Claudia Giambartolomei

Mendelian randomization of blood lipids for coronary heart disease.

Aims To investigate the causal role of high-density lipoprotein cholesterol (HDL-C) and triglycerides in coronary heart disease (CHD) using multiple instrumental variables for Mendelian randomization. Methods and results We developed weighted allele scores based on single nucleotide polymorphisms (SNPs) with established associations with HDL-C, triglycerides, and low-density lipoprotein cholesterol (LDL-C). For each trait, we constructed two scores. The first was unrestricted, including all independent SNPs associated with the lipid trait identified from a prior meta-analysis (threshold P < 2 × 10−6); and the second a restricted score, filtered to remove any SNPs also associated with either of the other two lipid traits at P ≤ 0.01. Mendelian randomization meta-analyses were conducted in 17 studies including 62,199 participants and 12,099 CHD events. Both the unrestricted and restricted allele scores for LDL-C (42 and 19 SNPs, respectively) associated with CHD. For HDL-C, the unrestricted allele score (48 SNPs) was associated with CHD (OR: 0.53; 95% CI: 0.40, 0.70), per 1 mmol/L higher HDL-C, but neither the restricted allele score (19 SNPs; OR: 0.91; 95% CI: 0.42, 1.98) nor the unrestricted HDL-C allele score adjusted for triglycerides, LDL-C, or statin use (OR: 0.81; 95% CI: 0.44, 1.46) showed a robust association. For triglycerides, the unrestricted allele score (67 SNPs) and the restricted allele score (27 SNPs) were both associated with CHD (OR: 1.62; 95% CI: 1.24, 2.11 and 1.61; 95% CI: 1.00, 2.59, respectively) per 1-log unit increment. However, the unrestricted triglyceride score adjusted for HDL-C, LDL-C, and statin use gave an OR for CHD of 1.01 (95% CI: 0.59, 1.75). Conclusion The genetic findings support a causal effect of triglycerides on CHD risk, but a causal role for HDL-C, though possible, remains less certain.

Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.

Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.

Genetic complexity in hypertrophic cardiomyopathy revealed by high-throughput sequencing

Background Clinical interpretation of the large number of rare variants identified by high throughput sequencing (HTS) technologies is challenging. The aim of this study was to explore the clinical implications of a HTS strategy for patients with hypertrophic cardiomyopathy (HCM) using a targeted HTS methodology and workflow developed for patients with a range of inherited cardiovascular diseases. By comparing the sequencing results with published findings and with sequence data from a large-scale exome sequencing screen of UK individuals, we sought to quantify the strength of the evidence supporting causality for detected candidate variants. Methods and results 223 unrelated patients with HCM (46±15 years at diagnosis, 74% males) were studied. In order to analyse coding, intronic and regulatory regions of 41 cardiovascular genes, we used solution-based sequence capture followed by massive parallel resequencing on Illumina GAIIx. Average read-depth in the 2.1 Mb target region was 120. Rare (frequency<0.5%) non-synonymous, loss-of-function and splice-site variants were defined as candidates. Excluding titin, we identified 152 distinct candidate variants in sarcomeric or associated genes (89 novel) in 143 patients (64%). Four sarcomeric genes (MYH7, MYBPC3, TNNI3, TNNT2) showed an excess of rare single non-synonymous single-nucleotide polymorphisms (nsSNPs) in cases compared to controls. The estimated probability that a nsSNP in these genes is pathogenic varied between 57% and near certainty depending on the location. We detected an additional 94 candidate variants (73 novel) in desmosomal, and ion-channel genes in 96 patients (43%). Conclusions This study provides the first large-scale quantitative analysis of the prevalence of sarcomere protein gene variants in patients with HCM using HTS technology. Inclusion of other genes implicated in inherited cardiac disease identifies a large number of non-synonymous rare variants of unknown clinical significance.

Cardiometabolic risk loci share downstream cis- and trans-gene regulation across tissues and diseases

Genetic variation and coronary artery disease Most genetic variants lie outside protein-coding genes, but their effects, especially in human health, are not well understood. Franzén et al. examined gene expression in tissues affected by coronary artery disease (CAD). They found that individuals with loci that have been associated with CAD in genome-wide analyses had different patterns of tissue-specific gene expression than individuals without these genetic variants. Similarly, tissues not associated with CAD did not have CAD-like expression patterns. Thus, tissue-specific data can be used to dissect the genetic effects that predispose individuals to CAD. Science, this issue p. 827 A gene expression survey in patients with coronary artery disease reveals how genetic variation affects the risk of heart failure. Genome-wide association studies (GWAS) have identified hundreds of cardiometabolic disease (CMD) risk loci. However, they contribute little to genetic variance, and most downstream gene-regulatory mechanisms are unknown. We genotyped and RNA-sequenced vascular and metabolic tissues from 600 coronary artery disease patients in the Stockholm-Tartu Atherosclerosis Reverse Networks Engineering Task study (STARNET). Gene expression traits associated with CMD risk single-nucleotide polymorphism (SNPs) identified by GWAS were more extensively found in STARNET than in tissue- and disease-unspecific gene-tissue expression studies, indicating sharing of downstream cis-/trans-gene regulation across tissues and CMDs. In contrast, the regulatory effects of other GWAS risk SNPs were tissue-specific; abdominal fat emerged as an important gene-regulatory site for blood lipids, such as for the low-density lipoprotein cholesterol and coronary artery disease risk gene PCSK9. STARNET provides insights into gene-regulatory mechanisms for CMD risk loci, facilitating their translation into opportunities for diagnosis, therapy, and prevention.

Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis

Summary Background Increased circulating plasma urate concentration is associated with an increased risk of coronary heart disease, but the extent of any causative effect of urate on risk of coronary heart disease is still unclear. In this study, we aimed to clarify any causal role of urate on coronary heart disease risk using Mendelian randomisation analysis. Methods We first did a fixed-effects meta-analysis of the observational association of plasma urate and risk of coronary heart disease. We then used a conventional Mendelian randomisation approach to investigate the causal relevance using a genetic instrument based on 31 urate-associated single nucleotide polymorphisms (SNPs). To account for potential pleiotropic associations of certain SNPs with risk factors other than urate, we additionally did both a multivariable Mendelian randomisation analysis, in which the genetic associations of SNPs with systolic and diastolic blood pressure, HDL cholesterol, and triglycerides were included as covariates, and an Egger Mendelian randomisation (MR-Egger) analysis to estimate a causal effect accounting for unmeasured pleiotropy. Findings In the meta-analysis of 17 prospective observational studies (166 486 individuals; 9784 coronary heart disease events) a 1 SD higher urate concentration was associated with an odds ratio (OR) for coronary heart disease of 1·07 (95% CI 1·04–1·10). The corresponding OR estimates from the conventional, multivariable adjusted, and Egger Mendelian randomisation analysis (58 studies; 198 598 individuals; 65 877 events) were 1·18 (95% CI 1·08–1·29), 1·10 (1·00–1·22), and 1·05 (0·92–1·20), respectively, per 1 SD increment in plasma urate. Interpretation Conventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. Funding UK National Institute for Health Research, British Heart Foundation, and UK Medical Research Council.

Associations of FTO and MC4R Variants with Obesity Traits in Indians and the Role of Rural/Urban Environment as a Possible Effect Modifier.

Few studies have investigated the association between genetic variation and obesity traits in Indian populations or the role of environmental factors as modifiers of these relationships. In the context of rapid urbanisation, resulting in significant lifestyle changes, understanding the aetiology of obesity is important. We investigated associations of FTO and MC4R variants with obesity traits in 3390 sibling pairs from four Indian cities, most of whom were discordant for current dwelling (rural or urban). The FTO variant rs9939609 predicted increased weight (0.09 Z-scores, 95% CI: 0.03, 0.15) and BMI (0.08 Z-scores, 95% CI: 0.02, 0.14). The MC4R variant rs17782313 was weakly associated with weight and hip circumference (P < .05). There was some indication that the association between FTO and weight was stronger in urban than that in rural dwellers (P for interaction = .03), but no evidence for effect modification by diet or physical activity. Further studies are needed to investigate ways in which urban environment may modify genetic risk of obesity.

Adult height, coronary heart disease and stroke: a multi-locus Mendelian randomization meta-analysis

Abstract Background: We investigated causal effect of completed growth, measured by adult height, on coronary heart disease (CHD), stroke and cardiovascular traits, using instrumental variable (IV) Mendelian randomization meta-analysis. Methods: We developed an allele score based on 69 single nucleotide polymorphisms (SNPs) associated with adult height, identified by the IBCCardioChip, and used it for IV analysis against cardiovascular risk factors and events in 21 studies and 60 028 participants. IV analysis on CHD was supplemented by summary data from 180 height-SNPs from the GIANT consortium and their corresponding CHD estimates derived from CARDIoGRAMplusC4D. Results: IV estimates from IBCCardioChip and GIANT-CARDIoGRAMplusC4D showed that a 6.5-cm increase in height reduced the odds of CHD by 10% [odds ratios 0.90; 95% confidence intervals (CIs): 0.78 to 1.03 and 0.85 to 0.95, respectively],which agrees with the estimate from the Emerging Risk Factors Collaboration (hazard ratio 0.93; 95% CI: 0.91 to 0.94). IV analysis revealed no association with stroke (odds ratio 0.97; 95% CI: 0.79 to 1.19). IV analysis showed that a 6.5-cm increase in height resulted in lower levels of body mass index (P < 0.001), triglycerides (P < 0.001), non high-density (non-HDL) cholesterol (P < 0.001), C-reactive protein (P = 0.042), and systolic blood pressure (P = 0.064) and higher levels of forced expiratory volume in 1 s and forced vital capacity (P < 0.001 for both). Conclusions: Taller individuals have a lower risk of CHD with potential explanations being that taller people have a better lung function and lower levels of body mass index, cholesterol and blood pressure.

A mini-review of familial ovarian germ cell tumors: An additional manifestation of the familial testicular germ cell tumor syndrome

INTRODUCTION While testicular germ cell tumors (TGCTs) are the most common malignancy in young men, germ cell tumors in women are uncommon. Familial clustering, epidemiologic evidence of increased risk with family or personal history of TGCT, and associations with genitourinary tract anomalies suggest an underlying genetic predisposition to TGCT, but traditional linkage studies have yet to identify a highly penetrant TGCT cancer susceptibility gene. In this paper, we investigate the familial occurrence of testicular and ovarian germ cell tumors. METHODS We report a family in which a TGCT and an ovarian germ cell tumor (OGCT) occurred in two siblings, summarize the existing literature on familial occurrences of OGCT, either alone or in combination with extragonadal or TGCTs, and compare the incidence of familial and sporadic testicular and ovarian GCTs. Sporadic GCT data were obtained from the US Surveillance Epidemiology and End Results (SEER) registry. RESULTS We identified 16 reports of OGCT occurring in conjunction with either ovarian, testicular or extragonadal GCT. In these familial cases, the mean age at onset of female dysgerminoma was younger than that noted in the general population (age 17 vs. age 24, p=0.01). In SEER, the incidence of TGCT was 15 times higher than that of OGCT. Histologic distributions in males and females showed distinctly different patterns. DISCUSSION Although the incidence of OGCTs in the general population is quite low, its occurrence in multiple members of the same family and in families with TGCT suggests that a gene conferring susceptibility to GCTs may exist in some families.

Population Genomics of Cardiometabolic Traits: Design of the University College London-London School of Hygiene and Tropical Medicine-Edinburgh-Bristol (UCLEB) Consortium

Substantial advances have been made in identifying common genetic variants influencing cardiometabolic traits and disease outcomes through genome wide association studies. Nevertheless, gaps in knowledge remain and new questions have arisen regarding the population relevance, mechanisms, and applications for healthcare. Using a new high-resolution custom single nucleotide polymorphism (SNP) array (Metabochip) incorporating dense coverage of genomic regions linked to cardiometabolic disease, the University College-London School-Edinburgh-Bristol (UCLEB) consortium of highly-phenotyped population-based prospective studies, aims to: (1) fine map functionally relevant SNPs; (2) precisely estimate individual absolute and population attributable risks based on individual SNPs and their combination; (3) investigate mechanisms leading to altered risk factor profiles and CVD events; and (4) use Mendelian randomisation to undertake studies of the causal role in CVD of a range of cardiovascular biomarkers to inform public health policy and help develop new preventative therapies.

Use of Allele-Specific FAIRE to Determine Functional Regulatory Polymorphism Using Large-Scale Genotyping Arrays

Following the widespread use of genome-wide association studies (GWAS), focus is turning towards identification of causal variants rather than simply genetic markers of diseases and traits. As a step towards a high-throughput method to identify genome-wide, non-coding, functional regulatory variants, we describe the technique of allele-specific FAIRE, utilising large-scale genotyping technology (FAIRE-gen) to determine allelic effects on chromatin accessibility and regulatory potential. FAIRE-gen was explored using lymphoblastoid cells and the 50,000 SNP Illumina CVD BeadChip. The technique identified an allele-specific regulatory polymorphism within NR1H3 (coding for LXR-α), rs7120118, coinciding with a previously GWAS-identified SNP for HDL-C levels. This finding was confirmed using FAIRE-gen with the 200,000 SNP Illumina Metabochip and verified with the established method of TaqMan allelic discrimination. Examination of this SNP in two prospective Caucasian cohorts comprising 15,000 individuals confirmed the association with HDL-C levels (combined beta = 0.016; p = 0.0006), and analysis of gene expression identified an allelic association with LXR-α expression in heart tissue. Using increasingly comprehensive genotyping chips and distinct tissues for examination, FAIRE-gen has the potential to aid the identification of many causal SNPs associated with disease from GWAS.