Four genetic loci influencing electrocardiographic indices of left ventricular hypertrophy

Genome-wide association analysis of left ventricular imaging-derived phenotypes identifies 72 risk loci and yields genetic insights into hypertrophic cardiomyopathy

Left ventricular regional wall thickness (LVRWT) is an independent predictor of morbidity and mortality in cardiovascular diseases (CVDs). To identify specific genetic influences on individual LVRWT, we established a novel deep learning algorithm to calculate 12 LVRWTs accurately in 42,194 individuals from the UK Biobank with cardiac magnetic resonance (CMR) imaging. Genome-wide association studies of CMR-derived 12 LVRWTs identified 72 significant genetic loci associated with at least one LVRWT phenotype (P < 5 × 10-8), which were revealed to actively participate in heart development and contraction pathways. Significant causal relationships were observed between the LVRWT traits and hypertrophic cardiomyopathy (HCM) using genetic correlation and Mendelian randomization analyses (P < 0.01). The polygenic risk score of inferoseptal LVRWT at end systole exhibited a notable association with incident HCM, facilitating the identification of high-risk individuals. The findings yield insights into the genetic determinants of LVRWT phenotypes and shed light on the biological basis for HCM etiology.

What Aspects of Phenotype Determine Risk for Sudden Cardiac Death in Pediatric Hypertrophic Cardiomyopathy?

Sudden cardiac death due to hypertrophic cardiomyopathy (HCM), is the most common autopsy-proven cause of unexpected medical death in children after infancy. This mode of death is preventable by implantation of an internal cardiac defibrillator (ICD), a procedure that has considerable morbidity in childhood patients, and even mortality. Since HCM is an inheritable disease (usually autosomal dominant, occasionally recessive), family screening may identify subjects at risk. This review summarizes published studies carried out to identify which phenotypic markers are important risk factors in childhood patients with HCM and reviews the performance of existing risk-stratification algorithms (HCM Risk-Kids, PRIMaCY) against those of single phenotypic markers. A significant proportion of HCM-patients diagnosed in childhood are associated with RASopathies such as Noonan syndrome, but a knowledge gap exists over risk stratification in this patient group. In conclusion, pediatric risk-stratification algorithms for sudden cardiac death perform better in children than adult HCM risk-stratification strategies. However, current multivariable algorithms overestimate risk substantially without having high sensitivity, and remain ‘a work in progress’. To include additional phenotypic parameters that can be reproducibly measured such as ECG-markers, e.g., ECG risk score (which has high sensitivity and negative predictive value), tissue Doppler diastolic function measurements, and quantification of myocardial scarring on cardiac magnetic resonance imaging, has the potential to improve risk-stratification algorithms. Until that work has been achieved, these are three factors that the clinician can combine with the current algorithm-calculated per cent risk, in order better to assess risk.

Novel SCN5A and GPD1L Variants Identified in Two Unrelated Han-Chinese Patients With Clinically Suspected Brugada Syndrome

Brugada syndrome (BrS) is a complexly genetically patterned, rare, malignant, life-threatening arrhythmia disorder. It is autosomal dominant in most cases and characterized by identifiable electrocardiographic patterns, recurrent syncope, nocturnal agonal respiration, and other symptoms, including sudden cardiac death. Over the last 2 decades, a great number of variants have been identified in more than 36 pathogenic or susceptibility genes associated with BrS. The present study used the combined method of whole exome sequencing and Sanger sequencing to identify pathogenic variants in two unrelated Han-Chinese patients with clinically suspected BrS. Minigene splicing assay was used to evaluate the effects of the splicing variant. A novel heterozygous splicing variant c.2437-2A>C in the sodium voltage-gated channel alpha subunit 5 gene (SCN5A) and a novel heterozygous missense variant c.161A>T [p.(Asp54Val)] in the glycerol-3-phosphate dehydrogenase 1 like gene (GPD1L) were identified in these two patients with BrS-1 and possible BrS-2, respectively. Minigene splicing assay indicated the deletion of 15 and 141 nucleotides in exon 16, resulting in critical amino acid deletions. These findings expand the variant spectrum of SCN5A and GPD1L, which can be beneficial to genetic counseling and prenatal diagnosis.

SD + SV4 diagnosis of left ventricular hypertrophy, a revaluation of ECG criterion by cardiac magnetic resonance imaging

Backgroud

Present electrocardiogram (ECG) criteria for diagnosing left ventricular hypertrophy (LVH) usually have low sensitivity, while the newly proposed SD + SV4 criterion, namely the deepest S‐wave amplitude in any lead (SD) plus SV4 amplitude, has been reported to have higher sensitivity and accuracy compared with other existing criteria. We aimed to further evaluate the diagnostic value of the SD + SV4 criterion in reference to the gold standard cardiac magnetic resonance imaging (CMR) in LVH diagnosis.

Methods

This retrospective study enrolled 138 patients who received CMR examination—60 patients with reduced ejection fraction (EF) and 78 patients with preserved EF. The left ventricular mass index (LVMI) measured by CMR was used as the gold standard for diagnosing LVH.

Result

The diagnostic value of the SD + SV4 criterion was compared with other 4 commonly used criteria. By CMR, 29 out of 138 people (21%) were diagnosed with LVH in reference to CMR. The SD + SV4 criterion had markedly higher sensitivity in diagnosing LVH compared with other criteria, but no higher specificity. There was no significant difference in area under receiver operating characteristic (ROC) curve among these criteria. The SD + SV4 criterion was not markedly consistent with CMR in diagnosing LVH. Compared to the other criteria, the SD + SV4 criterion had the highest sensitivity in patients with reduced ejection fraction; however, the area under the curve (AUC) of the SD + SV4 criterion in patients with reduced EF was significantly lower than in patients with preserved EF.

Conclusion

The newly proposed SD + SV4 criterion did not have a better diagnostic value compared with other existing criteria, and the statistical power of the SD + SV4 criterion was influenced by EF.

Genome-Wide Analysis of Left Ventricular Image-Derived Phenotypes Identifies Fourteen Loci Associated With Cardiac Morphogenesis and Heart Failure Development

BACKGROUND

The genetic basis of left ventricular (LV) image-derived phenotypes, which play a vital role in the diagnosis, management, and risk stratification of cardiovascular diseases, is unclear at present.

METHODS

The LV parameters were measured from the cardiovascular magnetic resonance studies of the UK Biobank. Genotyping was done using Affymetrix arrays, augmented by imputation. We performed genome-wide association studies of 6 LV traits-LV end-diastolic volume, LV end-systolic volume, LV stroke volume, LV ejection fraction, LV mass, and LV mass to end-diastolic volume ratio. The replication analysis was performed in the MESA study (Multi-Ethnic Study of Atherosclerosis). We identified the candidate genes at genome-wide significant loci based on the evidence from extensive bioinformatic analyses. Polygenic risk scores were constructed from the summary statistics of LV genome-wide association studies to predict the heart failure events.

RESULTS

The study comprised 16 923 European UK Biobank participants (mean age 62.5 years; 45.8% men) without prevalent myocardial infarction or heart failure. We discovered 14 genome-wide significant loci (3 loci each for LV end-diastolic volume, LV end-systolic volume, and LV mass to end-diastolic volume ratio; 4 loci for LV ejection fraction, and 1 locus for LV mass) at a stringent P<1×10-8. Three loci were replicated at Bonferroni significance and 7 loci at nominal significance (P<0.05 with concordant direction of effect) in the MESA study (n=4383). Follow-up bioinformatic analyses identified 28 candidate genes that were enriched in the cardiac developmental pathways and regulation of the LV contractile mechanism. Eight genes (TTN, BAG3, GRK5, HSPB7, MTSS1, ALPK3, NMB, and MMP11) supported by at least 2 independent lines of in silico evidence were implicated in the cardiac morphogenesis and heart failure development. The polygenic risk scores of LV phenotypes were predictive of heart failure in a holdout UK Biobank sample of 3106 cases and 224 134 controls (odds ratio 1.41, 95% CI 1.26 - 1.58, for the top quintile versus the bottom quintile of the LV end-systolic volume risk score).

CONCLUSIONS

We report 14 genetic loci and indicate several candidate genes that not only enhance our understanding of the genetic architecture of prognostically important LV phenotypes but also shed light on potential novel therapeutic targets for LV remodeling.

A combined linkage, microarray and exome analysis suggests MAP3K11 as a candidate gene for left ventricular hypertrophy

BACKGROUND

Electrocardiographic measures of left ventricular hypertrophy (LVH) are used as predictors of cardiovascular risk. We combined linkage and association analyses to discover novel rare genetic variants involved in three such measures and two principal components derived from them.

METHODS

The study was conducted among participants from the Erasmus Rucphen Family Study (ERF), a Dutch family-based sample from the southwestern Netherlands. Variance components linkage analyses were performed using Merlin. Regions of interest (LOD > 1.9) were fine-mapped using microarray and exome sequence data.

RESULTS

We observed one significant LOD score for the second principal component on chromosome 15 (LOD score = 3.01) and 12 suggestive LOD scores. Several loci contained variants identified in GWAS for these traits; however, these did not explain the linkage peaks, nor did other common variants. Exome sequence data identified two associated variants after multiple testing corrections were applied.

CONCLUSIONS

We did not find common SNPs explaining these linkage signals. Exome sequencing uncovered a relatively rare variant in MAPK3K11 on chromosome 11 (MAF = 0.01) that helped account for the suggestive linkage peak observed for the first principal component. Conditional analysis revealed a drop in LOD from 2.01 to 0.88 for MAP3K11, suggesting that this variant may partially explain the linkage signal at this chromosomal location. MAP3K11 is related to the JNK pathway and is a pro-apoptotic kinase that plays an important role in the induction of cardiomyocyte apoptosis in various pathologies, including LVH.

Quantitative trait mapping in Diversity Outbred mice identifies two genomic regions associated with heart size

Heart size is an important factor in cardiac health and disease. In particular, increased heart weight is predictive of adverse cardiovascular outcomes in multiple large community-based studies. We use two cohorts of Diversity Outbred (DO) mice to investigate the role of genetics, sex, age, and diet on heart size. DO mice (n = 289) of both sexes from generation 10 were fed a standard chow diet, and analyzed at 12-15 weeks of age. Another cohort of female DO mice (n = 258) from generation 11 were fed either a high-fat, cholesterol-containing (HFC) diet or a low-fat, high-protein diet, and analyzed at 24-25 weeks. We did not observe an effect of diet on body or heart weight in generation 11 mice, although we previously reported an effect on other cardiovascular risk factors, including cholesterol, triglycerides, and insulin. We do observe a significant genetic effect on heart weight in this population. We identified two quantitative trait loci for heart weight, one (Hwtf1) at a genome-wide significance level of p ≤ 0.05 on MMU15 and one (Hwtf2) at a genome-wide suggestive level of p ≤ 0.1 on MMU10, that together explain 13.3% of the phenotypic variance. Hwtf1 contained collagen type XXII alpha 1 chain (Col22a1), and the NZO/HlLtJ and WSB/EiJ haplotypes were associated with larger hearts. This is consistent with heart tissue Col22a1 expression in DO founders and SNP patterns within Hwtf1 for Col22a1. Col22a1 has been previously associated with cardiac fibrosis in mice, suggesting that Col22a1 may be involved in pathological cardiac hypertrophy.

Genetic Variation in Kruppel like Factor 15 Is Associated with Left Ventricular Hypertrophy in Patients with Type 2 Diabetes: Discovery and Replication Cohorts

Left ventricular (LV) hypertrophy (LVH) is a heritable trait that is common in type 2 diabetes and is associated with the development of heart failure. The transcriptional factor Kruppel like factor 15 (KLF15) is expressed in the heart and acts as a repressor of cardiac hypertrophy in experimental models. This study investigated if KLF15 gene variants were associated with LVH in type 2 diabetes. In stage 1 of a 2-stage approach, patients with type 2 diabetes and no known cardiac disease were prospectively recruited for a transthoracic echocardiographic assessment (Melbourne Diabetes Heart Cohort) (n = 318) and genotyping of two KLF15 single nucleotide polymorphisms (SNPs) (rs9838915, rs6796325). In stage 2, the association of KLF15 SNPs with LVH was investigated in the Genetics of Diabetes Audit and Research in Tayside Scotland (Go-DARTS) type 2 diabetes cohort (n = 5631). The KLF15 SNP rs9838915 A allele was associated in a dominant manner with LV mass before (P = 0.003) and after (P = 0.001) adjustment for age, gender, body mass index (BMI) and hypertension, and with adjusted septal (P < 0.0001) and posterior (P = 0.004) wall thickness. LVH was present in 35% of patients. Over a median follow up of 5.6 years, there were 22 (7%) first heart failure hospitalizations. The adjusted risk of heart failure hospitalization was 5.5-fold greater in those with LVH and the rs9838915 A allele compared to those without LVH and the GG genotype (hazard ratio (HR) 5.5 (1.6–18.6), P = 0.006). The association of rs9838915 A allele with LVH was replicated in the Go-DARTS cohort. We have identified the KLF15 SNP rs9838915 A allele as a marker of LVH in patients with type 2 diabetes, and replicated these findings in a large independent cohort. Studies are needed to characterize the functional importance of these results, and to determine if the SNP rs9838915 A allele is associated with LVH in other high risk patient cohorts.

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KLF15 SNP rs9838915 A allele is associated with increased LV mass in patients with 2 diabetes.

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KLF15 SNP rs9838915 predicts incident heart failure hospitalization.

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Genotyping KLF15 SNP rs9838915 allowed more precise stratification of the risk of heart failure hospitalization.

Left ventricular hypertrophy (LVH) is a heritable trait that is common in patients with diabetes. The Kruppel like factor 15 (KLF15) is expressed in the heart and acts as a repressor of cardiac hypertrophy and fibrosis. Our study provides evidence that genetic variation in KLF15 is associated with LVH in patients with type 2 diabetes and these findings were then replicated in an independent cohort of patients with type 2 diabetes. The KLF15 genetic variant was also associated with first heart failure hospitalization. These findings add to our understanding of the molecular mechanisms that contribute to increased LV mass.

An 'Omics' Perspective on Cardiomyopathies and Heart Failure

Pathological enlargement of the heart, represented by hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM), occurs in response to many genetic and non-genetic factors. The clinical course of cardiac hypertrophy is remarkably variable, ranging from lifelong absence of symptoms to rapidly declining heart function and sudden cardiac death (SCD). Unbiased omics studies have begun to provide a glimpse into the molecular framework underpinning altered mechanotransduction, mitochondrial energetics, oxidative stress, and extracellular matrix in the heart undergoing physiological and pathological hypertrophy. Omics analyses indicate that post-transcriptional regulation of gene expression plays an overriding role in the normal and diseased heart. Studies to date highlight a need for more effective bioinformatics to better integrate patient omics data with their comprehensive clinical histories.

Individual components of the Romhilt-Estes left ventricular hypertrophy score differ in their prediction of cardiovascular events: The Atherosclerosis Risk in Communities (ARIC) study

BACKGROUND

It has been recently reported that the Romhilt-Estes (R-E) score, originally proposed for detection of left ventricular hypertrophy from the electrocardiogram, is a strong predictor of all-cause mortality. Whether the R-E score is also predictive of cardiovascular disease (CVD) and whether its individual components differ in their ability to predict different CVD outcomes are not well established.

METHODS

This analysis includes 13,261 participants from the ARIC study who were free of CVD at baseline (1987-1989). Incident CVD, coronary heart disease (CHD), heart failure (HF), and stroke were ascertained by an adjudication committee through December 2010. The R-E left ventricular hypertrophy score was measured from automatically processed baseline electrocardiogram data. Cox proportional hazard models were used to examine the association between baseline the R-E overall score (overall) and each of its 6 individual components separately, with each of the CVD outcomes.

RESULTS

During a median follow-up of 21.8 years, 3,579, 2,205, 1,814, and 731 CVD, CHD, HF, and stroke events, respectively, occurred. In multivariable adjusted models, R-E score ≥4 points (compared with 0 points) was associated with increased risk of CVD, CHD, HF, and stroke (hazard ratio [95% CI] 1.66 [1.41-1.96], 1.66 [1.34-2.07], 1.97 [1.60-2.43], and 1.49 [1.07-2.07], respectively). The 6 component of the R-E score varied in their relationship to different CVD outcomes.

CONCLUSIONS

The R-E score is predictive of CVD outcomes. The 6 R-E score components differ in their associations with different CVD outcomes, indicating that they may be electrical biomarkers of different physiological events within the myocardium.

Genetic association of left ventricular mass assessed by M-mode and two-dimensional echocardiography

BACKGROUND

Left ventricular mass offers prognostic information for assessing cardiovascular disease risk. M-mode and two-dimensional (2D) echocardiographically-derived left ventricular mass values have shown high accuracy and reproducibility; however, no studies to date have compared left ventricular mass genetic association findings on the basis of both the methods. The aim of this study was to compare associations of single-nucleotide polymorphisms (SNPs) from genome-wide association study analyses of left ventricular mass using both methods in the same cohort.

METHODS AND RESULTS

Left ventricular mass was determined using 2D and M-mode echocardiography in 711 patients (390 women); SNP genotype data were obtained using the Genome-wide Human SNP Array 6.0. Genome-wide association study analyses were performed to obtain panels of SNPs associated with left ventricular mass and left ventricular mass index. The unindexed left ventricular mass showed excellent agreement [M-mode: 170 ± 47 vs. 2D: 178 ± 56 g; intraclass correlation coefficient 0.929 (95% confidence interval 0.932, 0.909)]. The presence of left ventricular hypertrophy based on M-mode and 2D-derived left ventricular mass index values showed moderate agreement (kappa = 0.49). Eleven SNPs showed suggestive association with at least two of the four left ventricular mass traits, with one SNP in CDH13 common to all four derived traits.

CONCLUSION

M-mode and 2D echocardiography left ventricular mass measurements in the same cohort identified suggestive genetic associations, both shared and unshared, suggesting common left ventricular mass biology underlying the two measures of left ventricular mass. The combined use of M-mode and 2D echo is a novel approach that may increase the yield of genetic association with left ventricular mass.

Heritabilities, proportions of heritabilities explained by GWAS findings, and implications of cross-phenotype effects on PR interval

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.

Functional importance of cardiac enhancer-associated noncoding RNAs in heart development and disease

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.

Genetic biomarkers in hypertrophic cardiomyopathy

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.

Genetic variants predicting left ventricular hypertrophy in a diabetic population: a Go-DARTS study including meta-analysis

Background

Left ventricular hypertrophy has multiple aetiologies including diabetes and genetic factors. We aimed to identify genetic variants predicting left ventricular hypertrophy in diabetic individuals.

Methods

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.

Results

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.

Conclusions

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.

Advances in blood pressure genomics

The elucidation of genes implicated in Mendelian forms of hypertension demonstrates rare variants with substantial effects are responsible, and often these genes lie within pathways managing sodium homeostasis. More recently with advances in affordable high-throughput genotyping strategies, multiple common genetic variants with modest effects on blood pressure (<1 mmHg systolic) have been discovered in the population. In aggregate, these common variants explain <3% of the variance of blood pressure. Although these findings may offer new mechanistic insights into the biology of blood pressure, a key question is can these findings translate into patient benefit? It is timely to reflect on recent advances in genomics, and the use of new resources, such as the 1000 Genomes Project and the Encyclopedia of DNA Elements, to annotate likely causal variants, and their relevance to cardiovascular disease. In this review, we discuss the advances in relation to our knowledge of the genetic architecture of blood pressure, and whether gene discoveries might influence cardiovascular risk assessment, help to stratify patient response to medicine, or identify new biological pathways for novel therapeutic targets.

Genome-wide haplotype analysis of cis expression quantitative trait loci in monocytes

In order to assess whether gene expression variability could be influenced by several SNPs acting in cis, either through additive or more complex haplotype effects, a systematic genome-wide search for cis haplotype expression quantitative trait loci (eQTL) was conducted in a sample of 758 individuals, part of the Cardiogenics Transcriptomic Study, for which genome-wide monocyte expression and GWAS data were available. 19,805 RNA probes were assessed for cis haplotypic regulation through investigation of ~2,1 × 10(9) haplotypic combinations. 2,650 probes demonstrated haplotypic p-values >10(4)-fold smaller than the best single SNP p-value. Replication of significant haplotype effects were tested for 412 probes for which SNPs (or proxies) that defined the detected haplotypes were available in the Gutenberg Health Study composed of 1,374 individuals. At the Bonferroni correction level of 1.2 × 10(-4) (~0.05/412), 193 haplotypic signals replicated. 1000 G imputation was then conducted, and 105 haplotypic signals still remained more informative than imputed SNPs. In-depth analysis of these 105 cis eQTL revealed that at 76 loci genetic associations were compatible with additive effects of several SNPs, while for the 29 remaining regions data could be compatible with a more complex haplotypic pattern. As 24 of the 105 cis eQTL have previously been reported to be disease-associated loci, this work highlights the need for conducting haplotype-based and 1000 G imputed cis eQTL analysis before commencing functional studies at disease-associated loci.

Common variation neighbouring micro-RNA 22 is associated with increased left ventricular mass

Aims

Previous genome-wide linkage analysis has suggested that chromosomal region 17p13.3 may harbour genes influencing left ventricular mass (LVM) in man. To date, the genetic factors accounting for LVM variability remain largely unknown but a non-coding RNA gene within this region, micro-RNA 22 (miR-22), has been implicated in cardiac hypertrophy and heart failure in animal models. We thus investigated the relationship between common genetic polymorphisms surrounding miR-22 and left ventricular mass in a family-based association study.

Methods and Results

We studied a cohort of 255 families comprising 1,425 individuals ascertained via a hypertensive proband. Ten single nucleotide polymorphisms which together tagged common genetic variation surrounding the miR-22 gene were genotyped. There was evidence of association between the rs7223247 polymorphism, which lies within the 3′UTR of a gene of unknown function, TLCD2, immediately downstream from miR-22, and left ventricular mass determined by Sokolow-Lyon voltage (Bonferroni corrected p-value = 0.038). The T allele at rs7223247 was associated with an 0.272 standard deviation higher Sokolow-Lyon voltage. Genotype was responsible for ∼1% of the population variability in LVM.

Conclusions

Genotype at the rs7223247 polymorphism affects left ventricular mass determined by Sokolow-Lyon voltage. The neighbouring genes miR-22 and TLCD2 are strong candidates to account for this observation.

Genetic epidemiology of left ventricular hypertrophy

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.

Comprehensive exploration of the effects of miRNA SNPs on monocyte gene expression

We aimed to assess whether pri-miRNA SNPs (miSNPs) could influence monocyte gene expression, either through marginal association or by interacting with polymorphisms located in 3'UTR regions (3utrSNPs). We then conducted a genome-wide search for marginal miSNPs effects and pairwise miSNPs × 3utrSNPs interactions in a sample of 1,467 individuals for which genome-wide monocyte expression and genotype data were available. Statistical associations that survived multiple testing correction were tested for replication in an independent sample of 758 individuals with both monocyte gene expression and genotype data. In both studies, the hsa-mir-1279 rs1463335 was found to modulate in cis the expression of LYZ and in trans the expression of CNTN6, CTRC, COPZ2, KRT9, LRRFIP1, NOD1, PCDHA6, ST5 and TRAF3IP2 genes, supporting the role of hsa-mir-1279 as a regulator of several genes in monocytes. In addition, we identified two robust miSNPs × 3utrSNPs interactions, one involving HLA-DPB1 rs1042448 and hsa-mir-219-1 rs107822, the second the H1F0 rs1894644 and hsa-mir-659 rs5750504, modulating the expression of the associated genes.

As some of the aforementioned genes have previously been reported to reside at disease-associated loci, our findings provide novel arguments supporting the hypothesis that the genetic variability of miRNAs could also contribute to the susceptibility to human diseases.

The genetics of coronary heart disease

BACKGROUND

Coronary heart disease (CHD) is a leading cause of death worldwide, yet many areas of its pathogenesis remain unknown or poorly understood, leaving potential for novel preventive and therapeutic interventions. Recent major advances in genomic science and technology have opened new avenues of investigation in the pathogenesis of CHD, some of which are leading to clinical translation.

SOURCES OF DATA

The published literature in CHD genetics has burgeoned in the last 5 years with the reporting of genome-wide association studies (GWASs) and many other findings.

AREAS OF AGREEMENT

Identification of many genetic variants with small effects on CHD risk has been a common finding. These have included several predicted loci, such as those involved in conventional CHD risk factors (e.g. plasma lipids) and many novel loci, where their mechanism of action is unclear. The need for large, collaborative approaches to research has also become clear and is now an accepted modus operandi.

AREAS OF CONTROVERSY

The clinical utility of novel GWAS findings remains uncertain. In particular, the relative contribution of common variants of modest effect and rare variants of larger effects to risk of CHD or response to drugs is unclear.

GROWING POINTS

As a greater number of larger GWASs are conducted in CHD and its related phenotypes, much effort is being made to find translational applications for their findings. Therapeutics, prediction and pathology are major areas of research endeavour.