The statistical genetics program in the Department of Biostatistics started in early 1960s. With the current explosion of new genetic technology and data, the field of statistical genetics has become a major research area in the department. The department collaborates with the School of Medicine Center for Genomic Science in this research effort. The current methodologic efforts in the Department of Biostatistics are in the following areas: quantitative genetics, human genetics, molecular evolution and functional genetics. Quantitative Trait Loci (QTL) analysis involves developing theoretical statistical methodologies to detect and map QTLs and measure their effect sizes. Traditional parametric QTL analysis methods have been extended semi-parametrically and non-parametrically. QTL mapping has long been known as a multiple test problem. Stochastic processes are used to model the dependency among the test statistics and to analytically calculate power and thresholds. Other issues include multiple QTL analysis, epistasis and optimal designs. The human genetics studies include family studies where current methods for linkage and association studies are extended to relax underlying assumptions and to deal with multiple markers simultaneously. The human genetics research also includes design for genome-wide scans where a combination of the affected sib-pair approach and the transmission-disequilibrium approach is used to map diseases. The studies in molecular evolution include the following: quantification of genetic diversity; detecting linked mutations along a genome; extending current phylogenetic methods to allow the phylogenetic reconstruction and the identification of correlated mutations simultaneously; assessing the reliability in phylogenetic reconstruction via Monte-Carlo Markov-chain methods; transmission studies; modeling evolution by fitting a wide variety of nucleotide substitution models to different parts of the genome of a specific organism (here, HIV). The Functional Genetics projects include the following: development methods to analyze two-dimensional electrophoretic gel images with the goals of compressing the information, removing the background noise, and identifying protein patters; development methods to analyze data generated by affymetrix chips and spot hybridization, which include normalization, quality control, identification of sources of variation, reduction of dimensionality, clustering and classification.