Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy

Potential causal association between serum vitamin D levels and intervertebral disc degeneration: A mendelian randomization study

OBJECTIVES

Intervertebral disc degeneration (IDD) is a prevalent musculoskeletal disorder with substantial implications for disability and healthcare expenditures. The role of serum vitamin D (25-Hydroxyvitamin D, 25(OH)D) levels in the pathogenesis of various musculoskeletal conditions has been explored in prior observational studies, suggesting a potential association. While previous observational studies have suggested an association between the two conditions, it might confound the effect of 25(OH)D on IDD. This Mendelian randomization (MR) study seeks to elucidate the causal relationship between 25(OH)D and IDD.

METHODS

We performed a MR analysis using summary-level data from genome-wide association studies (GWAS) of 25(OH)D (sample size = 441,291 European) and IDD (sample size = 336,439 (cases = 41,669, controls = 294,770) European). Single nucleotide polymorphisms (SNPs) significantly associated with 25(OH)D (p < 5 × 10-8) were selected as instrumental variables. The associations between genetically predicted 25(OH)D and IDD were estimated using the inverse-variance weighted (IVW) method, with sensitivity analyses employing the weighted median, MR-Egger, and MR-PRESSO approaches to assess the robustness of the findings.

RESULTS

In the primary IVW analysis, genetically predicted 25(OH)D was unrelated associated with IDD (odds ratio (OR) = 0.9671, 95% confidence interval (CI): 0.8956-1.0444, p = 0.39). The results remained consistent across the sensitivity analyses, and no significant directional pleiotropy was detected (MR-Egger intercept: p = 0.64).

CONCLUSIONS

This study found no obvious evidence that 25(OH)D is causally associated with IDD risks. We call for larger sample size studies to further unravel the potential causal relationship and the exact mechanism.

Blunted cortisol as a biomarker of depression based on the attenuation hypothesis: A Mendelian randomization analysis using depression as exposure

BACKGROUND

Both elevated and blunted cortisol responses have been associated with depression. Previous Mendelian randomization (MR) studies have largely ruled out cortisol as a cause of depression. Based on the attenuation hypothesis, this MR study used depression as exposure to assess whether cortisol might be a consequence and therefore a biomarker of depression.

METHODS

Strong (P < 5 × 10-8) and independent (r2 < 0.001) single nucleotide polymorphisms (SNPs) associated with broadly defined depression (294,322 cases, 741,438 controls) were used as instruments. These were applied to genetic associations with morning, fasting, and random plasma cortisol in the CORtisol NETwork (CORNET) consortium (n = 25,314), METabolic Syndrome in Men (METSIM) study (n = 6667), and Canadian Longitudinal Study on Aging (CLSA) cohort (n = 8299). Multivariable MR, adjusting for childhood maltreatment and major mental disorders, was conducted to address potential horizontal pleiotropy from dichotomous depression. Instruments were also selected by evidence of colocalization with major depressive disorder to address non-specificity.

RESULTS

Using 133 SNPs as instruments, depression was inversely associated with morning plasma cortisol (β per log-odds of genetic liability to depression = -0.107 [95 % CI, -0.181 to -0.032]) in the CORNET consortium. Replication in the METSIM study (β = -0.203 [95 % CI, -0.367 to -0.040]) and CLSA cohort (β = -0.091 [95 % CI, -0.220 to 0.039]) showed consistent but not always significant associations. Multivariable MR and follow-up analysis incorporating colocalization supported these findings.

CONCLUSIONS

Consistent with the attenuation hypothesis, blunted cortisol response appeared to be a consequence and potentially a biomarker of depression. Future studies are needed to provide more interpretable effect sizes and validate other biomarker measures.

Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research

Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions. In this review we discuss how estimates of comorbidity and identification of shared genetic loci between disorders can be influenced by how disorders are measured (phenotypic assessment) and the inclusion or exclusion criteria in individual genetic studies (sample ascertainment). Specifically, the depth of measurement, source of diagnosis, and time frame of disease trajectory have major implications for the clinical validity of the assessed phenotypes. Further, biases introduced in the ascertainment of both cases and controls can inflate or reduce estimates of genetic correlations. The impact of these design choices may have important implications for large meta-analyses of cohorts from diverse populations that use different forms of assessment and inclusion criteria, and subsequent cross-disorder analyses thereof. We review how assessment and ascertainment affect genetic findings in both univariate and multivariate analyses and conclude with recommendations for addressing them in future research.

Inflammatory bowel disease increases the risk of pancreatitis: a two-sample bidirectional Mendelian randomization analysis

BACKGROUND

Previous studies have suggested an association between inflammatory bowel disease (IBD), and pancreatitis, including acute pancreatitis (AP) and chronic pancreatitis (CP). We aimed to examine the potential causal relationship between IBD and pancreatitis using the Mendelian randomization (MR) method.

METHODS

We obtained data from genome-wide association studies (GWASs) in European individuals for IBD and its main subtypes, Crohn's disease (CD) and ulcerative colitis (UC) (31,665 IBD cases, 13,768 UC cases, 17,897 CD cases and 33,977 controls). Four independent summary statistics of pancreatitis from the the European Bioinformatics Institute (EMBL-EBI, 10,630 AP cases and 844,679 controls, 1,424 CP cases and 476,104 controls) and FinnGen Consortium (8,446 AP cases, 4,820 CP cases and 437,418 controls) were used for bidirectional MR analyses and sensitivity analysis. Finally, further meta-analysis was conducted on the MR results.

RESULTS

Generally, IBD is associated with an increased risk of pancreatitis (IBD-AP, OR = 1.050, 95% CI 1.020-1.080, P = 7.20 × 10-5; IBD-CP, OR = 1.050, 95% CI 1.010-1.090, P = 0.019). In addition, UC increased the risk of pancreatitis (UC-AP, OR = 1.050, 95% CI 1.020-1.070, P = 9.10 × 10-5; UC-CP, OR = 1.090, 95% CI 1.040-1.140, P = 1.44 × 10-4) and CD increased the risk of acute pancreatitis (OR = 1.040, 95% CI 1.020-1.060, P = 9.61 × 10-5). However, no causal association was found between CD and the risk of chronic pancreatitis (P > 0.05). The reverse MR results showed that AP may be associated with a reduced risk of IBD and CD (AP-IBD, OR = 0.880, 95% CI 0.810-0.960, P = 0.003; AP-CD, OR = 0.830, 95% CI 0.730-0.940, P = 0.003). However, there is no causal relationship between AP and the risk of UC, and there is no causal relationship between CP and the risk of IBD and its subtypes(P > 0.05).

CONCLUSION

In conclusion, based on MR analysis and meta-analysis, our results showed a positive causal effect of IBD on pancreatitis, and subgroup analyses showed that UC and CD may promote the development of acute pancreatitis, whereas UC may promote the development of chronic pancreatitis. Reverse MR analysis suggests that AP may have a potential protective effect on IBD and CD.

Association between telomere length and idiopathic normal pressure hydrocephalus: a Mendelian randomization study

Objective

Idiopathic normal pressure hydrocephalus (iNPH) is highly prevalent among elderly individuals, and there is a strong correlation between telomere length and biological aging. However, there is limited evidence to elucidate the relationship between telomere length and iNPH. This study aimed to investigate the associations between telomere length and iNPH using the Mendelian randomization (MR) method.

Methods

The genetic variants of telomere length were obtained from 472,174 UK Biobank individuals. Summary level data of iNPH were acquired from 218,365 individuals of the FinnGen consortium. Five MR estimation methods, including inverse-variance weighting (IVW), MR-Egger regression, weighted median, weighted mode and simple mode, were used for causal inference. Comprehensive sensitivity analyses were conducted to test the robustness of the results. In addition, multivariable MR was further implemented to identify potential mechanisms in the causal pathway from telomere length to iNPH.

Results

Genetically determined longer telomere length was significantly associated with decreased risk of iNPH (OR = 0.44, 95% CI 0.24-0.80; p = 0.008). No evident heterogeneity (Cochran Q = 138.11, p = 0.386) and pleiotropy (MR Egger intercept = 0.01, p = 0.514) were observed in the sensitivity analysis. In addition, multivariable MR indicated that the observed association was attenuated after adjustment for several vascular risk factors, including essential hypertension (IVW OR = 0.55, 95% CI 0.30-1.03; p = 0.061), type 2 diabetes (IVW OR = 0.71, 95% CI 0.09-5.39; p = 0.740) and coronary artery disease (IVW OR = 0.58, 95% CI 0.31-1.07; p = 0.082).

Conclusion

Our MR study revealed a strong negative correlation of telomere length with iNPH. The causal relationship might be driven by several vascular risk factors.

Enhancing the robustness of Mendelian randomization studies: lessons from a two-sample analysis of viral infections and colorectal cancer

This Matters Arising article critically examines the study "Genetic susceptibility association between viral infection and colorectal cancer risk: a two-sample Mendelian randomization analysis" by Li et al., highlighting both its contributions and methodological limitations. Their study employed two-sample Mendelian randomization (MR) to explore potential causal links between viral infections and colorectal cancer (CRC), identifying significant associations with infections such as herpes simplex virus and measles. However, several aspects of the methodology warrant scrutiny, including the relaxation of instrumental variable selection thresholds, the handling of potential pleiotropy, and the interpretation of biologically implausible findings. While leveraging advanced MR techniques such as MR-RAPS, cML, ConMix, and dIVW to address challenges like pleiotropy and weak instruments, the study encountered issues related to heterogeneity, insufficient exploration of biological plausibility, and a lack of detailed reporting on instrumental variable (IV) selection and preprocessing. This Matters Arising calls for more rigorous sensitivity analyses, improved transparency in IV selection criteria and harmonization of genome-wide association study (GWAS) datasets, particularly in addressing differences between self-reported and clinically diagnosed infections. Additionally, the Matters Arising article calls for a deeper exploration of biological mechanisms, such as the role of immune modulation and inflammation, to better interpret the observed associations. By addressing these limitations, future MR studies can enhance methodological rigor, improve reproducibility, and provide more robust insights into the causal pathways linking viral infections to CRC risk.

Bayesian Mendelian Randomization Analysis for Latent Exposures Leveraging GWAS Summary Statistics for Traits Co-Regulated by the Exposures

Mendelian Randomization analysis is a popular method to infer causal relationships between exposures and outcomes, utilizing data from genome-wide association studies (GWAS) to overcome limitations of observational research by treating genetic variants as instrumental variables. This study focuses on a specific problem setting, where causal signals may exist among a series of correlated traits, but the exposures of interest, such as biological functions or lower-dimensional latent factors that regulate the observable traits, are not directly observable. We propose a Bayesian Mendelian randomization analysis framework that allows joint analysis of the causal effects of multiple latent exposures on a disease outcome leveraging GWAS summary-level association statistics for traits co-regulated by the exposures. We conduct simulation studies to show the validity and superiority of the method in terms of type I error control and power due to a more flexible modeling framework and a more stable algorithm compared to an alternative approach and traditional single- and multi-exposure analysis approaches not specifically designed for the problem. We have also applied the method to reveal evidence of the causal effects of psychiatric factors, including compulsive, psychotic, neurodevelopmental, and internalizing factors, on neurodegenerative, autoimmune, digestive, and cardiometabolic diseases.

Mendelian randomization analysis using multiple biomarkers of an underlying common exposure

Mendelian randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on multiple biomarkers or other types of traits that are co-regulated by the exposure. We propose a method for MR analysis on latent exposures (MRLE), which tests the significance for, and the direction of, the effect of a latent exposure by leveraging information from multiple related traits. The method is developed by constructing a set of estimating functions based on the second-order moments of GWAS summary association statistics for the observable traits, under a structural equation model where genetic variants are assumed to have indirect effects through the latent exposure and potentially direct effects on the traits. Simulation studies show that MRLE has well-controlled type I error rates and enhanced power compared to single-trait MR tests under various types of pleiotropy. Applications of MRLE using genetic association statistics across five inflammatory biomarkers (CRP, IL-6, IL-8, TNF-α, and MCP-1) provide evidence for potential causal effects of inflammation on increasing the risk of coronary artery disease, colorectal cancer, and rheumatoid arthritis, while standard MR analysis for individual biomarkers fails to detect consistent evidence for such effects.

Metabolic Traits and Risk of Ischemic Stroke in Japanese and European Populations: A Two-Sample Mendelian Randomization Study

The role of metabolic traits in ischemic stroke (IS) has been explored through observational studies and a few Mendelian randomization (MR) studies employing limited methods in European populations. This study aimed to investigate the causal effects of metabolic traits on IS in both East Asian and European populations utilizing multiple MR methods based on genetic insights. Two-sample and multivariable MR were performed, and MR estimates were calculated as inverse-variance weighted (IVW), weighted median, and penalized weighted median. Pleiotropy was assessed by MR-Egger and Mendelian randomization pleiotropy residual sum and outlier tests. Systolic blood pressure (SBP) was associated with an increased risk of IS by IVW in both European (ORIVW: 1.032, 95% CI: 1.026-1.038, p < 0.001) and Japanese populations (ORIVW: 1.870, 95% CI: 1.122-3.116, p = 0.016), which was further confirmed by other methods. Unlike the European population, the evidence for the association of diastolic blood pressure (DBP) with IS in the Japanese population was not stable. No evidence supported an association between the other traits and IS (all Ps > 0.05) in both races. A positive association was found between SBP and IS in two races, while the results of DBP were only robust in Europeans.

The non-causative role of abnormal serum uric acid in intervertebral disc degeneration: A Mendelian randomization study

BACKGROUND

Intervertebral disc degeneration (IDD) is a common musculoskeletal disorder that contributes significantly to disability and healthcare costs. Serum urate concentration has been implicated in the development of various musculoskeletal conditions. While previous observational studies have suggested an association between the two conditions, it might confound the effect of serum urate concentrations on IDD. This Mendelian randomization (MR) study aimed to investigate the causal relationship between serum urate concentration and IDD.

METHODS

We performed a two-sample MR analysis using summary-level data from genome-wide association studies (GWAS) of serum urate concentration (n = 13 585 994 European ancestry) and IDD (n = 16 380 337 European ancestry). Single nucleotide polymorphisms (SNPs) significantly associated with serum urate concentration (p < 5 × 10-8) were selected as instrumental variables. The associations between genetically predicted serum urate concentration and IDD were estimated using the inverse-variance weighted (IVW) method, with sensitivity analyses employing the weighted median, MR-Egger, and MR-PRESSO approaches to assess the robustness of the findings.

RESULTS

In the primary IVW analysis, genetically predicted serum urate concentration was unrelated associated with IDD (odds ratio [OR] = 1.00, 95% confidence interval (CI): 1.00-1.00, p = 0.17)). The results remained consistent across the sensitivity analyses, and no significant directional pleiotropy was detected (MR-Egger intercept: p = 0.15).

CONCLUSIONS

This MR study provides evidence that there is no causal relationship between serum urate concentration and IDD. It suggests previous observational associations may be confounded. Serum urate levels are unlikely to be an important contributor to IDD.

Causal association of serum biomarkers with oral cavity and oropharyngeal cancer: a mendelian randomization study

BACKGROUND

Observational epidemiological studies revealed that multiple serum biomarkers can be associated with the risk of oral and oropharyngeal cancer (OC/OPC). However, the causal relationship between them remains largely unknown. This study aimed to investigate the causal relationship between potential serum biomarkers and (OC/OPC).

METHODS

A two-sample Mendelian randomization (MR) approach was performed to assess the causal association of 10 serum biomarkers with the risk of OC / OPC. Summary data on OC/OPC were obtained from a GWAS meta-analysis that included 2497 cases and 2928 controls. The TwoSampleMR package in R was used to perform MR analyzes. Inverse-variance weighted (IVW), Weighted median and MR-Egger methods were used to assess causal effects.

RESULTS

Suggestive associations with increased risk of C-reactive protein (CRP) (OR 1.52, 95% CI 1.14 to 2.02), using the IVW method. MR-Egger regression suggested that directional pleiotropy was unlikely to bias the result (P = 0.19). The findings were robust to sensitivity analyzes. The risk of OC/OPC was not associated with serum 25-hydroxyvitamin D, HDL cholesterol, LDL cholesterol, total cholesterol, triglycerides, adiponectin, leptin, HbA1C and Insulin-like growth factor 1 (IGF 1).

CONCLUSIONS

This study supports that CRP was causally associated with an increased risk of oral and oropharyngeal cancer.

Body Mass Index and the Risk of Rheumatic Disease: Linear and Nonlinear Mendelian Randomization Analyses

OBJECTIVE

Although the association between obesity and risk of rheumatic disease is well established, the precise causal relation has not been conclusively proven. Here, we estimate the causal effect of body mass index (BMI) on the risk of developing 5 different rheumatic diseases.

METHODS

Linear and nonlinear mendelian randomization (MR) were used to estimate the effect of BMI on risk of rheumatic disease, and sex-specific effects were identified. Analyses were performed in 361,952 participants from the UK Biobank cohort for 5 rheumatic diseases: rheumatoid arthritis (n = 8,381 cases), osteoarthritis (n = 87,430), psoriatic arthropathy (n = 933), gout (n = 13,638), and inflammatory spondylitis (n = 4,328).

RESULTS

Using linear MR, we found that 1 SD increase in BMI increases the incidence rate for rheumatoid arthritis (incidence rate ratio [IRR] 1.52 [95% confidence interval (95% CI) 1.36-1.69]), osteoarthritis (IRR 1.49 [95% CI 1.43-1.55]), psoriatic arthropathy (IRR 1.80 [95% CI 1.31-2.48]), gout (IRR 1.73 [95% CI 1.56-1.92]), and inflammatory spondylitis (IRR 1.34 [95% CI 1.14-1.57]) in all individuals. BMI was found to be a stronger risk factor in women compared to men for psoriatic arthropathy (P for sex interaction = 3.3 × 10-4 ) and gout (P for sex interaction = 4.3 × 10-3 ), and the effect on osteoarthritis was stronger in premenopausal compared to postmenopausal women (P = 1.8 × 10-3 ). Nonlinear effects of BMI were identified for osteoarthritis and gout in men, and for gout in women. The nonlinearity for gout was also more extreme in men compared to women (P = 0.03).

CONCLUSION

Higher BMI causes an increased risk for rheumatic disease, an effect that is more pronounced in women for both gout and psoriatic arthropathy. The novel sex- and BMI-specific causal effects identified here provide further insight into rheumatic disease etiology and mark an important step toward personalized medicine.

The gut-lung axis: Mendelian randomization identifies a causal association between inflammatory bowel disease and interstitial lung disease

BACKGROUND

Previous studies have suggested the association between interstitial lung disease (ILD) and inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC).

OBJECTIVES

To examine the potential bidirectional causal relationship between IBD and ILD using the Mendelian randomization (MR) method.

METHODS

We obtained the data from the genome-wide association studies (GWASs) in European individuals for IBD (25,042 cases and 34,915 controls) and ILD (21,806 cases and 196,986 controls) from the IEU GWAS database. We screened for instrumental variables based on the three assumptions of MR. The two-sample bidirectional MR analysis was performed using the inverse-variance weighted method and multiple sensitivity analyses.

RESULTS

Genetic liability to IBD was significantly associated with an increased ILD risk (odds ratio (OR) = 1.20, 95% confidence interval (CI) = 1.17-1.24, p = 3.67E-33). When considering the IBD subtypes, ILD risk was associated with genetic liability to both CD (OR = 1.14, 95% CI = 1.10-1.17, p = 1.91E-17) and UC (OR = 1.16, 95% CI = 1.12-1.21, p = 3.51E-13). There was weak evidence for the effect of genetic liability to ILD on IBD (OR = 1.32, 95% CI = 0.99-1.76, p = 0.062), CD (OR = 1.25, 95% CI = 1.00-1.55, p = 0.046), and UC (OR = 1.47, 95%CI = 1.01-2.14, p = 0.046).

CONCLUSION

The results indicate a strong causal effect of IBD (including CD and UC) on ILD.

Leveraging family data to design Mendelian randomization that is provably robust to population stratification

Mendelian randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases owing to weak instruments, as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We show in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, whereas standard MR methods yield inflated false positive rates. We then conduct an exploratory analysis of MR-Twin and other MR methods applied to 121 trait pairs in the UK Biobank data set. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, whereas MR-Twin is immune to this type of confounding, and that MR-Twin can help assess whether traditional approaches may be inflated owing to confounding from population stratification.

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.

Strategies to investigate and mitigate collider bias in genetic and Mendelian randomisation studies of disease progression

Genetic studies of disease progression can be used to identify factors that may influence survival or prognosis, which may differ from factors that influence on disease susceptibility. Studies of disease progression feed directly into therapeutics for disease, whereas studies of incidence inform prevention strategies. However, studies of disease progression are known to be affected by collider (also known as "index event") bias since the disease progression phenotype can only be observed for individuals who have the disease. This applies equally to observational and genetic studies, including genome-wide association studies and Mendelian randomisation (MR) analyses. In this paper, our aim is to review several statistical methods that can be used to detect and adjust for index event bias in studies of disease progression, and how they apply to genetic and MR studies using both individual- and summary-level data. Methods to detect the presence of index event bias include the use of negative controls, a comparison of associations between risk factors for incidence in individuals with and without the disease, and an inspection of Miami plots. Methods to adjust for the bias include inverse probability weighting (with individual-level data), or Slope-Hunter and Dudbridge et al.'s index event bias adjustment (when only summary-level data are available). We also outline two approaches for sensitivity analysis. We then illustrate how three methods to minimise bias can be used in practice with two applied examples. Our first example investigates the effects of blood lipid traits on mortality from coronary heart disease, while our second example investigates genetic associations with breast cancer mortality.

Leveraging family data to design Mendelian Randomization that is provably robust to population stratification

Mendelian Randomization (MR) has emerged as a powerful approach to leverage genetic instruments to infer causality between pairs of traits in observational studies. However, the results of such studies are susceptible to biases due to weak instruments as well as the confounding effects of population stratification and horizontal pleiotropy. Here, we show that family data can be leveraged to design MR tests that are provably robust to confounding from population stratification, assortative mating, and dynastic effects. We demonstrate in simulations that our approach, MR-Twin, is robust to confounding from population stratification and is not affected by weak instrument bias, while standard MR methods yield inflated false positive rates. We applied MR-Twin to 121 trait pairs in the UK Biobank dataset and found that MR-Twin identifies likely causal trait pairs and does not identify trait pairs that are unlikely to be causal. Our results suggest that confounding from population stratification can lead to false positives for existing MR methods, while MR-Twin is immune to this type of confounding.

An Overview of Methods and Exemplars of the Use of Mendelian Randomisation in Nutritional Research

Objectives: It is crucial to elucidate the causal relevance of nutritional exposures (such as dietary patterns, food intake, macronutrients intake, circulating micronutrients), or biomarkers in non-communicable diseases (NCDs) in order to find effective strategies for NCD prevention. Classical observational studies have found evidence of associations between nutritional exposures and NCD development, but such studies are prone to confounding and other biases. This has direct relevance for translation research, as using unreliable evidence can lead to the failure of trials of nutritional interventions. Facilitated by the availability of large-scale genetic data, Mendelian randomization studies are increasingly used to ascertain the causal relevance of nutritional exposures and biomarkers for many NCDs. Methods: A narrative overview was conducted in order to demonstrate and describe the utility of Mendelian randomization studies, for individuals with little prior knowledge engaged in nutritional epidemiological research. Results: We provide an overview, rationale and basic description of the methods, as well as strengths and limitations of Mendelian randomization studies. We give selected examples from the contemporary nutritional literature where Mendelian randomization has provided useful evidence on the potential causal relevance of nutritional exposures. Conclusions: The selected exemplars demonstrate the importance of well-conducted Mendelian randomization studies as a robust tool to prioritize nutritional exposures for further investigation.