Triangulating Evidence through the Inclusion of Genetically Informed Designs

The Association Between Smoking Behavior and the Risk of Hypertension: Review of the Observational and Genetic Evidence

Background

Cigarette smoking is one of the world's largest avoidable risk factors for morbidity and mortality. Numerous studies have investigated the association between smoking and hypertension (HTN). Although observational data and cross-sectional research often exhibit a link between smoking and HTN, establishing causation remains challenging owing to potential confounding variables. Mendelian randomization (MR), a genetic epidemiological technique that employs genetic variants as instrumental variables, offers a more robust approach for evaluating causal links. This review aimed to explore both the observational and causal relationships between smoking and the risk of HTN.

Methodology

A comprehensive literature search across major electronic databases was conducted to identify relevant observational and Mendelian randomization (MR) studies on smoking and HTN risk. Various characteristics were included during study selection, such as study design, exposure assessment, and age range. Standardized processes were used for data retrieval and quality evaluation.

Results

Analysis of observational data revealed a paradoxical association between smoking and the risk of HTN, where a lower risk was observed among current smokers when compared to non-smokers. However, observational analysis also presented a dose-response effect with greater smoking intensity showed a modest linear increase in HTN risk, and older smoking initiation was associated with a slight increase in HTN risk (compared with younger). In contrast, MR-based causal estimates provide inconsistent evidence regarding the causal relationship between smoking behavior and HTN. Some MR analyses indicated a potential causal link between smoking and HTN; but this was not consistent.

Conclusion

Observational studies suggest a paradoxical association between smoking and HTN. However, MR studies do not provide sufficient evidence to establish a causal relationship. Regardless, lifestyle variables remain crucial for overall health. Healthcare professionals should regularly assess smoking status and provide counseling for quitting. Further research is needed to clarify the underlying processes, identify mediators, and evaluate the interventions.

The genetic landscape of substance use disorders

Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.

Non-linear Mendelian randomization: detection of biases using negative controls with a focus on BMI, Vitamin D and LDL cholesterol

Mendelian randomisation (MR) is an established technique in epidemiological investigation, using the principle of random allocation of genetic variants at conception to estimate the causal linear effect of an exposure on an outcome. Extensions to this technique include non-linear approaches that allow for differential effects of the exposure on the outcome depending on the level of the exposure. A widely used non-linear method is the residual approach, which estimates the causal effect within different strata of the non-genetically predicted exposure (i.e. the "residual" exposure). These "local" causal estimates are then used to make inferences about non-linear effects. Recent work has identified that this method can lead to estimates that are seriously biased, and a new method-the doubly-ranked method-has been introduced as a possibly more robust approach. In this paper, we perform negative control outcome analyses in the MR context. These are analyses with outcomes onto which the exposure should have no predicted causal effect. Using both methods we find clearly biased estimates in certain situations. We additionally examined a situation for which there are robust randomised controlled trial estimates of effects-that of low-density lipoprotein cholesterol (LDL-C) reduction onto myocardial infarction, where randomised trials have provided strong evidence of the shape of the relationship. The doubly-ranked method did not identify the same shape as the trial data, and for LDL-C and other lipids they generated some highly implausible findings. Therefore, we suggest there should be extensive simulation and empirical methodological examination of performance of both methods for NLMR under different conditions before further use of these methods. In the interim, use of NLMR methods needs justification, and a number of sanity checks (such as analysis of negative and positive control outcomes, sensitivity analyses excluding removal of strata at the extremes of the distribution, examination of biological plausibility and triangulation of results) should be performed.

Assessing causal links between age at menarche and adolescent mental health: a Mendelian randomisation study

BACKGROUND

The timing of puberty may have an important impact on adolescent mental health. In particular, earlier age at menarche has been associated with elevated rates of depression in adolescents. Previous research suggests that this relationship may be causal, but replication and an investigation of whether this effect extends to other mental health domains is warranted.

METHODS

In this Registered Report, we triangulated evidence from different causal inference methods using a new wave of data (N = 13,398) from the Norwegian Mother, Father, and Child Cohort Study. We combined multiple regression, one- and two-sample Mendelian randomisation (MR), and negative control analyses (using pre-pubertal symptoms as outcomes) to assess the causal links between age at menarche and different domains of adolescent mental health.

RESULTS

Our results supported the hypothesis that earlier age at menarche is associated with elevated depressive symptoms in early adolescence based on multiple regression (β =  - 0.11, 95% CI [- 0.12, - 0.09], pone-tailed < 0.01). One-sample MR analyses suggested that this relationship may be causal (β =  - 0.07, 95% CI [- 0.13, 0.00], pone-tailed = 0.03), but the effect was small, corresponding to just a 0.06 standard deviation increase in depressive symptoms with each earlier year of menarche. There was also some evidence of a causal relationship with depression diagnoses during adolescence based on one-sample MR (OR = 0.74, 95% CI [0.54, 1.01], pone-tailed = 0.03), corresponding to a 29% increase in the odds of receiving a depression diagnosis with each earlier year of menarche. Negative control and two-sample MR sensitivity analyses were broadly consistent with this pattern of results. Multivariable MR analyses accounting for the genetic overlap between age at menarche and childhood body size provided some evidence of confounding. Meanwhile, we found little consistent evidence of effects on other domains of mental health after accounting for co-occurring depression and other confounding.

CONCLUSIONS

We found evidence that age at menarche affected diagnoses of adolescent depression, but not other domains of mental health. Our findings suggest that earlier age at menarche is linked to problems in specific domains rather than adolescent mental health in general.

Causal association between helicobacter pylori and atherosclerosis: a two-sample Mendelian randomization

BACKGROUND

Helicobacter pylori (H. pylori), according to a number of recent observational studies, is connected to atherosclerosis (AS). However, the link between H. pylori and AS is debatable.

METHODS

In order to calculate the causal relationship between H. pylori and AS, we employed a two-sample Mendelian randomization (MR) analysis. The data for H. pylori were obtained from the IEU GWAS database ( https://gwas.mrcieu.ac.uk/datasets/ ) and the data for AS were obtained from the Finngen GWAS database ( https://r5.finngen.fi/ ). We selected single nucleotide polymorphisms with a threshold of 5 × 10-6 from earlier genome-wide association studies. MR was performed mainly using the inverse variance weighted (IVW) method. To ensure the reliability of the findings, We performed a leave-one-out sensitivity analysis to test for sensitivity. F-value was used to test weak instrument.

RESULTS

A positive causal relationship between H. pylori OMP antibody levels and peripheral atherosclerosis was shown by our two-sample MR analysis (odds ratio (OR) = 1.33, 95% confidence interval (CI) = 1.14-1.54, P = 0.26E-03) using IVW. Additionally, there was a causative link between coronary atherosclerosis and H. pylori VacA antibody levels (IVW OR = 1.06, 95% CI = 1.01-1.10, P = 0.016). All the F-values were above 10.

CONCLUSIONS

This MR study discovered a causal link between H. pylori and AS. Different antibodies have different effects, so future researches are needed to figure out the exact mechanisms behind this link.

Systematic comparison of Mendelian randomisation studies and randomised controlled trials using electronic databases

OBJECTIVE

To scope the potential for (semi)-automated triangulation of Mendelian randomisation (MR) and randomised controlled trials (RCTs) evidence since the two methods have distinct assumptions that make comparisons between their results invaluable.

METHODS

We mined ClinicalTrials.Gov, PubMed and EpigraphDB databases and carried out a series of 26 manual literature comparisons among 54 MR and 77 RCT publications.

RESULTS

We found that only 13% of completed RCTs identified in ClinicalTrials.Gov submitted their results to the database. Similarly low coverage was revealed for Semantic Medline (SemMedDB) semantic triples derived from MR and RCT publications -36% and 12%, respectively. Among intervention types that can be mimicked by MR, only trials of pharmaceutical interventions could be automatically matched to MR results due to insufficient annotation with Medical Subject Headings ontology. A manual survey of the literature highlighted the potential for triangulation across a number of exposure/outcome pairs if these challenges can be addressed.

CONCLUSIONS

We conclude that careful triangulation of MR with RCT evidence should involve consideration of similarity of phenotypes across study designs, intervention intensity and duration, study population demography and health status, comparator group, intervention goal and quality of evidence.

Association between periodontitis and breast cancer: two-sample Mendelian randomization study

OBJECTIVES

The purpose of this study was to investigate whether there is a causal relationship between periodontitis and breast cancer by Mendelian randomization analysis.

MATERIALS AND METHODS

We performed a two-sample bidirectional Mendelian randomization (MR) analysis using publicly released genome-wide association studies (GWAS) statistics. The inverse-variance weighted (IVW) method was used as the primary analysis. We applied complementary methods, including weighted median, weighted mode, simple mode, MR-Egger regression, and MR-pleiotropy residual sum and outlier (MR-PRESSO) to detect and correct for the effect of horizontal pleiotropy.

RESULTS

IVW MR analysis showed no effect of periodontitis on breast cancer (IVW OR=0.99, P =0.14). Similarly, no significant causal relationship between breast cancer and periodontitis was found in reverse MR analysis (IVW OR=0.95, P =0.83). The results of MR-Egger regression, weighted median, and weighted mode methods were consistent with those of the IVW method. Based on sensitivity analyses, horizontal pleiotropy is unlikely to distort causal estimates.

CONCLUSIONS

Although observational studies have reported an association between periodontitis and breast cancer, the results of our MR analysis do not support a causal relationship between periodontitis and breast cancer.

CLINICAL RELEVANCE

Mendelian randomization study can more clearly analyze the causal relationship between periodontitis and breast cancer, in order to provide a certain reference for clinicians and deepen the understanding of the relationship between periodontitis and breast cancer, to explore more possible associations between periodontitis and systemic diseases.

What can Mendelian randomization contribute to biological anthropology?

Uncovering causal relationships between exposures and outcomes can be difficult in observational studies because of the potential for confounding and reverse causation to produce biased estimates. Conversely, randomized controlled trials (RCTs) provide the strongest evidence for causality but they are not always feasible. Mendelian randomization (MR) is a method that aims to strengthen causal inference using genetic variants as proxies or instrumental variables (IVs) for exposures, to overcome the above-mentioned biases. Since allele segregation occurs at random from parents to offspring, and alleles for a trait assort independently from those for other traits, MR studies have frequently been compared to "natural" RCTs. In biological anthropology (BA) relationships between variables of interest are usually evaluated using observational data, often remaining descriptive, and other approaches to causal inference have seldom been implemented. Here, we propose the use of MR to investigate cause and effect relationships in BA studies and provide examples to show how that can be done across areas of BA relevance, such as adaptation to the environment, nutrition and life history theory. While we consider MR a useful addition to the biological anthropologist's toolbox, we advocate the adoption of a wide range of methods, affected by different types of biases, in order to better answer the important causal questions for the discipline.

Comparison of intergenerational instrumental variable analyses of body mass index and mortality in UK Biobank

BACKGROUND

An increasing proportion of people have a body mass index (BMI) classified as overweight or obese and published studies disagree whether this will be beneficial or detrimental to health. We applied and evaluated two intergenerational instrumental variable methods to estimate the average causal effect of BMI on mortality in a cohort with many deaths: the parents of UK Biobank participants.

METHODS

In Cox regression models, parental BMI was instrumented by offspring BMI using an 'offspring as instrument' (OAI) estimation and by offspring BMI-related genetic variants in a 'proxy-genotype Mendelian randomization' (PGMR) estimation.

RESULTS

Complete-case analyses were performed in parents of 233 361 UK Biobank participants with full phenotypic, genotypic and covariate data. The PGMR method suggested that higher BMI increased mortality with hazard ratios per kg/m2 of 1.02 (95% CI: 1.01, 1.04) for mothers and 1.04 (95% CI: 1.02, 1.05) for fathers. The OAI method gave considerably higher estimates, which varied according to the parent-offspring pairing between 1.08 (95% CI: 1.06, 1.10; mother-son) and 1.23 (95% CI: 1.16, 1.29; father-daughter).

CONCLUSION

Both methods supported a causal role of higher BMI increasing mortality, although caution is required regarding the immediate causal interpretation of these exact values. Evidence of instrument invalidity from measured covariates was limited for the OAI method and minimal for the PGMR method. The methods are complementary for interrogating the average putative causal effects because the biases are expected to differ between them.

Genetic nurture versus genetic transmission of risk for ADHD traits in the Norwegian Mother, Father and Child Cohort Study

Identifying mechanisms underlying the intergenerational transmission of risk for attention-deficit/hyperactivity disorder (ADHD) traits can inform interventions and provide insights into the role of parents in shaping their children's outcomes. We investigated whether genetic transmission and genetic nurture (environmentally mediated effects) underlie associations between polygenic scores indexing parental risk and protective factors and their offspring's ADHD traits. This birth cohort study included 19,506 genotyped mother-father-offspring trios from the Norwegian Mother, Father and Child Cohort Study. Polygenic scores were calculated for parental factors previously associated with ADHD, including psychopathology, substance use, neuroticism, educational attainment, and cognitive performance. Mothers reported on their 8-year-old children's ADHD traits (n = 9,454 children) using the Parent/Teacher Rating Scale for Disruptive Behaviour Disorders. We found that associations between ADHD maternal and paternal polygenic scores and child ADHD traits decreased significantly when adjusting for the child polygenic score (pΔβ = 9.95 × 10-17 for maternal and pΔβ = 1.48 × 10-14 for paternal estimates), suggesting genetic transmission of ADHD risk. Similar patterns suggesting genetic transmission of risk were observed for smoking, educational attainment, and cognition. The maternal polygenic score for neuroticism remained associated with children's ADHD ratings even after adjusting for the child polygenic score, indicating genetic nurture. There was no robust evidence of genetic nurture for other parental factors. Our findings indicate that the intergenerational transmission of risk for ADHD traits is largely explained by the transmission of genetic variants from parents to offspring rather than by genetic nurture. Observational associations between parental factors and childhood ADHD outcomes should not be interpreted as evidence for predominantly environmentally mediated effects.

Cortisol and periodontitis: Prospective observational and Mendelian randomization studies

PURPOSE

Cortisol has obesogenic, hyperglycemic and immunomodulating effects. Preclinical and observational research suggested that it is associated with periodontitis but the evidence for potential causality in humans is sparse. We triangulated results from prospective observational and Mendelian randomization (MR) analyses to further explore this.

METHODS

Using pooled data from 3,388 participants of two population cohort studies embedded in the Study of Health in Pomerania (SHIP) project, we associated serum cortisol levels with periodontal outcomes measured after a median follow-up time of 6.9 years, adjusting for confounding and selection bias using propensity score weighting and multiple imputation. We further examined the effect of genetically proxied plasma morning cortisol levels on periodontitis using two-sample MR of 17,353 cases and 28,210 controls.

RESULTS

In SHIP, we found that cortisol levels were positively associated with follow-up levels of mean clinical attachment level (CAL), deep interdental CAL and bleeding on probing but were unrelated to mean probing pocket depth and deep periodontal pockets. In MR analysis, cortisol was not associated with periodontitis.

CONCLUSION

The observational study revealed a prospective association of spot cortisol with makers of periodontitis. Contrary to observational studies, genetically instrumented, long-term cortisol was unrelated to periodontitis. Our results find no univocal evidence that cortisol plays a role in periodontitis pathology, casting doubt on cortisol-related pathways.

The Genetically Informed Neurobiology of Addiction (GINA) model

Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.

A Guide for Understanding and Designing Mendelian Randomization Studies in the Musculoskeletal Field

Mendelian randomization (MR) is an increasingly popular component of an epidemiologist's toolkit, used to provide evidence of a causal effect of one trait (an exposure, eg, body mass index [BMI]) on an outcome trait or disease (eg, osteoarthritis). Identifying these effects is important for understanding disease etiology and potentially identifying targets for therapeutic intervention. MR uses genetic variants as instrumental variables for the exposure, which should not be influenced by the outcome or confounding variables, overcoming key limitations of traditional epidemiological analyses. For MR to generate a valid estimate of effect, key assumptions must be met. In recent years, there has been a rapid rise in MR methods that aim to test, or are robust to violations of, these assumptions. In this review, we provide an overview of MR for a non-expert audience, including an explanation of these key assumptions and how they are often tested, to aid a better reading and understanding of the MR literature. We highlight some of these new methods and how they can be useful for specific methodological challenges in the musculoskeletal field, including for conditions or traits that share underlying biological pathways, such as bone and joint disease. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Effects of general and central adiposity on circulating lipoprotein, lipid, and metabolite levels in UK Biobank: A multivariable Mendelian randomization study

Background

The direct effects of general adiposity (body mass index (BMI)) and central adiposity (waist-to-hip-ratio (WHR)) on circulating lipoproteins, lipids, and metabolites are unknown.

Methods

We used new metabolic data from UK Biobank (N=109,532, a five-fold higher N over previous studies). EDTA-plasma was used to quantify 249 traits with nuclear-magnetic-resonance spectroscopy including subclass-specific lipoprotein concentrations and lipid content, plus pre-glycemic and inflammatory metabolites. We used univariable and multivariable two-stage least-squares regression models with genetic risk scores for BMI and WHR as instruments to estimate total (unadjusted) and direct (mutually-adjusted) effects of BMI and WHR on metabolic traits; plus effects on statin use and interaction by sex, statin use, and age (proxy for medication use).

Findings

Higher BMI decreased apolipoprotein B and low-density lipoprotein cholesterol (LDL-C) before and after WHR-adjustment, whilst BMI increased triglycerides only before WHR-adjustment. These effects of WHR were larger and BMI-independent. Direct effects differed markedly by sex, e.g., triglycerides increased only with BMI among men, and only with WHR among women. Adiposity measures increased statin use and showed metabolic effects which differed by statin use and age. Among the youngest (38-53y, statins-5%), BMI and WHR (per-SD) increased LDL-C (total effects: 0.04-SD, 95%CI=-0.01,0.08 and 0.10-SD, 95%CI=0.02,0.17 respectively), but only WHR directly. Among the oldest (63-73y, statins-29%), BMI and WHR directly lowered LDL-C (-0.19-SD, 95%CI=-0.27,-0.11 and -0.05-SD, 95%CI=-0.16,0.06 respectively).

Interpretation

Excess adiposity likely raises atherogenic lipid and metabolite levels exclusively via adiposity stored centrally, particularly among women. Apparent effects of adiposity on lowering LDL-C are likely explained by an effect of adiposity on statin use.

Funding

UK Medical Research Council; British Heart Foundation; Novo Nordisk; National Institute for Health Research; Wellcome Trust; Cancer Research UK.

Phenotypic Causal Inference Using Genome-Wide Association Study Data: Mendelian Randomization and Beyond

statistics for genome-wide association studies (GWAS) are increasingly available for downstream analyses. Meanwhile, the popularity of causal inference methods has grown as we look to gather robust evidence for novel medical and public health interventions. This has led to the development of methods that use GWAS summary statistics for causal inference. Here, we describe these methods in order of their escalating complexity, from genetic associations to extensions of Mendelian randomization that consider thousands of phenotypes simultaneously. We also cover the assumptions and limitations of these approaches before considering the challenges faced by researchers performing causal inference using GWAS data. GWAS summary statistics constitute an important data source for causal inference research that offers a counterpoint to nongenetic methods when triangulating evidence. Continued efforts to address the challenges in using GWAS data for causal inference will allow the full impact of these approaches to be realized.

Relationship between periodontitis and psoriasis: A two-sample Mendelian randomization study

AIM

Observational research suggests that periodontitis affects psoriasis. However, observational studies are prone to reverse causation and confounding, which hampers drawing causal conclusions and the effect direction. We applied the Mendelian randomization (MR) method to comprehensively assess the potential bi-directional association between periodontitis and psoriasis.

MATERIALS AND METHODS

We used genetic instruments from the largest available genome-wide association study of European descent for periodontitis (17,353 cases, 28,210 controls) to investigate the relationship with psoriasis (13,229 cases, 21,543 controls), and vice versa. Causal Analysis Using Summary Effect (CAUSE) estimates and inverse variance-weighted (IVW) MR analyses were used for the primary analysis. Robust MR approaches were used for sensitivity analyses.

RESULTS

Both univariable methods, CAUSE and IVW MR analyses, did not reveal any impact of periodontitis on psoriasis (CAUSE odds ratio [OR] = 1.00, p = 1.00; IVW OR = 1.02, p = .6247), or vice versa (CAUSE OR = 1.01, p = .5135; IVW OR = 1.00, p = .7070). The null association was corroborated by pleiotropy-robust methods with ORs close to 1 and p-values >.59. Overall, MR analyses did not suggest any effect of periodontitis on psoriasis. Similarly, there was no evidence to support an effect of psoriasis on periodontitis.

CONCLUSIONS

Within the limitations of this MR study, the outcomes supported neither periodontitis affecting psoriasis nor psoriasis affecting periodontitis.

Identifying Novel Causes of Cancers to Enhance Cancer Prevention: New Strategies Are Needed

The burden of cancer from a clinical, societal, and economic viewpoint continues to increase in all parts of the world, along with much debate regarding how to confront this. Projected increases in cancer indicate a 50% increase in the number of cases over the next 2 decades, with the greatest proportional increase in low- and medium-income settings. In contrast to the historic high cancer burden due to viral and bacterial infections in these regions, future increases are expected to be due to cancers linked to westernization including breast, colorectum, lung, and prostate cancer. Identifying the reasons underlying these increases will be paramount to informing prevention efforts. Evidence from epidemiological and laboratory studies conducted in high-income countries over the last 70 years has led to the conclusion that approximately 40% of the cancer burden is explained by known risk factors-the 2 most important being tobacco and obesity in that order-raising the question of what is driving the rest of the cancer burden. International cancer statistics continue to show that approximately 80% of the cancer burden in high-income countries could be preventable in principle, implying that there are important environmental or lifestyle risk factors for cancer that have not yet been discovered. Emerging genomic evidence from population and experimental studies points to an important role for nonmutagenic promoters in driving cancer incidence rates. New research strategies and infrastructures that combine population-based and laboratory research at a global level are required to break this deadlock.

Causal Inference with Genetic Data: Past, Present, and Future

The set of methods discussed in this collection has emerged from the convergence of two scientific fields-genetics and causal inference. In this introduction, we discuss relevant aspects of each field and show how their convergence arises from the natural experiments that genetics offer. We present introductory concepts useful to readers unfamiliar with genetically informed methods for causal inference. We conclude that existing applications and foreseeable developments should ensure that we rapidly reap the rewards of this relatively new field, not only in terms of our understanding of human disease and development, but also in terms of tangible translational applications.

Mendelian randomization

Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel's laws of inheritance and instrumental variable estimation methods, which enable the inference of causal effects in the presence of unobserved confounding. In this Primer, we outline the principles of MR, the instrumental variable conditions underlying MR estimation and some of the methods used for estimation. We go on to discuss how the assumptions underlying an MR study can be assessed and give methods of estimation that are robust to certain violations of these assumptions. We give examples of a range of studies in which MR has been applied, the limitations of current methods of analysis and the outlook for MR in the future. The difference between the assumptions required for MR analysis and other forms of non-interventional epidemiological studies means that MR can be used as part of a triangulation across multiple sources of evidence for causal inference.

Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation

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.

Mendelian Randomization: Concepts and Scope

Mendelian randomization (MR) is a method of studying the causal effects of modifiable exposures (i.e., potential risk factors) on health, social, and economic outcomes using genetic variants associated with the specific exposures of interest. MR provides a more robust understanding of the influence of these exposures on outcomes because germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure-outcome associations. The genetic variant can therefore be used as a tool to link the proposed risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches. We describe the scope of MR, highlighting the range of applications being made possible as genetic data sets and resources become larger and more freely available. We outline the MR approach in detail, covering concepts, assumptions, and estimation methods. We cover some common misconceptions, provide strategies for overcoming violation of assumptions, and discuss future prospects for extending the clinical applicability, methodological innovations, robustness, and generalizability of MR findings.

A Mendelian randomization study on the effect of 25-hydroxyvitamin D levels on periodontitis

BACKGROUND

25-hydroxy vitamin D (25OHD) levels have been proposed to protect against periodontitis based on in vitro and observational studies but evidence from long-term randomized controlled trials (RCTs) is lacking. This study tested whether genetically proxied 25OHD is associated with periodontitis using Mendelian randomization (MR).

METHOD

Genetic variants strongly associated with 25OHD in a genome-wide association study (GWAS) of 417,580 participants of European ancestry were used as instrumental variables, and linked to GWAS summary data of 17,353 periodontitis cases and 28,210 controls. In addition to the main analysis using an inverse variance weighted (IVW) model, we applied additional robust methods to control for pleiotropy. We also undertook sensitivity analyses excluding single nucleotide polymorphisms (SNPs) used as instruments with potential pleiotropic effects and used a second 25OHD GWAS for replication. We identified 288 SNPs to be genome-wide significant for 25OHD, explaining 7.0% of the variance of 25OHD levels and providing ≥90% power to detect an odds ratio (OR) of ≤ 0.97.

RESULTS

MR analysis suggested that a 1 standard deviation increase in natural log-transformed 25OHD was not associated with periodontitis risk (IVW OR = 1.04; 95% confidence interval (CI): 0.97-1.12; P-value = 0.297). The robust models, replication, and sensitivity analyses were coherent with the primary analysis.

CONCLUSIONS

Collectively, our findings suggest that 25OHD levels are unlikely to have a substantial effect on the risk of periodontitis, but large long-term RCTs are needed to derive definitive evidence on the causal role of 25OHD in periodontitis. This article is protected by copyright. All rights reserved.

Computational Tools for Causal Inference in Genetics

The advent of large-scale, phenotypically rich, and readily accessible data provides an unprecedented opportunity for epidemiologists, statistical geneticists, bioinformaticians, and also behavioral and social scientists to investigate the causes and consequences of disease. Computational tools and resources are an integral component of such endeavors, which will become increasingly important as these data continue to grow exponentially. In this review, we have provided an overview of computational software and databases that have been developed to assist with analyses in causal inference. This includes online tools that can be used to help generate hypotheses, publicly accessible resources that store summary-level information for millions of genetic markers, and computational approaches that can be used to leverage this wealth of data to study causal relationships.

Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments

Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that "all genes affect every complex trait" complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and potential pleiotropic pathways involved.

Periodontitis and pulmonary function: a Mendelian randomization study

Objectives

Observational research suggests that periodontitis affects pulmonary function; however, observational studies are subject to confounding and reverse causation, making causal inference and the direction of these associations difficult. We used Mendelian randomization (MR) to assess the potential causal association between genetic liability to periodontitis and pulmonary function.

Materials and methods

We used six single-nucleotide polymorphisms (SNPs) associated with periodontitis (P < 5 × 10⁻⁶) from a genome-wide association study (GWAS) of 17,353 European descent periodontitis cases and 28,210 controls from the GeneLifestyle Interactions in Dental Endpoints consortium and the UK Biobank, and related these to SNPs from a lung function GWAS including 79,055 study participants of the SpiroMeta Consortium.

Results

MR analysis suggested no effect of periodontitis on the ratio of forced expiratory volume in one second to lower forced vital capacity (standard deviation increment in outcome per doubling of the odds of the exposure (95% confidence interval) =  − 0.004 (− 0.028; 0.020)). Replication analysis using genetic instruments from two different GWAS and sensitivity analyses to address potential pleiotropy led to no substantial changes in estimates.

Conclusions

Collectively, these findings do not support a relationship between genetic liability for periodontitis and pulmonary function.

Clinical relevance

Periodontitis does not seem to be a risk factor for worsening of pulmonary function.

Causal inference with observational data: the need for triangulation of evidence

The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an underestimate or overestimate of the effect of interest. Various advanced statistical approaches exist that offer certain advantages in terms of addressing these potential biases. However, although these statistical approaches have different underlying statistical assumptions, in practice they cannot always completely remove key sources of bias; therefore, using design-based approaches to improve causal inference is also important. Here it is the design of the study that addresses the problem of potential bias - either by ensuring it is not present (under certain assumptions) or by comparing results across methods with different sources and direction of potential bias. The distinction between statistical and design-based approaches is not an absolute one, but it provides a framework for triangulation - the thoughtful application of multiple approaches (e.g. statistical and design based), each with their own strengths and weaknesses, and in particular sources and directions of bias. It is unlikely that any single method can provide a definite answer to a causal question, but the triangulation of evidence provided by different approaches can provide a stronger basis for causal inference. Triangulation can be considered part of wider efforts to improve the transparency and robustness of scientific research, and the wider scientific infrastructure and system of incentives.