Comparison of unrestrained plethysmography and forced oscillation for identifying genetic variability of airway responsiveness in inbred mice.

Lung function detection in mice is currently most accurately measured by invasive techniques, which are costly, labor intensive, and terminal. This limits their use for large-scale or longitudinal studies. Noninvasive assays are often used instead, but their accuracy for measuring lung function parameters such as resistance and elastance has been questioned in studies involving small numbers of mouse strains. Here we compared parameters detected by two different methods using 29 inbred mouse strains: enhanced pause (Penh), detected by unrestrained plethysmography, and central airway resistance and lung elastance, detected by a forced oscillation technique. We further tested whether the phenotypic variations were determined by the same genomic location in genome-wide association studies using a linear mixed model algorithm. Penh, resistance, and elastance were measured in nonexposed mice or mice exposed to saline and increasing doses of aerosolized methacholine. Because Penh differed from airway resistance in several strains and because the peak genetic associations found for Penh, resistance, or elastance were located at different genomic regions, we conclude that using Penh as an indicator for lung function changes in high-throughput genetic studies (i.e., genome-wide association studies or quantitative trait locus studies) measures something fundamentally different than airway resistance and lung elastance.

R/qtlbim: QTL with Bayesian Interval Mapping in experimental crosses.

UNLABELLED: R/qtlbim is an extensible, interactive environment for the Bayesian Interval Mapping of QTL, built on top of R/qtl (Broman et al., 2003), providing Bayesian analysis of multiple interacting quantitative trait loci (QTL) models for continuous, binary and ordinal traits in experimental crosses. It includes several efficient Markov chain Monte Carlo (MCMC) algorithms for evaluating the posterior of genetic architectures, i.e. the number and locations of QTL, their main and epistatic effects and gene-environment interactions. R/qtlbim provides extensive informative graphical and numerical summaries, and model selection and convergence diagnostics of the MCMC output, illustrated through the vignette, example and demo capabilities of R (R Development Core Team 2006). AVAILABILITY: The package is freely available from cran.r-project.org.

Novel method for high-throughput phenotyping of sleep in mice.

Assessment of sleep in mice currently requires initial implantation of chronic electrodes for assessment of electroencephalogram (EEG) and electromyogram (EMG) followed by time to recover from surgery. Hence, it is not ideal for high-throughput screening. To address this deficiency, a method of assessment of sleep and wakefulness in mice has been developed based on assessment of activity/inactivity either by digital video analysis or by breaking infrared beams in the mouse cage. It is based on the algorithm that any episode of continuous inactivity of > or =40 s is predicted to be sleep. The method gives excellent agreement in C57BL/6J male mice with simultaneous assessment of sleep by EEG/EMG recording. The average agreement over 8,640 10-s epochs in 24 h is 92% (n = 7 mice) with agreement in individual mice being 88-94%. Average EEG/EMG determined sleep per 2-h interval across the day was 59.4 min. The estimated mean difference (bias) per 2-h interval between inactivity-defined sleep and EEG/EMG-defined sleep was only 1.0 min (95% confidence interval for mean bias -0.06 to +2.6 min). The standard deviation of differences (precision) was 7.5 min per 2-h interval with 95% limits of agreement ranging from -13.7 to +15.7 min. Although bias significantly varied by time of day (P = 0.0007), the magnitude of time-of-day differences was not large (average bias during lights on and lights off was +5.0 and -3.0 min per 2-h interval, respectively). This method has applications in chemical mutagenesis and for studies of molecular changes in brain with sleep/wakefulness.

Multiple trait measurements in 43 inbred mouse strains capture the phenotypic diversity characteristic of human populations.

The breadth of genetic and phenotypic variation among inbred strains is often underappreciated because assessments include only a limited number of strains. Evaluation of a larger collection of inbred strains provides not only a greater understanding of this variation but collectively mimics much of the variation observed in human populations. We used a high-throughput phenotyping protocol to measure females and males of 43 inbred strains for body composition (weight, fat, lean tissue mass, and bone mineral density), plasma triglycerides, high-density lipoprotein and total cholesterol, glucose, insulin, and leptin levels while mice consumed a high-fat, high-cholesterol diet. Mice were fed a chow diet until they were 6-8 wk old and then fed the high-fat diet for an additional 18 wk. As expected, broad phenotypic diversity was observed among these strains. Significant variation between the sexes was also observed for most traits measured. Additionally, the response to the high-fat diet differed considerably among many strains. By the testing of such a large set of inbred strains for many traits, multiple phenotypes can be considered simultaneously and thereby aid in the selection of certain inbred strains as models for complex human diseases. These data are publicly available in the web-accessible Mouse Phenome Database (http://www.jax.org/phenome), an effort established to promote systematic characterization of biochemical and behavioral phenotypes of commonly used and genetically diverse inbred mouse strains. Data generated by this effort builds on the value of inbred mouse strains as a powerful tool for biomedical research.

Large-scale, high-throughput screening for coagulation and hematologic phenotypes in mice.

The Mouse Phenome Project is an international effort to systematically gather phenotypic data for a defined set of inbred mouse strains. For such large-scale projects the development of high-throughput screening protocols that allow multiple tests to be performed on a single mouse is essential. Here we report hematologic and coagulation data for more than 30 inbred strains. Complete blood counts were performed using an Advia 120 analyzer. For coagulation testing, we successfully adapted the Dade Behring BCS automated coagulation analyzer for use in mice by lowering sample and reagent volume requirements. Seven automated assay procedures were developed. Small sample volume requirements make it possible to perform multiple tests on a single animal without euthanasia, while reductions in reagent volume requirements reduce costs. The data show that considerable variation in many basic hematological and coagulation parameters exists among the inbred strains. These data, freely available on the World Wide Web, allow investigators to knowledgeably select the most appropriate strain(s) to meet their individual study designs and goals.

CGDSNPdb: a database resource for error-checked and imputed mouse SNPs.

The Center for Genome Dynamics Single Nucleotide Polymorphism Database (CGDSNPdb) is an open-source value-added database with more than nine million mouse single nucleotide polymorphisms (SNPs), drawn from multiple sources, with genotypes assigned to multiple inbred strains of laboratory mice. All SNPs are checked for accuracy and annotated for properties specific to the SNP as well as those implied by changes to overlapping protein-coding genes. CGDSNPdb serves as the primary interface to two unique data sets, the 'imputed genotype resource' in which a Hidden Markov Model was used to assess local haplotypes and the most probable base assignment at several million genomic loci in tens of strains of mice, and the Affymetrix Mouse Diversity Genotyping Array, a high density microarray with over 600,000 SNPs and over 900,000 invariant genomic probes. CGDSNPdb is accessible online through either a web-based query tool or a MySQL public login. Database URL: http://cgd.jax.org/cgdsnpdb/