scholarly journals MR-TRYX: A Mendelian randomization framework that exploits horizontal pleiotropy to infer novel causal pathways

2018 ◽  
Author(s):  
Yoonsu Cho ◽  
Philip C Haycock ◽  
Eleanor Sanderson ◽  
Tom R Gaunt ◽  
Jie Zheng ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Causal Effects ◽  
Outcome Analysis ◽  
Causal Relationships ◽  
Single Exposure ◽  
Causal Pathways ◽  
William Bateson

AbstractIn Mendelian randomization (MR) analysis, variants that exert horizontal pleiotropy are typically treated as a nuisance. However, they could be valuable in identifying novel pathways to the traits under investigation. Here, we developed the MR-TRYX framework, following the advice of William Bateson to “TReasure Your eXceptions”. We begin by detecting outliers in a single exposure-outcome MR analysis, hypothesising they are due to horizontal pleiotropy. We search across thousands of complete GWAS summary datasets in the MR-Base database to systematically identify other (“candidate”) traits that associate with the outliers. We developed a multi-trait pleiotropy model of the heterogeneity in the exposure-outcome analysis due to pathways through candidate traits. Through detailed investigation of several causal relationships, many pleiotropic pathways were uncovered with already established causal effects, validating the approach, but also novel putative causal pathways. Adjustment for pleiotropic pathways reduced the heterogeneity across the analyses.

scholarly journals Investigating causal pathways between liability to ADHD and substance use, and liability to substance use and ADHD risk, using Mendelian randomization

2019 ◽  
Author(s):  
Jorien L Treur ◽  
Ditte Demontis ◽  
George Davey Smith ◽  
Hannah Sallis ◽  
Tom G Richardson ◽  
...  
Keyword(s):  
Substance Use ◽  
Alcohol Use ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Smoking Initiation ◽  
Cannabis Use ◽  
Causal Effects ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Causal Pathways

ABSTRACTBackgroundAttention-deficit hyperactivity disorder (ADHD) has consistently been associated with substance (ab)use, but the nature of this association is not fully understood. In view of preventive efforts, a vital question is whether there are causal effects, from ADHD to substance use and/or from substance use to ADHD.MethodsWe applied bidirectional Mendelian randomization using summary-level data from the largest available genome-wide association studies (GWASs) on ADHD, smoking (initiation, cigarettes/day, cessation, and a compound measure of lifetime smoking), alcohol use (drinks/week and alcohol use disorder), cannabis use (initiation and cannabis use disorder (CUD)) and coffee consumption (cups/day). Genetic variants robustly associated with the ‘exposure’ were selected as instruments and then identified in the ‘outcome’ GWAS. Effect estimates from individual genetic variants were combined with inverse-variance weighted regression and five sensitivity analyses were applied (weighted median, weighted mode, MR-Egger, generalized summary-data-based MR, and Steiger filtering).ResultsWe found strong evidence that liability to ADHD increases likelihood of smoking initiation and also cigarettes per day among smokers, decreases likelihood of smoking cessation, and increases likelihood of cannabis initiation and CUD. In the other direction, there was evidence that liability to smoking initiation and CUD increase ADHD risk. There was no clear evidence of causal effects between liability to ADHD and alcohol or caffeine consumption.ConclusionsWe find evidence for causal effects of liability to ADHD on smoking and cannabis use, and of liability to smoking and cannabis use on ADHD risk, indicating bidirectional pathways. Further work is needed to explore causal mechanisms.

scholarly journals Are there causal relationships between ADHD and BMI? Evidence from multiple genetically informed designs

2020 ◽  
Author(s):  
Chao-Yu Liu ◽  
Tabea Schoeler ◽  
Neil M Davies ◽  
Hugo Peyre ◽  
Kai-Xiang Lim ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Association Studies ◽  
Monozygotic Twins ◽  
Causal Effects ◽  
List Type ◽  
Genome Wide Association Studies ◽  
Causal Relationships ◽  
Adhd Symptoms ◽  
Multiple Sources ◽  
Polygenic Score

AbstractBackgroundAttention-deficit/hyperactivity disorder (ADHD) and Body Mass Index (BMI) are associated. However, it remains unclear whether this association reflects causal relationships in either direction, or confounding. Here, we implemented genetically informed methods to examine bidirectional causality and potential confounding.MethodsThree genetically informed methods were employed: (1) cross-lagged twin-differences analyses to assess bidirectional effects of ADHD symptoms and BMI at ages 8, 12, 14 and 16 years in 2386 pairs of monozygotic twins from the Twins Early Development Study (TEDS), (2) within- and between-family ADHD and BMI polygenic score (PS) analyses in 3320 pairs of dizygotic TEDS twins and (3) two-sample bidirectional Mendelian randomization (MR) using summary statistics from Genome-Wide Association Studies (GWAS) on ADHD (N=55,374) and BMI (N=806,834).ResultsMixed results were obtained across the three methods. Twin-difference analyses provided little support for cross-lagged associations between ADHD symptoms and BMI over time. PS analyses were consistent with bidirectional relationships between ADHD and BMI with plausible time-varying effects from childhood to adolescence. MR findings were also consistent with bidirectional causal effects between ADHD and BMI. Multivariable MR suggested the presence of substantial confounding in bidirectional relationships.ConclusionsThe three methods converged to highlight multiple sources of confounding in the association between ADHD and BMI. PS and MR analyses suggested plausible causal relationships in both directions. Possible explanations for mixed causal findings across methods are discussed.Key messagesPolygenic score and Mendelian randomization analyses were consistent with bidirectional causal effects between ADHD and BMI.Findings from different genetically informed methods suggested that multiple sources of confounding are at play, including genetic and shared environmental confounding, population stratification, assortative mating and dynastic effects.The ADHD polygenic score increasingly associated with BMI phenotype from childhood to adolescence, suggesting an increasing role of ADHD in the aetiology of BMI. Findings were reversed between the BMI polygenic score and ADHD.Addressing mixed evidence will require increased sample sizes to implement novel methods such as within-family MR.

The bidirectional association between Internet addiction and depression: a large-sample longitudinal study among Chinese university students (Preprint)

2020 ◽  
Author(s):  
Wan-Jun Guo ◽  
Xia Yang ◽  
Yu-Jie Tao ◽  
Ya-Jing Meng ◽  
Hui-Yao Wang ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
Internet Addiction ◽  
Causal Effects ◽  
Causal Relationships ◽  
Depression Severity ◽  
Large Sample ◽  
Panel Model ◽  
Bidirectional Association ◽  
Predictive Effect

BACKGROUND Evidence indicates that Internet addiction (IA) is associated with depression, but longitudinal studies have rarely been reported, and no studies have yet investigated potential common vulnerability or a possible specific causal relationship between these disorders. OBJECTIVE To overcome these gaps, the present 12-month longitudinal study based on a large-sample employed a cross-lagged panel model (CLPM) approach to investigate the potential common vulnerability and specific cross-causal relationships between IA and CSD (or depression). METHODS IA and clinically-significant depression (CSD) among 12 043 undergraduates were surveyed at baseline (as freshmen) and in follow-up after 12 months (as sophomores). Application of CLPM revealed two well-fitted design between IA and CSD, and between severities of IA and depression, adjusting for demographics. RESULTS Rates of baseline IA and CSD were 5.47% and 3.85%, respectively; increasing to 9.47% and 5.58%, respectively at follow-up. Among those with baseline IA and CSD, 44.61% and 34.48% remained stable at the time of the follow-up survey, respectively. Rates of new-incidences of IA and CSD over 12 months were 7.43% and 4.47%, respectively. Application of a cross-lagged panel model approach (CLPM, a discrete time structural equation model used primarily to assess causal relationships in real-world settings) revealed two well-fitted design between IA and CSD, and between severities of IA and depression, adjusting for demographics. Models revealed that baseline CSD (or depression severity) had a significant net-predictive effect on follow-up IA (or IA severity), and baseline IA (or IA severity) had a significant net-predictive effect on follow-up CSD (or depression severity). CONCLUSIONS These correlational patterns using a CLPM indicate that both common vulnerability and bidirectional specific cross-causal effects between them may contribute to the association between IA and depression. As the path coefficients of the net-cross-predictive effects were significantly smaller than those of baseline to follow-up cross-section associations, vulnerability may play a more significant role than bidirectional-causal effects. CLINICALTRIAL Ethics Committee of West China Hospital, Sichuan University (NO. 2016-171)

scholarly journals A comprehensive evaluation of methods for Mendelian randomization using realistic simulations and an analysis of 38 biomarkers for risk of type 2 diabetes

2021 ◽  
Author(s):  
Guanghao Qi ◽  
Nilanjan Chatterjee
Keyword(s):  
Type 2 Diabetes ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Real Data ◽  
Causal Effects ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Sample Sizes

Abstract Background Previous studies have often evaluated methods for Mendelian randomization (MR) analysis based on simulations that do not adequately reflect the data-generating mechanisms in genome-wide association studies (GWAS) and there are often discrepancies in the performance of MR methods in simulations and real data sets. Methods We use a simulation framework that generates data on full GWAS for two traits under a realistic model for effect-size distribution coherent with the heritability, co-heritability and polygenicity typically observed for complex traits. We further use recent data generated from GWAS of 38 biomarkers in the UK Biobank and performed down sampling to investigate trends in estimates of causal effects of these biomarkers on the risk of type 2 diabetes (T2D). Results Simulation studies show that weighted mode and MRMix are the only two methods that maintain the correct type I error rate in a diverse set of scenarios. Between the two methods, MRMix tends to be more powerful for larger GWAS whereas the opposite is true for smaller sample sizes. Among the other methods, random-effect IVW (inverse-variance weighted method), MR-Robust and MR-RAPS (robust adjust profile score) tend to perform best in maintaining a low mean-squared error when the InSIDE assumption is satisfied, but can produce large bias when InSIDE is violated. In real-data analysis, some biomarkers showed major heterogeneity in estimates of their causal effects on the risk of T2D across the different methods and estimates from many methods trended in one direction with increasing sample size with patterns similar to those observed in simulation studies. Conclusion The relative performance of different MR methods depends heavily on the sample sizes of the underlying GWAS, the proportion of valid instruments and the validity of the InSIDE assumption. Down-sampling analysis can be used in large GWAS for the possible detection of bias in the MR methods.

Assessing the Causal Relationships between Insulin Resistance and Hyperuricemia and Gout Using Bidirectional Mendelian Randomization

2021 ◽  
Author(s):  
Natalie McCormick ◽  
Mark J. O’Connor ◽  
Chio Yokose ◽  
Tony R. Merriman ◽  
David B. Mount ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Mendelian Randomization ◽  
Causal Relationships

Psychological factors as the risk factor of mouth ulcers: A two-sample Mendelian randomization study

2021 ◽  
pp. 135910532199969
Author(s):  
Yueqi Shi ◽  
Shaoyi Wang ◽  
Shunying Yu ◽  
Guan Ning Lin ◽  
Weichen Song
Keyword(s):  
Multiple Testing ◽  
Mendelian Randomization ◽  
Oral Care ◽  
Genetic Correlations ◽  
Well Being ◽  
Causal Effects ◽  
Mental Condition ◽  
Summary Statistics ◽  
Protective Effects ◽  
Psychological Traits

To examine whether psychological traits (PT) had causal effects on Mouth Ulcers (MU), we applied two-sample Mendelian randomization (MR) to genetics association summary statistics of eleven PT and MU. After the adjustment of outlier variants, genetic correlations and multiple testing, well-being (WB) spectrum PT like life satisfactory (odds ratio [OR] = 0.638 per one standard deviation increment of PT score) had protective effects on MU. Reverse WB traits like neuroticism (OR = 1.60) increased the risk of MU. The lack of well-being characteristics may increase the risk of MU, which highlighted the value of preventive oral care for people who have a reverse mental condition.

scholarly journals Causal Effects of Body Mass Index on Cardiometabolic Traits and Events: A Mendelian Randomization Analysis

2014 ◽  
Vol 94 (2) ◽  
pp. 312 ◽  
Author(s):  
Michael V. Holmes ◽  
Leslie A. Lange ◽  
Tom Palmer ◽  
Matthew B. Lanktree ◽  
Kari E. North ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Mendelian Randomization ◽  
Causal Effects ◽  
Mendelian Randomization Analysis ◽  
Randomization Analysis ◽  
Cardiometabolic Traits

scholarly journals Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

2019 ◽  
Author(s):  
Simon Haworth ◽  
Pik Fang Kho ◽  
Pernilla Lif Holgerson ◽  
Liang-Dar Hwang ◽  
Nicholas J. Timpson ◽  
...  
Keyword(s):  
Dental Caries ◽  
Causal Inference ◽  
Mendelian Randomization ◽  
Association Studies ◽  
List Type ◽  
Genome Wide Association Studies ◽  
Causal Relationships ◽  
Link Type ◽  
Alternative Method ◽  
Causal Variable

AbstractBackgroundHypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments.MethodsWe developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed a causal architecture plot. We apply this process to body mass index and lipid traits as exemplars of traits where there is strong prior expectation for causal effects and dental caries and periodontitis as exemplars of traits where there is a need for causal inference.ResultsThe results for lipids and BMI suggest that these traits are best viewed as creating consequences on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health.ConclusionsThe automated process is available as part of the MASSIVE pipeline from the Complex-Traits Genetics Virtual Lab (https://vl.genoma.io) and results are available in (https://view.genoma.io). We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a way visualizing the overall causal map of the human phenome.Key messagesThe latent causal variable approach uses summary statistics from genome-wide association studies to estimate a parameter termed genetic causality proportion.Systematic estimation of genetic causality proportion for many pairs of traits provides an alternative method for phenome-wide causal inference with some theoretical and practical advantages compared to phenome-wide Mendelian randomization.Using this approach, we confirm that lipid traits are an upstream risk factor for other traits and diseases, and we identify that dental diseases are predominantly a downstream consequence of other traits rather than a cause of poor systemic health.The method assumes no bidirectional causality and no confounding by environmental correlates of genotypes, so care is needed when these assumptions are not met.We developed an automated and accessible pipeline for estimating phenome-wide causal relationships and generating interactive visual summaries.

scholarly journals Causal Effect Between Total Cholesterol and HDL Cholesterol as Risk Factors for Chronic Kidney Disease: A Mendelian Randomization Study

2020 ◽  
Author(s):  
Liu Miao ◽  
Yan Min ◽  
Chuan-Meng Zhu ◽  
Jian-Hong Chen ◽  
Bin Qi ◽  
...  
Keyword(s):  
Total Cholesterol ◽  
Serum Lipid ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Hdl Cholesterol ◽  
Causal Effects ◽  
Lipid Levels ◽  
Serum Lipid Levels ◽  
Genome Wide ◽  
Mr Study

Abstract Background/Aims: While observational studies show an association between serum lipid levels and cardiovascular disease (CVD), intervention studies that examine the preventive effects of serum lipid levels on the development of CKD are lacking. Methods: To estimate the role of serum lipid levels in the etiology of CKD, we conducted a two-sample Mendelian randomization (MR) study on serum lipid levels. Single nucleotide polymorphisms (SNPs), which were significantly associated genome-wide with plasma serum lipid levels from the GLGC and CKDGen consortium genome-wide association study (GWAS), including total cholesterol (TC, n = 187365), triglyceride (TG, n = 177861), HDL cholesterol (HDL-C, n = 187167), LDL cholesterol (LDL-C, n = 173082), apolipoprotein A1 (ApoA1, n = 20687), apolipoprotein B (ApoB, n = 20690) and CKD (n = 117165), were used as instrumental variables. None of the lipid-related SNPs was associated with CKD (all P > 0.05). Results: MR analysis genetically predicted the causal effect between TC/HDL-C and CKD. The odds ratio (OR) and 95% confidence interval (CI) of TC within CKD was 0.756 (0.579 to 0.933) (P = 0.002), and HDL-C was 0.85 (0.687 to 1.012) (P = 0.049). No causal effects between TG, LDL-C- ApoA1, ApoB and CKD were observed. Sensitivity analyses confirmed that TC and HDL-C were significantly associated with CKD. Conclusions: The findings from this MR study indicate causal effects between TC, HDL-C and CKD. Decreased TC and elevated HDL-C may reduce the incidence of CKD but need to be further confirmed by using a genetic and environmental approach.

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