Commentary: Proxy gene-by-environment Mendelian randomization for assessing causal effects of maternal exposures on offspring outcomes

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.

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

Journal of Health Psychology ◽

10.1177/1359105321999697 ◽

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.

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

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2014.01.012 ◽

2014 ◽

Vol 94 (2) ◽

pp. 312 ◽

Cited By ~ 1

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

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

10.21203/rs.3.rs-52857/v1 ◽

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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Investigating genetic correlations and causal effects between caffeine consumption and sleep behaviours

10.1101/199828 ◽

2017 ◽

Author(s):

Jorien L. Treur ◽

Mark Gibson ◽

Amy E Taylor ◽

Peter J Rogers ◽

Marcus R Munafò

Keyword(s):

Genetic Correlation ◽

Sleep Duration ◽

Genetic Variants ◽

Mendelian Randomization ◽

Association Studies ◽

Genetic Correlations ◽

Causal Effects ◽

Genome Wide Association Studies ◽

Caffeine Consumption ◽

Insomnia Complaints

AbstractStudy Objectives:Higher caffeine consumption has been linked to poorer sleep and insomnia complaints. We investigated whether these observational associations are the result of genetic risk factors influencing both caffeine consumption and poorer sleep, and/or whether they reflect (possibly bidirectional) causal effects.Methods:Summary-level data were available from genome-wide association studies (GWAS) on caffeine consumption (n=91,462), sleep duration, and chronotype (i.e., being a ‘morning’ versus an ‘evening’ person) (both n=128,266), and insomnia complaints (n=113,006). Linkage disequilibrium (LD) score regression was used to calculate genetic correlations, reflecting the extent to which genetic variants influencing caffeine consumption and sleep behaviours overlap. Causal effects were tested with bidirectional, two-sample Mendelian randomization (MR), an instrumental variable approach that utilizes genetic variants robustly associated with an exposure variable as an instrument to test causal effects. Estimates from individual genetic variants were combined using inverse-variance weighted meta-analysis, weighted median regression and MR Egger regression methods.Results:There was no clear evidence for genetic correlation between caffeine consumption and sleep duration (rg=0.000,p=0.998), chronotype (rg=0.086,p=0.192) or insomnia (rg=-0.034,p=0.700). Two-sample Mendelian randomization analyses did not support causal effects from caffeine consumption to sleep behaviours, or the other way around.Conclusions:We found no evidence in support of genetic correlation or causal effects between caffeine consumption and sleep. While caffeine may have acute effects on sleep when taken shortly before habitual bedtime, our findings suggest that a more sustained pattern of high caffeine consumption is likely associated with poorer sleep through shared environmental factors.

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Causal Associations Between Blood Lipids and COVID-19 Risk: A Two-Sample Mendelian Randomization Study

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvbaha.121.316324 ◽

2021 ◽

Author(s):

Kun Zhang ◽

Shan-Shan Dong ◽

Yan Guo ◽

Shi-Hao Tang ◽

Hao Wu ◽

...

Keyword(s):

Total Cholesterol ◽

Blood Lipids ◽

Mendelian Randomization ◽

Low Density Lipoprotein ◽

Density Lipoprotein ◽

Outcome Data ◽

Causal Effects ◽

Lipoprotein Cholesterol ◽

Global Pandemic ◽

The Uk

Objective: Coronavirus disease 2019 (COVID-19) is a global pandemic caused by the severe acute respiratory syndrome coronavirus 2. It has been reported that dyslipidemia is correlated with COVID-19, and blood lipids levels, including total cholesterol, HDL-C (high-density lipoprotein cholesterol), and LDL-C (low-density lipoprotein cholesterol) levels, were significantly associated with disease severity. However, the causalities of blood lipids on COVID-19 are not clear. Approach and Results: We performed 2-sample Mendelian randomization (MR) analyses to explore the causal effects of blood lipids on COVID-19 susceptibility and severity. Using the outcome data from the UK Biobank (1221 cases and 4117 controls), we observed potential positive causal effects of dyslipidemia (odds ratio [OR], 1.27 [95% CI, 1.08–1.49], P =3.18×10 −3 ), total cholesterol (OR, 1.19 [95% CI, 1.07–1.32], P =8.54×10 −4 ), and ApoB (apolipoprotein B; OR, 1.18 [95% CI, 1.07–1.29], P =1.01×10 −3 ) on COVID-19 susceptibility after Bonferroni correction. In addition, the effects of total cholesterol (OR, 1.01 [95% CI, 1.00–1.02], P =2.29×10 −2 ) and ApoB (OR, 1.01 [95% CI, 1.00–1.02], P =2.22×10 −2 ) on COVID-19 susceptibility were also identified using outcome data from the host genetics initiative (14 134 cases and 1 284 876 controls). Conclusions: In conclusion, we found that higher total cholesterol and ApoB levels might increase the risk of COVID-19 infection.

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Education, intelligence and Alzheimer’s disease: Evidence from a multivariable two-sample Mendelian randomization study

10.1101/401042 ◽

2018 ◽

Cited By ~ 13

Author(s):

Emma L Anderson ◽

Laura D Howe ◽

Kaitlin H Wade ◽

Yoav Ben-Shlomo ◽

W. David Hill ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Educational Attainment ◽

Mendelian Randomization ◽

Causal Effect ◽

Univariate Analysis ◽

Causal Effects ◽

Training Interventions ◽

Bidirectional Relationship ◽

Years Of Schooling

AbstractObjectivesTo examine whether educational attainment and intelligence have causal effects on risk of Alzheimer’s disease (AD), independently of each other.DesignTwo-sample univariable and multivariable Mendelian Randomization (MR) to estimate the causal effects of education on intelligence and vice versa, and the total and independent causal effects of both education and intelligence on risk of AD.Participants17,008 AD cases and 37,154 controls from the International Genomics of Alzheimer’s Project (IGAP) consortiumMain outcome measureOdds ratio of AD per standardised deviation increase in years of schooling and intelligenceResultsThere was strong evidence of a causal, bidirectional relationship between intelligence and educational attainment, with the magnitude of effect being similar in both directions. Similar overall effects were observed for both educational attainment and intelligence on AD risk in the univariable MR analysis; with each SD increase in years of schooling and intelligence, odds of AD were, on average, 37% (95% CI: 23% to 49%) and 35% (95% CI: 25% to 43%) lower, respectively. There was little evidence from the multivariable MR analysis that educational attainment affected AD risk once intelligence was taken into account, but intelligence affected AD risk independently of educational attainment to a similar magnitude observed in the univariate analysis.ConclusionsThere is robust evidence for an independent, causal effect of intelligence in lowering AD risk, potentially supporting a role for cognitive training interventions to improve aspects of intelligence. However, given the observed causal effect of educational attainment on intelligence, there may also be support for policies aimed at increasing length of schooling to lower incidence of AD.

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Causal effects from non‐alcoholic fatty liver disease on kidney function: A Mendelian randomization study

Liver International ◽

10.1111/liv.15118 ◽

2021 ◽

Author(s):

Sehoon Park ◽

Soojin Lee ◽

Yaerim Kim ◽

Semin Cho ◽

Kwangsoo Kim ◽

...

Keyword(s):

Liver Disease ◽

Fatty Liver ◽

Kidney Function ◽

Fatty Liver Disease ◽

Mendelian Randomization ◽

Causal Effects ◽

Alcoholic Fatty Liver ◽

Alcoholic Fatty Liver Disease

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Genetically Predicted Type 2 Diabetes Mellitus Liability, Glycated Hemoglobin and Cardiovascular Diseases: A Wide-Angled Mendelian Randomization Study

Genes ◽

10.3390/genes12101644 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1644

Author(s):

Bowen Liu ◽

Amy M. Mason ◽

Luanluan Sun ◽

Emanuele Di Angelantonio ◽

Dipender Gill ◽

...

Keyword(s):

Cardiovascular Disease ◽

Coronary Artery Disease ◽

Coronary Artery ◽

Cardiovascular Diseases ◽

General Population ◽

Mendelian Randomization ◽

Causal Effects ◽

Disease Outcomes ◽

Artery Disease ◽

Diagnosed Diabetes

(1) Aim: To investigate the causal effects of T2DM liability and glycated haemoglobin (HbA1c) levels on various cardiovascular disease outcomes, both in the general population and in non-diabetic individuals specifically. (2) Methods: We selected 243 variants as genetic instruments for T2DM liability and 536 variants for HbA1c. Linear Mendelian randomization analyses were performed to estimate the associations of genetically-predicted T2DM liability and HbA1c with 12 cardiovascular disease outcomes in 367,703 unrelated UK Biobank participants of European ancestries. We performed secondary analyses in participants without diabetes (HbA1c < 6.5% with no diagnosed diabetes), and in participants without diabetes or pre-diabetes (HbA1c < 5.7% with no diagnosed diabetes). (3) Results: Genetically-predicted T2DM liability was positively associated (p < 0.004, 0.05/12) with peripheral vascular disease, aortic valve stenosis, coronary artery disease, heart failure, ischaemic stroke, and any stroke. Genetically-predicted HbA1c was positively associated with coronary artery disease and any stroke. Mendelian randomization estimates generally shifted towards the null when excluding diabetic and pre-diabetic participants from analyses. (4) Conclusions: This genetic evidence supports causal effects of T2DM liability and HbA1c on a range of cardiovascular diseases, suggesting that improving glycaemic control could reduce cardiovascular risk in a general population, with greatest benefit in individuals with diabetes.

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