Larson, Martin G.

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Moritz F Sinner, 2012 Finalist and Presenting Author

Abstract In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. We identified metabolites associated with higher or lower risk of HF incidence, the associations that were not confounded by the other metabolites, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. We revealed the underlying relationships of the findings. For example, asparagine directly influenced glycine, and both were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids which are not synthesized in the human body and come directly from the diet. Metabolites may play a critical role in linking genetic background and lifestyle factors to HF progression. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates a mechanistic understanding of HF progression.

Background Tools for the prediction of atrial fibrillation ( AF ) may identify high‐risk individuals more likely to benefit from preventive interventions and serve as a benchmark to test novel putative risk factors. Methods and Results Individual‐level data from 3 large cohorts in the U nited S tates (Atherosclerosis Risk in Communities [ ARIC ] study, the Cardiovascular Health Study [ CHS ], and the Framingham Heart Study [ FHS ]), including 18 556 men and women aged 46 to 94 years (19% A frican A mericans, 81% whites) were pooled to derive predictive models for AF using clinical variables. Validation of the derived models was performed in 7672 participants from the Age, Gene and Environment—Reykjavik study ( AGES ) and the Rotterdam Study ( RS ). The analysis included 1186 incident AF cases in the derivation cohorts and 585 in the validation cohorts. A simple 5‐year predictive model including the variables age, race, height, weight, systolic and diastolic blood pressure, current smoking, use of antihypertensive medication, diabetes, and history of myocardial infarction and heart failure had good discrimination (C‐statistic, 0.765; 95% CI , 0.748 to 0.781). Addition of variables from the electrocardiogram did not improve the overall model discrimination (C‐statistic, 0.767; 95% CI , 0.750 to 0.783; categorical net reclassification improvement, −0.0032; 95% CI , −0.0178 to 0.0113). In the validation cohorts, discrimination was acceptable ( AGES C‐statistic, 0.664; 95% CI , 0.632 to 0.697 and RS C‐statistic, 0.705; 95% CI , 0.664 to 0.747) and calibration was adequate. Conclusion A risk model including variables readily available in primary care settings adequately predicted AF in diverse populations from the U nited S tates and E urope.

Background— Data regarding the familial aggregation of left ventricular (LV) geometry and its relations to parental heart failure (HF) are limited. Methods and Results— We evaluated concordance of LV geometry within 1093 nuclear families in 5758 participants of the original (parents) (n=2351) and offspring (n=3407) cohorts of the Framingham Heart Study undergoing routine echocardiography in mid- to late adulthood. LV geometry was categorized based on cohort- and sex-specific 80th percentile cutoffs of LV mass and relative wall thickness (RWT) into normal (both <80th percentile), concentric remodeling (LV mass <80th percentile; RWT >80th percentile), concentric hypertrophy (both >80th percentile), and eccentric hypertrophy (LV mass >80th percentile; RWT <80th percentile). Within nuclear families, LV geometry was concordant among related pairs (parent-child, sibling-sibling) ( P =0.0015) but not among unrelated spousal pairs ( P =0.60), a finding that remained unchanged after adjusting for clinical covariates known to influence LV remodeling (age, systolic blood pressure, body mass index), excluding individuals with prevalent HF and myocardial infarction, and varying the thresholds for defining LV geometry. The prevalence of abnormal LV geometry was higher in family members of affected individuals, with recurrence risks of 1.4 for concentric remodeling (95% CI, 1.2 to 1.7) and eccentric hypertrophy (95% CI, 1.1 to 1.8) and 3.9 (95% CI, 3.2 to 4.6) for concentric hypertrophy. In a subset of 1497 offspring, we observed an association between parental HF (n=458) and eccentric hypertrophy in offspring ( P <0.0001). Conclusions— Our investigation of a 2-generational community-based sample demonstrates familial aggregation of LV geometry, with the greatest recurrence risk for concentric LV geometry, and establishes an association between eccentric LV geometry and parental HF.