Four Genetic Loci Influencing Electrocardiographic Indices of Left Ventricular Hypertrophy

Department of Genetics, Evolution and Environment, University College London, London, United Kingdom.
Circulation Cardiovascular Genetics (Impact Factor: 4.6). 09/2011; 4(6):626-35. DOI: 10.1161/CIRCGENETICS.111.960203
Source: PubMed


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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    • "d corresponds well to the estimate provided by Mayosi et al . ( 2002 ) ( ~40 % ) and Shah et al . ( 2011 ) ( ~39 % ) . With respect to CV , our esti - mate corresponds with previous estimates ranging from 23 to 40 % ( Mayosi et al . 2002 ; Shah et al . 2011 ) . For 12LS , our estimate was higher ( 0 . 46 % ) than previously reported ( 0 . 32 % ) ( Shah et al . 2011 ) . This is the first study that provides direct estimates of the proportion of heritability attributable to common variants discovered by GWAS . The heritability explained is particularly low for PR and SL , a finding that is not uncommon for complex traits ( Manolio et al . 2009 ) . For QRS ( 17 % ) , a substantial portion of trait he"
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    ABSTRACT: Electrocardiogram (ECG) measurements are a powerful tool for evaluating cardiac function and are widely used for the diagnosis and prediction of a variety of conditions, including myocardial infarction, cardiac arrhythmias, and sudden cardiac death. Recently, genome-wide association studies (GWASs) identified a large number of genes related to ECG parameter variability, specifically for the QT, QRS, and PR intervals. The aims of this study were to establish the heritability of ECG traits, including indices of left ventricular hypertrophy, and to directly assess the proportion of those heritabilities explained by GWAS variants. These analyses were conducted in a large, Dutch family-based cohort study, the Erasmus Rucphen Family study using variance component methods implemented in the SOLAR (Sequential Oligogenic Linkage Analysis Routines) software package. Heritability estimates ranged from 34 % for QRS and Cornell voltage product to 49 % for 12-lead sum. Trait-specific GWAS findings for each trait explained a fraction of their heritability (17 % for QRS, 4 % for QT, 2 % for PR, 3 % for Sokolow-Lyon index, and 4 % for 12-lead sum). The inclusion of all ECG-associated single nucleotide polymorphisms explained an additional 6 % of the heritability of PR. In conclusion, this study shows that, although GWAS explain a portion of ECG trait variability, a large amount of heritability remains to be explained. In addition, larger GWAS for PR are likely to detect loci already identified, particularly those observed for QRS and 12-lead sum.
    Human Genetics 09/2015; DOI:10.1007/s00439-015-1595-9 · 4.82 Impact Factor
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    • "In a initial genome-wide joint linkage and association study, conducted on up to 1238 individuals participating in the Framingham Heart Study based on only ~71,000 SNPs, significant linkage was obtained for echocardiographic LV mass on chromosome 5 [68]. The study found interesting SNP associations on chromosome 2 and chromosome 11 (with a SNP near heatshock 70-KD protein 8, HSPA8), but these associations did not reach strict genome-wide significance . "
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    ABSTRACT: Left ventricular (LV) hypertrophy is a strong independent predictor of increased cardiovascular morbidity and mortality in clinical and population-based samples. Clinical and hemodynamic stimuli to LV hypertrophy induce not only an increase in cardiac mass and wall thickness but also a fundamental reconfiguration of the protein, cellular and molecular components of the myocardium. Several studies have indicated that LV mass is influenced by genetic factors. The substantial heritability (h(2)) for LV mass in population-based samples of varying ethnicity indicates robust genetic influences on LV hypertrophy. Genome-wide linkage and association studies in diverse populations have been performed to identify genes influencing LV mass, and although several chromosomal regions have been found to be significantly associated with LV mass, the specific genes and functional variants contained in these chromosomal regions have yet to be identified. In addition, multiple studies have tried to link single-nucleotide polymorphisms (SNPs) in regulatory and pathway genes with common forms of LV hypertrophy, but there is little evidence that these genetic variations are functional. Up to this point in time, the results obtained in genetic studies are of limited clinical value. Much of the heritability remains unexplained, the identity of the underlying gene pathways, genes, and functional variants remains unknown, and the promise of genetically-based risk prediction and personalized medicine remain unfulfilled. However, molecular biological technologies continue to improve rapidly, and the long-term potential of sophisticated genetic investigations using these modern genomic technologies, coupled with smart study designs, remains intact. Ultimately, genetic investigations offer much promise for future prevention, early intervention and treatment of this major public health issue.
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