Abstract Body: Introduction
Heart disease continues to be a leading cause of death and disability worldwide. A multitude of proteins that mechanically link the cytoskeleton, sarcomeres, and the intercalated discs are key structural components that contribute to disease risk. However, how these subcellular complexes are functionally interdependent is unclear. Despite technological advances, identifying the genetic basis driving variation of heart structure and function remains a challenge.
Hypothesis
We hypothesize that genetic variation relevant to cardiac disease risk will manifest as anatomical and functional changes of the heart. Our goal was to identify loci contributing to phenotypic variation in clinical heart imaging traits from a Hispanic population using a novel family-based genetic mapping approach.
Methods
We conducted genome-wide covariance-kernel based scans, under a variance component framework, of 19 cardiac imaging traits from 210 participants of the San Antonio Family Heart Study cohort. Rare, common, de novo, and all-variant kernels were evaluated (n = 2733 of each), accounting for sex and age, at a mapping resolution of 1 Mbp.
Results
Genome-wide significant associations were observed for 17 kernels and 5 cardiac imaging traits (α = 1.83 × 10^-5). These kernels mapped to 5 loci and exposed the contribution from different types of variants to trait variability. Strikingly, multiple traits were significantly associated with a kernel in the same loci which may indicate pleiotropy, shared biological pathways, or closely linked causal variants. The highest association found was between an all-variant kernel at 15:30.5 Mbp (p = 6.60 × 10^-7) and the systolic left-ventricular mass normalized by body shape area. All-variant and rare kernels, at 18:3.5Mbp (p = 2.66 × 10^-6) and 3:61.5Mbp (p = 8.55 × 10^-6) respectively, were also significantly associated with this trait. These kernel signals led to the identification of 4 candidate genes known to be involved in muscle pathophysiology: TJP1(ZO-1) at 15:30.1Mbp, MYOM1 at 18:3.2Mbp, MYL12A at 18:3.2Mbp and PTPRG at 3:61.6Mbp.
Conclusions
We identified loci that influence traits closely related to heart structure and function. Future work will identify the variants driving these kernel signals, and their functional significance will be established in cell-based assays.