Germline variants in HEY2 functional domains lead to congenital heart defects and thoracic aortic aneurysms.

PURPOSE: In this study we aimed to establish the genetic cause of a myriad of cardiovascular defects prevalent in individuals from a genetically isolated population, who were found to share a common ancestor in 1728. METHODS: Trio genome sequencing was carried out in an index patient with critical congenital heart disease (CHD); family members had either exome or Sanger sequencing. To confirm enrichment, we performed a gene-based association test and meta-analysis in two independent validation cohorts: one with 2685 CHD cases versus 4370 . These controls were also ancestry-matched (same as FTAA controls), and the other with 326 cases with familial thoracic aortic aneurysms (FTAA) and dissections versus 570 ancestry-matched controls. Functional consequences of identified variants were evaluated using expression studies. RESULTS: We identified a loss-of-function variant in the Notch target transcription factor-encoding gene HEY2. The homozygous state (n = 3) causes life-threatening congenital heart defects, while 80% of heterozygous carriers (n = 20) had cardiovascular defects, mainly CHD and FTAA of the ascending aorta. We confirm enrichment of rare risk variants in HEY2 functional domains after meta-analysis (MetaSKAT p = 0.018). Furthermore, we show that several identified variants lead to dysregulation of repression by HEY2. CONCLUSION: A homozygous germline loss-of-function variant in HEY2 leads to critical CHD. The majority of heterozygotes show a myriad of cardiovascular defects.

Disentangling Genetic Risks for Metabolic Syndrome.

A quarter of the world's population is estimated to meet the criteria for metabolic syndrome (MetS), a cluster of cardiometabolic risk factors that promote development of coronary artery disease and type 2 diabetes, leading to increased risk of premature death and significant health costs. In this study we investigate whether the genetics associated with MetS components mirror their phenotypic clustering. A multivariate approach that leverages genetic correlations of fasting glucose, HDL cholesterol, systolic blood pressure, triglycerides, and waist circumference was used, which revealed that these genetic correlations are best captured by a genetic one factor model. The common genetic factor genome-wide association study (GWAS) detects 235 associated loci, 174 more than the largest GWAS on MetS to date. Of these loci, 53 (22.5%) overlap with loci identified for two or more MetS components, indicating that MetS is a complex, heterogeneous disorder. Associated loci harbor genes that show increased expression in the brain, especially in GABAergic and dopaminergic neurons. A polygenic risk score drafted from the MetS factor GWAS predicts 5.9% of the variance in MetS. These results provide mechanistic insights into the genetics of MetS and suggestions for drug targets, especially fenofibrate, which has the promise of tackling multiple MetS components.