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Most, if not all, human phenotypes exhibit a temporal, dosage-dependent, or age effect. In this work, I explore and showcase the use different analytical methods for assessing the genetic contribution to traits with temporal trends, or what I refer to as 'dynamic complex traits' (DCTs). The study of DCTs could offer insights into disease pathogenesis that are not achievable in other research settings. I describe the development and application of a method of DCT analysis termed ̀Curve- Based Multivariate Distance Matrix Regression' (CMDMR) using data from a structured longitudinal clinical study to demonstrate the approach in genetic association analysis (Chapter 2). The method was found to perform as well as or better than traditional statistical methods that might be applied to DCTs. I also applied the CMDMR method in conducting a genome wide association (GWA) study of height that essentially exploits dissimilarity among the longitudinal height profiles of individuals with different genotypes (Chapter 3). This framework is applied to height growth data from the Bogalusa Heart Study. I identified 7 novel variants in 6 loci (FAM19A1, FGF20, SCD5, MAP3K7, GLCCI1 and TJP2) associated with height profiles using parametric curves (all p-values