Four genetic loci influencing electrocardiographic indices of left ventricular hypertrophy.
ABSTRACT Presence of left ventricular hypertrophy on an ECG (ECG-LVH) is widely assessed clinically and provides prognostic information in some settings. There is evidence for significant heritability of ECG-LVH. We conducted a large-scale gene-centric association analysis of 4 commonly measured indices of ECG-LVH.
We calculated the Sokolow-Lyon index, Cornell product, 12-lead QRS voltage sum, and 12-lead QRS voltage product in 10 256 individuals from 3 population-based cohorts and typed their DNA using a customized gene array (the Illumina HumanCVD BeadChip 50K array), containing 49 094 genetic variants in ≈2100 genes of cardiovascular relevance. We followed-up promising associations in 11 777 additional individuals. We identified and replicated 4 loci associated with ECG-LVH indices: 3p22.2 (SCN5A, rs6797133, P=1.22 × 10(-7)) with Cornell product and 12q13.3 (PTGES3, rs2290893, P=3.74 × 10(-8)), 15q25.2 (NMB, rs2292462, P=3.23 × 10(-9)), and 15q26.3 (IGF1R, rs4966014, P=1.26 × 10(-7)) with the 12-lead QRS voltage sum. The odds ratio of being in the top decile for the 12-lead QRS voltage sum for those carrying 6 trait-raising alleles at the 12q13.3, 15q25.2, and 15q26.3 loci versus those carrying 0 to 1 alleles was 1.60 (95% CI: 1.20 to 2.29). Lead single-nucleotide polymorphisms at the 12q13.3 and 15q25.2 loci showed significant expression quantitative trait loci effects in monocytes.
These findings provide novel insights into the genetic determination of ECG-LVH. The findings could help to improve our understanding of the mechanisms determining this prognostically important trait.
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ABSTRACT: Left ventricular hypertrophy has multiple aetiologies including diabetes and genetic factors. We aimed to identify genetic variants predicting left ventricular hypertrophy in diabetic individuals. Demographic, echocardiographic, prescribing, morbidity, mortality and genotyping databases connected with the Genetics of Diabetes Audit and Research in Tayside, Scotland project were accurately linked using a patient-specific identifier. Left ventricular hypertrophy cases were identified using echocardiographic data. Genotyping data from 973 cases and 1443 non-left ventricular hypertrophy controls were analysed, investigating whether single nucleotide polymorphisms associated with left ventricular hypertrophy in previous Genome Wide Association Studies predicted left ventricular hypertrophy in our population of individuals with type 2 diabetes. Meta-analysis assessed overall significance of these single nucleotide polymorphisms, which were also used to create gene scores. Logistic regression assessed whether these scores predicted left ventricular hypertrophy. Two single nucleotide polymorphisms previously associated with left ventricular hypertrophy were significant: rs17132261: OR 2.03, 95%CI 1.10-3.73, p-value 0.02 and rs2292462: OR 0.82, 95% CI 0.73-0.93 and p-value 2.26x10-3. Meta-analysis confirmed rs17132261 and rs2292462 were associated with left ventricular hypertrophy (p=1.03x10-8 and p=5.86x10-10 respectively) and one single nucleotide polymorphisms in IGF1R (rs4966014) became genome wide significant upon meta-analysis although was not significant in our study. Gene scoring based on published single nucleotide polymorphisms also predicted left ventricular hypertrophy in our study. Rs17132261, within SLC25A46, encodes a mitochondrial phosphate transporter, implying abnormal myocardial energetics contribute to left ventricular hypertrophy development. Rs2292462 lies within the obesity-implicated neuromedin B gene. Rs4966014 lies within the IGF1R1 gene. IGF1 signalling is an established factor in cardiac hypertrophy. We created a resource to study genetics of left ventricular hypertrophy in diabetes and validated our left ventricular hypertrophy phenotype in replicating single nucleotide polymorphisms identified by previous genome wide association studies investigating left ventricular hypertrophy.Cardiovascular Diabetology 07/2013; 12(1):109. DOI:10.1186/1475-2840-12-109 · 3.71 Impact Factor
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ABSTRACT: The key information processing units within gene regulatory networks are enhancers. Enhancer activity is associated with the production of tissue specific noncoding RNAs, yet the existence of such transcripts during cardiac development has not been established. Using an integrated genomic approach, we demonstrate that fetal cardiac enhancers generate long noncoding RNAs (lncRNAs) during cardiac differentiation and morphogenesis. Enhancer expression correlates with the emergence of active enhancer chromatin states, the initiation of RNA polymerase II at enhancer loci and expression of target genes. Orthologous human sequences are also transcribed in fetal human hearts and cardiac progenitor cells. Through a systematic bioinformatic analysis, we identified and characterized, for the first time, a catalog of lncRNAs that are expressed during embryonic stem cell differentiation into cardiomyocytes and associated with active cardiac enhancer sequences. RNA-sequencing demonstrates that many of these transcripts are polyadenylated, multi-exonic long noncoding RNAs. Moreover, knockdown of two enhancer-associated lncRNAs resulted in the specific downregulation of their predicted target genes. Interestingly, the reactivation of the fetal gene program, a hallmark of the stress response in the adult heart, is accompanied by increased expression of fetal cardiac enhancer transcripts. Altogether, these findings demonstrate that the activity of cardiac enhancers and expression of their target genes are associated with the production of enhancer-derived lncRNAs.Journal of Molecular and Cellular Cardiology 08/2014; 76. DOI:10.1016/j.yjmcc.2014.08.009 · 5.22 Impact Factor
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ABSTRACT: Hypertrophic cardiomyopathy is a common inherited heart muscle disorder associated with sudden cardiac death, arrhythmias and heart failure. Genetic mutations can be identified in approximately 60% of patients; these are commonest in genes that encode proteins of the cardiac sarcomere. Similar to other Mendelian diseases these mutations are characterized by incomplete penetrance and variable clinical expression. Our knowledge of this genetic diversity is rapidly evolving as high-throughput DNA sequencing technology is now used to characterize an individual patient's disease. In addition, the genomic basis of several multisystem diseases associated with a hypertrophic cardiomyopathy phenotype has been elucidated. Genetic biomarkers can be helpful in making an accurate diagnosis and in identifying relatives at risk of developing the condition. In the clinical setting, genetic testing and genetic screening should be used pragmatically with appropriate counseling. Here we review the current role of genetic biomarkers in hypertrophic cardiomyopathy, highlight recent progress in the field and discuss future challenges.Biomarkers in Medicine 08/2013; 7(4):505-16. DOI:10.2217/bmm.13.79 · 2.86 Impact Factor