Standardized experiments in mutant mice reveal behavioural similarity on 129S5 and C57BL/6J backgrounds.

Behavioural analysis of mice carrying engineered mutations is widely used to identify roles of specific genes in components of the mammalian behavioural repertoire. The reproducibility and robustness of phenotypic measures has become a concern that undermines the use of mouse genetic models for translational studies. Contributing factors include low individual study power, non-standardized behavioural testing, failure to address confounds and differences in genetic background of mutant mice. We have examined the importance of these factors using a statistically robust approach applied to behavioural data obtained from three mouse mutations on 129S5 and C57BL/6J backgrounds generated in a standardized battery of five behavioural assays. The largest confounding effect was sampling variation, which partially masked the genetic background effect. Our observations suggest that strong interaction of mutation with genetic background in mice in innate and learned behaviours is not necessarily to be expected. We found composite measures of innate and learned behaviour were similarly impacted by mutations across backgrounds. We determined that, for frequently used group sizes, a single retest of a significant result conforming to the commonly used P < 0.05 threshold results in a reproducibility of 60% between identical experiments. Reproducibility was reduced in the presence of strain differences. We also identified a P-value threshold that maximized reproducibility of mutant phenotypes across strains. This study illustrates the value of standardized approaches for quantitative assessment of behavioural phenotypes and highlights approaches that may improve the translational value of mouse behavioural studies.

Human cognitive ability is influenced by genetic variation in components of postsynaptic signalling complexes assembled by NMDA receptors and MAGUK proteins.

Differences in general cognitive ability (intelligence) account for approximately half of the variation in any large battery of cognitive tests and are predictive of important life events including health. Genome-wide analyses of common single-nucleotide polymorphisms indicate that they jointly tag between a quarter and a half of the variance in intelligence. However, no single polymorphism has been reliably associated with variation in intelligence. It remains possible that these many small effects might be aggregated in networks of functionally linked genes. Here, we tested a network of 1461 genes in the postsynaptic density and associated complexes for an enriched association with intelligence. These were ascertained in 3511 individuals (the Cognitive Ageing Genetics in England and Scotland (CAGES) consortium) phenotyped for general cognitive ability, fluid cognitive ability, crystallised cognitive ability, memory and speed of processing. By analysing the results of a genome wide association study (GWAS) using Gene Set Enrichment Analysis, a significant enrichment was found for fluid cognitive ability for the proteins found in the complexes of N-methyl-D-aspartate receptor complex; P=0.002. Replication was sought in two additional cohorts (N=670 and 2062). A meta-analytic P-value of 0.003 was found when these were combined with the CAGES consortium. The results suggest that genetic variation in the macromolecular machines formed by membrane-associated guanylate kinase (MAGUK) scaffold proteins and their interaction partners contributes to variation in intelligence.

Deriving structural and functional insights from a ligand-based hierarchical classification of G protein-coupled receptors.

G protein-coupled receptors (GPCRs) constitute the largest known family of cell-surface receptors. With hundreds of members populating the rhodopsin-like GPCR superfamily and many more awaiting discovery in the human genome, they are of interest to the pharmaceutical industry because of the opportunities they afford for yielding potentially lucrative drug targets. Typical sequence analysis strategies for identifying novel GPCRs tend to involve similarity searches using standard primary database search tools. This will reveal the most similar sequence, generally without offering any insight into its family or superfamily relationships. Conversely, searches of most 'pattern' or family databases are likely to identify the superfamily, but not the closest matching subtype. Here we describe a diagnostic resource that allows identification of GPCRs in a hierarchical fashion, based principally upon their ligand preference. This resource forms part of the PRINTS database, which now houses approximately 250 GPCR-specific fingerprints (http://www.bioinf.man.ac.uk/dbbrowser/gpcrPRINTS/). This collection of fingerprints is able to provide more sensitive diagnostic opportunities than have been realized by related approaches and is currently the only diagnostic tool for assigning GPCR subtypes. Mapping such fingerprints on to three-dimensional GPCR models offers powerful insights into the structural and functional determinants of subtype specificity.