A wealth of genotypic and phenotypic information about inbred strains of laboratory mice is being collected and assembled in large databases. Sophisticated mining of this information can be useful in generation of hypotheses regarding the sources and nature of phenotypic variability, both environmental and genetic. As genotypic databases become complete, computational methods for identification of the genetic loci associated with complex polygenic traits may be possible. The common genetic origin of the inbred strains, and the genetic similarity of members of these strains make possible these approaches to the genetic study of pain and other complex phenotypes. In the first study, the relative role of laboratory environmental factors and genetic factors in pain related phenotypes are explored in a large data archive containing over 8000 observations of a single pain related phenotype. Classification and Regression Tree Analysis revealed that the experimenter was a more important factor than genotype and that other laboratory factors also influence studies of pain. Linear modeling allowed parametric estimation of some of the effects, and results of the CART analysis were confirmed in a balanced prospective experiment. In the second study, the possibility of detecting genetic loci contributing to trait variability through the use of databased genetic information and inbred strain phenotype studies is evaluated. Two algorithms are considered, and compared to results from more commonly employed experimental crosses. Statistical power issues and methods of controlling error-rates are evaluated for each method. The use of permutation analysis for the empirical derivation of significance thresholds may enhance the performance of inbred strain based mapping, potentially making this theoretically interesting method viable for use in practice.
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