Brión, Marı́a

BACKGROUND: Bicuspid aortic valve (BAV) is a frequent congenital heart defect with a high heritability. Despite this, only a limited number of genes have been associated with the disease, and the molecular mechanisms remain unexplained in most cases. This study aimed to further understand the genetic architecture of BAV. METHODS: A genome-wide association study meta-analysis including 9631 cases among 65 677 participants was performed. Genes were prioritized using transcriptomic analyses based on RNA sequencing in relevant tissues, including human fetal and adult aortic valves. The impact of the knockdown or knockout of 4 candidate genes on cardiac development was verified in zebrafish. A polygenic risk score was developed, its association with BAV was evaluated in an independent cohort, and its association with a wide range of phenotypes (n=976) was evaluated in UK Biobank (n=355 618 individuals). RESULTS: Thirty-six genomic loci were identified, including 32 that were not described previously. Among the prioritized genes, KANK2 and ERBB4 were identified as potentially causal through transcriptomic analyses, colocalization, and Mendelian randomization based on gene expression in human aortic valves (n=484), whereas PRDM6 and STRN were prioritized using similar analyses from aortic (n=326) and left ventricular tissues (n=326), respectively. Targeting 4 candidate genes ( WNT4 , LEF1 , STRN , and KANK2 ) in zebrafish led to disruption in cardiac development. A polygenic risk score was associated with an odds ratio of 2.07 (95% CI, 1.90–2.25; P =5.43×10 -62 ) per SD for BAV and significantly associated with thoracic aortic aneurysm and atrial fibrillation in UK Biobank. CONCLUSIONS: This study supports a significant polygenic contribution to BAV, where the combination of multiple common variants in genes involved in heart morphogenesis disrupts aortic valve development.

Abstract Background Cancer therapy-related cardiac dysfunction, which commonly includes varying degrees of asymptomatic ventricular dysfunction and/or heart failure, is the main adverse effect of anticancer therapy. The mechanism by which it develops is still quite uncertain. In recent years several genetic association studies have identified common genetic variants associated with cardiotoxicity in genes related to DNA damage, drug transportation and metabolism, antioxidant mechanism, sarcomere dysfunction and impaired iron metabolism, but the evidence is still limited. Rare variants in familial cardiomyopathies genes have also been identified. Purpose This uncertainly requires larger studies to identify genetic variants allowing to predict the risk of cardiotoxicity. In this sense, we carry out a genome-wide association meta-analysis to identify genetic variants associated with cancer therapy-related cardiac dysfunction. The development of a risk predictor model combining clinical and genetic factors would make it possible to anticipate the adverse effects of this therapy. Methods Genome wide meta-analysis was performed with a total of 594 patients from the CARDIOTOX registry and 3545 healthy individuals from the National DNA Bank, together with three previously published independent cohorts with 1446, 227 and 52 individuals receiving cancer therapy. Patients were classified with different cardiotoxicity degrees based on the CARDIOTOX registry and exhaustive clinical information was obtained from medical records. The study was ethically approved by local institutional review boards and all participants provided written informed consent. DNA samples were genotyped and imputed to perform a GWAS meta-analysis for case-control (n=5864) and extreme phenotypes (n=5317) looking for genetic variants that predispose to developing cancer therapy-related cardiac dysfunction. A functional characterization of the significant loci was performed by colocalization with eQTLs. Results The case-control analysis showed 17 loci that achieved suggestive significance (< P=1 x10-5) (Figure 1). The rs6765460, an intergenic variant between TPRG1 and TP63 genes, had the strongest relationship with the development of cardiotoxicity (P=1.89x10-7). The extreme phenotypes analysis showed additional 8 loci that achieved suggestive significance. The rs12904266, an intron variant of SLCO3A1, had the strongest relationship with severe cardiotoxicity (P=3.20E-07). eQTLs colocalization of the significant loci revealed association with genes involved in the mitochondria, inflammation, immune system, and fibrosis. Conclusions Our GWAS meta-analysis identified 25 novel genetic variants that may be associated with cancer therapy-related cardiac dysfunction. Genotyping of these markers could be considered in a genetic risk score which, together with the already known clinical risk factors, would improve the cardiotoxicity prediction in cardio-oncology units.Figure 1.Miami plot with P values

We executed a genome-wide association scan for age-related macular degeneration (AMD) in 2,157 cases and 1,150 controls. Our results validate AMD susceptibility loci near CFH ( P < 10 −75 ), ARMS2 ( P < 10 −59 ), C2/CFB ( P < 10 −20 ), C3 ( P < 10 −9 ), and CFI ( P < 10 −6 ). We compared our top findings with the Tufts/Massachusetts General Hospital genome-wide association study of advanced AMD (821 cases, 1,709 controls) and genotyped 30 promising markers in additional individuals (up to 7,749 cases and 4,625 controls). With these data, we identified a susceptibility locus near TIMP3 (overall P = 1.1 × 10 −11 ), a metalloproteinase involved in degradation of the extracellular matrix and previously implicated in early-onset maculopathy. In addition, our data revealed strong association signals with alleles at two loci ( LIPC , P = 1.3 × 10 −7 ; CETP , P = 7.4 × 10 −7 ) that were previously associated with high-density lipoprotein cholesterol (HDL-c) levels in blood. Consistent with the hypothesis that HDL metabolism is associated with AMD pathogenesis, we also observed association with AMD of HDL-c—associated alleles near LPL ( P = 3.0 × 10 −3 ) and ABCA1 ( P = 5.6 × 10 −4 ). Multilocus analysis including all susceptibility loci showed that 329 of 331 individuals (99%) with the highest-risk genotypes were cases, and 85% of these had advanced AMD. Our studies extend the catalog of AMD associated loci, help identify individuals at high risk of disease, and provide clues about underlying cellular pathways that should eventually lead to new therapies.