RESUMEN
We conducted whole-genome sequencing of four inbred mouse strains initially selected for high (H1, H2) or low (L1, L2) open-field activity (OFA), and then examined strain distribution patterns for all DNA variants that differed between their BALB/cJ and C57BL/6J parental strains. Next, we assessed genome-wide sharing (3,678,826 variants) both between and within the High and Low Activity strains. Results suggested that about 10% of these DNA variants may be associated with OFA, and clearly demonstrated its polygenic nature. Finally, we conducted bioinformatic analyses of functional genomics data from mouse, rat, and human to refine previously identified quantitative trait loci (QTL) for anxiety-related measures. This combination of sequence analysis and genomic-data integration facilitated refinement of previously intractable QTL findings, and identified possible genes for functional follow-up studies.
Asunto(s)
Ansiedad/genética , Ratones Endogámicos/genética , Prueba de Campo Abierto/fisiología , Animales , Trastornos de Ansiedad/genética , Mapeo Cromosómico/métodos , Biología Computacional/métodos , Modelos Animales de Enfermedad , Genómica/métodos , Genotipo , Humanos , Ratones , Ratones Endogámicos BALB C/genética , Ratones Endogámicos C57BL/genética , Fenotipo , Polimorfismo de Nucleótido Simple/genética , Sitios de Carácter Cuantitativo/genética , Ratas , Secuenciación del Exoma/métodosRESUMEN
Anxiety disorders are common and can be debilitating, with effective treatments remaining hampered by an incomplete understanding of the underlying genetic etiology. Improvements have been made in understanding the genetic influences on mouse behavioral models of anxiety, yet it is unclear the extent to which genes identified in these experimental systems contribute to genetic variation in human anxiety phenotypes. Leveraging new and existing large-scale human genome-wide association studies, we tested whether sets of genes previously identified in mouse anxiety-like behavior studies contribute to a range of human anxiety disorders. When tested as individual genes, 13 mouse-identified genes were associated with human anxiety phenotypes, suggesting an overlap of individual genes contributing to both mouse models of anxiety-like behaviors and human anxiety traits. When genes were tested as sets, we did identify 14 significant associations between mouse gene sets and human anxiety, but the majority of gene sets showed no significant association with human anxiety phenotypes. These few significant associations indicate a need to identify and develop more translatable mouse models by identifying sets of genes that "match" between model systems and specific human phenotypes of interest. We suggest that continuing to develop improved behavioral paradigms and finer-scale experimental data, for instance from individual neuronal subtypes or cell-type-specific expression data, is likely to improve our understanding of the genetic etiology and underlying functional changes in anxiety disorders.
Asunto(s)
Trastornos de Ansiedad , Estudio de Asociación del Genoma Completo , Humanos , Ratones , Animales , Trastornos de Ansiedad/genética , Ansiedad/genética , FenotipoRESUMEN
The abundance of existing functional genomics studies permits an integrative approach to interpreting and resolving the results of diverse systems genetics studies. However, a major challenge lies in assembling and harmonizing heterogeneous data sets across species for facile comparison to the positional candidate genes and coexpression networks that come from systems genetic studies. GeneWeaver is an online database and suite of tools at www.geneweaver.org that allows for fast aggregation and analysis of gene set-centric data. GeneWeaver contains curated experimental data together with resource-level data such as GO annotations, MP annotations, and KEGG pathways, along with persistent stores of user entered data sets. These can be entered directly into GeneWeaver or transferred from widely used resources such as GeneNetwork.org. Data are analyzed using statistical tools and advanced graph algorithms to discover new relations, prioritize candidate genes, and generate function hypotheses. Here we use GeneWeaver to find genes common to multiple gene sets, prioritize candidate genes from a quantitative trait locus, and characterize a set of differentially expressed genes. Coupling a large multispecies repository curated and empirical functional genomics data to fast computational tools allows for the rapid integrative analysis of heterogeneous data for interpreting and extrapolating systems genetics results.
Asunto(s)
Bases de Datos Genéticas , Genética de Población/métodos , Genómica/métodos , Programas Informáticos , Navegador Web , Mapeo Cromosómico , Epistasis Genética , Regulación de la Expresión Génica , Redes Reguladoras de Genes , Estudios de Asociación Genética/métodos , Sitios de Carácter Cuantitativo , Motor de Búsqueda , Interfaz Usuario-ComputadorRESUMEN
The goal of systems genetics is to understand the impact of genetic variation across all levels of biological organization, from mRNAs, proteins, and metabolites, to higher-order physiological and behavioral traits. This approach requires the accumulation and integration of many types of data, and also requires the use of many types of statistical tools to extract relevant patterns of covariation and causal relations as a function of genetics, environment, stage, and treatment. In this protocol we explain how to use the GeneNetwork web service, a powerful and free online resource for systems genetics. We provide workflows and methods to navigate massive multiscalar data sets and we explain how to use an extensive systems genetics toolkit for analysis and synthesis. Finally, we provide two detailed case studies that take advantage of human and mouse cohorts to evaluate linkage between gene variants, addiction, and aging.