Abstract 439: Trajectory Analysis of Left Ventricular Dimensions From Biobank Data Uncovers Novel Genetic Associations

Abstract

Background: Approximately 6 million adults in the United States have heart failure. The progression of heart failure is variable arising from differences in sex, age, genetic background including ancestry, and medication response. Many population-based genetic studies of heart failure have been cross-sectional in nature, failing to gain additional power from longitudinal analyses. As heart failure is known to change over time, using longitudinal data trajectories as a quantitative trait will increase power in genome wide association studies (GWAS). Methods: We used the electronic health record in a racially and ethnically diverse medical biobank from a single, metropolitan US center. We used whole genome data from 896 unrelated participants analyzed, including 494 who had at least 1 electrocardiogram and 324 who had more than 1 echocardiogram (average of 3 observations per person). A mixture model based semiparametric latent growth curve model was used to cluster outcome measures used for genome-wide analyses. Results: GWAS on the trajectory probability of QTc interval identified significant associations with variants in regulatory regions proximal to the WLS gene, which encodes Wntless, a Wnt ligand secretion mediator. WLS was previously associated with QTc and myocardial infarction, thus confirming the power of the method. GWAS on the trajectory probability of left ventricular diameter (LVIDd) identified significant associations with variants in regulatory regions near MYO10 , which encodes unconventional Myosin-10. MYO10 was previously associated with obesity and metabolic syndrome. Conclusions: This is the first study to show an association with variants in or near MYO10 and left ventricular dimension changes over time. Further, we found that using trajectory probabilities can provide increased power to find novel associations with longitudinal data. This reduces the need for larger cohorts, and increases yield from smaller, well-phenotyped cohorts, such as those found in biobanks. This approach should be useful in the study of rare diseases and underrepresented populations.