCS with application of high-throughput methods such as genome-wide association studies analysis, proteomics, and transcriptomics.

Laboratory Codes in Nomenclature and Scientific Communication (Advancing Organism Nomenclature in Scientific Communication to Improve Research Reporting and Reproducibility)

Laboratory registration codes, also known as laboratory codes or lab codes, are a key element in standardized laboratory animal and genetic nomenclature. As such they are critical to accurate scientific communication and to research reproducibility and integrity. The original committee on Mouse Genetic Nomenclature published nomenclature conventions for mice genetics in 1940, and then conventions for inbred strains in 1952. Unique designations were needed, and have been in use since the 1950s, for the sources of animals and substrains, for the laboratories that identified new alleles or mutations, and then for developers of transgenes and induced mutations. Current laboratory codes are typically a 2- to 4-letter acronym for an institution or an investigator. Unique codes are assigned from the International Laboratory Code Registry, which was developed and is maintained by ILAR in the National Academies (National Academies of Sciences Engineering and Medicine and previously National Academy of Sciences). As a resource for the global research community, the registry has been online since 1997. Since 2003 mouse and rat genetic and strain nomenclature rules have been reviewed and updated annually as a joint effort of the International Committee on Standardized Genetic Nomenclature for Mice and the Rat Genome and Nomenclature Committee. The current nomenclature conventions (particularly conventions for non-inbred animals) are applicable beyond rodents, although not widely adopted. Ongoing recognition, since at least the 1930s, of the research relevance of genetic backgrounds and origins of animals, and of spontaneous and induced genetic variants speaks to the need for broader application of standardized nomenclature for animals in research, particularly given the increasing numbers and complexities of genetically modified swine, nonhuman primates, fish, and other species.