Biostatistics and Data Science

RESEARCH INTEREST

Statistical Genetics and Genetic Epidemiology; High-throughput Data Analysis; Computational genomics; Bioinformatics and Biostatistics; Human Population and Evolutionary Genetics .

Dr. Xu received his B.S. in biophysics at Fudan University, Shanghai, China. Then he moved on to genetic research for his M.S. in genetics. During this period, he participated in many joint research projects between the Institute of Genetics, Fudan University and Chinese National Human Genome Center at Shanghai, where he received training in human genome research from data generation to data analysis. Upon graduation, he was admitted to the Human and Molecular Genetics program at the University of Texas-Graduate School of Biomedical Sciences at Houston as a Ph.D. student and worked as a research assistant in the Human Genetics Center, UT-Health Science Center at Houston, where he received systematic trainings in human population and statistical genetics. He did his postdoctoral training at the Computational Genetics Section, Department of Epidemiology, the University of Texas M. D. Anderson Cancer Center. The major focus of his postdoctoral training is statistical genetics and genetic epidemiology. His postdoctoral training was supported by donor-sponsored Dauphin Postdoctoral Fellowship in Cancer Prevention.

The long-term research goals of Dr. Xu's laboratory are to characterize patterns of genetic variation from high-throughput genomic data and to use this information to address fundamental problems in the context of human complex diseases and evolution. To this end, the research projects are pursued in two broad and interrelated areas: 1) human population and evolutionary genomics and 2) elucidating the genetic architecture of complex diseases of public health significance, such as cardiovascular disease, type 2 diabetes, various cancers and mental diseases, and studying functional genomics. Theoretical, statistical, and computational tools are being developed, including linkage & linkage disequilibrium based approaches and admixture mapping of complex diseases.

Here are some recent software by Dr. Xu's group.

• haplotype analysis
• GIFT: for differentially methylated regions

TEACHING AREAS

Biological Sequence Analysis, Statistical Genetics, High-throughput Data Analysis, Computational Statistics

SELECTED PUBLICATIONS

• Mathew G, George V, Xu H (2014): Comparison of Several Sequence-based Association Methods in Pedigrees. BMC Proceedings 8:S48.
• Tan YD, Xu H (2014): A General Method for Accurate Estimation of False Discovery Rates in Identification of Differentially Expressed Genes. Bioinformatics In press, PMID: 24632499.
• Ryu D, Xu H, George V, Su S, Wang X, Podolsky R (2013): Quantifying and Normalizing Methylation Levels in Illumina Arrays. Journal of Biometrics & Biostatistics4:164.
• Xu H, Podolsky R, Ryu D, Wang X, Su S, Shi H, George V (2013): A Method to Detect Differentially Methylated Loci With Next Generation Sequencing. Genetic Epidemiology 37:377-382.
• Das I, Mukhopadhyay S, Xu H (2013): Individualized Dosing for Multiple Ordered Groups of Patients. Journal of Statistical Theory and Practice 7:95-106.
• Jin B, Ernst J, Tiedemann RL, Xu H, Kellis M, Dalton S, Liu C, Choi JH, Robertson KD. (2012): Linking DNAmethyltransferases to epigenetic marks and nucleosome structure genome-wide in human tumor cells. Cell Reports 2:1411-1424.
• Nandram B, Xu H (2011): Bayesian Corrections of a Selection Bias in Genetics. Journal of Biometrics & Biostatistics 2:112.
• Xu H, George V (2011): A Monte Carlo test of linkage disequilibrium for single nucleotide polymorphisms. BMC Research Notes 4:124.
• Nandram B, Choi JW, Xu H (2011): Maximum likelihood estimation for ascertainment bias in sampling siblings. Journal of Data Science 9:23-41.
• Mukhopadhyay S, George V, Xu H (2010): Variable selection method for quantitative trait analysis based on parallel genetic algorithm. Annals of Human Genetics74:88-96.