scholarly journals Mendelian Randomization with Instrumental Variable Synthesis (IVY)

2019 ◽  
Author(s):  
Zhaobin Kuang ◽  
Aldo Cordova-Palomera ◽  
Fred Sala ◽  
Sen Wu ◽  
Jared Dunnmon ◽  
...  
Keyword(s):  
Graphical Models ◽  
Instrumental Variable ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Uk Biobank ◽  
Inference Method ◽  
Real World Data ◽  
Individual Level ◽  
Artery Disease ◽  
Effect Estimation

SUMMARYMendelian Randomization (MR) is an important causal inference method primarily used in biomedical research. This work applies contemporary techniques in machine learning to improve the robustness and power of traditional MR tools. By denoising and combining candidate genetic variants through techniques from unsupervised probabilistic graphical models, an influential latent instrumental variable is constructed for causal effect estimation. We present results on identifying relationships between biomarkers and the occurrence of coronary artery disease using individual-level real-world data from UK-BioBank via the proposed method. The approach, termed Instrumental Variable sYnthesis (IVY) is proposed as a complement to current methods, and is able to improve results based on allele scoring, particularly at moderate sample sizes.

scholarly journals Constrained Instruments and their Application to Mendelian Randomization with Pleiotropy

2017 ◽  
Author(s):  
Lai Jiang ◽  
Karim Oualkacha ◽  
Vanessa Didelez ◽  
Antonio Ciampi ◽  
Pedro Rosa ◽  
...  
Keyword(s):  
Instrumental Variables ◽  
Genetic Variants ◽  
Instrumental Variable ◽  
Genetic Variant ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Naive Method ◽  
Effect Estimation ◽  
Selection Of ◽  
Better Than

AbstractIn Mendelian randomization (MR), genetic variants are used to construct instrumental variables, which enable inference about the causal relationship between a phenotype of interest and a response or disease outcome. However, standard MR inference requires several assumptions, including the assumption that the genetic variants only influence the response through the phenotype of interest. Pleiotropy occurs when a genetic variant has an effect on more than one phenotype; therefore, a pleiotropic genetic variant may be an invalid instrumental variable. Hence, a naive method for constructing instrumental variables may lead to biased estimation of the causality between the phenotype and the response. Here, we present a set of intuitive methods (Constrained Instrumental Variable methods [CIV]) to construct valid instrumental variables and perform adjusted causal effect estimation when pleiotropy exists, focusing particularly on the situation where pleiotropic phenotypes have been measured. Our approach includes an automatic and valid selection of genetic variants when building the instrumental variables. We also provide details of the features of many existing methods, together with a comparison of their performance in a large series of simulations. CIV methods performed consistently better than many comparators across four different pleiotropic violations of the MR assumptions. We analyzed data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) Mueller et al. (2005) to disentangle causal relationships of several biomarkers with AD progression. The results showed that CIV methods can provide causal effect estimates, as well as selection of valid instruments while accounting for pleiotropy.

scholarly journals Causal effect of renal function on venous thromboembolism: a two-sample Mendelian randomization investigation

2021 ◽  
Author(s):  
Shuai Yuan ◽  
Maria Bruzelius ◽  
Susanna C. Larsson
Keyword(s):  
Renal Function ◽  
Venous Thromboembolism ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genome Wide ◽  
Increased Risk ◽  
Mr Study

AbstractWhether renal function is causally associated with venous thromboembolism (VTE) is not yet fully elucidated. We conducted a two-sample Mendelian randomization (MR) study to determine the causal effect of renal function, measured as estimated glomerular filtration rate (eGFR), on VTE. Single-nucleotide polymorphisms associated with eGFR were selected as instrumental variables at the genome-wide significance level (p < 5 × 10−8) from a meta-analysis of 122 genome-wide association studies including up to 1,046,070 individuals. Summary-level data for VTE were obtained from the FinnGen consortium (6913 VTE cases and 169,986 non-cases) and UK Biobank study (4620 VTE cases and 356,574 non-cases). MR estimates were calculated using the random-effects inverse-variance weighted method and combined using fixed-effects meta-analysis. Genetically predicted decreased eGFR was significantly associated with an increased risk of VTE in both FinnGen and UK Biobank. For one-unit decrease in log-transformed eGFR, the odds ratios of VTE were 2.93 (95% confidence interval (CI) 1.25, 6.84) and 4.46 (95% CI 1.59, 12.5) when using data from FinnGen and UK Biobank, respectively. The combined odds ratio was 3.47 (95% CI 1.80, 6.68). Results were consistent in all sensitivity analyses and no horizontal pleiotropy was detected. This MR-study supported a casual role of impaired renal function in VTE.

Associations of observational and genetically determined caffeine intake with coronary artery disease and diabetes

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Said ◽  
Y.J Van De Vegte ◽  
N Verweij ◽  
P Van Der Harst
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Mendelian Randomization ◽  
Cox Regression ◽  
Caffeine Intake ◽  
Uk Biobank ◽  
Genome Wide ◽  
Artery Disease ◽  
Genetically Determined

Abstract Background Caffeine is the most widely consumed psychostimulant and is associated with lower risk of coronary artery disease (CAD) and type 2 diabetes (T2D). However, whether these associations are causal remains unknown. Objectives This study aimed to identify genetic variants associated with caffeine intake, and to investigate possible causal links between genetically determined caffeine intake and CAD or T2D. Additionally, we aimed to replicate previous observational findings between caffeine intake and CAD or T2D. Methods Genome wide associated studies (GWAS) were performed on caffeine intake from coffee, tea or both in 407,072 UK Biobank participants. Identified variants were used in a two-sample Mendelian randomization (MR) approach to investigate evidence for causal links between caffeine intake and CAD in CARDIoGRAMplusC4D (60,801 cases; 123,504 controls) or T2D in DIAGRAM (26,676 cases; 132,532 controls). Observational associations were tested within UK Biobank using Cox regression analyses. Results Moderate observational caffeine intakes from coffee or tea were associated with lower risks of CAD or T2D compared to no or high intake, with the lowest risks at intakes of 120–180 mg/day from coffee for CAD (HR=0.77 [95% CI: 0.73–0.82; P&lt;1e-16]), and 300–360 mg/day for T2D (HR=0.76 [95% CI: 0.67–0.86]; P=1.57e-5). GWAS identified 51 novel genetic loci associated with caffeine intake, enriched for central nervous system genes. In contrast to observational analyses, MR analyses in CARDIoGRAMplusC4D and DIAGRAM yielded no evidence for causal links between caffeine intake and the development of CAD or T2D. Conclusions MR analyses indicate caffeine intake might not protect against CAD or T2D, despite protective associations in observational analyses. Manhattan_plot_CaffeineIntake Funding Acknowledgement Type of funding source: None

scholarly journals Mendelian randomization analyses reveal mediating factors of the causal effect of height on coronary artery disease

2021 ◽  
Author(s):  
Daniel Hui ◽  
Christopher S. Thom ◽  
Kimberly Lorenz ◽  
Scott M. Damrauer ◽  
Themistocles L. Assimes ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Lung Function ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Multivariable Analysis ◽  
Inverse Correlation ◽  
Artery Disease ◽  
Cad Risk

An inverse correlation between stature and risk of coronary artery disease (CAD) has been observed in several epidemiologic studies, and recent Mendelian randomization (MR) experiments have suggested evidence that this association may be causal. However, the extent to which the effect estimated by MR can be explained by established cardiovascular risk factors is unclear, with a recent report suggesting that lung function traits could fully explain the height-CAD effect. To clarify this relationship, we utilized the largest set of genetic instruments for human stature to date, comprising >2,000 genetic variants for height and CAD. In univariable analysis, we confirmed that a one standard deviation decrease in height (~6.5 cm) was associated with a 12.0% increase in the risk of CAD, consistent with previous reports. In multivariable analysis accounting for effects from up to 12 established risk factors, we observed a >3-fold attenuation in the causal effect of height on CAD susceptibility (3.7%, p = 2.1x10-2). We observed minimal effects of lung function traits on CAD risk in our analyses, indicating that these traits are unlikely to explain the residual association between height and CAD risk. In sum, these results suggest that height does not add meaningful clinical impact on CAD risk prediction beyond established risk factors.

Causal Association Between Atopic Dermatitis and Inflammatory Bowel Disease: A 2-Sample Bidirectional Mendelian Randomization Study

2021 ◽  
Author(s):  
Christa Meisinger ◽  
Dennis Freuer
Keyword(s):  
Inflammatory Bowel Disease ◽  
Atopic Dermatitis ◽  
Bowel Disease ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
The Other ◽  
Uk Biobank ◽  
Inflammatory Bowel ◽  
The Uk

Abstract Background Observational studies postulated an association between atopic dermatitis (AD) and inflammatory bowel disease (IBD). However, it remains unclear whether this relationship is causal. Methods To determine whether AD is causally related to IBD and vice versa, a 2-sample Mendelian randomization study was conducted. Independent genetic instruments from the largest available genome-wide association study for AD (EAGLE eczema consortium without the 23andMe study including 10,788 cases and 30,047 controls) were used to investigate the association with IBD in the UK Biobank study (7045 cases, 456,327 controls) and a second European IBD sample (12,882 cases, 21,770 controls). Results Atopic dermatitis was strongly associated with higher risk of IBD as a whole (odds ratio [OR], 1.107; 95% confidence interval [CI], 1.035; 1.183; P = .003) in the UK Biobank study. The positive association was not significant in the other IBD study (OR, 1.114; 95% CI, 0.956; 1.298), but in meta-analyses of results from the 2 studies, the strong association could be confirmed (OR, 1.11; 95% CI, 1.04; 1.18). When evaluating the causal relationship in the other direction, IBD as a whole did not show an association with AD. Subtype analyses revealed that AD was suggestively associated with ulcerative colitis (UC; OR, 1.149; 95% CI, 1.018; 1.297) but not Crohn’s disease (CD). However, there was a suggestive association between CD and AD (OR, 1.034; 95% CI, 1.004; 1.064) but not UC and AD. Conclusions This study supports a causal effect between AD and IBD—but not between IBD and AD. There seems to be considerable differences between UC and CD regarding their specific associations with AD. These findings have implications for the management of IBD and AD in clinical practice.

scholarly journals Polynomial Mendelian Randomization reveals widespread non-linear causal effects in the UK Biobank

2021 ◽  
Author(s):  
Jonathan Sulc ◽  
Jenny Sjaarda ◽  
Zoltan Kutalik
Keyword(s):  
Causal Inference ◽  
Large Scale ◽  
Causal Effect ◽  
Causal Effects ◽  
Mendelian Randomisation ◽  
Uk Biobank ◽  
Individual Level ◽  
Glucose Levels ◽  
Causal Function ◽  
The Uk

Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

scholarly journals Instrumental Heterogeneity in Sex-Specific Two-Sample Mendelian Randomization: Empirical Results From the Relationship Between Anthropometric Traits and Breast/Prostate Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Yixin Gao ◽  
Jinhui Zhang ◽  
Huashuo Zhao ◽  
Fengjun Guan ◽  
Ping Zeng
Keyword(s):  
Breast Cancer ◽  
Prostate Cancer ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Correlation Coefficients ◽  
Causal Effects ◽  
Mr Studies ◽  
Pearson’S Correlation ◽  
Pearson's Correlation ◽  
Effect Estimation

BackgroundIn two-sample Mendelian randomization (MR) studies, sex instrumental heterogeneity is an important problem needed to address carefully, which however is often overlooked and may lead to misleading causal inference.MethodsWe first employed cross-trait linkage disequilibrium score regression (LDSC), Pearson’s correlation analysis, and the Cochran’s Q test to examine sex genetic similarity and heterogeneity in instrumental variables (IVs) of exposures. Simulation was further performed to explore the influence of sex instrumental heterogeneity on causal effect estimation in sex-specific two-sample MR analyses. Furthermore, we chose breast/prostate cancer as outcome and four anthropometric traits as exposures as an illustrative example to illustrate the importance of taking sex heterogeneity of instruments into account in MR studies.ResultsThe simulation definitively demonstrated that sex-combined IVs can lead to biased causal effect estimates in sex-specific two-sample MR studies. In our real applications, both LDSC and Pearson’s correlation analyses showed high genetic correlation between sex-combined and sex-specific IVs of the four anthropometric traits, while nearly all the correlation coefficients were larger than zero but less than one. The Cochran’s Q test also displayed sex heterogeneity for some instruments. When applying sex-specific instruments, significant discrepancies in the magnitude of estimated causal effects were detected for body mass index (BMI) on breast cancer (P = 1.63E-6), for hip circumference (HIP) on breast cancer (P = 1.25E-20), and for waist circumference (WC) on prostate cancer (P = 0.007) compared with those generated with sex-combined instruments.ConclusionOur study reveals that the sex instrumental heterogeneity has non-ignorable impact on sex-specific two-sample MR studies and the causal effects of anthropometric traits on breast/prostate cancer would be biased if sex-combined IVs are incorrectly employed.

scholarly journals Causal Effect of the Triglyceride-Glucose Index and the Joint Exposure of Higher Glucose and Triglyceride With Extensive Cardio-Cerebrovascular Metabolic Outcomes in the UK Biobank: A Mendelian Randomization Study

2021 ◽  
Vol 7 ◽  
Author(s):  
Shucheng Si ◽  
Jiqing Li ◽  
Yunxia Li ◽  
Wenchao Li ◽  
Xiaolu Chen ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Heart Diseases ◽  
Cerebrovascular Diseases ◽  
European Ancestry ◽  
Uk Biobank ◽  
Tyg Index ◽  
Metabolic Outcomes ◽  
Meta Regression ◽  
The Uk

Background: The causal evidence of the triglyceride–glucose (TyG) index, as well as the joint exposure of higher glucose and triglyceride on the risk of cardio-cerebrovascular diseases (CVD), was lacking.Methods: A comprehensive factorial Mendelian randomization (MR) was performed in the UK Biobank cohort involving 273,368 individuals with European ancestry to assess and quantify these effects. The factorial MR, MR-PRESSO, MR-Egger, meta-regression, sensitivity analysis, positive control, and external verification were utilized. Outcomes include major outcomes [overall CVD, ischemic heart diseases (IHD), and cerebrovascular diseases (CED)] and minor outcomes [angina pectoris (AP), acute myocardial infarction (AMI), chronic IHD (CIHD), heart failure (HF), hemorrhagic stroke (HS), and ischemic stroke (IS)].Results: The TyG index significantly increased the risk of overall CVD [OR (95% CI): 1.20 (1.14–1.25)], IHD [OR (95% CI): 1.22 (1.15–1.29)], CED [OR (95% CI): 1.14 (1.05–1.23)], AP [OR (95% CI): 1.29 (1.20–1.39)], AMI [OR (95% CI): 1.27 (1.16–1.39)], CIHD [OR (95% CI): 1.21 (1.13–1.29)], and IS [OR (95% CI): 1.22 (1.06–1.40)]. Joint exposure to genetically higher GLU and TG was significantly associated with a higher risk of overall CVD [OR (95% CI): 1.17 (1.12–1.23)] and IHD [OR (95% CI): 1.22 (1.16–1.29)], but not with CED. The effect of GLU and TG was independent of each other genetically and presented dose–response effects in bivariate meta-regression analysis.Conclusions: Lifelong genetic exposure to higher GLU and TG was jointly associated with higher cardiac metabolic risk while the TyG index additionally associated with several cerebrovascular diseases. The TyG index could serve as a more sensitive pre-diagnostic indicator for CVD while the joint GLU and TG could offer a quantitative risk for cardiac metabolic outcomes.

scholarly journals Using genetic variants to evaluate the causal effect of cholesterol lowering on head and neck cancer risk: A Mendelian randomization study

PLoS Genetics ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. e1009525
Author(s):  
Mark Gormley ◽  
James Yarmolinsky ◽  
Tom Dudding ◽  
Kimberley Burrows ◽  
Richard M. Martin ◽  
...  
Keyword(s):  
Cancer Risk ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Meta Analysis ◽  
Uk Biobank ◽  
Limited Evidence ◽  
Cholesterol Lowering ◽  
Increased Risk ◽  
Secondary Analyses

Head and neck squamous cell carcinoma (HNSCC), which includes cancers of the oral cavity and oropharynx, is a cause of substantial global morbidity and mortality. Strategies to reduce disease burden include discovery of novel therapies and repurposing of existing drugs. Statins are commonly prescribed for lowering circulating cholesterol by inhibiting HMG-CoA reductase (HMGCR). Results from some observational studies suggest that statin use may reduce HNSCC risk. We appraised the relationship of genetically-proxied cholesterol-lowering drug targets and other circulating lipid traits with oral (OC) and oropharyngeal (OPC) cancer risk using two-sample Mendelian randomization (MR). For the primary analysis, germline genetic variants in HMGCR, NPC1L1, CETP, PCSK9 and LDLR were used to proxy the effect of low-density lipoprotein cholesterol (LDL-C) lowering therapies. In secondary analyses, variants were used to proxy circulating levels of other lipid traits in a genome-wide association study (GWAS) meta-analysis of 188,578 individuals. Both primary and secondary analyses aimed to estimate the downstream causal effect of cholesterol lowering therapies on OC and OPC risk. The second sample for MR was taken from a GWAS of 6,034 OC and OPC cases and 6,585 controls (GAME-ON). Analyses were replicated in UK Biobank, using 839 OC and OPC cases and 372,016 controls and the results of the GAME-ON and UK Biobank analyses combined in a fixed-effects meta-analysis. We found limited evidence of a causal effect of genetically-proxied LDL-C lowering using HMGCR, NPC1L1, CETP or other circulating lipid traits on either OC or OPC risk. Genetically-proxied PCSK9 inhibition equivalent to a 1 mmol/L (38.7 mg/dL) reduction in LDL-C was associated with an increased risk of OC and OPC combined (OR 1.8 95%CI 1.2, 2.8, p = 9.31 x10-05), with good concordance between GAME-ON and UK Biobank (I2 = 22%). Effects for PCSK9 appeared stronger in relation to OPC (OR 2.6 95%CI 1.4, 4.9) than OC (OR 1.4 95%CI 0.8, 2.4). LDLR variants, resulting in genetically-proxied reduction in LDL-C equivalent to a 1 mmol/L (38.7 mg/dL), reduced the risk of OC and OPC combined (OR 0.7, 95%CI 0.5, 1.0, p = 0.006). A series of pleiotropy-robust and outlier detection methods showed that pleiotropy did not bias our findings. We found limited evidence for a role of cholesterol-lowering in OC and OPC risk, suggesting previous observational results may have been confounded. There was some evidence that genetically-proxied inhibition of PCSK9 increased risk, while lipid-lowering variants in LDLR, reduced risk of combined OC and OPC. This result suggests that the mechanisms of action of PCSK9 on OC and OPC risk may be independent of its cholesterol lowering effects; however, this was not supported uniformly across all sensitivity analyses and further replication of this finding is required.

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