9707395 Muse With the advent of fast, inexpensive DNA sequence data, the field of molecular evolution has blossomed, finding diverse applications in topics ranging from ecology to drug design. To this point, however, most analyses have been somewhat one-dimensional; while several genes are often used in a study, the DNA sequence data for each locus are usually analyzed independently. Such a "single locus" approach can be of limited utility. Many interesting phenomena are expected to leave their signature effects across multiple loci in systematic and sometimes predictable manners. In addition, we know that genes evolve not as isolated entities, but as arrays of interacting loci that give rise to form and function. These facts demand that inferences incorporate data from all relevant loci. The central theme of the proposed research is to develop and evaluate statistical methods for performing molecular evolutionary analyses on multiple genetic loci. These procedures will help to identify mechanisms, both biological and demographic, that affect evolutionary processes at the DNA sequence level. The potential applications of the methods span many areas of biological study. For instance, these procedures will provide ways for testing whether or not short-lived organisms evolve more quickly than long-lived ones. The methods can also be used to look for evolutionary responses at the genetic level of a host organism to a genetic change in a parasite. Another important area of application involves the identification of functionally important regions of genes.
