The overall mean response to montelukast may be skewed towards a response phenotype by a small subset (<15%) of asthma patients. CYSLTR2 and ALOX5 polymorphisms may predispose a minority of individuals to excessive cysteinyl-leukotriene concentrations, yielding a distinct asthma phenotype most likely to respond to leukotriene modifier pharmacotherapy. These findings require replication to establish validity and clinical utility.

BackgroundThe complex trait of prepulse inhibition (PPI) is a sensory gating measure related to schizophrenia and can be measured in mice. Large-scale public repositories of inbred mouse strain genotypes and phenotypes such as PPI can be used to detect Quantitative Trait Loci (QTLs) in silico. However, the method has been criticized for issues including insufficient number of strains, not controlling for false discoveries, the complex haplotype structure of inbred mice, and failing to account for genotypic and phenotypic subgroups.Methodology/Principal FindingsWe have implemented a method that addresses these issues by incorporating phylogenetic analyses, multilevel regression with mixed effects, and false discovery rate (FDR) control. A genome-wide scan for PPI was conducted using over 17,000 single nucleotide polymorphisms (SNPs) in 37 strains phenotyped. Eighty-nine SNPs were significant at a false discovery rate (FDR) of 5%. After accounting for long-range linkage disequilibrium, we found 3 independent QTLs located on murine chromosomes 1 and 13. One of the PPI positives corresponds to a region of human chromosome 6p which includes DTNBP1, a gene implicated in schizophrenia. Another region includes the gene Tsn which alters PPI when knocked out. These genes also appear to have correlated expression with PPI.Conclusions/SignificanceThese results support the usefulness of using an improved in silico mapping method to identify QTLs for complex traits such as PPI which can be then be used for to help identify loci influencing schizophrenia in humans.

The presence of subgroups of patients that do not respond to the drug was an important reason for nonresponse. Additional analyses using finite mixture models in pharmacogenetic studies may provide insight into drug nonresponse and a better discrimination between true and false discoveries.

The techniques currently available for studying drug self-administration in animals offer the unique opportunity to carry out micro-analysis of initial episodes of drug use which are extremely difficult to obtain for human subjects. Nonetheless, traditional self-administration techniques do not allow a cost-effective output of large sample sizes needed for genetic analysis. Additionally, the statistical techniques that allow the integration of within-subject temporal data with genetic information are scant. We therefore propose a two-stage method for analyzing strain differences in dynamic phenotypes for a high-throughput version of the self-administration procedure. On a first phenotype-refinement stage, a change-point algorithm (Gallistel et al. (2004) Proc. Natl Acad. Sci. USA 101:13124-13131) was used to separate individual drug self-administration response curves into three distinct components. In a second stage, strains differences in these indexes were assessed. This two-stage approach is illustrated with drug self-administration data and through a computer simulation.

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