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. Therefore, research 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 has immense potential for addressing these burdens.
Additional integration of “omics” data such as genomics, epigenetics, transcriptomics, and microbiomics can help to identify patterns, allowing scientists to predict outcomes and to understand disease mechanisms.
Experts at Columbia are using integrative statistical methods to design more powerful association tests, including ongoing studies of autism. They are also using comprehensive patient electronic health records to integrate, capture and quantify similarities between pairs of patients according to their comprehensive information. Similarity-based case identification can help stratify patients and lead to more precise diagnosis and more effective treatment choices.
Learn more about the Department of Biostatistics, Genomics@Columbia, Dr. Iuliana Ionita-Laza’s research on statistical and computational methods for the analysis of high-dimensional omics data, Dr. Shuang Wang’s Laboratory of Computational Methods, and Dr. Mary Beth Terry’s work on cancer genomics.