Identifying the biological substrates of complex neurobehavioral traits such as alcohol dependency pose a tremendous challenge given the diverse model systems and phenotypic assessments used. To address this problem we have developed a platform for integrated analysis of high-throughput or genome-wide functional genomics studies. A wealth of such data exists, but it is often found in disparate, non-computable forms. Our interactive web-based software system, Gene Weaver (http://www.geneweaver.org), couples curated results from genomic studies to graph-theoretical tools for combinatorial analysis. Using this system we identified a gene underlying multiple alcohol-related phenotypes in four species. A search of over 60,000 gene sets in GeneWeaver's database revealed alcohol-related experimental results including genes identified in mouse genetic mapping studies, alcohol selected Drosophila lines, Rattus differential expression, and human alcoholic brains. We identified highly connected genes and compared these to genes currently annotated to alcohol-related behaviors and processes. The most highly connected gene not annotated to alcohol was Pafah1b1. Experimental validation using a Pafah1b1 conditional knock-out mouse confirmed that this gene is associated with an increased preference for alcohol and an altered thermoregulatory response to alcohol. Although this gene has not been previously implicated in alcohol-related behaviors, its function in various neural mechanisms makes a role in alcohol-related phenomena plausible. By making diverse cross-species functional genomics data readily computable, we were able to identify and confirm a novel alcohol-related gene that may have implications for alcohol use disorders and other effects of alcohol.

The Pax6 transcription factor is expressed in cerebellar granule cells and when mutated, as in the Sey/Sey mouse, produces granule cells with disturbed survival and migration and with defects in neurite extension. The impact of Pax6 on other genes in the context of cerebellar development has not been identified. In this study, we performed transcriptome comparisons between wildtype and Pax6-null whole cerebellar tissue at embryonic day (E) 13.5, 15.5 and 18.5 using Affymetrix arrays (U74Av2). Statistical analyses identified 136 differentially regulated transcripts (FDR 0.05, 1.2-fold change cutoff) over time in Pax6-null cerebellar tissue. In parallel we examined the Math1-null granuloprival cerebellum and identified 228 down-regulated transcripts (FDR 0.05, 1.2-fold change cutoff). The intersection of these two microarray datasets produced a total of 21 differentially regulated transcripts. For a subset of the identified transcripts, we used qRT-PCR to validate the microarray data and demonstrated the expression in the rhombic lip lineage and differential expression in Pax6-null cerebellum with in situ hybridisation analysis. The candidate genes identified in this way represent direct or indirect Pax6-downstream genes involved in cerebellar development.

The Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines were developed to address the lack of reproducibility in biomedical animal studies and improve the communication of research findings. While intended to guide the preparation of peer-reviewed manuscripts, the principles of transparent reporting are also fundamental for in vivo databases. Here, we describe the benefits and challenges of applying the guidelines for the International Mouse Phenotyping Consortium (IMPC), whose goal is to produce and phenotype 20,000 knockout mouse strains in a reproducible manner across ten research centres. In addition to ensuring the transparency and reproducibility of the IMPC, the solutions to the challenges of applying the ARRIVE guidelines in the context of IMPC will provide a resource to help guide similar initiatives in the future.

A persistent challenge lies in the interpretation of consensus and discord from functional genomics experimentation. Harmonizing and analyzing this data will enable investigators to discover relations of many genes to many diseases, and from many phenotypes and experimental paradigms to many diseases through their genomic substrates. The GeneWeaver.org system provides a platform for cross-species integration and interrogation of heterogeneous curated and experimentally derived functional genomics data. GeneWeaver enables researchers to store, share, analyze, and compare results of their own genome-wide functional genomics experiments in an environment containing rich companion data obtained from major curated repositories, including the Mouse Genome Database and other model organism databases, along with derived data from highly specialized resources, publications, and user submissions. The data, largely consisting of gene sets and putative biological networks, are mapped onto one another through gene identifiers and homology across species. A versatile suite of interactive tools enables investigators to perform a variety of set analysis operations to find consilience among these often noisy experimental results. Fast algorithms enable real-time analysis of large queries. Specific applications include prioritizing candidate genes for quantitative trait loci, identifying biologically valid mouse models and phenotypic assays for human disease, finding the common biological substrates of related diseases, classifying experiments and the biological concepts they represent from empirical data, and applying patterns of genomic evidence to implicate novel genes in disease. These results illustrate an alternative to strict emphasis on replicability, whereby researchers classify experimental results to identify the conditions that lead to their similarity.

Since the publication of the first comprehensive linkage map for the laboratory mouse, the architecture of recombination as a basic biological process has become amenable to investigation in mammalian model organisms. Here we take advantage of high-density genotyping and the unique pedigree structure of the incipient Collaborative Cross to investigate the roles of sex and genetic background in mammalian recombination. Our results confirm the observation that map length is longer when measured through female meiosis than through male meiosis, but we find that this difference is modified by genotype at loci on both the X chromosome and the autosomes. In addition, we report a striking concentration of crossovers in the distal ends of autosomes in male meiosis that is absent in female meiosis. The presence of this pattern in both single- and double-recombinant chromosomes, combined with the absence of a corresponding asymmetry in the distribution of double-strand breaks, indicates a regulated sequence of events specific to male meiosis that is anchored by chromosome ends. This pattern is consistent with the timing of chromosome pairing and evolutionary constraints on male recombination. Finally, we identify large regions of reduced crossover frequency that together encompass 5% of the genome. Many of these "cold regions" are enriched for segmental duplications, suggesting an inverse local correlation between recombination rate and mutation rate for large copy number variants.

Gene expression microarray data can be used for the assembly of genetic coexpression network graphs. Using mRNA samples obtained from recombinant inbred Mus musculus strains, it is possible to integrate allelic variation with molecular and higher-order phenotypes. The depth of quantitative genetic analysis of microarray data can be vastly enhanced utilizing this mouse resource in combination with powerful computational algorithms, platforms, and data repositories. The resulting network graphs transect many levels of biological scale. This approach is illustrated with the extraction of cliques of putatively co-regulated genes and their annotation using gene ontology analysis and cis-regulatory element discovery. The causal basis for co-regulation is detected through the use of quantitative trait locus mapping.

The EMBL Nucleotide Sequence Database (www.ebi.ac.uk/embl) at the EMBL European Bioinformatics Institute, UK, offers a comprehensive set of publicly available nucleotide sequence and annotation, freely accessible to all. Maintained in collaboration with partners DDBJ and GenBank, coverage includes whole genome sequencing project data, directly submitted sequence, sequence recorded in support of patent applications and much more. The database continues to offer submission tools, data retrieval facilities and user support. In 2005, the volume of data offered has continued to grow exponentially. In addition to the newly presented data, the database encompasses a range of new data types generated by novel technologies, offers enhanced presentation and searchability of the data and has greater integration with other data resources offered at the EBI and elsewhere. In stride with these developing data types, the database has continued to develop submission and retrieval tools to maximise the information content of submitted data and to offer the simplest possible submission routes for data producers. New developments, the submission process, data retrieval and access to support are presented in this paper, along with links to sources of further information.

The wealth of genomic technologies has enabled biologists to rapidly ascribe phenotypic characters to biological substrates. Central to effective biological investigation is the operational definition of the process under investigation. We propose an elucidation of categories of biological characters, including disease relevant traits, based on natural endogenous processes and experimentally observed biological networks, pathways and systems rather than on externally manifested constructs and current semantics such as disease names and processes. The Ontological Discovery Environment (ODE) is an Internet accessible resource for the storage, sharing, retrieval and analysis of phenotype-centered genomic data sets across species and experimental model systems. Any type of data set representing gene-phenotype relationships, such quantitative trait loci (QTL) positional candidates, literature reviews, microarray experiments, ontological or even meta-data, may serve as inputs. To demonstrate a use case leveraging the homology capabilities of ODE and its ability to synthesize diverse data sets, we conducted an analysis of genomic studies related to alcoholism. The core of ODE's gene set similarity, distance and hierarchical analysis is the creation of a bipartite network of gene-phenotype relations, a unique discrete graph approach to analysis that enables set-set matching of non-referential data. Gene sets are annotated with several levels of metadata, including community ontologies, while gene set translations compare models across species. Computationally derived gene sets are integrated into hierarchical trees based on gene-derived phenotype interdependencies. Automated set identifications are augmented by statistical tools which enable users to interpret the confidence of modeled results. This approach allows data integration and hypothesis discovery across multiple experimental contexts, regardless of the face similarity and semantic annotation of the experimental systems or species domain.

Genes with common functions often exhibit correlated expression levels, which can be used to identify sets of interacting genes from microarray data. Microarrays typically measure expression across genomic space, creating a massive matrix of co-expression that must be mined to extract only the most relevant gene interactions. We describe a graph theoretical approach to extracting co-expressed sets of genes, based on the computation of cliques. Unlike the results of traditional clustering algorithms, cliques are not disjoint and allow genes to be assigned to multiple sets of interacting partners, consistent with biological reality. A graph is created by thresholding the correlation matrix to include only the correlations most likely to signify functional relationships. Cliques computed from the graph correspond to sets of genes for which significant edges are present between all members of the set, representing potential members of common or interacting pathways. Clique membership can be used to infer function about poorly annotated genes, based on the known functions of better-annotated genes with which they share clique membership (i.e., "guilt-by-association"). We illustrate our method by applying it to microarray data collected from the spleens of mice exposed to low-dose ionizing radiation. Differential analysis is used to identify sets of genes whose interactions are impacted by radiation exposure. The correlation graph is also queried independently of clique to extract edges that are impacted by radiation. We present several examples of multiple gene interactions that are altered by radiation exposure and thus represent potential molecular pathways that mediate the radiation response.

Cross-species translational approaches to human genomic analyses are lacking. The present study uses an integrative framework to investigate how genes associated with nicotine use in model organisms contribute to the genetic architecture of human tobacco consumption. First, we created a model organism geneset by collecting results from five animal models of nicotine exposure (RNA expression changes in brain) and then tested the relevance of these genes and flanking genetic variation using genetic data from human cigarettes per day (UK BioBank N = 123,844; all European Ancestry). We tested three hypotheses: (1) DNA variation in, or around, the 'model organism geneset' will contribute to the heritability to human tobacco consumption, (2) that the model organism genes will be enriched for genes associated with human tobacco consumption, and (3) that a polygenic score based off our model organism geneset will predict tobacco consumption in the AddHealth sample (N = 1667; all European Ancestry). Our results suggested that: (1) model organism genes accounted for ~5-36% of the observed SNP-heritability in human tobacco consumption (enrichment: 1.60-31.45), (2) model organism genes, but not negative control genes, were enriched for the gene-based associations (MAGMA, H-MAGMA, SMultiXcan) for human cigarettes per day, and (3) polygenic scores based on our model organism geneset predicted cigarettes per day in an independent sample. Altogether, these findings highlight the advantages of using multiple species evidence to isolate genetic factors to better understand the etiological complexity of tobacco and other nicotine consumption.

Impulsive behavior and impulsivity are heritable phenotypes that are strongly associated with risk for substance use disorders. Identifying the neurogenetic mechanisms that influence impulsivity may also reveal novel biological insights into addiction vulnerability. Our past studies using the BXD and Collaborative Cross (CC) recombinant inbred mouse panels have revealed that behavioral indicators of impulsivity measured in a reversal-learning task are heritable and are genetically correlated with aspects of intravenous cocaine self-administration. Genome-wide linkage studies in the BXD panel revealed a quantitative trait locus (QTL) on chromosome 10, but we expect to identify additional QTL by testing in a population with more genetic diversity. To this end, we turned to Diversity Outbred (DO) mice; 392 DO mice (156 males, 236 females) were phenotyped using the same reversal learning test utilized previously. Our primary indicator of impulsive responding, a measure that isolates the relative difficulty mice have with reaching performance criteria under reversal conditions, revealed a genome-wide significant QTL on chromosome 7 (max LOD score = 8.73, genome-wide corrected p<0.05). A measure of premature responding akin to that implemented in the 5-choice serial reaction time task yielded a suggestive QTL on chromosome 17 (max LOD score = 9.14, genome-wide corrected <0.1). Candidate genes were prioritized (2900076A07Rik, Wdr73 and Zscan2) based upon expression QTL data we collected in DO and CC mice and analyses using publicly available gene expression and phenotype databases. These findings may advance understanding of the genetics that drive impulsive behavior and enhance risk for substance use disorders.

Circadian variability is driven by genetics and Diversity Outbred (DO) mice is a powerful tool for examining the genetics of complex traits because their high genetic and phenotypic diversity compared to conventional mouse crosses. The DO population combines the genetic diversity of eight founder strains including five common inbred and three wild-derived strains. In DO mice and their founders, we established a high-throughput system to measure cellular rhythms using in vitro preparations of skin fibroblasts. Among the founders, we observed strong heritability for rhythm period, robustness, phase and amplitude. We also found significant sex and strain differences for these rhythms. Extreme differences in period for molecular and behavioral rhythms were found between the inbred A/J strain and the wild-derived CAST/EiJ strain, where A/J had the longest period and CAST/EiJ had the shortest. In addition, we measured cellular rhythms in 329 DO mice, which displayed far greater phenotypic variability than the founders-80% of founders compared to only 25% of DO mice had periods of ~ 24 h. Collectively, our findings demonstrate that genetic diversity contributes to phenotypic variability in circadian rhythms, and high-throughput characterization of fibroblast rhythms in DO mice is a tractable system for examining the genetics of circadian traits.

The emergence of the SARS-CoV-2 virus and subsequent COVID-19 pandemic initiated intense research into the mechanisms of action for this virus. It was quickly noted that COVID-19 presents more seriously in conjunction with other human disease conditions such as hypertension, diabetes, and lung diseases. We conducted a bioinformatics analysis of COVID-19 comorbidity-associated gene sets, identifying genes and pathways shared among the comorbidities, and evaluated current knowledge about these genes and pathways as related to current information about SARS-CoV-2 infection. We performed our analysis using GeneWeaver (GW), Reactome, and several biomedical ontologies to represent and compare common COVID-19 comorbidities. Phenotypic analysis of shared genes revealed significant enrichment for immune system phenotypes and for cardiovascular-related phenotypes, which might point to alleles and phenotypes in mouse models that could be evaluated for clues to COVID-19 severity. Through pathway analysis, we identified enriched pathways shared by comorbidity datasets and datasets associated with SARS-CoV-2 infection.

Sensitivity to rewarding and reinforcing drug effects has a critical role in initial use, but the role of initial aversive drug effects has received less attention. Methamphetamine effects on dopamine re-uptake and efflux are associated with its addiction potential. However, methamphetamine also serves as a substrate for the trace amine-associated receptor 1 (TAAR1). Growing evidence in animal models indicates that increasing TAAR1 function reduces drug self-administration and intake. We previously determined that a non-synonymous single nucleotide polymorphism (SNP) in Taar1 predicts a conformational change in the receptor that has functional consequences. A Taar1 m1J mutant allele existing in DBA/2J mice expresses a non-functional receptor. In comparison to mice that possess one or more copies of the reference Taar1 allele (Taar1 +/+ or Taar1 +/m1J ), mice with the Taar1 m1J/m1J genotype readily consume methamphetamine, express low sensitivity to aversive effects of methamphetamine, and lack sensitivity to acute methamphetamine-induced hypothermia. We used three sets of knock-in and control mice in which one Taar1 allele was exchanged with the alternative allele to determine if other methamphetamine-related traits and an opioid trait are impacted by the same Taar1 SNP proven to affect MA consumption and hypothermia. First, we measured sensitivity to conditioned rewarding and aversive effects of methamphetamine to determine if an impact of the Taar1 SNP on these traits could be proven. Next, we used multiple genetic backgrounds to study the consistency of Taar1 allelic effects on methamphetamine intake and hypothermia. Finally, we studied morphine-induced hypothermia to confirm prior data suggesting that a gene in linkage disequilibrium with Taar1, rather than Taar1, accounts for prior observed differences in sensitivity. We found that a single SNP exchange reduced sensitivity to methamphetamine conditioned reward and increased sensitivity to conditioned aversion. Profound differences in methamphetamine intake and hypothermia consistently corresponded with genotype at the SNP location, with only slight variation in magnitude across genetic backgrounds. Morphine-induced hypothermia was not dependent on Taar1 genotype. Thus, Taar1 genotype and TAAR1 function impact multiple methamphetamine-related effects that likely predict the potential for methamphetamine use. These data support further investigation of their potential roles in risk for methamphetamine addiction and therapeutic development.

The Mouse Phenome Database continues to serve as a curated repository and analysis suite for measured attributes of members of diverse mouse populations. The repository includes annotation to community standard ontologies and guidelines, a database of allelic states for 657 mouse strains, a collection of protocols, and analysis tools for flexible, interactive, user directed analyses that increasingly integrates data across traits and populations. The database has grown from its initial focus on a standard set of inbred strains to include heterogeneous mouse populations such as the Diversity Outbred and mapping crosses and well as Collaborative Cross, Hybrid Mouse Diversity Panel, and recombinant inbred strains. Most recently the system has expanded to include data from the International Mouse Phenotyping Consortium. Collectively these data are accessible by API and provided with an interactive tool suite that enables users' persistent selection, storage, and operation on collections of measures. The tool suite allows basic analyses, advanced functions with dynamic visualization including multi-population meta-analysis, multivariate outlier detection, trait pattern matching, correlation analyses and other functions. The data resources and analysis suite provide users a flexible environment in which to explore the basis of phenotypic variation in health and disease across the lifespan.

Understanding the source of genetic variation in aging and using this variation to define the molecular mechanisms of healthy aging require deep and broad quantification of a host of physiological, morphological, and behavioral endpoints. The murine model is a powerful system in which to understand the relations across age-related phenotypes and to identify research models with variation in life span and health span. The Jackson Laboratory Nathan Shock Center of Excellence in the Basic Biology of Aging has performed broad characterization of aging in genetically diverse laboratory mice and has placed these data, along with data from several other major aging initiatives, into the interactive Mouse Phenome Database. The data may be accessed and analyzed by researchers interested in finding mouse models for specific aging processes, age-related health and disease states, and for genetic analysis of aging variation and trait covariation. We expect that by placing these data in the hands of the aging community that there will be (a) accelerated genetic analyses of aging processes, (b) discovery of genetic loci regulating life span, (c) identification of compelling correlations between life span and susceptibility for age-related disorders, and (d) discovery of concordant genomic loci influencing life span and aging phenotypes between mouse and humans.

Significant departures from expected Mendelian inheritance ratios (transmission ratio distortion, TRD) are frequently observed in both experimental crosses and natural populations. TRD on mouse Chromosome (Chr) 2 has been reported in multiple experimental crosses, including the Collaborative Cross (CC). Among the eight CC founder inbred strains, we found that Chr 2 TRD was exclusive to females that were heterozygous for the WSB/EiJ allele within a 9.3 Mb region (Chr 2 76.9 - 86.2 Mb). A copy number gain of a 127 kb-long DNA segment (designated as responder to drive, R2d) emerged as the strongest candidate for the causative allele. We mapped R2d sequences to two loci within the candidate interval. R2d1 is located near the proximal boundary, and contains a single copy of R2d in all strains tested. R2d2 maps to a 900 kb interval, and the number of R2d copies varies from zero in classical strains (including the mouse reference genome) to more than 30 in wild-derived strains. Using real-time PCR assays for the copy number, we identified a mutation (R2d2WSBdel1) that eliminates the majority of the R2d2WSB copies without apparent alterations of the surrounding WSB/EiJ haplotype. In a three-generation pedigree segregating for R2d2WSBdel1, the mutation is transmitted to the progeny and Mendelian segregation is restored in females heterozygous for R2d2WSBdel1, thus providing direct evidence that the copy number gain is causal for maternal TRD. We found that transmission ratios in R2d2WSB heterozygous females vary between Mendelian segregation and complete distortion depending on the genetic background, and that TRD is under genetic control of unlinked distorter loci. Although the R2d2WSB transmission ratio was inversely correlated with average litter size, several independent lines of evidence support the contention that female meiotic drive is the cause of the distortion. We discuss the implications and potential applications of this novel meiotic drive system.

Allergic airway diseases (AADs) such as asthma are characterized in part by granulocytic airway inflammation. The gene regulatory networks that govern granulocyte recruitment are poorly understood, but evidence is accruing that microRNAs (miRNAs) play an important role. To identify miRNAs that may underlie AADs, we used two complementary approaches that leveraged the genotypic and phenotypic diversity of the Collaborative Cross (CC) mouse population. In the first approach, we sought to identify miRNA expression quantitative trait loci (eQTL) that overlap QTL for AAD-related phenotypes. Specifically, CC founder strains and incipient lines of the CC were sensitized and challenged with house dust mite allergen followed by measurement of granulocyte recruitment to the lung. Total lung RNA was isolated and miRNA was measured using arrays for CC founders and qRT-PCR for incipient CC lines.

A selective sweep is the result of strong positive selection driving newly occurring or standing genetic variants to fixation, and can dramatically alter the pattern and distribution of allelic diversity in a population. Population-level sequencing data have enabled discoveries of selective sweeps associated with genes involved in recent adaptations in many species. In contrast, much debate but little evidence addresses whether "selfish" genes are capable of fixation-thereby leaving signatures identical to classical selective sweeps-despite being neutral or deleterious to organismal fitness. We previously described R2d2, a large copy-number variant that causes nonrandom segregation of mouse Chromosome 2 in females due to meiotic drive. Here we show population-genetic data consistent with a selfish sweep driven by alleles of R2d2 with high copy number (R2d2(HC)) in natural populations. We replicate this finding in multiple closed breeding populations from six outbred backgrounds segregating for R2d2 alleles. We find that R2d2(HC) rapidly increases in frequency, and in most cases becomes fixed in significantly fewer generations than can be explained by genetic drift. R2d2(HC) is also associated with significantly reduced litter sizes in heterozygous mothers, making it a true selfish allele. Our data provide direct evidence of populations actively undergoing selfish sweeps, and demonstrate that meiotic drive can rapidly alter the genomic landscape in favor of mutations with neutral or even negative effects on overall Darwinian fitness. Further study will reveal the incidence of selfish sweeps, and will elucidate the relative contributions of selfish genes, adaptation and genetic drift to evolution.

The European Nucleotide Archive (ENA; http://www.ebi.ac.uk/ena) is Europe's primary nucleotide-sequence repository. The ENA consists of three main databases: the Sequence Read Archive (SRA), the Trace Archive and EMBL-Bank. The objective of ENA is to support and promote the use of nucleotide sequencing as an experimental research platform by providing data submission, archive, search and download services. In this article, we outline these services and describe major changes and improvements introduced during 2010. These include extended EMBL-Bank and SRA-data submission services, extended ENA Browser functionality, support for submitting data to the European Genome-phenome Archive (EGA) through SRA, and the launch of a new sequence similarity search service.