Welcome to Dr. Rodriguez-Zas's Laboratory of Statistical genetics and bioinformatics at the University of Illinois.

Curriculum Vitae 2008 (PDF)

My research program is a continuous quest to understand the genetic architecture of health, social behavior, agricultural and other complex characteristics through statistical genomics and bioinformatics. This quest is directed by a methodical process of evaluating complementary approaches and identifying parsimonious models to study complex characteristics in humans, laboratory animals, insects, livestock and plants including pain, social behavior, neuropeptides and livestock productions traits such as milk production and meat quality. I provide approaches suitable to present scenarios such as analysis of gene expression and proteomic experiments. and anticipate future needs including meta-analysis of gene expression and proteomic data and gene networks. I created Beehive a free integrated system for management, analysis and inrepretation of microarray data that removes technical barriers so that experimenters can easily perfrom complex analysis.

Outcomes of my research help to discriminate nature from nurture and enhancing the opportunities to cure and prevent diseases, and to understand complex biological systems.

My specific research goals are:

  1. to develop innovative statistical genomic approaches to understand of the genetic basis of multifactorial phenotypes
  2. to optimize available approaches statistical genomics and bioinformatics
  3. to foster a research environment where the bright and motivated students choose to learn and work
  4. to communicate the research outcomes in a timely fashion to scientists and the community
  5. to collaborate in innovative research

Statistical genomics: Statistical methods for analyzing genomic data that integrate molecular markers, genetic linkage maps, gene expression profiles, protein expression, pedigrees and complex phenotypic traits.

Bioinformatics: Development and application of information and computer sciences to enhance the understanding and utility of the vast, diverse, and complex life sciences data. Includes tools to acquire, store, organize, archive, analyze, and visualize the data.