With advances in DNA sequencing, genotyping, gene annotation,expression profiling, and other high throughput measurement technologies, viable scientific questions are widening in scope and often involve data collected from numerous sources. Statisticians working at the interface of statistics and biology are consequently faced with the challenge of developing analytic methods for investigations involving numerous types of data. 1 important example concerns studies to identify the genetic basis of gene expression (eQTL mapping studies), where relevant data consists of expression measurements, genetic maps, information on co-regulation of transcripts, and on transcription factors and their binding sites. Another collection of studies addresses genetic regulation of physiological systems. The data includes time course expression measurements, physiological variables, and information on co-regulation of transcripts. Expression QTL mapping and time course microarray studies provide fundamental insight into both normal and diseased biological systems. They are being used to identify regulatory genes, to gain insight into the mechanisms of regulation, and to determine ways in which regulation can be modified to promote or maintain a beneficial response. The full extent of biomedical information in these studies cannot be realized without effective statistical methods. The goal of this proposal is to develop, evaluate, and disseminate statistical methods to address questions from these 2 types of studies. This goal will be accomplished through 3 specific aims: the development of methods to identify mapping transcripts and the genomic locations to which they map; the extension of these methods to the cases of both sparse maps and dense maps; and the development of methods to identify temporal paths of expression and affecting covariates. Successful completion of the proposed research will result in substantially improved statistical methods for these 2 important categories of genomic studies. ? ? ?
| Dawson, John A; Ye, Shuyun; Kendziorski, Christina (2012) R/EBcoexpress: an empirical Bayesian framework for discovering differential co-expression. Bioinformatics 28:1939-40 |
| Newton, Michael A; He, Qiuling; Kendziorski, Christina (2012) A model-based analysis to infer the functional content of a gene list. Stat Appl Genet Mol Biol 11: |
| Ruan, Lingyan; Yuan, Ming; Zou, Hui (2011) Regularized parameter estimation in high-dimensional gaussian mixture models. Neural Comput 23:1605-22 |
| Ruan, Lingyan; Yuan, Ming (2011) An empirical Bayes' approach to joint analysis of multiple microarray gene expression studies. Biometrics 67:1617-26 |
| Andrei, Alina; Kendziorski, Christina (2009) An efficient method for identifying statistical interactors in gene association networks. Biostatistics 10:706-18 |
| Chen, Meng; Kendziorski, Christina (2007) A statistical framework for expression quantitative trait loci mapping. Genetics 177:761-71 |
