Grant: Discovery Grants Program - Individual

Longitudinal studies are research studies that involve repeated observations of the same factors over a period of time. As observational studies, they are applied in health science to uncover predictors of certain diseases. In longitudinal association studies, long-term outcomes are determined by multiple genetic and environmental factors collected over time that interact with one another. The existing studies examine such factors under the assumption that they are measured accurately. However, unobserved or error-prone environmental factors and/or misclassification in genotyping are unavoidable. In reality, both genetics and environmental factors are likely measured with errors. It is now well known that these errors can influence the results of the study. The impact of ignoring the errors varies from bias and large variability in estimators to low power or even false-negative results in detecting associations. In fact, in the presence of measurement error and misclassification, detecting the interaction term is more challenging than either the genetic or the environmental factors. In order to improve the accuracy and precision in the assessment of both genetic and environmental factors, one needs to take into account these errors. The main objective of this proposal is to develop appropriate statistical techniques in longitudinal association studies when the genetic and environmental factors are subject to errors. More specifically, we aim to develop novel measurement error techniques that will improve the identification of genetic variants, environmental factors, and their interactions associated with adulthood diseases.