Public Health Risk Assessment: Validation of risk assessment matrix limitations and an analytical approach to gene set reduction for continuous phenotype in microarray studies

  • Author / Creator
  • Although risk is a core element of public health practice, its definition varies greatly among various public health programs. Several methods have been developed for risk assessment and management in different contexts of public health to better understand disease progression and outcome development. My dissertation consists of two rather different approaches to risk assessment: the first part deals with a flaw in the current public health risk assessment via risk matrices, the second part addresses a methodological gap in the analysis of data measured by DNA microarray technology. We first evaluated the risk assessment matrix which is a semi-quantitative tool for assessing risks, and setting priorities in risk management. Although the method can be useful in promoting discussion to distinguish high risks from low risks, a published critique described a problem when the frequency and severity of risks are negatively correlated. A theoretical analysis showed that risk predictions could be misleading. We explored this predicted problem by constructing a risk assessment matrix using a public health risk scenario, tainted blood transfusion infection risk that provides negative correlation between harm frequency and severity. We estimated the risk from the experiential data and compared these estimates with those provided by the risk assessment matrix. We concluded that the risk assessment matrix should not be abandoned, but users must address the source of problem in applying the matrix to inform decision makers. We then focused on DNA microarray studies which open a new platform with an opportunity to study and compare thousands of genes at the same time, leading to early and more accurate disease risk assessment, diagnosis, as well as improved tailored treatment. Advances in DNA microarray technology have stimulated methodological research on data analysis in biomedical studies. Using microarray data analysis, researchers are able to assess the association of a priori defined gene sets sharing a common biological theme (pathways) with an outcome of interest (phenotype) and gain insights into biological functions of genes and pathways influencing disease mechanisms. Gene set analysis (GSA) is a popular approach to examine the association between a predefined gene set and a phenotype. Few GSA methods have been developed for continuous phenotypes. However, often not all the genes within a significant gene set contribute to its significance. While a few methods have been developed to extract core genes from gene sets in the case of binary phenotypes studies, such as diseased versus disease-free subjects, no attention has been paid to studies measuring a continuous phenotype. We developed a computationally efficient gene set reduction method to identify core subsets of gene sets associated with a continuous phenotype. Identifying the core subset enhances our understanding of the biological mechanism and reduces costs of disease risk assessment, diagnosis and treatment. To evaluate the performance of the method, we applied our method to two real microarray data sets. First, we examined the association between pathway expressions and tumor volume in a cohort of lethal prostate cancer patients from Swedish Watchful Waiting cohort, and extracted main genes from significant pathways. Second, we assessed whether there is an association between pathways expression in newborns’ blood and their birth weight in Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE) study, and reduced the significant pathways to their core subsets.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Doctor of Philosophy
  • DOI
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Public Health Sciences
  • Specialization
    • Public Health
  • Supervisor / co-supervisor and their department(s)
    • Dinu, Irina (Department of Public Health Sciences)
    • Hrudey, Steve (Faculty of Medicine & Dentistry)
  • Examining committee members and their departments
    • Pyne, Saumyadipta (Statistics and Computer Science)
    • Kong, Linglong (Mathematical and Statistical Sciences)
    • Hrudey, Steve (Medicine & Dentistry)
    • Kozyrskyj, Anita (Pediatrics)
    • Dang, Sanjeena (Mathematics and Statistics)
    • Dinu, Irina (Public Health Sciences)