scholarly journals MR-pheWAS with stratification and interaction: Searching for the causal effects of smoking heaviness identified an effect on facial aging

2018 ◽  
Author(s):  
Louise A C Millard ◽  
Marcus R Munafò ◽  
Kate Tilling ◽  
Robyn E Wootton ◽  
George Davey Smith
Keyword(s):  
Lung Function ◽  
Detrimental Effect ◽  
Genetic Variant ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Causal Effects ◽  
Uk Biobank ◽  
Facial Aging ◽  
Heavy Smoking ◽  
Never Smokers

AbstractMendelian randomization (MR) is an established approach for estimating the causal effect of an environmental exposure on a downstream outcome. The gene x environment (GxE) study design can be used within an MR framework to determine whether MR estimates may be biased if the genetic instrument affects the outcome through pathways other than via the exposure of interest (known as horizontal pleiotropy). MR phenome-wide association studies (MR-pheWAS) search for the effects of an exposure, and a recently published tool (PHESANT) means that it is now possible to do this comprehensively, across thousands of traits in UK Biobank. In this study, we introduce the GxE MR-pheWAS approach, and search for the causal effects of smoking heaviness – stratifying on smoking status (ever versus never) – as an exemplar. If a genetic variant is associated with smoking heaviness (but not smoking initiation), and this variant affects an outcome (at least partially) via tobacco intake, we would expect the effect of the variant on the outcome to differ in ever versus never smokers. If this effect is entirely mediated by tobacco intake, we would expect to see an effect in ever smokers but not never smokers. We used PHESANT to search for the causal effects of smoking heaviness, instrumented by genetic variant rs16969968, among never and ever smokers respectively, in UK Biobank. We ranked results by: 1) strength of effect of rs16969968 among ever smokers, and 2) strength of interaction between ever and never smokers. We replicated previously established causal effects of smoking heaviness, including a detrimental effect on lung function and pulse rate. Novel results included a detrimental effect of heavier smoking on facial aging. We have demonstrated how GxE MR-pheWAS can be used to identify causal effects of an exposure, while simultaneously assessing the extent that results may be biased by horizontal pleiotropy.Author summaryMendelian randomization uses genetic variants associated with an exposure to investigate causality. For instance, a genetic variant that relates to how heavily a person smokes has been used to test whether smoking causally affects health outcomes. Mendelian randomization is biased if the genetic variant also affects the outcome via other pathways. We exploit additional information – that the effect of heavy smoking only occurs in people who actually smoke – to overcome this problem. By testing associations in ever and never smokers separately we can assess whether the genetic variant affects an outcome via smoking or another pathway. If the effect is entirely via smoking heaviness, we would expect to see an effect in ever but not never smokers, and this would suggest that smoking causally influences the outcome. Previous Mendelian randomization studies of smoking heaviness focused on specific outcomes – here we searched for the causal effects of smoking heaviness across over 18,000 traits. We identified previously established effects (e.g. a detrimental effect on lung function) and novel results including a detrimental effect of heavier smoking on facial aging. Our approach can be used to search for the causal effects of other exposures, where the exposure only occurs in known subsets of the population.

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.

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

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.

scholarly journals Interpretation of mendelian randomization using one measure of an exposure that varies over time.

2021 ◽  
Author(s):  
Tim T Morris ◽  
Jon Heron ◽  
Eleanor Sanderson ◽  
George Davey Smith ◽  
Kate Tilling
Keyword(s):  
Blood Pressure ◽  
Systolic Blood Pressure ◽  
Observational Data ◽  
Genetic Variant ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
Time Points ◽  
The Life Course ◽  
Over Time

Background Mendelian randomization (MR) is a powerful tool through which the causal effects of modifiable exposures on outcomes can be estimated from observational data. Most exposures vary throughout the life course, but MR is commonly applied to one measurement of an exposure (e.g., weight measured once between ages 40 and 60). It has been argued that MR provides biased causal effect estimates when applied to one measure of an exposure that varies over time. Methods We propose an approach that emphasises the liability that causes the entire exposure trajectory. We demonstrate this approach using simulations and an applied example. Results We show that rather than estimating the direct or total causal effect of changing the exposure value at a given time, MR estimates the causal effect of changing the liability as induced by a specific genotype that gives rise to the exposure at that time. As such, results from MR conducted at different time points are expected to differ (unless the liability of exposure is constant over time), as we demonstrate by estimating the effect of BMI measured at different ages on systolic blood pressure. Conclusions Practitioners should not interpret MR results as timepoint-specific direct or total causal effects, but as the effect of changing the liability that causes the entire exposure trajectory. Estimates of how the effects of a genetic variant on an exposure vary over time are needed to interpret timepoint-specific causal effects.

scholarly journals Prioritization of candidate causal genes in GWAS signals of asthma in UK Biobank

2020 ◽  
Author(s):  
Kim Valette ◽  
Zhonglin Li ◽  
Valentin Bon-Baret ◽  
Arnaud Chignon ◽  
Jean-Christophe Bérubé ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Uk Biobank ◽  
Gamma Chain ◽  
Gene Encoding ◽  
Putative Gene ◽  
A Genome ◽  
Causal Genes ◽  
High Affinity Ige Receptor

Abstract To identify susceptibility loci and candidate causal genes of asthma, we performed a genome-wide association study (GWAS) in UK Biobank on a broad asthma definition (n = 56,167 asthma cases and 352,255 controls). We then carried out functional mapping through transcriptome-wide association studies (TWAS) and Mendelian randomization in lung (n = 1,038) and blood (n = 31,684) tissues. The GWAS revealed 72 asthma-associated loci from 116 independent significant variants (PGWAS<5.0E-8). As expected, the yield of exonic variants associated with asthma was low, but nine were identified as potentially deleterious (CADD > 20) including a stop-gain mutation in the filaggrin (FLG) gene. The top lung TWAS gene on 17q12-q21 was GSDMB (PTWAS=1.42E-54). Other TWAS genes of interest include TSLP on 5q22, RERE on 1p36, CLEC16A on 16p13, and IL4R on 16p12, which all replicated in GTEx lung (n = 515). A novel risk locus was also revealed by the lung asthma TWAS on 1q23.3 with the putative gene encoding the gamma chain of the high-affinity IgE receptor (FCER1G, PTWAS=2.13E-6), which was also replicated in GTEx lung (PTWAS=3.71E-7). By testing a comprehensive set of cells and tissues, we then demonstrated that the largest fold enrichment of regulatory and functional annotations among asthma-associated variants was in the blood. We mapped 485 eQTL-regulated genes associated with asthma in the blood and 50 of them were shown to be causally associated with asthma by Mendelian randomization. Prioritization of druggable genes revealed known (IL4R, TSLP, IL6, TNFSF4) and potentially new therapeutic targets for asthma.

scholarly journals Causal Associations Between Educational Attainment and 14 Urological and Reproductive Health Outcomes: A Mendelian Randomization Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Menghua Wang ◽  
Zhongyu Jian ◽  
Xiaoshuai Gao ◽  
Chi Yuan ◽  
Xi Jin ◽  
...  
Keyword(s):  
Reproductive Health ◽  
Educational Attainment ◽  
Health Outcomes ◽  
Lower Risk ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Causal Effects ◽  
Genome Wide Association Studies ◽  
Bonferroni Correction ◽  
The Impact

Background: The impact of educational attainment (EA) on multiple urological and reproductive health outcomes has been explored in observational studies. Here we used Mendelian randomization (MR) to investigate whether EA has causal effects on 14 urological and reproductive health outcomes.Methods: We obtained summary statistics for EA and 14 urological and reproductive health outcomes from genome-wide association studies (GWAS). MR analyses were applied to explore the potential causal association between EA and them. Inverse variance weighted was the primary analytical method.Results: Genetically predicted one standard deviation (SD) increase in EA was causally associated with a higher risk of prostate cancer [odds ratio (OR) 1.14, 95% confidence interval (CI) 1.05–1.25, P = 0.003] and a reduced risk of kidney stone (OR 0.73, 95% CI 0.62–0.87, P &lt; 0.001) and cystitis (OR 0.76, 95% CI 0.67–0.86, P &lt; 0.001) after Bonferroni correction. EA was also suggestively correlated with a lower risk of prostatitis (OR 0.76, 95% CI 0.59–0.98, P = 0.037) and incontinence (OR 0.64, 95% CI 0.47–0.87, P = 0.004). For the bioavailable testosterone levels and infertility, sex-specific associations were observed, with genetically determined increased EA being related to higher levels of testosterone in men (β 0.07, 95% CI 0.04–0.10, P &lt; 0.001), lower levels of testosterone in women (β −0.13, 95% CI−0.16 to−0.11, P &lt; 0.001), and a lower risk of infertility in women (OR 0.74, 95% CI 0.64–0.86, P &lt; 0.001) but was not related to male infertility (OR 0.79, 95% CI 0.52–1.20, P = 0.269) after Bonferroni correction. For bladder cancer, kidney cancer, testicular cancer, benign prostatic hyperplasia, and erectile dysfunction, no causal effects were observed.Conclusions: EA plays a vital role in urological diseases, especially in non-oncological outcomes and reproductive health. These findings should be verified in further studies when GWAS data are sufficient.

scholarly journals New genetic signals for lung function highlight pathways and pleiotropy, and chronic obstructive pulmonary disease associations across multiple ancestries

2018 ◽  
Author(s):  
Nick Shrine ◽  
Anna L Guyatt ◽  
A Mesut Erzurumluoglu ◽  
Victoria E Jackson ◽  
Brian D Hobbs ◽  
...  
Keyword(s):  
Lung Function ◽  
Drug Targets ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
European Ancestry ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
A Genome ◽  
Disease Associations ◽  
Never Smokers

AbstractReduced lung function predicts mortality and is key to the diagnosis of COPD. In a genome-wide association study in 400,102 individuals of European ancestry, we define 279 lung function signals, one-half of which are new. In combination these variants strongly predict COPD in deeply-phenotyped patient populations. Furthermore, the combined effect of these variants showed generalisability across smokers and never-smokers, and across ancestral groups. We highlight biological pathways, known and potential drug targets for COPD and, in phenome-wide association studies, autoimmune-related and other pleiotropic effects of lung function associated variants. This new genetic evidence has potential to improve future preventive and therapeutic strategies for COPD.

scholarly journals Phenome-wide Mendelian-randomization study of genetically determined vitamin D on multiple health outcomes using the UK Biobank study

2019 ◽  
Vol 48 (5) ◽  
pp. 1425-1434 ◽  
Author(s):  
Xiangrui Meng ◽  
Xue Li ◽  
Maria N Timofeeva ◽  
Yazhou He ◽  
Athina Spiliopoulou ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Vitamin D ◽  
Health Outcomes ◽  
Mendelian Randomization ◽  
Causal Effects ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Polygenic Risk ◽  
Disease Outcomes ◽  
Multiple Health

Abstract Background Vitamin D deficiency is highly prevalent across the globe. Existing studies suggest that a low vitamin D level is associated with more than 130 outcomes. Exploring the causal role of vitamin D in health outcomes could support or question vitamin D supplementation. Methods We carried out a systematic literature review of previous Mendelian-randomization studies on vitamin D. We then implemented a Mendelian Randomization–Phenome Wide Association Study (MR-PheWAS) analysis on data from 339 256 individuals of White British origin from UK Biobank. We first ran a PheWAS analysis to test the associations between a 25(OH)D polygenic risk score and 920 disease outcomes, and then nine phenotypes (i.e. systolic blood pressure, diastolic blood pressure, risk of hypertension, T2D, ischaemic heart disease, body mass index, depression, non-vertebral fracture and all-cause mortality) that met the pre-defined inclusion criteria for further analysis were examined by multiple MR analytical approaches to explore causality. Results The PheWAS analysis did not identify any health outcome associated with the 25(OH)D polygenic risk score. Although a selection of nine outcomes were reported in previous Mendelian-randomization studies or umbrella reviews to be associated with vitamin D, our MR analysis, with substantial study power (>80% power to detect an association with an odds ratio >1.2 for per standard deviation increase of log-transformed 25[OH]D), was unable to support an interpretation of causal association. Conclusions We investigated the putative causal effects of vitamin D on multiple health outcomes in a White population. We did not support a causal effect on any of the disease outcomes tested. However, we cannot exclude small causal effects or effects on outcomes that we did not have enough power to explore due to the small number of cases.

Causal Effects of Positive Affect, Life Satisfaction, Depressive Symptoms, and Neuroticism on Kidney Function: A Mendelian Randomization Study

2021 ◽  
pp. ASN.2020071086 ◽  
Author(s):  
Sehoon Park ◽  
Soojin Lee ◽  
Yaerim Kim ◽  
Yeonhee Lee ◽  
Min Woo Kang ◽  
...  
Keyword(s):  
Life Satisfaction ◽  
Depressive Symptoms ◽  
Confidence Interval ◽  
Positive Affect ◽  
Kidney Function ◽  
Mendelian Randomization ◽  
Causal Effects ◽  
European Ancestry ◽  
Uk Biobank ◽  
Lower Egfr

BackgroundFurther investigation of the causal effects of psychologic wellbeing on kidney function is warranted.MethodsIn this Mendelian randomization (MR) study, genetic instruments for positive affect, life satisfaction, depressive symptoms, and neuroticism were introduced from a previous genome-wide association study meta-analysis of European individuals. Summary-level MR was performed using the CKDGen data of European ancestry (n=567,460), and additional allele score–based MR was performed in the individual-level data of White British UK Biobank participants (n=321,024).ResultsIn summary-level MR with the CKDGen data, depressive symptoms were a significant causative factor for kidney function impairment (CKD OR, 1.45; 95% confidence interval, 1.07 to 1.96; eGFR change [%] beta −2.18; 95% confidence interval, −3.61 to −0.72) and pleiotropy-robust sensitivity analysis results supported the causal estimates. A genetic predisposition for positive affect was significantly associated with better kidney function (CKD OR, 0.69; 95% confidence interval, 0.52 to 0.91), eGFR change [%] beta 1.50; 95% confidence interval, 0.09 to 2.93) and sensitivity MR analysis results supported the finding for CKD outcome, but was nonsignificant for eGFR. Life satisfaction and neuroticism exposures showed nonsignificant causal estimates. In the UK Biobank with covariate-adjusted allele score MR analysis, allele scores for positive affect and life satisfaction were causally associated with reduced risk of CKD and higher eGFR. In contrast, neuroticism allele score was associated with increased risk of CKD and lower eGFR, and depressive symptoms allele score was associated with lower eGFR, but showed nonsignificant association with CKD.ConclusionsHealth care providers in the nephrology field should be aware of the causal linkage between psychologic wellbeing and kidney function.

scholarly journals Childhood obesity and multiple sclerosis: A Mendelian randomization study

2021 ◽  
pp. 135245852110017
Author(s):  
Adil Harroud ◽  
Ruth E Mitchell ◽  
Tom G Richardson ◽  
John A Morris ◽  
Vincenzo Forgetta ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Childhood Obesity ◽  
Mendelian Randomization ◽  
Pubertal Timing ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Increased Risk ◽  
Age At Puberty

Background: Higher childhood body mass index (BMI) has been associated with an increased risk of multiple sclerosis (MS). Objective: To evaluate whether childhood BMI has a causal influence on MS, and whether this putative effect is independent from early adult obesity and pubertal timing. Methods: We performed Mendelian randomization (MR) using summary genetic data on 14,802 MS cases and 26,703 controls. Large-scale genome-wide association studies provided estimates for BMI in childhood ( n = 47,541) and adulthood ( n = 322,154). In multivariable MR, we examined the direct effects of each timepoint and further adjusted for age at puberty. Findings were replicated using the UK Biobank ( n = 453,169). Results: Higher genetically predicted childhood BMI was associated with increased odds of MS (odds ratio (OR) = 1.26/SD BMI increase, 95% confidence interval (CI): 1.07–1.50). However, there was little evidence of a direct effect after adjusting for adult BMI (OR = 1.03, 95% CI: 0.70–1.53). Conversely, the effect of adult BMI persisted independent of childhood BMI (OR = 1.43; 95% CI: 1.01–2.03). The addition of age at puberty did not alter the findings. UK Biobank analyses showed consistent results. Sensitivity analyses provided no evidence of pleiotropy. Conclusion: Genetic evidence supports an association between childhood obesity and MS susceptibility, mediated by persistence of obesity into early adulthood but independent of pubertal timing.

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