Statistical Genetics, Epigenetics, and Bioinformatics

Statistical genetics is a scientific field concerned with the development and application of advanced modern statistical methods for understanding the genetic basis of human/animal diseases and traits. It overlaps with a variety of fields such as biology, epidemiology, bioinformatics, genetics, etc. Epigenetics, including DNA methylation, is the study of how human/animal’s behaviors and/or environmental exposures can affect how the genes are expressed. Unlike genetic changes, epigenetic and gene expression changes are reversible and more responsive to environmental exposures, e.g., smoking, aging, etc.

An interdisciplinary field for biological studies, bioinformatics focuses on computer programming as a major part of its methodology for genomics to assist the identification of candidates’ genes and single nucleotide polymorphisms.

Faculty and students at UNMC Department of Biostatistics are working to develop new statistical designs, models, as well as computational methods, to analyze genetic, epigenetic and gene expression data on candidate genes or the whole genome including genome-wide association studies, targeted/ whole genome sequencing studies, and expression studies. 

Our Research:

• Herath, S., Dai, H., Au, A., Taylor, K., Succar, L., Endre, Z., and Erlich J. (2020). Validation of housekeeper genes for normalisation for RT-qPCR gene expression studies in ischaemic and toxicological conditions in rat models. PLOS ONE. 
• Chen, S., Refaey, H., Mukherjee, N., Solatikia, F., Jiang, Y., Arshad, H., Ewart, S., Holloway, J., Zhang, H., and Karmaus, W. (2020). Age at onset of different pubertal signs in boys and girls and deferential DNA methylation at age 10 and 18 years: an epigenome-wide follow-up study. Human Reproduction Open. Doi: 10.1093/hropen/hoaa006.
• Chen, S., Mukherjee, N., Janjanam, V., Arshad, H., Holloway, J., and Karmaus, W. (2017). Consistency and variability of DNA methylation in women during puberty, young adulthood and pregnancy. Genetics & Epigenetics. 9:1-9.
• Dai, , Wu, G., Wu, M., and Zhi, D. (2016) An Optimal Bahadur-efficient Method in Detection of Sparse Signals with Applications to Pathway Analysis in Sequencing Association Studies. PLOS ONE. 
• Yu, F., Chen, MH., Kuo, L., Tabbot. H., and Davis J (2015) Confident Difference Criterion: A new Bayesian differentially expressed gene selection algorithm, BMC Bioinformatics, 16: 245.
• Dai, , Charnigo, R., Vyhlidal, C., Jones, B., and Bhandary M. (2013). Mixed Modeling and Sample Size Calculations for Identifying Housekeeping Genes. Statistics in Medicine. 32(18): 3115-3125.
• Yu F., Chen MH., Kuo, L., Peng, H., and Yang W. (2011), Bayesian Hierarchical Modeling and Selection of Differential Expression Genes for the EST Data, Biometrics. 67(1):142-150.
• Dai, and Charnigo R. (2010). Contaminated Normal Modeling with Application in Microarray Experiments. Canadian Journal of Statistics. 38(2): 315-332.
• Yu F., Chen MH., and Kuo L. (2008). Detecting Differentially Expressed Genes Using Calibrated Bayes Factors, Statistica Sinica. 18: 783-802.