The challenge of identifying meaningful diagnostic or prognostic genotypic biomarkers for many diseases is complicated by the range of phenotypes that are observed in the patient population. Such phenotypic variation is often captured through clinical records, but these are not commonly employed in the analysis of genomic data. To address this limitation, we have developed a Bayesian hierarchical B-spline approach to fit disease trajectory models for primates exposed to low doses of Mycobacterium tuberculosis (Mtb) based on their clinical profiles. Disease severity estimates derived from these fitted curves are employed to identify genes significantly associated with disease progression, increasing the value of information extracted from the expression profiles and contributing to the identification of predictive biomarkers for TB susceptibility. We also present a second application of our approach to the analysis of gene expression profiles associated with induced colitis in both wild type (WT) and genetically modified mice lacing the TNFR1 receptor. Through an integrated analysis of clinical trajectories, pathology data, and gene expression profiles, we show significant associations between the severity and duration of symptomatic illness and tumor development. Our results demonstrate that the incorporation of individual disease trajectory estimates enhances existing approaches for biomarker identification and offers the potential to provide insights into personalized treatment strategies for complex diseases.
Michelle Lacey is an interdisciplinary statistician with over 15 years of experience in collaborating with biomedical researchers. She received her A.B. in mathematics from Bryn Mawr College, and after working in industry for two years she returned to graduate school in 1996 in the Department of Statistics at Yale University and earned her Ph.D. in 2003. Dr. Lacey is currently appointed as Associate Professor of Mathematics and Adjunct Associate Professor of Biostatistics at Tulane University, and in addition to regularly teaching graduate courses in statistical modeling and data analysis for the School of Science and Engineering she is a contributing lecturer for courses at the Tulane University School of Medicine and the School of Public Health and Tropical Medicine. Dr. Lacey directs the Tulane Cancer Center Genomics Analysis Core to provide statistical support to researchers conducting high-throughput experiments, and she maintains an independent research program in the areas of epigenetics and the development of methods for the integrated analysis of biological data. She also serves as a consultant with the United Nations World Food Programme, providing guidance for the modeling and analysis of food security in vulnerable populations.
Tulane University, Department of Mathematics