Abstract
Due to the relatively recent explosion of interest in the field of bioinformatics, many software tools have appeared which attempt to manage and evaluate the overwhelming public data stream. Many of the tools are created and then abandoned due to the difficulty of integrating them with current research practices. Other tools are often misused due to misunderstanding of their design principles and inadequate description of the applications usage. In order to remedy these problems and create applications which will be equally valuable to the research public as to their creators, Discovery Driven Design is proposed and applied to show how application development and data dispersal can be aided through the modeling of research oriented questions.

USE OF THE EXPECTED LOG LIKELIHOOD TO EVALUATE DESIGNS FOR MAPPING QUANTITATIVE TRAIT LOCI

Tammy J. Benson B.S. Animal Science, Truman State University M.S. Genetics, Iowa State University Home Dept: Statistics Major Professor: Dr. Alicia Carriquiry Iowa State University

Friday, February 8, 2002 1:10 p.m. 1420 Molecular Biology Building

Abstract
Many traits of economic importance are determined by quantitative trait loci(QTL). QTL can be detected by their linkage to marker loci. Many different experimental designs have been proposed for detecting QTL. The relative merits of these designs can be evaluated by their power to detect a QTL segregating in a population. The test statistic most commonly used for detection of QTL is the log likelihood ratio. It has been suggested that the expected log likelihood ratio may be a good indicator of the power of a particular experimental design. In this study, two methods were used to calculate this expected value and their usefulness evaluated by simulation. The first approach approximated the expected value of the log likelihood assuming y is distributed as a mixture of multivariate normal distributions. The second approach calculated the exact log likelihood by approximating the distribution of y with a single multivariate normal distribution that has the same mean and variance-covariance structure as the mixture distribution. The utility of these approximations were studied by computing the power and the mean of the log likelihood ratio for several designs by computer simulation and relating these results to the expected value of the log likelihood for both approaches. The expected value of the log likelihood calculated using the first approach had a poor relationship with power, but the expected value of the log likelihood ratio calculated using the second approach had a strong correlation with the mean of the log likelihood ratio. Because the mean of the log likelihood ratio did not have a strong correlation with the power, this mean may not be a good indicator of the power of a design. Therefore, any approximation of the mean of the log likelihood ratio also may not be a good indicator of the power.