DF/HCC Program Affiliation

Biostatistics

Research Abstract

I am a biostatistician with diverse interests, including molecular epidemiology, biomarker discovery, and environmental exposure assessment. Much of my statistical research involves latent variable methods, model-based clustering, and high-dimensional data analysis. Recent work has focused on computationally efficient methods for epigenomics research.

Many current projects involve statistical methods for epigenetics, especially in a genome-wide context. Epigenetics is the study of heritable changes in gene function not involving changes in DNA sequence, the best examples of which are DNA methylation and histone modification. Because these processes help to regulate gene expression, but also may be affected by the expression of certain genes, miRNAs, and other molecular factors, their genome-wide study necessarily requires methods for integrative genomics, and thus provides a rich opportunity for challenging statistical work. In addition, because environmental factors can influence epigenetic processes, epigenetics may play a central role in gene-environment interactions, and thus provides additional interesting opportunities for epidemiologic research.

I have extensive experience analyzing array data generated by the GoldenGate DNA methylation panel (Illumina, San Diego, CA). I also have experience with copy number data from the Affymetrix 500K array and various a-CGH platforms. An active interest is the analysis of associations between DNA methylation and copy number variation, using pathway analysis software such as Ingenuity (Redwood City, CA).

Publications

No representative citations available.