Genetic Instrumental Variable Analysis Research Articles

Cross-ancestry genetic investigation of schizophrenia, cannabis use disorder, and tobacco smoking

Individuals with schizophrenia frequently experience co-occurring substance use, including tobacco smoking and heavy cannabis use, and substance use disorders. There is interest in understanding the extent to which these relationships are causal, and to what extent shared genetic factors play a role. We explored the relationships between schizophrenia (Scz; European ancestry N = 161,405; African ancestry N = 15,846), cannabis use disorder (CanUD; European ancestry N = 886,025; African ancestry N = 120,208), and ever-regular tobacco smoking (Smk; European ancestry N = 805,431; African ancestry N = 24,278) using the largest available genome-wide studies of these phenotypes in individuals of African and European ancestries. All three phenotypes were positively genetically correlated (rgs = 0.17–0.62). Genetic instrumental variable analyses suggested the presence of shared heritable factors, but evidence for bidirectional causal relationships was also found between all three phenotypes even after correcting for these shared genetic factors. We identified 327 pleiotropic loci with 439 lead SNPs in the European ancestry data, 150 of which were novel (i.e., not genome-wide significant in the original studies). Of these pleiotropic loci, 202 had lead variants which showed convergent effects (i.e., same direction of effect) on Scz, CanUD, and Smk. Genetic variants convergent across all three phenotypes showed strong genetic correlations with risk-taking, executive function, and several mental health conditions. Our results suggest that both shared genetic factors and causal mechanisms may play a role in the relationship between CanUD, Smk, and Scz, but longitudinal, prospective studies are needed to confirm a causal relationship.

Sjögren’s Disease and Oral Health: A Genetic Instrumental Variable Analysis

Epidemiological studies have consistently shown that Sjögren’s disease (SjD) increases the risk of dental caries. Despite similar evidence indicating an elevated risk of periodontitis, SjD remains a disputed risk factor for this disease. The risk of bias in observational research is a major impediment to confirming this link. Within an instrumental variable framework, genetic variants associated with a risk factor can be used to proxy its effect on an outcome while avoiding common sources of observational study bias. In this study, we leveraged an instrumental variable approach to investigate whether SjD affects the risk of caries and periodontitis. A total of 57 genetic variants strongly associated with SjD were identified from a genome-wide association study of 2,247 European descent cases and 332,115 controls. We tested for associations of these genetic instruments with caries (measured as the number of decayed, missing, and filled surfaces in 26,792 individuals) and periodontitis (17,353 clinical periodontitis cases and 28,210 European controls). Several sensitivity analyses were used to further validate the primary inverse variance weighted (IVW) estimate. IVW analysis revealed an adverse effect of SjD on caries (β = 0.039, P = 6.3e-16) and periodontitis (odds ratio = 1.033, P = 2.3e-05). Sensitivity analyses, conducted to assess the robustness to potential violations of instrumental variable assumptions, further support these findings. Our results showed that SjD has a detrimental effect on caries and also suggest that SjD promotes periodontitis.

Characterizing causal relationships of visceral fat and body shape on multiple sclerosis risk

BackgroundEpidemiologic studies have established obesity as a risk factor for multiple sclerosis (MS). These studies relied on body-mass index (BMI) and body size silhouettes as the primary measures of obesity. Unfortunately, the causal mechanisms through which obesity confers MS risk are not yet known. ObjectivesTo investigate the causal effects of multiple specific measures of body fat on MS risk in populations of European descent, using Mendelian randomization (MR). MethodsMR is a genetic instrumental variable analysis utilizing genome-wide association (GWA) summary statistics to infer causality between phenotypes. MR analyses were performed to investigate the relationships between seven measures of body fat (BMI, waist-hip ratio, visceral adipose tissue [VAT], subcutaneous adipose tissue, and arm-, leg-, and trunk-fat to total body fat ratio) and MS risk. ResultsOnly BMI and VAT were significantly associated with MS risk in separate MR analyses (βBMI=0.27, pBMI<0.001; βVAT=0.28, pVAT=0.006). High correlation between BMI and VAT instruments suggest that two-sample MR associations for BMI and VAT likely capture the same causal mechanisms. ConclusionsBMI and VAT were causally associated with MS risk in European populations, though their effects do not appear independent, suggesting overlap in the role of overall body mass and visceral obesity in MS pathogenesis.

A safe level of alcohol consumption: the right answer demands the right question.

Alcohol has been produced by humans for nearly ten millennia, but gold-standard evidence by which to judge the health effects of limited alcohol consumption remains elusive, introducing serious difficulty in considering the safety of alcohol consumption. To do so, physicians and policymakers must consider the population, dose and context of alcohol consumption and the end-point, preferably a holistic composite, of interest. The limitations of new research trends, such as mega-cohorts, genetic instrumental variable analysis and modelling studies, must also be viewed against the much larger backdrop of existing evidence. Some existing guidelines, such as the 2015-2020 Dietary Guidelines for Americans, succeed remarkably in this task. Nonetheless, large-scale randomized trials are urgently needed if future generations are to enjoy any greater insight into the health effects of population-wide alcohol consumption than the current one has.

Genetic instrumental variable analysis: time to call mendelian randomization what it is. The example of alcohol and cardiovascular disease

In recent years, epidemiologists have increasingly sought to employ genetic data to identify 'causal' relationships between exposures of interest and various endpoints - an instrumental variable approach sometimes termed Mendelian randomization. However, this approach is subject to all of the limitations of instrumental variable analysis and to several limitations specific to its genetic underpinnings, including confounding, weak instrument bias, pleiotropy, adaptation, and failure of replication. Although the approach enjoys some utility in testing the etiological role of discrete biochemical pathways, like folate metabolism, examples like that of alcohol consumption and cardiovascular disease demonstrate that it must be treated with all of the circumspection that should accompany all forms of observational epidemiology. Going forward, we urge the elimination of randomization or causality in reports of its use and suggest that Mendelian randomization instead be termed exactly what it is - genetic instrumental variable analysis.

Association of Body Mass Index with DNA Methylation and Gene Expression in Blood Cells and Relations to Cardiometabolic Disease: A Mendelian Randomization Approach.

BackgroundThe link between DNA methylation, obesity, and adiposity-related diseases in the general population remains uncertain.Methods and FindingsWe conducted an association study of body mass index (BMI) and differential methylation for over 400,000 CpGs assayed by microarray in whole-blood-derived DNA from 3,743 participants in the Framingham Heart Study and the Lothian Birth Cohorts, with independent replication in three external cohorts of 4,055 participants. We examined variations in whole blood gene expression and conducted Mendelian randomization analyses to investigate the functional and clinical relevance of the findings. We identified novel and previously reported BMI-related differential methylation at 83 CpGs that replicated across cohorts; BMI-related differential methylation was associated with concurrent changes in the expression of genes in lipid metabolism pathways. Genetic instrumental variable analysis of alterations in methylation at one of the 83 replicated CpGs, cg11024682 (intronic to sterol regulatory element binding transcription factor 1 [SREBF1]), demonstrated links to BMI, adiposity-related traits, and coronary artery disease. Independent genetic instruments for expression of SREBF1 supported the findings linking methylation to adiposity and cardiometabolic disease. Methylation at a substantial proportion (16 of 83) of the identified loci was found to be secondary to differences in BMI. However, the cross-sectional nature of the data limits definitive causal determination.ConclusionsWe present robust associations of BMI with differential DNA methylation at numerous loci in blood cells. BMI-related DNA methylation and gene expression provide mechanistic insights into the relationship between DNA methylation, obesity, and adiposity-related diseases.

Elevated Lipoprotein(a) Levels, LPA Risk Genotypes, and Increased Risk of Heart Failure in the General Population

ObjectivesThis study sough to test whether elevated lipoprotein(a) levels and corresponding LPA risk genotypes (low number of kringle IV type 2 repeats, rs3798220 and rs10455872, minor allele carriers) are associated with an increased risk of heart failure (HF). BackgroundElevated lipoprotein(a) levels represent a genetically determined risk factor for myocardial infarction (MI) and aortic valve stenosis (AVS). It is presently unknown whether elevated lipoprotein(a) levels also cause heart failure (HF). MethodsWe combined 2 general population studies, the Copenhagen City Heart Study (n = 10,855) and the Copenhagen General Population Study (n = 87,242), which totaled 98,097 Danish participants, of whom 4,122 were diagnosed with HF (1976 to 2013). We conducted observational and genetic instrumental variable analyses in a Mendelian randomization study design, assessing evidence of causality, and we performed mediation analyses. ResultsElevated lipoprotein(a) levels were associated with multivariable adjusted hazard ratios for HF of 1.10 (95% CI: 0.97 to 1.25) for the 34th to 66th percentiles (8 to 19 mg/dl), 1.24 (95% CI: 1.08 to 1.42) for the 67th to 90th percentiles (20 to 67 mg/dl), 1.57 (95% CI: 1.32 to 1.87) for the 91st to 99th percentiles (68 to 153 mg/dl), and 1.79 (95% CI: 1.18 to 2.73) for levels >99th percentile (>153 mg/dl) versus levels <34th percentile (<8 mg/dl) (trend, p < 0.001), corresponding to a population-attributable risk of 9%. By combining all LPA risk genotypes, instrumental variable analysis yielded a genetic relative risk for HF of 1.18 (95% CI: 1.04 to 1.34) per 10-fold higher lipoprotein(a) levels, which was comparable to the corresponding observational hazard ratio of 1.22 (95% CI: 1.11 to 1.35). Upon exclusion of participants diagnosed with MI or AVS, risk estimates were attenuated. Accordingly, 63% (95% CI: 45% to 99%) of HF risk was mediated via MI and AVS combined. ConclusionsElevated lipoprotein(a) levels and corresponding LPA risk genotypes were associated with an increased risk of HF consistent with a causal association. The association appeared to be partly mediated by MI and AVS.

Diabetes Pathology and Risk of Primary Open-Angle Glaucoma: Evaluating Causal Mechanisms by Using Genetic Information.

Although type 2 diabetes (T2D) predicts glaucoma, the potential for unmeasured confounding has hampered causal conclusions. We performed separate sample genetic instrumental variable analyses using the Genetic Epidemiology Research Study on Adult Health and Aging cohort (n = 69,685; 1995-2013) to estimate effects of T2D on primary open-angle glaucoma (POAG; 3,554 cases). Genetic instrumental variables for overall and mechanism-specific (i.e., linked to T2D via influences on adiposity, β-cell function, insulin regulation, or other metabolic processes) T2D risk were constructed by using 39 genetic polymorphisms established to predict T2D in other samples. Instrumental variable estimates indicated that T2D increased POAG risk (odds ratio = 2.53, 95% confidence interval: 1.04, 6.11). The instrumental variable for β-cell dysregulation also significantly predicted POAG (odds ratioβ-cell = 5.26, 95% confidence interval: 1.75, 15.85), even among individuals without diagnosed T2D, suggesting that metabolic dysregulation may increase POAG risk prior to T2D diagnosis. The T2D risk variant in the melatonin receptor 1B gene (MTNR1B) predicted risk of POAG independently of T2D status, indicating possible pleiotropic physiological functions of melatonin, but instrumental variable effect estimates were significant even excluding MTNR1B variants. To our knowledge, this is the first genetic instrumental variable study of T2D and glaucoma, providing a novel approach to evaluating this hypothesized relationship. Our findings substantially bolster observational evidence that T2D increases POAG risk.

Secretory Phospholipase A2-IIA and Cardiovascular Disease: A Mendelian Randomization Study

ObjectivesThis study sought to investigate the role of secretory phospholipase A2 (sPLA2)-IIA in cardiovascular disease.BackgroundHigher circulating levels of sPLA2-IIA mass or sPLA2 enzyme activity have been associated with increased risk of cardiovascular events. However, it is not clear if this association is causal. A recent phase III clinical trial of an sPLA2 inhibitor (varespladib) was stopped prematurely for lack of efficacy.MethodsWe conducted a Mendelian randomization meta-analysis of 19 general population studies (8,021 incident, 7,513 prevalent major vascular events [MVE] in 74,683 individuals) and 10 acute coronary syndrome (ACS) cohorts (2,520 recurrent MVE in 18,355 individuals) using rs11573156, a variant in PLA2G2A encoding the sPLA2-IIA isoenzyme, as an instrumental variable.ResultsPLA2G2A rs11573156 C allele associated with lower circulating sPLA2-IIA mass (38% to 44%) and sPLA2 enzyme activity (3% to 23%) per C allele. The odds ratio (OR) for MVE per rs11573156 C allele was 1.02 (95% confidence interval [CI]: 0.98 to 1.06) in general populations and 0.96 (95% CI: 0.90 to 1.03) in ACS cohorts. In the general population studies, the OR derived from the genetic instrumental variable analysis for MVE for a 1-log unit lower sPLA2-IIA mass was 1.04 (95% CI: 0.96 to 1.13), and differed from the non-genetic observational estimate (OR: 0.69; 95% CI: 0.61 to 0.79). In the ACS cohorts, both the genetic instrumental variable and observational ORs showed a null association with MVE. Instrumental variable analysis failed to show associations between sPLA2 enzyme activity and MVE.ConclusionsReducing sPLA2-IIA mass is unlikely to be a useful therapeutic goal for preventing cardiovascular events.