Iuliana Ionita-Laza’s research lies at the interface of statistics and genetics, centered around developing and applying efficient statistical and computational methods for the analysis of high-dimensional omics data at different levels, such as genome, epigenome and transcriptome. Understanding the genetic causes of complex traits is the critical barrier in designing more efficient disease treatment and prevention strategies. These diseases collectively carry a tremendous public health burden, imparting a severe economic and social impact globally.
Dr. Ionita-Laza has developed new approaches to study design for genetic variant discovery, including capture-recapture methods, as well as spatial statistics techniques for identifying clusters of risk variants in the genome. She is also interested in developing integrative statistical methods for different types of data that can help make important scientific advances in complex disease with the overall goal to understand the precise genes underlying complex diseases.
Current Precision Prevention Work
Dr. Ionita-Laza and her colleagues are currently working on integrative statistical methods for accurate prediction of functional effects of genetic variants in noncoding genomic regions, and for designing more powerful association tests, including ongoing studies of autism. A better understanding of the biological mechanisms underlying complex diseases will likely have an important impact on precise treatment and prevention strategies, and public health as a whole.