Jorgen Engmann

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.

Investigating the possible causal association of smoking with depression and anxiety using Mendelian randomisation meta-analysis: the CARTA consortium

Objectives To investigate whether associations of smoking with depression and anxiety are likely to be causal, using a Mendelian randomisation approach. Design Mendelian randomisation meta-analyses using a genetic variant (rs16969968/rs1051730) as a proxy for smoking heaviness, and observational meta-analyses of the associations of smoking status and smoking heaviness with depression, anxiety and psychological distress. Participants Current, former and never smokers of European ancestry aged ≥16 years from 25 studies in the Consortium for Causal Analysis Research in Tobacco and Alcohol (CARTA). Primary outcome measures Binary definitions of depression, anxiety and psychological distress assessed by clinical interview, symptom scales or self-reported recall of clinician diagnosis. Results The analytic sample included up to 58 176 never smokers, 37 428 former smokers and 32 028 current smokers (total N=127 632). In observational analyses, current smokers had 1.85 times greater odds of depression (95% CI 1.65 to 2.07), 1.71 times greater odds of anxiety (95% CI 1.54 to 1.90) and 1.69 times greater odds of psychological distress (95% CI 1.56 to 1.83) than never smokers. Former smokers also had greater odds of depression, anxiety and psychological distress than never smokers. There was evidence for positive associations of smoking heaviness with depression, anxiety and psychological distress (ORs per cigarette per day: 1.03 (95% CI 1.02 to 1.04), 1.03 (95% CI 1.02 to 1.04) and 1.02 (95% CI 1.02 to 1.03) respectively). In Mendelian randomisation analyses, there was no strong evidence that the minor allele of rs16969968/rs1051730 was associated with depression (OR=1.00, 95% CI 0.95 to 1.05), anxiety (OR=1.02, 95% CI 0.97 to 1.07) or psychological distress (OR=1.02, 95% CI 0.98 to 1.06) in current smokers. Results were similar for former smokers. Conclusions Findings from Mendelian randomisation analyses do not support a causal role of smoking heaviness in the development of depression and anxiety.

Sixty-Five Common Genetic Variants and Prediction of Type 2 Diabetes

We developed a 65 type 2 diabetes (T2D) variant–weighted gene score to examine the impact on T2D risk assessment in a U.K.-based consortium of prospective studies, with subjects initially free from T2D (N = 13,294; 37.3% women; mean age 58.5 [38–99] years). We compared the performance of the gene score with the phenotypically derived Framingham Offspring Study T2D risk model and then the two in combination. Over the median 10 years of follow-up, 804 participants developed T2D. The odds ratio for T2D (top vs. bottom quintiles of gene score) was 2.70 (95% CI 2.12–3.43). With a 10% false-positive rate, the genetic score alone detected 19.9% incident cases, the Framingham risk model 30.7%, and together 37.3%. The respective area under the receiver operator characteristic curves were 0.60 (95% CI 0.58–0.62), 0.75 (95% CI 0.73 to 0.77), and 0.76 (95% CI 0.75 to 0.78). The combined risk score net reclassification improvement (NRI) was 8.1% (5.0 to 11.2; P = 3.31 × 10−7). While BMI stratification into tertiles influenced the NRI (BMI ≤24.5 kg/m2, 27.6% [95% CI 17.7–37.5], P = 4.82 × 10−8; 24.5–27.5 kg/m2, 11.6% [95% CI 5.8–17.4], P = 9.88 × 10−5; >27.5 kg/m2, 2.6% [95% CI −1.4 to 6.6], P = 0.20), age categories did not. The addition of the gene score to a phenotypic risk model leads to a potentially clinically important improvement in discrimination of incident T2D.

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.

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.

The druggable genome and support for target identification and validation in drug development

The druggable genome and genome-wide association study data reveal new drug development and repurposing opportunities. An organized way to drug the genome Many drugs that are already approved for specific diseases have known protein targets, which may be relevant for other disease types as well. In addition, a systematic way of identifying druggable genes in various diseases should help streamline the process of developing new drugs for these targets, even if no specific drugs are available for them yet. Finan et al. designed a computational approach to do this, combining data from numerous existing genome-wide association studies to identify druggable proteins, connect them with known drugs where available, and facilitate the design of new targeted therapeutics. Target identification (determining the correct drug targets for a disease) and target validation (demonstrating an effect of target perturbation on disease biomarkers and disease end points) are important steps in drug development. Clinically relevant associations of variants in genes encoding drug targets model the effect of modifying the same targets pharmacologically. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome-wide association studies to an updated set of genes encoding druggable human proteins, to agents with bioactivity against these targets, and, where there were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, which will enable association studies of druggable genes for drug target selection and validation in human disease.

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.

A systematic review and meta-analysis of 130,000 individuals shows smoking does not modify the association of APOE genotype on risk of coronary heart disease

Background Conflicting evidence exists on whether smoking acts as an effect modifier of the association between APOE genotype and risk of coronary heart disease (CHD). Methods and results We searched PubMed and EMBASE to June 11, 2013 for published studies reporting APOE genotype, smoking status and CHD events and added unpublished data from population cohorts. We tested for presence of effect modification by smoking status in the relationship between APOE genotype and risk of CHD using likelihood ratio test. In total 13 studies (including unpublished data from eight cohorts) with 10,134 CHD events in 130,004 individuals of European descent were identified. The odds ratio (OR) for CHD risk from APOE genotype (ε4 carriers versus non-carriers) was 1.06 (95% confidence interval (CI): 1.01, 1.12) and for smoking (present vs. past/never smokers) was OR 2.05 (95%CI: 1.95, 2.14). When the association between APOE genotype and CHD was stratified by smoking status, compared to non-ε4 carriers, ε4 carriers had an OR of 1.11 (95%CI: 1.02, 1.21) in 28,789 present smokers and an OR of 1.04 (95%CI 0.98, 1.10) in 101,215 previous/never smokers, with no evidence of effect modification (P-value for heterogeneity = 0.19). Analysis of pack years in individual participant data of >60,000 with adjustment for cardiovascular traits also failed to identify evidence of effect modification. Conclusions In the largest analysis to date, we identified no evidence for effect modification by smoking status in the association between APOE genotype and risk of CHD.

Exome-wide analysis of rare coding variation identifies novel associations with COPD and airflow limitation in MOCS3, IFIT3 and SERPINA12

Background Several regions of the genome have shown to be associated with COPD in genome-wide association studies of common variants. Objective To determine rare and potentially functional single nucleotide polymorphisms (SNPs) associated with the risk of COPD and severity of airflow limitation. Methods 3226 current or former smokers of European ancestry with lung function measures indicative of Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2 COPD or worse were genotyped using an exome array. An analysis of risk of COPD was carried out using ever smoking controls (n=4784). Associations with %predicted FEV1 were tested in cases. We followed-up signals of interest (p<10−5) in independent samples from a subset of the UK Biobank population and also undertook a more powerful discovery study by meta-analysing the exome array data and UK Biobank data for variants represented on both arrays. Results Among the associated variants were two in regions previously unreported for COPD; a low frequency non-synonymous SNP in MOCS3 (rs7269297, pdiscovery=3.08×10−6, preplication=0.019) and a rare SNP in IFIT3, which emerged in the meta-analysis (rs140549288, pmeta=8.56×10−6). In the meta-analysis of % predicted FEV1 in cases, the strongest association was shown for a splice variant in a previously unreported region, SERPINA12 (rs140198372, pmeta=5.72×10−6). We also confirmed previously reported associations with COPD risk at MMP12, HHIP, GPR126 and CHRNA5. No associations in novel regions reached a stringent exome-wide significance threshold (p<3.7×10−7). Conclusions This study identified several associations with the risk of COPD and severity of airflow limitation, including novel regions MOCS3, IFIT3 and SERPINA12, which warrant further study.

Mendelian Randomisation study of the influence of eGFR on coronary heart disease

Impaired kidney function, as measured by reduced estimated glomerular filtration rate (eGFR), has been associated with increased risk of coronary heart disease (CHD) in observational studies, but it is unclear whether this association is causal or the result of confounding or reverse causation. In this study we applied Mendelian randomisation analysis using 17 genetic variants previously associated with eGFR to investigate the causal role of kidney function on CHD. We used 13,145 participants from the UCL-LSHTM-Edinburgh-Bristol (UCLEB) Consortium and 194,427 participants from the Coronary ARtery DIsease Genome-wide Replication and Meta-analysis plus Coronary Artery Disease (CARDIoGRAMplusC4D) consortium. We observed significant association of an unweighted gene score with CHD risk (odds ratio = 0.983 per additional eGFR-increasing allele, 95% CI = 0.970–0.996, p = 0.008). However, using weights calculated from UCLEB, the gene score was not associated with disease risk (p = 0.11). These conflicting results could be explained by a single SNP, rs653178, which was not associated with eGFR in the UCLEB sample, but has known pleiotropic effects that prevent us from drawing a causal conclusion. The observational association between low eGFR and increased CHD risk was not explained by potential confounders, and there was no evidence of reverse causation, therefore leaving the remaining unexplained association as an open question.