Mendelian randomization (MR) uses genetic variants as instrumental variables to infer whether a risk factor causally affects a health outcome. Meta‐analysis has been used historically in MR to ...combine results from separate epidemiological studies, with each study using a small but select group of genetic variants. In recent years, it has been used to combine genome‐wide association study (GWAS) summary data for large numbers of genetic variants. Heterogeneity among the causal estimates obtained from multiple genetic variants points to a possible violation of the necessary instrumental variable assumptions. In this article, we provide a basic introduction to MR and the instrumental variable theory that it relies upon. We then describe how random effects models, meta‐regression, and robust regression are being used to test and adjust for heterogeneity in order to improve the rigor of the MR approach.
Large-scale molecular profiling and genotyping provide a unique opportunity to systematically compare the genetically predicted effects of therapeutic targets on the human metabolome. We firstly ...constructed genetic risk scores for 8 drug targets on the basis that they primarily modify low-density lipoprotein (LDL) cholesterol (HMGCR, PCKS9, and NPC1L1), high-density lipoprotein (HDL) cholesterol (CETP), or triglycerides (APOC3, ANGPTL3, ANGPTL4, and LPL). Conducting mendelian randomisation (MR) provided strong evidence of an effect of drug-based genetic scores on coronary artery disease (CAD) risk with the exception of ANGPTL3. We then systematically estimated the effects of each score on 249 metabolic traits derived using blood samples from an unprecedented sample size of up to 115,082 UK Biobank participants. Genetically predicted effects were generally consistent among drug targets, which were intended to modify the same lipoprotein lipid trait. For example, the linear fit for the MR estimates on all 249 metabolic traits for genetically predicted inhibition of LDL cholesterol lowering targets HMGCR and PCSK9 was r2 = 0.91. In contrast, comparisons between drug classes that were designed to modify discrete lipoprotein traits typically had very different effects on metabolic signatures (for instance, HMGCR versus each of the 4 triglyceride targets all had r2 < 0.02). Furthermore, we highlight this discrepancy for specific metabolic traits, for example, finding that LDL cholesterol lowering therapies typically had a weak effect on glycoprotein acetyls, a marker of inflammation, whereas triglyceride modifying therapies assessed provided evidence of a strong effect on lowering levels of this inflammatory biomarker. Our findings indicate that genetically predicted perturbations of these drug targets on the blood metabolome can drastically differ, despite largely consistent effects on risk of CAD, with potential implications for biomarkers in clinical development and measuring treatment response.
Physical activity and sleep duration are established risk factors for many diseases, but their aetiology is poorly understood, partly due to relying on self-reported evidence. Here we report a ...genome-wide association study (GWAS) of device-measured physical activity and sleep duration in 91,105 UK Biobank participants, finding 14 significant loci (7 novel). These loci account for 0.06% of activity and 0.39% of sleep duration variation. Genome-wide estimates of ~ 15% phenotypic variation indicate high polygenicity. Heritability is higher in women than men for overall activity (23 vs. 20%, p = 1.5 × 10
) and sedentary behaviours (18 vs. 15%, p = 9.7 × 10
). Heritability partitioning, enrichment and pathway analyses indicate the central nervous system plays a role in activity behaviours. Two-sample Mendelian randomisation suggests that increased activity might causally lower diastolic blood pressure (beta mmHg/SD: -0.91, SE = 0.18, p = 8.2 × 10
), and odds of hypertension (Odds ratio/SD: 0.84, SE = 0.03, p = 4.9 × 10
). Our results advocate the value of physical activity for reducing blood pressure.
Circulating lipoprotein lipids cause coronary heart disease (CHD). However, the precise way in which one or more lipoprotein lipid-related entities account for this relationship remains unclear. ...Using genetic instruments for lipoprotein lipid traits implemented through multivariable Mendelian randomisation (MR), we sought to compare their causal roles in the aetiology of CHD.
We conducted a genome-wide association study (GWAS) of circulating non-fasted lipoprotein lipid traits in the UK Biobank (UKBB) for low-density lipoprotein (LDL) cholesterol, triglycerides, and apolipoprotein B to identify lipid-associated single nucleotide polymorphisms (SNPs). Using data from CARDIoGRAMplusC4D for CHD (consisting of 60,801 cases and 123,504 controls), we performed univariable and multivariable MR analyses. Similar GWAS and MR analyses were conducted for high-density lipoprotein (HDL) cholesterol and apolipoprotein A-I. The GWAS of lipids and apolipoproteins in the UKBB included between 393,193 and 441,016 individuals in whom the mean age was 56.9 y (range 39-73 y) and of whom 54.2% were women. The mean (standard deviation) lipid concentrations were LDL cholesterol 3.57 (0.87) mmol/L and HDL cholesterol 1.45 (0.38) mmol/L, and the median triglycerides was 1.50 (IQR = 1.11) mmol/L. The mean (standard deviation) values for apolipoproteins B and A-I were 1.03 (0.24) g/L and 1.54 (0.27) g/L, respectively. The GWAS identified multiple independent SNPs associated at P < 5 × 10-8 for LDL cholesterol (220), apolipoprotein B (n = 255), triglycerides (440), HDL cholesterol (534), and apolipoprotein A-I (440). Between 56%-93% of SNPs identified for each lipid trait had not been previously reported in large-scale GWASs. Almost half (46%) of these SNPs were associated at P < 5 × 10-8 with more than one lipid-related trait. Assessed individually using MR, LDL cholesterol (odds ratio OR 1.66 per 1-standard-deviation-higher trait; 95% CI: 1.49-1.86; P < 0.001), triglycerides (OR 1.34; 95% CI: 1.25-1.44; P < 0.001) and apolipoprotein B (OR 1.73; 95% CI: 1.56-1.91; P < 0.001) had effect estimates consistent with a higher risk of CHD. In multivariable MR, only apolipoprotein B (OR 1.92; 95% CI: 1.31-2.81; P < 0.001) retained a robust effect, with the estimate for LDL cholesterol (OR 0.85; 95% CI: 0.57-1.27; P = 0.44) reversing and that of triglycerides (OR 1.12; 95% CI: 1.02-1.23; P = 0.01) becoming weaker. Individual MR analyses showed a 1-standard-deviation-higher HDL cholesterol (OR 0.80; 95% CI: 0.75-0.86; P < 0.001) and apolipoprotein A-I (OR 0.83; 95% CI: 0.77-0.89; P < 0.001) to lower the risk of CHD, but these effect estimates attenuated substantially to the null on accounting for apolipoprotein B. A limitation is that, owing to the nature of lipoprotein metabolism, measures related to the composition of lipoprotein particles are highly correlated, creating a challenge in making exclusive interpretations on causation of individual components.
These findings suggest that apolipoprotein B is the predominant trait that accounts for the aetiological relationship of lipoprotein lipids with risk of CHD.
Mendelian randomization (MR) is a burgeoning field that involves the use of genetic variants to assess causal relationships between exposures and outcomes. MR studies can be straightforward; for ...example, genetic variants within or near the encoding locus that is associated with protein concentrations can help to assess their causal role in disease. However, a more complex relationship between the genetic variants and an exposure can make findings from MR more difficult to interpret. In this Review, we describe some of these challenges in interpreting MR analyses, including those from studies using genetic variants to assess causality of multiple traits (such as branched-chain amino acids and risk of diabetes mellitus); studies describing pleiotropic variants (for example, C-reactive protein and its contribution to coronary heart disease); and those investigating variants that disrupt normal function of an exposure (for example, HDL cholesterol or IL-6 and coronary heart disease). Furthermore, MR studies on variants that encode enzymes responsible for the metabolism of an exposure (such as alcohol) are discussed, in addition to those assessing the effects of variants on time-dependent exposures (extracellular superoxide dismutase), cumulative exposures (LDL cholesterol), and overlapping exposures (triglycerides and non-HDL cholesterol). We elaborate on the molecular features of each relationship, and provide explanations for the likely causal associations. In doing so, we hope to contribute towards more reliable evaluations of MR findings.
Obesity is observationally associated with altered risk of many female reproductive conditions. These include polycystic ovary syndrome (PCOS), abnormal uterine bleeding, endometriosis, infertility, ...and pregnancy-related disorders. However, the roles and mechanisms of obesity in the aetiology of reproductive disorders remain unclear. Thus, we aimed to estimate observational and genetically predicted causal associations between obesity, metabolic hormones, and female reproductive disorders.
Logistic regression, generalised additive models, and Mendelian randomisation (MR) (2-sample, non-linear, and multivariable) were applied to obesity and reproductive disease data on up to 257,193 women of European ancestry in UK Biobank and publicly available genome-wide association studies (GWASs). Body mass index (BMI), waist-to-hip ratio (WHR), and WHR adjusted for BMI were observationally (odds ratios ORs = 1.02-1.87 per 1-SD increase in obesity trait) and genetically (ORs = 1.06-2.09) associated with uterine fibroids (UF), PCOS, heavy menstrual bleeding (HMB), and pre-eclampsia. Genetically predicted visceral adipose tissue (VAT) mass was associated with the development of HMB (OR 95% CI per 1-kg increase in predicted VAT mass = 1.32 1.06-1.64, P = 0.0130), PCOS (OR 95% CI = 1.15 1.08-1.23, P = 3.24 × 10-05), and pre-eclampsia (OR 95% CI = 3.08 1.98-4.79, P = 6.65 × 10-07). Increased waist circumference posed a higher genetic risk (ORs = 1.16-1.93) for the development of these disorders and UF than did increased hip circumference (ORs = 1.06-1.10). Leptin, fasting insulin, and insulin resistance each mediated between 20% and 50% of the total genetically predicted association of obesity with pre-eclampsia. Reproductive conditions clustered based on shared genetic components of their aetiological relationships with obesity. This study was limited in power by the low prevalence of female reproductive conditions among women in the UK Biobank, with little information on pre-diagnostic anthropometric traits, and by the susceptibility of MR estimates to genetic pleiotropy.
We found that common indices of overall and central obesity were associated with increased risks of reproductive disorders to heterogenous extents in a systematic, large-scale genetics-based analysis of the aetiological relationships between obesity and female reproductive conditions. Our results suggest the utility of exploring the mechanisms mediating the causal associations of overweight and obesity with gynaecological health to identify targets for disease prevention and treatment.
Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data. As ...with all epidemiological approaches, findings from Mendelian randomisation studies depend on specific assumptions. We provide explanations of the information typically reported in Mendelian randomisation studies that can be used to assess the plausibility of these assumptions and guidance on how to interpret findings from Mendelian randomisation studies in the context of other sources of evidence
Cholesteryl ester transfer protein (CETP) facilitates exchange of triglycerides and cholesteryl ester between high-density lipoprotein (HDL) and apolipoprotein B100-containing lipoproteins. Evidence ...from genetic studies that variants in the CETP gene were associated with higher blood HDL cholesterol, lower low-density lipoprotein cholesterol, and lower risk of coronary heart disease suggested that pharmacological inhibition of CETP may be beneficial. To date, 4 CETP inhibitors have entered phase 3 cardiovascular outcome trials. Torcetrapib was withdrawn due to unanticipated off-target effects that increased risk of death, and major trials of dalcetrapib and evacetrapib were terminated early for futility. In the 30,000-patient REVEAL (Randomized Evaluation of the Effects of Anacetrapib through Lipid Modification) trial, anacetrapib doubled HDL cholesterol, reduced non-HDL cholesterol by 17 mg/dl (0.44 mmol/l), and reduced major vascular events by 9% over 4 years, but anaceptrapib was found to accumulate in adipose tissue, and regulatory approval is not being sought. Therefore, despite considerable initial promise, CETP inhibition provides insufficient cardiovascular benefit for routine use.
Obesity traits are causally implicated with risk of cardiometabolic diseases. It remains unclear whether there are similar causal effects of obesity traits on other non-communicable diseases. Also, ...it is largely unexplored whether there are any sex-specific differences in the causal effects of obesity traits on cardiometabolic diseases and other leading causes of death. We constructed sex-specific genetic risk scores (GRS) for three obesity traits; body mass index (BMI), waist-hip ratio (WHR), and WHR adjusted for BMI, including 565, 324, and 337 genetic variants, respectively. These GRSs were then used as instrumental variables to assess associations between the obesity traits and leading causes of mortality in the UK Biobank using Mendelian randomization. We also investigated associations with potential mediators, including smoking, glycemic and blood pressure traits. Sex-differences were subsequently assessed by Cochran's Q-test (Phet). A Mendelian randomization analysis of 228,466 women and 195,041 men showed that obesity causes coronary artery disease, stroke (particularly ischemic), chronic obstructive pulmonary disease, lung cancer, type 2 and 1 diabetes mellitus, non-alcoholic fatty liver disease, chronic liver disease, and acute and chronic renal failure. Higher BMI led to higher risk of type 2 diabetes in women than in men (Phet = 1.4×10-5). Waist-hip-ratio led to a higher risk of chronic obstructive pulmonary disease (Phet = 3.7×10-6) and higher risk of chronic renal failure (Phet = 1.0×10-4) in men than women. Obesity traits have an etiological role in the majority of the leading global causes of death. Sex differences exist in the effects of obesity traits on risk of type 2 diabetes, chronic obstructive pulmonary disease, and renal failure, which may have downstream implications for public health.
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