Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression

Bowden, Jack; Spiller, Wes; M, Fabiola Del Greco; Sheehan, Nuala A.; Thompson, John F.; Minelli, Cosetta; Smith, George Davey

doi:10.1093/ije/dyy265

Cited by 252 publications

(264 citation statements)

References 0 publications

Supporting

Mentioning

262

Contrasting

Order By: Relevance

“…This is an indicator of potential horizontal pleiotropy that violates the traditional IV assumptions. Therefore, we applied MR-Egger regression on the radial plot scale as a sensitivity analysis 54 . The mean I 2 GX statistic is 0.89, indicating that instruments are sufficiently strong for this analysis 71 .…”

Section: Methodsmentioning

confidence: 99%

“…However, there was evidence of variant heterogeneity potentially due to horizontal pleiotropy (Cochran's Q = 677.17; P = 1.09 × 10 −37 ; Supplementary Table 21). Therefore, we performed sensitivity analysis using the Radial MR-Egger approach (Methods) 54 to control for bias due to pleiotropy, and observed an effect that was consistent with our main IVW analyses but less precisely estimated (wider confidence intervals) because this method is statistically relatively inefficient (MR-Egger β = 0.025; 95% CI [−0.005, 0.055]; P = 0.103; Fig. 4 and Supplementary Table 21).…”

Section: Clustering Of Sleepiness Loci Suggest Biological Subtypesmentioning

confidence: 99%

See 1 more Smart Citation

Genome-wide association analysis of self-reported daytime sleepiness identifies 42 loci that suggest biological subtypes

et al. 2019

View full text Add to dashboard Cite

Excessive daytime sleepiness (EDS) affects 10-20% of the population and is associated with substantial functional deficits. Here, we identify 42 loci for self-reported daytime sleepiness in GWAS of 452,071 individuals from the UK Biobank, with enrichment for genes expressed in brain tissues and in neuronal transmission pathways. We confirm the aggregate effect of a genetic risk score of 42 SNPs on daytime sleepiness in independent Scandinavian cohorts and on other sleep disorders (restless legs syndrome, insomnia) and sleep traits (duration, chronotype, accelerometer-derived sleep efficiency and daytime naps or inactivity). However, individual daytime sleepiness signals vary in their associations with objective short vs long sleep, and with markers of sleep continuity. The 42 sleepiness variants primarily cluster into two predominant composite biological subtypes-sleep propensity and sleep fragmentation. Shared genetic links are also seen with obesity, coronary heart disease, psychiatric diseases, cognitive traits and reproductive ageing.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Clustering Of Sleepiness Loci Suggest Biological Subtypesmentioning

confidence: 99%

Genome-wide association analysis of self-reported daytime sleepiness identifies 42 loci that suggest biological subtypes

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The presence of horizontal pleiotropy may give rise to significant heterogeneity. When significant heterogeneity was detected (inferred using Cochran Q), the MR-Radial method 31 was used to identify SNPs responsible for heterogeneity (P = 0.05/number of SNPs) and in sensitivity analyses, these SNPs were removed and effect estimates were recalculated.…”

Section: Creation Of a New Instrument For The Prediction Of Coffee Comentioning

confidence: 99%

Coffee Consumption and Kidney Function: A Mendelian Randomization Study

Kennedy

Pirastu

Poole

et al. 2020

American Journal of Kidney Diseases

View full text Add to dashboard Cite

Section: Instrumental Variables (Ivs) Selectionmentioning

confidence: 99%

“…After data harmonization, we then removed outlier pleiotropic SNPs using RadialMR [15] with the p-value threshold of 0.05. RadialMR [15] identi ed outlying genetic instruments via heterogeneity test (modi ed Q-statistics). After the removal of pleiotropy, the remaining exposure related SNPs for each outcome as instrumental variables (IVs) were utilized to perform MR analyses.…”

Section: Instrumental Variables (Ivs) Selectionmentioning

confidence: 99%

Causally Associations of Blood Lipids Levels with COVID-19 Risk: Mendelian Randomization Study

Zhang

Guo

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundCoronavirus disease 2019 (COVID-19) is a global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2). It has been found that coronary artery disease (CAD) is a comorbid condition for COVID-19. As the risk factors of CAD, whether blood lipids levels are causally related to increasing susceptibility and severity of COVID-19 is still unknown.ObjectiveWe aim to measure the causal effects between blood lipids and COVID-19 using two-sample Mendelian Randomization (MR) methods.MethodsWe performed two-sample MR analyses to explore whether dyslipidemia, low density lipoprotein cholesterol (LDL-c), high density lipoprotein cholesterol (HDL-c), triglyceride (TG) and total cholesterol (TC) were causally related to COVID-19 risk and severity. The GWAS summary data of blood lipids involving in 312571 individuals and dyslipidemia in a total of 53991 individuals were used as exposures, respectively. Two COVID-19 GWASs including 1221 infected patients and 1610 severe patients defined as respiratory failure were employed as outcomes. ResultsThe MR results showed that dyslipidemia was casually associated with the susceptibility of COVID-19 and induced 27% higher odds for COVID-19 infection (MR-IVW OR = 1.27, 95% CI: 1.08 to 1.49, p-value = 3·18 × 10-3). For blood lipids, the increasing level of TC will raise 18 % higher odds for the susceptibility of COVID-19 (MR-IVW OR = 1.18, 95% CI: 1.06 to 1.31, p-value = 3.08 × 10-3). Based on MR estimates, we further carried out gene-based analysis and found that ABO gene was associated with TC.Conclusions Dyslipidemia is casually associated with the susceptibility of COVID-19 and the blood TC level is a risk factor for the susceptibility of COVID-19. In addition, the different susceptibility of COVID-19 in specific blood group may be partly explained by the TC concentration in diverse ABO blood groups.

show abstract

Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression

Cited by 252 publications

References 0 publications

Genome-wide association analysis of self-reported daytime sleepiness identifies 42 loci that suggest biological subtypes

Genome-wide association analysis of self-reported daytime sleepiness identifies 42 loci that suggest biological subtypes

Coffee Consumption and Kidney Function: A Mendelian Randomization Study

Causally Associations of Blood Lipids Levels with COVID-19 Risk: Mendelian Randomization Study

Contact Info

Product

Resources

About