Statistical methods for high-throughput genomics

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High-throughput genomics technologies (e.g. microarray and sequencing technology) enable genome-wide evaluation of genes expression and their regulatory mechanism. Such technologies have revolutionized the fields of biology and medicine in recent years, but there remains an urgent need for the development of efficient and rigorous statistical methods to analyze the resultant large and complex datasets. As the new sequencing data are inherently discrete, we first develop powerful multiple testing methods that are suitable for discrete data. A second problem arises when multiple sets of genes are tested for differential expression across conditions, which is a common practice in high-throughput genomic data analysis. Because gene sets can share genes, the test results should satisfy certain logical constraints. We will develop computationally efficient statistical methods for multiple testing of these logically constrained hypotheses. As sequencing technology emerges as the primary tool for investigating genome-wide regulation events, it becomes increasingly important to develop statistical methodology for error rate estimation and control for genomic event detection. Finally, as more and more high-throughput genomic data accumulate, we will develop methods for joint analysis of multiple datasets to improve the sensitivity and specificity of genomic discoveries.****The proposed research will have a significant impact on the analysis of high-throughput genomic data that leads to more powerful and better quality genomic regulatory element detection and more reproducible scientific discoveries. The applications of our research results can advance understanding of the genetic basis of many human diseases including cancer, rheumatic diseases, and developmental disorders. Furthermore, the statistical theory and methodology to be developed have an important impact on a wide range of scientific applications including statistical genetics (quantitative trait locus (QTL), linkage disequilibrium, proteomics, etc.) and engineering.**********